mixOmics 6.35.2
multidrug study
library(BiocParallel)
If you are following our online course, the following vignette will be useful as a complementary learning tool. This vignette also covers the essential use cases of various methods in this package for the general mixOmcis user. The below methods will be covered:
As outlined in 1.3, this is not an exhaustive list of all the methods found within mixOmics. More information can be found at our website and you can ask questions via our discourse forum.
Figure 1: Different types of analyses with mixOmics (Rohart, Gautier, Singh, and Le Cao 2017).The biological questions, the number of data sets to integrate, and the type of response variable, whether qualitative (classification), quantitative (regression), one (PLS1) or several (PLS) responses, all drive the choice of analytical method
All methods featured in this diagram include variable selection except rCCA. In N-integration, rCCA and PLS enable the integration of two quantitative data sets, whilst the block PLS methods (that derive from the methods from Tenenhaus and Tenenhaus (2011)) can integrate more than two data sets. In P-integration, our method MINT is based on multi-group PLS (Eslami et al. 2014).The following activities cover some of these methods.
mixOmics is an R toolkit dedicated to the exploration and integration of biological data sets with a specific focus on variable selection. The package currently includes more than twenty multivariate methodologies, mostly developed by the mixOmics team (see some of our references in 1.2.3). Originally, all methods were designed for omics data, however, their application is not limited to biological data only. Other applications where integration is required can be considered, but mostly for the case where the predictor variables are continuous (see also 1.1).
In mixOmics, a strong focus is given to graphical representation to better translate and understand the relationships between the different data types and visualize the correlation structure at both sample and variable levels.
Note the data pre-processing requirements before analysing data with mixOmics:
Types of data. Different types of biological data can be explored and integrated with mixOmics. Our methods can handle molecular features measured on a continuous scale (e.g. microarray, mass spectrometry-based proteomics and metabolomics) or sequenced-based count data (RNA-seq, 16S, shotgun metagenomics) that become `continuous’ data after pre-processing and normalisation.
Normalisation. The package does not handle normalisation as it is platform-specific and we cover a too wide variety of data! Prior to the analysis, we assume the data sets have been normalised using appropriate normalisation methods and pre-processed when applicable.
Prefiltering. While mixOmics methods can handle large data sets (several tens of thousands of predictors), we recommend pre-filtering the data to less than 10K predictor variables per data set, for example by using Median Absolute Deviation (Teng et al. 2016) for RNA-seq data, by removing consistently low counts in microbiome data sets (Lê Cao et al. 2016) or by removing near-zero variance predictors. Such step aims to lessen the computational time during the parameter tuning process.
Data format. Our methods use matrix decomposition techniques. Therefore, the numeric data matrix or data frames have \(n\) observations or samples in rows and \(p\) predictors or variables (e.g. genes, proteins, OTUs) in columns.
Covariates. In the current version of mixOmics, covariates that may confound the analysis are not included in the methods. We recommend correcting for those covariates beforehand using appropriate univariate or multivariate methods for batch effect removal. Contact us for more details as we are currently working on this aspect.
We list here the main methodological or theoretical concepts you need to know to be able to efficiently apply mixOmics:
Individuals, observations or samples: the experimental units on which information are collected, e.g. patients, cell lines, cells, faecal samples etc.
Variables, predictors: read-out measured on each sample, e.g. gene (expression), protein or OTU (abundance), weight etc.
Variance: measures the spread of one variable. In our methods, we estimate the variance of components rather that variable read-outs. A high variance indicates that the data points are very spread out from the mean, and from one another (scattered).
Covariance: measures the strength of the relationship between two variables, i.e. whether they co-vary. A high covariance value indicates a strong relationship, e.g. weight and height in individuals frequently vary roughly in the same way; roughly, the heaviest are the tallest. A covariance value has no lower or upper bound.
Correlation: a standardized version of the covariance that is bounded by -1 and 1.
Linear combination: variables are combined by multiplying each of them by a coefficient and adding the results. A linear combination of height and weight could be \(2 * weight - 1.5 * height\) with the coefficients \(2\) and \(-1.5\) assigned with weight and height respectively.
Component: an artificial variable built from a linear combination of the observed variables in a given data set. Variable coefficients are optimally defined based on some statistical criterion. For example in Principal Component Analysis, the coefficients of a (principal) component are defined so as to maximise the variance of the component.
Loadings: variable coefficients used to define a component.
Sample plot: representation of the samples projected in a small space spanned (defined) by the components. Samples coordinates are determined by their components values or scores.
Correlation circle plot: representation of the variables in a space spanned by the components. Each variable coordinate is defined as the correlation between the original variable value and each component. A correlation circle plot enables to visualise the correlation between variables - negative or positive correlation, defined by the cosine angle between the centre of the circle and each variable point) and the contribution of each variable to each component - defined by the absolute value of the coordinate on each component. For this interpretation, data need to be centred and scaled (by default in most of our methods except PCA). For more details on this insightful graphic, see Figure 1 in (González et al. 2012).
Unsupervised analysis: the method does not take into account any known sample groups and the analysis is exploratory. Examples of unsupervised methods covered in this vignette are Principal Component Analysis (PCA, Chapter 3), Projection to Latent Structures (PLS, Chapter 4), and also Canonical Correlation Analysis (CCA, not covered here but see the website page).
Supervised analysis: the method includes a vector indicating the class membership of each sample. The aim is to discriminate sample groups and perform sample class prediction. Examples of supervised methods covered in this vignette are PLS Discriminant Analysis (PLS-DA, Chapter 5), DIABLO (Chapter 6) and also MINT (Chapter 7).
If the above descriptions were not comprehensive enough and you have some more questions, feel free to explore the glossary on our website.
Here is an overview of the most widely used methods in mixOmics that will be further detailed in this vignette, with the exception of rCCA. We depict them along with the type of data set they can handle.
FIGURE 1: An overview of what quantity and type of dataset each method within mixOmics requires. Thin columns represent a single variable, while the larger blocks represent datasets of multiple samples and variables.
Figure 2: List of methods in mixOmics, sparse indicates methods that perform variable selection
Figure 3: Main functions and parameters of each method
The methods implemented in mixOmics are described in detail in the following publications. A more extensive list can be found at this link.
Overview and recent integrative methods: Rohart F., Gautier, B, Singh, A, Le Cao, K. A. mixOmics: an R package for ’omics feature selection and multiple data integration. PLoS Comput Biol 13(11): e1005752.
Graphical outputs for integrative methods: (González et al. 2012) Gonzalez I., Le Cao K.-A., Davis, M.D. and Dejean S. (2012) Insightful graphical outputs to explore relationships between two omics data sets. BioData Mining 5:19.
DIABLO: Singh A, Gautier B, Shannon C, Vacher M, Rohart F, Tebbutt S, K-A. Le Cao. DIABLO - multi-omics data integration for biomarker discovery.
sparse PLS: Le Cao K.-A., Martin P.G.P, Robert-Granie C. and Besse, P. (2009) Sparse Canonical Methods for Biological Data Integration: application to a cross-platform study. BMC Bioinformatics, 10:34.
sparse PLS-DA: Le Cao K.-A., Boitard S. and Besse P. (2011) Sparse PLS Discriminant Analysis: biologically relevant feature selection and graphical displays for multiclass problems. BMC Bioinformatics, 22:253.
Multilevel approach for repeated measurements: Liquet B, Le Cao K-A, Hocini H, Thiebaut R (2012). A novel approach for biomarker selection and the integration of repeated measures experiments from two assays. BMC Bioinformatics, 13:325
sPLS-DA for microbiome data: Le Cao K-A\(^*\), Costello ME \(^*\), Lakis VA , Bartolo F, Chua XY, Brazeilles R and Rondeau P. (2016) MixMC: Multivariate insights into Microbial Communities.PLoS ONE 11(8): e0160169
Each methods chapter has the following outline:
Other methods not covered in this document are described on our website and the following references:
regularised Canonical Correlation Analysis, see the Methods and Case study tabs, and (González et al. 2008) that describes CCA for large data sets.
Microbiome (16S, shotgun metagenomics) data analysis, see also (Lê Cao et al. 2016) and kernel integration for microbiome data. The latter is in collaboration with Drs J Mariette and Nathalie Villa-Vialaneix (INRA Toulouse, France), an example is provided for the Tara ocean metagenomics and environmental data.
First, download the latest version from Bioconductor:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("mixOmics")
Alternatively, you can install the latest GitHub version of the package:
BiocManager::install("mixOmicsTeam/mixOmics")
The mixOmics package should directly import the following packages:
igraph, rgl, ellipse, corpcor, RColorBrewer, plyr, parallel, dplyr, tidyr, reshape2, methods, matrixStats, rARPACK, gridExtra.
For Apple mac users: if you are unable to install the imported package rgl, you will need to install the XQuartz software first.
library(mixOmics)
Check that there is no error when loading the package, especially for the rgl library (see above).
The examples we give in this vignette use data that are already part of the package. To upload your own data, check first that your working directory is set, then read your data from a .txt or .csv format, either by using File > Import Dataset in RStudio or via one of these command lines:
# from csv file
data <- read.csv("your_data.csv", row.names = 1, header = TRUE)
# from txt file
data <- read.table("your_data.txt", header = TRUE)
For more details about the arguments used to modify those functions, type ?read.csv or ?read.table in the R console.
mixOmicsEach analysis should follow this workflow:
Then use your critical thinking and additional functions and visual tools to make sense of your data! (some of which are listed in 1.2.2) and will be described in the next Chapters.
For instance, for Principal Components Analysis, we first load the data:
data(nutrimouse)
X <- nutrimouse$gene
Then use the following steps:
MyResult.pca <- pca(X) # 1 Run the method
plotIndiv(MyResult.pca) # 2 Plot the samples
plotVar(MyResult.pca) # 3 Plot the variables
This is only a first quick-start, there will be many avenues you can take to deepen your exploratory and integrative analyses. The package proposes several methods to perform variable, or feature selection to identify the relevant information from rather large omics data sets. The sparse methods are listed in the Table in 1.2.2.
Following our example here, sparse PCA can be applied to select the top 5 variables contributing to each of the two components in PCA. The user specifies the number of variables to selected on each component, for example, here 5 variables are selected on each of the first two components (keepX=c(5,5)):
MyResult.spca <- spca(X, keepX=c(5,5)) # 1 Run the method
plotIndiv(MyResult.spca) # 2 Plot the samples
plotVar(MyResult.spca) # 3 Plot the variables
You can see know that we have considerably reduced the number of genes in the plotVar correlation circle plot.
Do not stop here! We are not done yet. You can enhance your analyses with the following:
Have a look at our manual and each of the functions and their examples, e.g. ?pca, ?plotIndiv, ?sPCA, …
Run the examples from the help file using the example function: example(pca), example(plotIndiv), …
Have a look at our website that features many tutorials and case studies,
Keep reading this vignette, this is just the beginning!
multidrug studyTo illustrate PCA and is variants, we will analyse the multidrug case study available in the package. This pharmacogenomic study investigates the patterns of drug activity in cancer cell lines (Szakács et al. 2004). These cell lines come from the NCI-60 Human Tumor Cell Lines established by the Developmental Therapeutics Program of the National Cancer Institute (NCI) to screen for the toxicity of chemical compound repositories in diverse cancer cell lines. NCI-60 includes cell lines derived from cancers of colorectal (7 cell lines), renal (8), ovarian (6), breast (8), prostate (2), lung (9) and central nervous system origin (6), as well as leukemia (6) and melanoma (8).
Two separate data sets (representing two types of measurements) on the same NCI-60 cancer cell lines are available in multidrug (see also ?multidrug):
$ABC.trans: Contains the expression of 48 human ABC transporters measured by quantitative real-time PCR (RT-PCR) for each cell line.
$compound: Contains the activity of 1,429 drugs expressed as GI50, which is the drug concentration that induces 50% inhibition of cellular growth for the tested cell line.
Additional information will also be used in the outputs:
$comp.name: The names of the 1,429 compounds.
$cell.line: Information on the cell line names ($Sample) and the cell line types ($Class).
In this activity, we illustrate PCA performed on the human ABC transporters ABC.trans, and sparse PCA on the compound data compound.
The input data matrix \(\boldsymbol{X}\) is of size \(N\) samples in rows and \(P\) variables (e.g. genes) in columns. We start with the ABC.trans data.
library(mixOmics)
data(multidrug)
X <- multidrug$ABC.trans
dim(X) # Check dimensions of data
## [1] 60 48
Contrary to the minimal code example, here we choose to also scale the variables for the reasons detailed earlier. The function tune.pca() calculates the cumulative proportion of explained variance for a large number of principal components (here we set ncomp = 10). A screeplot of the proportion of explained variance relative to the total amount of variance in the data for each principal component is output (Fig. 4):
tune.pca.multi <- tune.pca(X, ncomp = 10, scale = TRUE)
plot(tune.pca.multi)
Figure 4: Screeplot from the PCA performed on the ABC.trans data: Amount of explained variance for each principal component on the ABC transporter data.
# tune.pca.multidrug$cum.var # Outputs cumulative proportion of variance
From the numerical output (not shown here), we observe that the first two principal components explain 22.87% of the total variance, and the first three principal components explain 29.88% of the total variance. The rule of thumb for choosing the number of components is not so much to set a hard threshold based on the cumulative proportion of explained variance (as this is data-dependent), but to observe when a drop, or elbow, appears on the screeplot. The elbow indicates that the remaining variance is spread over many principal components and is not relevant in obtaining a low dimensional ‘snapshot’ of the data. This is an empirical way of choosing the number of principal components to retain in the analysis. In this specific example we could choose between 2 to 3 components for the final PCA, however these criteria are highly subjective and the reader must keep in mind that visualisation becomes difficult above three dimensions.
Based on the preliminary analysis above, we run a PCA with three components. Here we show additional input, such as whether to center or scale the variables.
final.pca.multi <- pca(X, ncomp = 3, center = TRUE, scale = TRUE)
# final.pca.multi # Lists possible outputs
The output is similar to the tuning step above. Here the total variance in the data is:
final.pca.multi$var.tot
## [1] 47.98305
By summing the variance explained from all possible components, we would achieve the same amount of explained variance. The proportion of explained variance per component is:
final.pca.multi$prop_expl_var$X
## PC1 PC2 PC3
## 0.12677541 0.10194929 0.07011818
The cumulative proportion of variance explained can also be extracted (as displayed in Figure 4):
final.pca.multi$cum.var
## PC1 PC2 PC3
## 0.1267754 0.2287247 0.2988429
To calculate components, we use the variable coefficient weights indicated in the loading vectors. Therefore, the absolute value of the coefficients in the loading vectors inform us about the importance of each variable in contributing to the definition of each component. We can extract this information through the selectVar() function which ranks the most important variables in decreasing order according to their absolute loading weight value for each principal component.
# Top variables on the first component only:
head(selectVar(final.pca.multi, comp = 1)$value)
## value.var
## ABCE1 0.3242162
## ABCD3 0.2647565
## ABCF3 0.2613074
## ABCA8 -0.2609394
## ABCB7 0.2493680
## ABCF1 0.2424253
Note:
We project the samples into the space spanned by the principal components to visualise how the samples cluster and assess for biological or technical variation in the data. We colour the samples according to the cell line information available in multidrug$cell.line$Class by specifying the argument group (Fig. 5).
plotIndiv(final.pca.multi,
comp = c(1, 2), # Specify components to plot
ind.names = TRUE, # Show row names of samples
group = multidrug$cell.line$Class,
title = 'ABC transporters, PCA comp 1 - 2',
legend = TRUE, legend.title = 'Cell line')
Figure 5: Sample plot from the PCA performed on the ABC.trans data. Samples are projected into the space spanned by the first two principal components, and coloured according to cell line type. Numbers indicate the rownames of the data.
Because we have run PCA on three components, we can examine the third component, either by plotting the samples onto the principal components 1 and 3 (PC1 and PC3) in the code above (comp = c(1, 3)) or by using the 3D interactive plot (code shown below). The addition of the third principal component only seems to highlight a potential outlier (sample 8, not shown). Potentially, this sample could be removed from the analysis, or, noted when doing further downstream analysis. The removal of outliers should be exercised with great caution and backed up with several other types of analyses (e.g. clustering) or graphical outputs (e.g. boxplots, heatmaps, etc).
# Interactive 3D plot will load the rgl library.
plotIndiv(final.pca.multi, style = '3d',
group = multidrug$cell.line$Class,
title = 'ABC transporters, PCA comp 1 - 3')
These plots suggest that the largest source of variation explained by the first two components can be attributed to the melanoma cell line, while the third component highlights a single outlier sample. Hence, the interpretation of the following outputs should primarily focus on the first two components.
Note:
Correlation circle plots indicate the contribution of each variable to each component using the plotVar() function, as well as the correlation between variables (indicated by a ‘cluster’ of variables). Note that to interpret the latter, the variables need to be centered and scaled in PCA:
plotVar(final.pca.multi, comp = c(1, 2),
var.names = TRUE,
cex = 3, # To change the font size
# cutoff = 0.5, # For further cutoff
title = 'Multidrug transporter, PCA comp 1 - 2')
ABC.trans data. The plot shows groups of transporters that are highly correlated, and also contribute to PC1 - near the big circle on the right hand side of the plot (transporters grouped with those in orange), or PC1 and PC2 - top left and top bottom corner of the plot, transporters grouped with those in pink and yellow." width="75%" />
Figure 6: Correlation Circle plot from the PCA performed on the ABC.trans data. The plot shows groups of transporters that are highly correlated, and also contribute to PC1 - near the big circle on the right hand side of the plot (transporters grouped with those in orange), or PC1 and PC2 - top left and top bottom corner of the plot, transporters grouped with those in pink and yellow.
The plot in Figure 6 highlights a group of ABC transporters that contribute to PC1: ABCE1, and to some extent the group clustered with ABCB8 that contributes positively to PC1, while ABCA8 contributes negatively. We also observe a group of transporters that contribute to both PC1 and PC2: the group clustered with ABCC2 contributes negatively both to PC1 and PC2, and a cluster of ABCC12 and ABCD2 that contributes negatively to PC1 and positively to PC2. We observe that several transporters are inside the small circle. However, examining the third component (argument comp = c(1, 3)) does not appear to reveal further transporters that contribute to this third component. The additional argument cutoff = 0.5 could further simplify this plot.
A biplot allows us to display both samples and variables simultaneously to further understand their relationships. Samples are displayed as dots while variables are displayed at the tips of the arrows. Similar to correlation circle plots, data must be centered and scaled to interpret the correlation between variables (as a cosine angle between variable arrows).
biplot(final.pca.multi, group = multidrug$cell.line$Class,
legend.title = 'Cell line')
ABS.trans data. The plot highlights which transporter expression levels may be related to specific cell lines, such as melanoma." width="75%" />
Figure 7: Biplot from the PCA performed on the ABS.trans data. The plot highlights which transporter expression levels may be related to specific cell lines, such as melanoma.
The biplot in Figure 7 shows that the melanoma cell lines seem to be characterised by a subset of transporters such as the cluster around ABCC2 as highlighted previously in Figure 6. Further examination of the data, such as boxplots (as shown in Fig. 8), can further elucidate the transporter expression levels for these specific samples.
ABCC2.scale <- scale(X[, 'ABCC2'], center = TRUE, scale = TRUE)
boxplot(ABCC2.scale ~
multidrug$cell.line$Class, col = color.mixo(1:9),
xlab = 'Cell lines', ylab = 'Expression levels, scaled',
par(cex.axis = 0.5), # Font size
main = 'ABCC2 transporter')
Figure 8: Boxplots of the transporter ABCC2 identified from the PCA correlation circle plot (Fig. 6) and the biplot (Fig. 7) show the level of ABCC2 expression related to cell line types. The expression level of ABCC2 was centered and scaled in the PCA, but similar patterns are also observed in the original data.
In the ABC.trans data, there is only one missing value. Missing values can be handled by sPCA via the NIPALS algorithm . However, if the number of missing values is large, we recommend imputing them with NIPALS, as we describe in our website in www.mixOmics.org.
First, we must decide on the number of components to evaluate. The previous tuning step indicated that ncomp = 3 was sufficient to explain most of the variation in the data, which is the value we choose in this example. We then set up a grid of keepX values to test, which can be thin or coarse depending on the total number of variables. We set up the grid to be thin at the start, and coarse as the number of variables increases. The ABC.trans data includes a sufficient number of samples to perform repeated 5-fold cross-validation to define the number of folds and repeats (leave-one-out CV is also possible if the number of samples \(N\) is small by specifying folds = \(N\)). The computation may take a while if you are not using parallelisation (see additional parameters in tune.spca()), here we use a small number of repeats for illustrative purposes. We then plot the output of the tuning function.
grid.keepX <- c(seq(5, 30, 5))
# grid.keepX # To see the grid
set.seed(30) # For reproducibility with this handbook, remove otherwise
tune.spca.result <- tune.spca(X, ncomp = 3,
folds = 5,
test.keepX = grid.keepX, nrepeat = 10)
# Consider adding up to 50 repeats for more stable results
tune.spca.result$choice.keepX
## comp1 comp2 comp3
## 15 15 30
plot(tune.spca.result)
ABC.trans data. For a grid of number of variables to select indicated on the x-axis, the average correlation between predicted and actual components based on cross-validation is calculated and shown on the y-axis for each component. The optimal number of variables to select per component is assessed via one-sided \(t-\)tests and is indicated with a diamond." width="75%" />
Figure 9: Tuning the number of variables to select with sPCA on the ABC.trans data. For a grid of number of variables to select indicated on the x-axis, the average correlation between predicted and actual components based on cross-validation is calculated and shown on the y-axis for each component. The optimal number of variables to select per component is assessed via one-sided \(t-\)tests and is indicated with a diamond.
The tuning function outputs the averaged correlation between predicted and actual components per keepX value for each component. It indicates the optimal number of variables to select for which the averaged correlation is maximised on each component. Figure 9 shows that this is achieved when selecting 15 transporters on the first component, and 15 on the second. Given the drop in values in the averaged correlations for the third component, we decide to only retain two components.
Note:
Based on the tuning above, we perform the final sPCA where the number of variables to select on each component is specified with the argument keepX. Arbitrary values can also be input if you would like to skip the tuning step for more exploratory analyses:
# By default center = TRUE, scale = TRUE
keepX.select <- tune.spca.result$choice.keepX[1:2]
final.spca.multi <- spca(X, ncomp = 2, keepX = keepX.select)
# Proportion of explained variance:
final.spca.multi$prop_expl_var$X
## PC1 PC2
## 0.1171694 0.1004163
Overall when considering two components, we lose approximately 1.1 % of explained variance compared to a full PCA, but the aim of this analysis is to identify key transporters driving the variation in the data, as we show below.
We first examine the sPCA sample plot:
plotIndiv(final.spca.multi,
comp = c(1, 2), # Specify components to plot
ind.names = TRUE, # Show row names of samples
group = multidrug$cell.line$Class,
title = 'ABC transporters, sPCA comp 1 - 2',
legend = TRUE, legend.title = 'Cell line')
Figure 10: Sample plot from the sPCA performed on the ABC.trans data. Samples are projected onto the space spanned by the first two sparse principal components that are calculated based on a subset of selected variables. Samples are coloured by cell line type and numbers indicate the sample IDs.
In Figure 10, component 2 in sPCA shows clearer separation of the melanoma samples compared to the full PCA. Component 1 is similar to the full PCA. Overall, this sample plot shows that little information is lost compared to a full PCA.
A biplot can also be plotted that only shows the selected transporters (Figure 11):
biplot(final.spca.multi, group = multidrug$cell.line$Class,
legend =FALSE)
Figure 11: Biplot from the sPCA performed on the ABS.trans data after variable selection. The plot highlights in more detail which transporter expression levels may be related to specific cell lines, such as melanoma, compared to a classical PCA.
The correlation circle plot highlights variables that contribute to component 1 and component 2 (Fig. 12):
plotVar(final.spca.multi, comp = c(1, 2), var.names = TRUE,
cex = 3, # To change the font size
title = 'Multidrug transporter, sPCA comp 1 - 2')
ABC.trans data. Only the transporters selected by the sPCA are shown on this plot. Transporters coloured in green are discussed in the text." width="75%" />
Figure 12: Correlation Circle plot from the sPCA performed on the ABC.trans data. Only the transporters selected by the sPCA are shown on this plot. Transporters coloured in green are discussed in the text.
The transporters selected by sPCA are amongst the most important ones in PCA. Those coloured in green in Figure 6 (ABCA9, ABCB5, ABCC2 and ABCD1) show an example of variables that contribute positively to component 2, but with a larger weight than in PCA. Thus, they appear as a clearer cluster in the top part of the correlation circle plot compared to PCA. As shown in the biplot in Figure 11, they contribute in explaining the variation in the melanoma samples.
We can extract the variable names and their positive or negative contribution to a given component (here 2), using the selectVar() function:
# On the first component, just a head
head(selectVar(final.spca.multi, comp = 2)$value)
## value.var
## ABCA9 0.4510621
## ABCB5 0.4184759
## ABCC2 0.4046096
## ABCD1 0.3921438
## ABCA3 -0.2779984
## ABCD2 -0.2255945
The loading weights can also be visualised with plotLoading(), where variables are ranked from the least important (top) to the most important (bottom) in Figure 13). Here on component 2:
plotLoadings(final.spca.multi, comp = 2)
Figure 13: sPCA loading plot of the ABS.trans data for component 2. Only the transporters selected by sPCA on component 2 are shown, and are ranked from least important (top) to most important. Bar length indicates the loading weight in PC2.
The data come from a liver toxicity study in which 64 male rats were exposed to non-toxic (50 or 150 mg/kg), moderately toxic (1500 mg/kg) or severely toxic (2000 mg/kg) doses of acetaminophen (paracetamol) (Bushel, Wolfinger, and Gibson 2007). Necropsy was performed at 6, 18, 24 and 48 hours after exposure and the mRNA was extracted from the liver. Ten clinical measurements of markers for liver injury are available for each subject. The microarray data contain expression levels of 3,116 genes. The data were normalised and preprocessed by Bushel, Wolfinger, and Gibson (2007).
liver toxicity contains the following:
$gene: A data frame with 64 rows (rats) and 3116 columns (gene expression levels),$clinic: A data frame with 64 rows (same rats) and 10 columns (10 clinical variables),$treatment: A data frame with 64 rows and 4 columns, describe the different treatments, such as doses of acetaminophen and times of necropsy.We can analyse these two data sets (genes and clinical measurements) using sPLS1, then sPLS2 with a regression mode to explain or predict the clinical variables with respect to the gene expression levels.
library(mixOmics)
data(liver.toxicity)
X <- liver.toxicity$gene
Y <- liver.toxicity$clinic
As we have discussed previously for integrative analysis, we need to ensure that the samples in the two data sets are in the same order, or matching, as we are performing data integration:
head(data.frame(rownames(X), rownames(Y)))
## rownames.X. rownames.Y.
## 1 ID202 ID202
## 2 ID203 ID203
## 3 ID204 ID204
## 4 ID206 ID206
## 5 ID208 ID208
## 6 ID209 ID209
We first start with a simple case scenario where we wish to explain one \(\boldsymbol Y\) variable with a combination of selected \(\boldsymbol X\) variables (transcripts). We choose the following clinical measurement which we denote as the \(\boldsymbol y\) single response variable:
y <- liver.toxicity$clinic[, "ALB.g.dL."]
Defining the ‘best’ number of dimensions to explain the data requires we first launch a PLS1 model with a large number of components. Some of the outputs from the PLS1 object are then retrieved in the perf() function to calculate the \(Q^2\) criterion using repeated 10-fold cross-validation.
tune.pls1.liver <- pls(X = X, Y = y, ncomp = 4, mode = 'regression')
set.seed(33) # For reproducibility with this handbook, remove otherwise
Q2.pls1.liver <- perf(tune.pls1.liver, validation = 'Mfold',
folds = 10, nrepeat = 5)
plot(Q2.pls1.liver, criterion = 'Q2')
Figure 14: \(Q^2\) criterion to choose the number of components in PLS1. For each dimension added to the PLS model, the \(Q^2\) value is shown. The horizontal line of 0.0975 indicates the threshold below which adding a dimension may not be beneficial to improve accuracy in PLS.
The plot in Figure 14 shows that the \(Q^2\) value varies with the number of dimensions added to PLS1, with a decrease to negative values from 2 dimensions. Based on this plot we would choose only one dimension, but we will still add a second dimension for the graphical outputs.
Note:
We now set a grid of values - thin at the start, but also restricted to a small number of genes for a parsimonious model, which we will test for each of the two components in the tune.spls() function, using the MAE criterion.
# Set up a grid of values:
list.keepX <- c(5:10, seq(15, 50, 5))
# list.keepX # Inspect the keepX grid
set.seed(33) # For reproducibility with this handbook, remove otherwise
tune.spls1.MAE <- tune.spls(X, y, ncomp= 2,
test.keepX = list.keepX,
validation = 'Mfold',
folds = 10,
nrepeat = 5,
progressBar = FALSE,
measure = 'MAE')
plot(tune.spls1.MAE)
keepX is indicated with a diamond." 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" alt="Mean Absolute Error criterion to choose the number of variables to select in PLS1, using repeated CV times for a grid of variables to select. The MAE increases with the addition of a second dimension comp 1 to 2, suggesting that only one dimension is sufficient. The optimal keepX is indicated with a diamond." width="75%" />
Figure 15: Mean Absolute Error criterion to choose the number of variables to select in PLS1, using repeated CV times for a grid of variables to select. The MAE increases with the addition of a second dimension comp 1 to 2, suggesting that only one dimension is sufficient. The optimal keepX is indicated with a diamond.
Figure 15 confirms that one dimension is sufficient to reach minimal MAE. Based on the tune.spls() function we extract the final parameters:
choice.ncomp <- tune.spls1.MAE$choice.ncomp$ncomp
# Optimal number of variables to select in X based on the MAE criterion
# We stop at choice.ncomp
choice.keepX <- tune.spls1.MAE$choice.keepX[1:choice.ncomp]
choice.ncomp
## [1] 1
choice.keepX
## comp1
## 40
Note:
measure = 'MSE, the optimal keepX was rather unstable, and is often smaller than when using the MAE criterion. As we have highlighted before, there is some back and forth in the analyses to choose the criterion and parameters that best fit our biological question and interpretation.Here is our final model with the tuned parameters:
spls1.liver <- spls(X, y, ncomp = choice.ncomp, keepX = choice.keepX,
mode = "regression")
The list of genes selected on component 1 can be extracted with the command line (not output here):
selectVar(spls1.liver, comp = 1)$X$name
We can compare the amount of explained variance for the \(\boldsymbol X\) data set based on the sPLS1 (on 1 component) versus PLS1 (that was run on 4 components during the tuning step):
spls1.liver$prop_expl_var$X
## comp1
## 0.08177942
tune.pls1.liver$prop_expl_var$X
## comp1 comp2 comp3 comp4
## 0.11079101 0.14010577 0.21714518 0.06433377
The amount of explained variance in \(\boldsymbol X\) is lower in sPLS1 than PLS1 for the first component. However, we will see in this case study that the Mean Squared Error Prediction is also lower (better) in sPLS1 compared to PLS1.
For further graphical outputs, we need to add a second dimension in the model, which can include the same number of keepX variables as in the first dimension. However, the interpretation should primarily focus on the first dimension. In Figure 16 we colour the samples according to the time of treatment and add symbols to represent the treatment dose. Recall however that such information was not included in the sPLS1 analysis.
spls1.liver.c2 <- spls(X, y, ncomp = 2, keepX = c(rep(choice.keepX, 2)),
mode = "regression")
plotIndiv(spls1.liver.c2,
group = liver.toxicity$treatment$Time.Group,
pch = as.factor(liver.toxicity$treatment$Dose.Group),
legend = TRUE, legend.title = 'Time', legend.title.pch = 'Dose')
liver.toxicity data with two dimensions. Components associated to each data set (or block) are shown. Focusing only on the projection of the sample on the first component shows that the genes selected in \(\boldsymbol X\) tend to explain the 48h length of treatment vs the earlier time points. This is somewhat in agreement with the levels of the \(\boldsymbol y\) variable. However, more insight can be obtained by plotting the first components only, as shown in Figure 17." width="75%" />
Figure 16: Sample plot from the PLS1 performed on the liver.toxicity data with two dimensions. Components associated to each data set (or block) are shown. Focusing only on the projection of the sample on the first component shows that the genes selected in \(\boldsymbol X\) tend to explain the 48h length of treatment vs the earlier time points. This is somewhat in agreement with the levels of the \(\boldsymbol y\) variable. However, more insight can be obtained by plotting the first components only, as shown in Figure 17.
The alternative is to plot the component associated to the \(\boldsymbol X\) data set (here corresponding to a linear combination of the selected genes) vs. the component associated to the \(\boldsymbol y\) variable (corresponding to the scaled \(\boldsymbol y\) variable in PLS1 with one dimension), or calculate the correlation between both components:
# Define factors for colours matching plotIndiv above
time.liver <- factor(liver.toxicity$treatment$Time.Group,
levels = c('18', '24', '48', '6'))
dose.liver <- factor(liver.toxicity$treatment$Dose.Group,
levels = c('50', '150', '1500', '2000'))
# Set up colours and symbols
col.liver <- color.mixo(time.liver)
pch.liver <- as.numeric(dose.liver)
plot(spls1.liver$variates$X, spls1.liver$variates$Y,
xlab = 'X component', ylab = 'y component / scaled y',
col = col.liver, pch = pch.liver)
legend('topleft', col = color.mixo(1:4), legend = levels(time.liver),
lty = 1, title = 'Time')
legend('bottomright', legend = levels(dose.liver), pch = 1:4,
title = 'Dose')
liver.toxicity data on one dimension. A reduced representation of the 40 genes selected and combined in the \(\boldsymbol X\) component on the \(x-\)axis with respect to the \(\boldsymbol y\) component value (equivalent to the scaled values of \(\boldsymbol y\)) on the \(y-\)axis. We observe a separation between the high doses 1500 and 2000 mg/kg (symbols \(+\) and \(\times\)) at 48h and 18h while low and medium doses cluster in the middle of the plot. High doses for 6h and 18h have high scores for both components." width="75%" />
Figure 17: Sample plot from the sPLS1 performed on the liver.toxicity data on one dimension. A reduced representation of the 40 genes selected and combined in the \(\boldsymbol X\) component on the \(x-\)axis with respect to the \(\boldsymbol y\) component value (equivalent to the scaled values of \(\boldsymbol y\)) on the \(y-\)axis. We observe a separation between the high doses 1500 and 2000 mg/kg (symbols \(+\) and \(\times\)) at 48h and 18h while low and medium doses cluster in the middle of the plot. High doses for 6h and 18h have high scores for both components.
cor(spls1.liver$variates$X, spls1.liver$variates$Y)
## comp1
## comp1 0.7625307
Figure 17 is a reduced representation of a multivariate regression with PLS1. It shows that PLS1 effectively models a linear relationship between \(\boldsymbol y\) and the combination of the 40 genes selected in \(\boldsymbol X\).
The performance of the final model can be assessed with the perf() function, using repeated cross-validation (CV). Because a single performance value has little meaning, we propose to compare the performances of a full PLS1 model (with no variable selection) with our sPLS1 model based on the MSEP (other criteria can be used):
set.seed(33) # For reproducibility with this handbook, remove otherwise
# PLS1 model and performance
pls1.liver <- pls(X, y, ncomp = choice.ncomp, mode = "regression")
perf.pls1.liver <- perf(pls1.liver, validation = "Mfold", folds =10,
nrepeat = 5, progressBar = FALSE)
perf.pls1.liver$measures$MSEP$summary
## feature comp mean sd
## 1 Y 1 0.7255745 0.05380427
# To extract values across all repeats:
# perf.pls1.liver$measures$MSEP$values
# sPLS1 performance
perf.spls1.liver <- perf(spls1.liver, validation = "Mfold", folds = 10,
nrepeat = 5, progressBar = FALSE)
perf.spls1.liver$measures$MSEP$summary
## feature comp mean sd
## 1 Y 1 0.6109679 0.02554252
The MSEP is lower with sPLS1 compared to PLS1, indicating that the \(\boldsymbol{X}\) variables selected (listed above with selectVar()) can be considered as a good linear combination of predictors to explain \(\boldsymbol y\).
PLS2 is a more complex problem than PLS1, as we are attempting to fit a linear combination of a subset of \(\boldsymbol{Y}\) variables that are maximally covariant with a combination of \(\boldsymbol{X}\) variables. The sparse variant allows for the selection of variables from both data sets.
As a reminder, here are the dimensions of the \(\boldsymbol{Y}\) matrix that includes clinical parameters associated with liver failure.
dim(Y)
## [1] 64 10
Similar to PLS1, we first start by tuning the number of components to select by using the perf() function and the \(Q^2\) criterion using repeated cross-validation.
tune.pls2.liver <- pls(X = X, Y = Y, ncomp = 5, mode = 'regression')
set.seed(33) # For reproducibility with this handbook, remove otherwise
Q2.pls2.liver <- perf(tune.pls2.liver, validation = 'Mfold', folds = 10,
nrepeat = 5)
plot(Q2.pls2.liver, criterion = 'Q2.total')
Figure 18: \(Q^2\) criterion to choose the number of components in PLS2. For each component added to the PLS2 model, the averaged \(Q^2\) across repeated cross-validation is shown, with the horizontal line of 0.0975 indicating the threshold below which the addition of a dimension may not be beneficial to improve accuracy.
Figure 18 shows that one dimension should be sufficient in PLS2. We will include a second dimension in the graphical outputs, whilst focusing our interpretation on the first dimension.
Note:
nrepeat = 1.Using the tune.spls() function, we can perform repeated cross-validation to obtain some indication of the number of variables to select. We show an example of code below which may take some time to run (see ?tune.spls() to use parallel computing). We had refined the grid of tested values as the tuning function tended to favour a very small signature. Hence we decided to constrain the start of the grid to 3 for a more insightful signature. Both measure = 'cor and RSS gave similar signature sizes, but this observation might differ for other case studies.
The optimal parameters can be output, along with a plot showing the tuning results, as shown in Figure 19:
# This code may take several min to run, parallelisation option is possible
list.keepX <- c(seq(5, 50, 5))
list.keepY <- c(3:10)
set.seed(33) # For reproducibility with this handbook, remove otherwise
tune.spls.liver <- tune.spls(X, Y, test.keepX = list.keepX,
test.keepY = list.keepY, ncomp = 2,
nrepeat = 1, folds = 10, mode = 'regression',
measure = 'cor',
# the following uses two CPUs for faster computation
# it can be commented out
BPPARAM = BiocParallel::SnowParam(workers = 2)
)
plot(tune.spls.liver)
keepX and keepY, the averaged correlation coefficients between the \(\boldsymbol t\) and \(\boldsymbol u\) components are shown across repeated CV, with optimal values (here corresponding to the highest mean correlation) indicated in a green square for each dimension and data set." width="60%" />
Figure 19: Tuning plot for sPLS2. For every grid value of keepX and keepY, the averaged correlation coefficients between the \(\boldsymbol t\) and \(\boldsymbol u\) components are shown across repeated CV, with optimal values (here corresponding to the highest mean correlation) indicated in a green square for each dimension and data set.
Here is our final model with the tuned parameters for our sPLS2 regression analysis. Note that if you choose to not run the tuning step, you can still decide to set the parameters of your choice here.
#Optimal parameters
choice.keepX <- tune.spls.liver$choice.keepX
choice.keepY <- tune.spls.liver$choice.keepY
choice.ncomp <- length(choice.keepX)
spls2.liver <- spls(X, Y, ncomp = choice.ncomp,
keepX = choice.keepX,
keepY = choice.keepY,
mode = "regression")
The amount of explained variance can be extracted for each dimension and each data set:
spls2.liver$prop_expl_var
## $X
## comp1 comp2
## 0.19377955 0.08686379
##
## $Y
## comp1 comp2
## 0.3649944 0.2195915
The selected variables can be extracted from the selectVar() function, for example for the \(\boldsymbol X\) data set, with either their $name or the loading $value (not output here):
selectVar(spls2.liver, comp = 1)$X$value
The VIP measure is exported for all variables in \(\boldsymbol X\), here we only subset those that were selected (non null loading value) for component 1:
vip.spls2.liver <- vip(spls2.liver)
# just a head
head(vip.spls2.liver[selectVar(spls2.liver, comp = 1)$X$name,1])
## A_42_P620915 A_43_P14131 A_42_P578246 A_43_P11724 A_42_P840776 A_42_P675890
## 24.20292 22.10492 15.72275 14.98876 13.92233 13.11236
The (full) output shows that most \(\boldsymbol X\) variables that were selected are important for explaining \(\boldsymbol Y\), since their VIP is greater than 1.
We can examine how frequently each variable is selected when we subsample the data using the perf() function to measure how stable the signature is (Table 1). The same could be output for other components and the \(\boldsymbol Y\) data set.
perf.spls2.liver <- perf(spls2.liver, validation = 'Mfold', folds = 10, nrepeat = 5)
# Extract stability
stab.spls2.liver.comp1 <- perf.spls2.liver$features$stability.X$comp1
# Averaged stability of the X selected features across CV runs, as shown in Table
stab.spls2.liver.comp1[1:choice.keepX[1]]
# We extract the stability measures of only the variables selected in spls2.liver
extr.stab.spls2.liver.comp1 <- stab.spls2.liver.comp1[selectVar(spls2.liver,
comp =1)$X$name]
| x |
|---|
| 0.64 |
| 0.70 |
| 0.50 |
| 0.42 |
| 0.48 |
| NA |
| NA |
| NA |
| NA |
| NA |
| NA |
| NA |
| NA |
| NA |
| NA |
| NA |
| NA |
| NA |
| NA |
| NA |
We recommend to mainly focus on the interpretation of the most stable selected variables (with a frequency of occurrence greater than 0.8).
Using the plotIndiv() function, we display the sample and metadata information using the arguments group (colour) and pch (symbol) to better understand the similarities between samples modelled with sPLS2.
The plot on the left hand side corresponds to the projection of the samples from the \(\boldsymbol X\) data set (gene expression) and the plot on the right hand side the \(\boldsymbol Y\) data set (clinical variables).
plotIndiv(spls2.liver, ind.names = FALSE,
group = liver.toxicity$treatment$Time.Group,
pch = as.factor(liver.toxicity$treatment$Dose.Group),
col.per.group = color.mixo(1:4),
legend = TRUE, legend.title = 'Time',
legend.title.pch = 'Dose')
liver.toxicity data. Samples are projected into the space spanned by the components associated to each data set (or block). We observe some agreement between the data sets, and a separation of the 1500 and 2000 mg doses (\(+\) and \(\times\)) in the 18h, 24h time points, and the 48h time point." width="75%" />
Figure 20: Sample plot for sPLS2 performed on the liver.toxicity data. Samples are projected into the space spanned by the components associated to each data set (or block). We observe some agreement between the data sets, and a separation of the 1500 and 2000 mg doses (\(+\) and \(\times\)) in the 18h, 24h time points, and the 48h time point.
From Figure 20 we observe an effect of low vs. high doses of acetaminophen (component 1) as well as time of necropsy (component 2). There is some level of agreement between the two data sets, but it is not perfect!
If you run an sPLS with three dimensions, you can consider the 3D plotIndiv() by specifying style = '3d in the function.
The plotArrow() option is useful in this context to visualise the level of agreement between data sets. Recall that in this plot:
plotArrow(spls2.liver, ind.names = FALSE,
group = liver.toxicity$treatment$Time.Group,
col.per.group = color.mixo(1:4),
legend.title = 'Time.Group')
liver.toxicity data. The start of the arrow indicates the location of a given sample in the space spanned by the components associated to the gene data set, and the tip of the arrow the location of that same sample in the space spanned by the components associated to the clinical data set. We observe large shifts for 18h, 24 and 48h samples for the high doses, however the clusters of samples remain the same, as we observed in Figure 20." width="75%" />
Figure 21: Arrow plot from the sPLS2 performed on the liver.toxicity data. The start of the arrow indicates the location of a given sample in the space spanned by the components associated to the gene data set, and the tip of the arrow the location of that same sample in the space spanned by the components associated to the clinical data set. We observe large shifts for 18h, 24 and 48h samples for the high doses, however the clusters of samples remain the same, as we observed in Figure 20.
In Figure 21 we observe that specific groups of samples seem to be located far apart from one data set to the other, indicating a potential discrepancy between the information extracted. However the groups of samples according to either dose or treatment remains similar.
Correlation circle plots illustrate the correlation structure between the two types of variables. To display only the name of the variables from the \(\boldsymbol{Y}\) data set, we use the argument var.names = c(FALSE, TRUE) where each boolean indicates whether the variable names should be output for each data set. We also modify the size of the font, as shown in Figure 22:
plotVar(spls2.liver, cex = c(3,4), var.names = c(FALSE, TRUE))
liver.toxicity data. The plot highlights correlations within selected genes (their names are not indicated here), within selected clinical parameters, and correlations between genes and clinical parameters on each dimension of sPLS2. This plot should be interpreted in relation to Figure 20 to better understand how the expression levels of these molecules may characterise specific sample groups." width="75%" />
Figure 22: Correlation circle plot from the sPLS2 performed on the liver.toxicity data. The plot highlights correlations within selected genes (their names are not indicated here), within selected clinical parameters, and correlations between genes and clinical parameters on each dimension of sPLS2. This plot should be interpreted in relation to Figure 20 to better understand how the expression levels of these molecules may characterise specific sample groups.
To display variable names that are different from the original data matrix (e.g. gene ID), we set the argument var.names as a list for each type of label, with geneBank ID for the \(\boldsymbol X\) data set, and TRUE for the \(\boldsymbol Y\) data set:
plotVar(spls2.liver,
var.names = list(X.label = liver.toxicity$gene.ID[,'geneBank'],
Y.label = TRUE), cex = c(3,4))
$gene.ID (Note: some gene names are missing)." 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alt="Correlation circle plot from the sPLS2 performed on the liver.toxicity data. A variant of Figure 22 with gene names that are available in $gene.ID (Note: some gene names are missing)." width="75%" />
Figure 23: Correlation circle plot from the sPLS2 performed on the liver.toxicity data. A variant of Figure 22 with gene names that are available in $gene.ID (Note: some gene names are missing).
The correlation circle plots highlight the contributing variables that, together, explain the covariance between the two data sets. In addition, specific subsets of molecules can be further investigated, and in relation with the sample group they may characterise. The latter can be examined with additional plots (for example boxplots with respect to known sample groups and expression levels of specific variables, as we showed in the PCA case study previously. The next step would be to examine the validity of the biological relationship between the clusters of genes with some of the clinical variables that we observe in this plot.
A 3D plot is also available in plotVar() with the argument style = '3d. It requires an sPLS2 model with at least three dimensions.
Other plots are available to complement the information from the correlation circle plots, such as Relevance networks and Clustered Image Maps (CIMs), as described in Module 2.
The network in sPLS2 displays only the variables selected by sPLS, with an additional cutoff similarity value argument (absolute value between 0 and 1) to improve interpretation. Because Rstudio sometimes struggles with the margin size of this plot, we can either launch X11() prior to plotting the network, or use the arguments save and name.save as shown below:
# Define red and green colours for the edges
color.edge <- color.GreenRed(50)
# X11() # To open a new window for Rstudio
network(spls2.liver, comp = 1:2,
cutoff = 0.7,
shape.node = c("rectangle", "circle"),
color.node = c("cyan", "pink"),
color.edge = color.edge,
# To save the plot, unotherwise:
# save = 'pdf', name.save = 'network_liver'
)
cutoff argument (optional)." 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alt="Network representation from the sPLS2 performed on the liver.toxicity data. The networks are bipartite, where each edge links a gene (rectangle) to a clinical variable (circle) node, according to a similarity matrix described in Module 2. Only variables selected by sPLS2 on the two dimensions are represented and are further filtered here according to a cutoff argument (optional)." width="75%" />
Figure 24: Network representation from the sPLS2 performed on the liver.toxicity data. The networks are bipartite, where each edge links a gene (rectangle) to a clinical variable (circle) node, according to a similarity matrix described in Module 2. Only variables selected by sPLS2 on the two dimensions are represented and are further filtered here according to a cutoff argument (optional).
Figure 24 shows two distinct groups of variables. The first cluster groups four clinical parameters that are mostly positively associated with selected genes. The second group includes one clinical parameter negatively associated with other selected genes. These observations are similar to what was observed in the correlation circle plot in Figure 22.
Note:
igraph R package Csardi, Nepusz, et al. (2006)).The Clustered Image Map also allows us to visualise correlations between variables. Here we choose to represent the variables selected on the two dimensions and we save the plot as a pdf figure.
# X11() # To open a new window if the graphic is too large
cim(spls2.liver, comp = 1:2, xlab = "clinic", ylab = "genes",
# To save the plot, uncomment:
# save = 'pdf', name.save = 'cim_liver'
)
liver.toxicity data. The plot displays the similarity values (as described in Module 2) between the \(\boldsymbol X\) and \(\boldsymbol Y\) variables selected across two dimensions, and clustered with a complete Euclidean distance method." width="75%" />
Figure 25: Clustered Image Map from the sPLS2 performed on the liver.toxicity data. The plot displays the similarity values (as described in Module 2) between the \(\boldsymbol X\) and \(\boldsymbol Y\) variables selected across two dimensions, and clustered with a complete Euclidean distance method.
The CIM in Figure 25 shows that the clinical variables can be separated into three clusters, each of them either positively or negatively associated with two groups of genes. This is similar to what we have observed in Figure 22. We would give a similar interpretation to the relevance network, had we also used a cutoff threshold in cim().
Note:
To finish, we assess the performance of sPLS2. As an element of comparison, we consider sPLS2 and PLS2 that includes all variables, to give insights into the different methods.
# Comparisons of final models (PLS, sPLS)
## PLS
pls.liver <- pls(X, Y, mode = 'regression', ncomp = 2)
perf.pls.liver <- perf(pls.liver, validation = 'Mfold', folds = 10,
nrepeat = 5)
## Performance for the sPLS model ran earlier
perf.spls.liver <- perf(spls2.liver, validation = 'Mfold', folds = 10,
nrepeat = 5)
plot(c(1,2), perf.pls.liver$measures$cor.upred$summary$mean,
col = 'blue', pch = 16,
ylim = c(0.6,1), xaxt = 'n',
xlab = 'Component', ylab = 't or u Cor',
main = 's/PLS performance based on Correlation')
axis(1, 1:2) # X-axis label
points(perf.pls.liver$measures$cor.tpred$summary$mean, col = 'red', pch = 16)
points(perf.spls.liver$measures$cor.upred$summary$mean, col = 'blue', pch = 17)
points(perf.spls.liver$measures$cor.tpred$summary$mean, col = 'red', pch = 17)
legend('bottomleft', col = c('blue', 'red', 'blue', 'red'),
pch = c(16, 16, 17, 17), c('u PLS', 't PLS', 'u sPLS', 't sPLS'))
Figure 26: Comparison of the performance of PLS2 and sPLS2, based on the correlation between the actual and predicted components \(\boldsymbol{t,u}\) associated to each data set for each component.
We extract the correlation between the actual and predicted components \(\boldsymbol{t,u}\) associated to each data set in Figure 26. The correlation remains high on the first dimension, even when variables are selected. On the second dimension the correlation coefficients are equivalent or slightly lower in sPLS compared to PLS. Overall this performance comparison indicates that the variable selection in sPLS still retains relevant information compared to a model that includes all variables.
Note:
The Small Round Blue Cell Tumours (SRBCT) data set from (Khan et al. 2001) includes the expression levels of 2,308 genes measured on 63 samples. The samples are divided into four classes: 8 Burkitt Lymphoma (BL), 23 Ewing Sarcoma (EWS), 12 neuroblastoma (NB), and 20 rhabdomyosarcoma (RMS). The data are directly available in a processed and normalised format from the mixOmics package and contains the following:
$gene: A data frame with 63 rows and 2,308 columns. These are the expression levels of 2,308 genes in 63 subjects,
$class: A vector containing the class of tumour for each individual (4 classes in total),
$gene.name: A data frame with 2,308 rows and 2 columns containing further information on the genes.
More details can be found in ?srbct. We will illustrate PLS-DA and sPLS-DA which are suited for large biological data sets where the aim is to identify molecular signatures, as well as classify samples. We will analyse the gene expression levels of srbct$gene to discover which genes may best discriminate the 4 groups of tumours.
We first load the data from the package. We then set up the data so that \(\boldsymbol X\) is the gene expression matrix and \(\boldsymbol y\) is the factor indicating sample class membership. \(\boldsymbol y\) will be transformed into a dummy matrix \(\boldsymbol Y\) inside the function. We also check that the dimensions are correct and match both \(\boldsymbol X\) and \(\boldsymbol y\):
library(mixOmics)
data(srbct)
X <- srbct$gene
# Outcome y that will be internally coded as dummy:
Y <- srbct$class
dim(X); length(Y)
## [1] 63 2308
## [1] 63
summary(Y)
## EWS BL NB RMS
## 23 8 12 20
As covered in Module 3, PCA is a useful tool to explore the gene expression data and to assess for sample similarities between tumour types. Remember that PCA is an unsupervised approach, but we can colour the samples by their class to assist in interpreting the PCA (Figure 27). Here we center (default argument) and scale the data:
pca.srbct <- pca(X, ncomp = 3, scale = TRUE)
plotIndiv(pca.srbct, group = srbct$class, ind.names = FALSE,
legend = TRUE,
title = 'SRBCT, PCA comp 1 - 2')
Figure 27: Preliminary (unsupervised) analysis with PCA on the SRBCT gene expression data. Samples are projected into the space spanned by the principal components 1 and 2. The tumour types are not clustered, meaning that the major source of variation cannot be explained by tumour types. Instead, samples seem to cluster according to an unknown source of variation.
We observe almost no separation between the different tumour types in the PCA sample plot, with perhaps the exception of the NB samples that tend to cluster with other samples. This preliminary exploration teaches us two important findings:
The perf() function evaluates the performance of PLS-DA - i.e., its ability to rightly classify ‘new’ samples into their tumour category using repeated cross-validation. We initially choose a large number of components (here ncomp = 10) and assess the model as we gradually increase the number of components. Here we use 3-fold CV repeated 10 times. In Module 2, we provided further guidelines on how to choose the folds and nrepeat parameters:
plsda.srbct <- plsda(X,Y, ncomp = 10)
set.seed(30) # For reproducibility with this handbook, remove otherwise
perf.plsda.srbct <- perf(plsda.srbct, validation = 'Mfold', folds = 3,
progressBar = FALSE, # Set to TRUE to track progress
nrepeat = 10) # We suggest nrepeat = 50
plot(perf.plsda.srbct, sd = TRUE, legend.position = 'horizontal')
max.dist, centroids.dist and mahalanobis.dist. Bars show the standard deviation across the repeated folds. The plot shows that the error rate reaches a minimum from 3 components." 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alt="Tuning the number of components in PLS-DA on the SRBCT gene expression data. For each component, repeated cross-validation (10 \(\times 3-\)fold CV) is used to evaluate the PLS-DA classification performance (overall and balanced error rate BER), for each type of prediction distance; max.dist, centroids.dist and mahalanobis.dist. Bars show the standard deviation across the repeated folds. The plot shows that the error rate reaches a minimum from 3 components." width="75%" />
Figure 28: Tuning the number of components in PLS-DA on the SRBCT gene expression data. For each component, repeated cross-validation (10 \(\times 3-\)fold CV) is used to evaluate the PLS-DA classification performance (overall and balanced error rate BER), for each type of prediction distance; max.dist, centroids.dist and mahalanobis.dist. Bars show the standard deviation across the repeated folds. The plot shows that the error rate reaches a minimum from 3 components.
From the classification performance output presented in Figure 28, we observe that:
There are some slight differences between the overall and balanced error rates (BER) with BER > overall, suggesting that minority classes might be ignored from the classification task when considering the overall performance (summary(Y) shows that BL only includes 8 samples). In general the trend is the same, however, and for further tuning with sPLS-DA we will consider the BER.
The error rate decreases and reaches a minimum for ncomp = 3 for the max.dist distance. These parameters will be included in further analyses.
Notes:
ncomp) as a ‘compounding’ model (i.e. PLS-DA with component 3 includes the trained model on the previous two components).Additional numerical outputs from the performance results are listed and can be reported as performance measures (not output here):
perf.plsda.srbct
We now run our final PLS-DA model that includes three components:
final.plsda.srbct <- plsda(X,Y, ncomp = 3)
We output the sample plots for the dimensions of interest (up to three). By default, the samples are coloured according to their class membership. We also add confidence ellipses (ellipse = TRUE, confidence level set to 95% by default, see the argument ellipse.level) in Figure 29. A 3D plot could also be insightful (use the argument type = '3D').
plotIndiv(final.plsda.srbct, ind.names = FALSE, legend=TRUE,
comp=c(1,2), ellipse = TRUE,
title = 'PLS-DA on SRBCT comp 1-2',
X.label = 'PLS-DA comp 1', Y.label = 'PLS-DA comp 2')
plotIndiv(final.plsda.srbct, ind.names = FALSE, legend=TRUE,
comp=c(2,3), ellipse = TRUE,
title = 'PLS-DA on SRBCT comp 2-3',
X.label = 'PLS-DA comp 2', Y.label = 'PLS-DA comp 3')
SRBCT gene expression data. Samples are projected into the space spanned by the first three components. (a) Components 1 and 2 and (b) Components 1 and 3. Samples are coloured by their tumour subtypes. Component 1 discriminates RMS + EWS vs. NB + BL, component 2 discriminates RMS + NB vs. EWS + BL, while component 3 discriminates further the NB and BL groups. It is the combination of all three components that enables us to discriminate all classes." width="50%" />SRBCT gene expression data. Samples are projected into the space spanned by the first three components. (a) Components 1 and 2 and (b) Components 1 and 3. Samples are coloured by their tumour subtypes. Component 1 discriminates RMS + EWS vs. NB + BL, component 2 discriminates RMS + NB vs. EWS + BL, while component 3 discriminates further the NB and BL groups. It is the combination of all three components that enables us to discriminate all classes." width="50%" />
Figure 29: Sample plots from PLS-DA performed on the SRBCT gene expression data. Samples are projected into the space spanned by the first three components. (a) Components 1 and 2 and (b) Components 1 and 3. Samples are coloured by their tumour subtypes. Component 1 discriminates RMS + EWS vs. NB + BL, component 2 discriminates RMS + NB vs. EWS + BL, while component 3 discriminates further the NB and BL groups. It is the combination of all three components that enables us to discriminate all classes.
We can observe improved clustering according to tumour subtypes, compared with PCA. This is to be expected since the PLS-DA model includes the class information of each sample. We observe some discrimination between the NB and BL samples vs. the others on the first component (x-axis), and EWS and RMS vs. the others on the second component (y-axis). From the plotIndiv() function, the axis labels indicate the amount of variation explained per component. However, the interpretation of this amount is not as important as in PCA, as PLS-DA aims to maximise the covariance between components associated to \(\boldsymbol X\) and \(\boldsymbol Y\), rather than the variance \(\boldsymbol X\).
We can rerun a more extensive performance evaluation with more repeats for our final model:
set.seed(30) # For reproducibility with this handbook, remove otherwise
perf.final.plsda.srbct <- perf(final.plsda.srbct, validation = 'Mfold',
folds = 3,
progressBar = FALSE, # TRUE to track progress
nrepeat = 10) # we recommend 50
Retaining only the BER and the max.dist, numerical outputs of interest include the final overall performance for 3 components:
perf.final.plsda.srbct$error.rate$BER[, 'max.dist']
## comp1 comp2 comp3
## 0.57826087 0.26414855 0.06282609
As well as the error rate per class across each component:
perf.final.plsda.srbct$error.rate.class$max.dist
## comp1 comp2 comp3
## EWS 0.3130435 0.07826087 0.09130435
## BL 0.8250000 0.57500000 0.00000000
## NB 0.4750000 0.33333333 0.05000000
## RMS 0.7000000 0.07000000 0.11000000
From this output, we can see that the first component tends to classify EWS and NB better than the other classes. As components 2 and then 3 are added, the classification improves for all classes. However we see a slight increase in classification error in component 3 for EWS and RMS while BL is perfectly classified. A permutation test could also be conducted to conclude about the significance of the differences between sample groups, but is not currently implemented in the package.
A prediction background can be added to the sample plot by calculating a background surface first, before overlaying the sample plot (Figure 30, see Extra Reading material, or ?background.predict). We give an example of the code below based on the maximum prediction distance:
background.max <- background.predict(final.plsda.srbct,
comp.predicted = 2,
dist = 'max.dist')
plotIndiv(final.plsda.srbct, comp = 1:2, group = srbct$class,
ind.names = FALSE, title = 'Maximum distance',
legend = TRUE, background = background.max)
Figure 30: Sample plots from PLS-DA on the SRBCT gene expression data and prediction areas based on prediction distances. From our usual sample plot, we overlay a background prediction area based on permutations from the first two PLS-DA components using the three different types of prediction distances. The outputs show how the prediction distance can influence the quality of the prediction, with samples projected into a wrong class area, and hence resulting in predicted misclassification. (Currently, the prediction area background can only be calculated for the first two components).
Figure 30 shows the differences in prediction according to the prediction distance, and can be used as a further diagnostic tool for distance choice. It also highlights the characteristics of the distances. For example the max.dist is a linear distance, whereas both centroids.dist and mahalanobis.dist are non linear. Our experience has shown that as discrimination of the classes becomes more challenging, the complexity of the distances (from maximum to Mahalanobis distance) should increase, see details in the Extra reading material.
In high-throughput experiments, we expect that many of the 2308 genes in \(\boldsymbol X\) are noisy or uninformative to characterise the different classes. An sPLS-DA analysis will help refine the sample clusters and select a small subset of variables relevant to discriminate each class.
We estimate the classification error rate with respect to the number of selected variables in the model with the function tune.splsda(). The tuning is being performed one component at a time inside the function and the optimal number of variables to select is automatically retrieved after each component run, as described in Module 2.
Previously, we determined the number of components to be ncomp = 3 with PLS-DA. Here we set ncomp = 4 to further assess if this would be the case for a sparse model, and use 5-fold cross validation repeated 10 times. We also choose the maximum prediction distance.
Note:
We first define a grid of keepX values. For example here, we define a fine grid at the start, and then specify a coarser, larger sequence of values:
# Grid of possible keepX values that will be tested for each comp
list.keepX <- c(1:10, seq(20, 100, 10))
list.keepX
## [1] 1 2 3 4 5 6 7 8 9 10 20 30 40 50 60 70 80 90 100
# This chunk takes ~ 2 min to run
# Some convergence issues may arise but it is ok as this is run on CV folds
tune.splsda.srbct <- tune.splsda(X, Y, ncomp = 4, validation = 'Mfold',
folds = 5, dist = 'max.dist',
test.keepX = list.keepX, nrepeat = 10)
The following command line will output the mean error rate for each component and each tested keepX value given the past (tuned) components.
# Just a head of the classification error rate per keepX (in rows) and comp
head(tune.splsda.srbct$error.rate)
## comp1 comp2 comp3 comp4
## 1 0.6271739 0.3153442 0.05048007 0.009601449
## 2 0.5507609 0.3128442 0.03210145 0.009601449
## 3 0.5484692 0.3042572 0.03131341 0.008514493
## 4 0.5389946 0.3001359 0.02793478 0.011014493
## 5 0.5285145 0.2915489 0.01409420 0.011014493
## 6 0.5274275 0.2854620 0.01300725 0.012101449
When we examine each individual row, this output globally shows that the classification error rate continues to decrease after the third component in sparse PLS-DA.
We display the mean classification error rate on each component, bearing in mind that each component is conditional on the previous components calculated with the optimal number of selected variables. The diamond in Figure 31 indicates the best keepX value to achieve the lowest error rate per component.
# To show the error bars across the repeats:
plot(tune.splsda.srbct, sd = TRUE)
keepX value chosen for the previous component (comp 1)." 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" alt="Tuning keepX for the sPLS-DA performed on the SRBCT gene expression data. Each coloured line represents the balanced error rate (y-axis) per component across all tested keepX values (x-axis) with the standard deviation based on the repeated cross-validation folds. The diamond indicates the optimal keepX value on a particular component which achieves the lowest classification error rate as determined with a one-sided \(t-\)test. As sPLS-DA is an iterative algorithm, values represented for a given component (e.g. comp 1 to 2) include the optimal keepX value chosen for the previous component (comp 1)." width="75%" />
Figure 31: Tuning keepX for the sPLS-DA performed on the SRBCT gene expression data. Each coloured line represents the balanced error rate (y-axis) per component across all tested keepX values (x-axis) with the standard deviation based on the repeated cross-validation folds. The diamond indicates the optimal keepX value on a particular component which achieves the lowest classification error rate as determined with a one-sided \(t-\)test. As sPLS-DA is an iterative algorithm, values represented for a given component (e.g. comp 1 to 2) include the optimal keepX value chosen for the previous component (comp 1).
The tuning results depend on the tuning grid list.keepX, as well as the values chosen for folds and nrepeat. Therefore, we recommend assessing the performance of the final model, as well as examining the stability of the selected variables across the different folds, as detailed in the next section.
Figure 31 shows that the error rate decreases when more components are included in sPLS-DA. To obtain a more reliable estimation of the error rate, the number of repeats should be increased (between 50 to 100). This type of graph helps not only to choose the ‘optimal’ number of variables to select, but also to confirm the number of components ncomp. From the code below, we can assess that in fact, the addition of a fourth component does not improve the classification (no statistically significant improvement according to a one-sided \(t-\)test), hence we can choose ncomp = 3.
# The optimal number of components according to our one-sided t-tests
tune.splsda.srbct$choice.ncomp$ncomp
## [1] 3
# The optimal keepX parameter according to minimal error rate
tune.splsda.srbct$choice.keepX
## comp1 comp2 comp3 comp4
## 50 20 30 3
Here is our final sPLS-DA model with three components and the optimal keepX obtained from our tuning step.
You can choose to skip the tuning step, and input your arbitrarily chosen parameters in the following code (simply specify your own ncomp and keepX values):
# Optimal number of components based on t-tests on the error rate
ncomp <- tune.splsda.srbct$choice.ncomp$ncomp
ncomp
## [1] 3
# Optimal number of variables to select
select.keepX <- tune.splsda.srbct$choice.keepX[1:ncomp]
select.keepX
## comp1 comp2 comp3
## 50 20 30
splsda.srbct <- splsda(X, Y, ncomp = ncomp, keepX = select.keepX)
The performance of the model with the ncomp and keepX parameters is assessed with the perf() function. We use 5-fold validation (folds = 5), repeated 10 times (nrepeat = 10) for illustrative purposes, but we recommend increasing to nrepeat = 50. Here we choose the max.dist prediction distance, based on our results obtained with PLS-DA.
The classification error rates that are output include both the overall error rate, as well as the balanced error rate (BER) when the number of samples per group is not balanced - as is the case in this study.
set.seed(34) # For reproducibility with this handbook, remove otherwise
perf.splsda.srbct <- perf(splsda.srbct, folds = 5, validation = "Mfold",
dist = "max.dist", progressBar = FALSE, nrepeat = 10)
# perf.splsda.srbct # Lists the different outputs
perf.splsda.srbct$error.rate
## $overall
## max.dist
## comp1 0.431746032
## comp2 0.198412698
## comp3 0.006349206
##
## $BER
## max.dist
## comp1 0.522146739
## comp2 0.260670290
## comp3 0.004673913
We can also examine the error rate per class:
perf.splsda.srbct$error.rate.class
## $max.dist
## comp1 comp2 comp3
## EWS 0.02608696 0.004347826 0.008695652
## BL 0.58750000 0.375000000 0.000000000
## NB 0.95000000 0.483333333 0.000000000
## RMS 0.52500000 0.180000000 0.010000000
These results can be compared with the performance of PLS-DA and show the benefits of variable selection to not only obtain a parsimonious model, but also to improve the classification error rate (overall and per class).
During the repeated cross-validation process in perf() we can record how often the same variables are selected across the folds. This information is important to answer the question: How reproducible is my gene signature when the training set is perturbed via cross-validation?.
par(mfrow=c(1,2))
# For component 1
stable.comp1 <- perf.splsda.srbct$features$stable$comp1
barplot(stable.comp1, xlab = 'variables selected across CV folds',
ylab = 'Stability frequency',
main = 'Feature stability for comp = 1')
# For component 2
stable.comp2 <- perf.splsda.srbct$features$stable$comp2
barplot(stable.comp2, xlab = 'variables selected across CV folds',
ylab = 'Stability frequency',
main = 'Feature stability for comp = 2')
par(mfrow=c(1,1))
Figure 32: Stability of variable selection from the sPLS-DA on the SRBCT gene expression data. We use a by-product from perf() to assess how often the same variables are selected for a given keepX value in the final sPLS-DA model. The barplot represents the frequency of selection across repeated CV folds for each selected gene for component 1 and 2. The genes are ranked according to decreasing frequency.
Figure 32 shows that the genes selected on component 1 are moderately stable (frequency < 0.5) whereas those selected on component 2 are more stable (frequency < 0.7). This can be explained as there are various combinations of genes that are discriminative on component 1, whereas the number of combinations decreases as we move to component 2 which attempts to refine the classification.
The function selectVar() outputs the variables selected for a given component and their loading values (ranked in decreasing absolute value). We concatenate those results with the feature stability, as shown here for variables selected on component 1:
# First extract the name of selected var:
select.name <- selectVar(splsda.srbct, comp = 1)$name
# Then extract the stability values from perf:
stability <- perf.splsda.srbct$features$stable$comp1[select.name]
# Just the head of the stability of the selected var:
head(cbind(selectVar(splsda.srbct, comp = 1)$value, stability))
## value.var Var1 Freq
## g123 0.3043756 g123 0.46
## g846 0.2678550 g846 0.46
## g758 0.2372147 g758 0.46
## g836 0.2360363 g836 0.46
## g1606 0.2350958 g1606 0.46
## g335 0.2334439 g335 0.46
As we hinted previously, the genes selected on the first component are not necessarily the most stable, suggesting that different combinations can lead to the same discriminative ability of the model. The stability increases in the following components, as the classification task becomes more refined.
Note:
vip() function on splsda.srbct.Previously, we showed the ellipse plots displayed for each class. Here we also use the star argument (star = TRUE), which displays arrows starting from each group centroid towards each individual sample (Figure 33).
plotIndiv(splsda.srbct, comp = c(1,2),
ind.names = FALSE,
ellipse = TRUE, legend = TRUE,
star = TRUE,
title = 'SRBCT, sPLS-DA comp 1 - 2')
plotIndiv(splsda.srbct, comp = c(2,3),
ind.names = FALSE,
ellipse = TRUE, legend = TRUE,
star = TRUE,
title = 'SRBCT, sPLS-DA comp 2 - 3')
SRBCT gene expression data. Samples are projected into the space spanned by the first three components. The plots represent 95% ellipse confidence intervals around each sample class. The start of each arrow represents the centroid of each class in the space spanned by the components. (a) Components 1 and 2 and (b) Components 2 and 3. Samples are coloured by their tumour subtype. Component 1 discriminates BL vs. the rest, component 2 discriminates EWS vs. the rest, while component 3 further discriminates NB vs. RMS vs. the rest. The combination of all three components enables us to discriminate all classes." width="50%" />SRBCT gene expression data. Samples are projected into the space spanned by the first three components. The plots represent 95% ellipse confidence intervals around each sample class. The start of each arrow represents the centroid of each class in the space spanned by the components. (a) Components 1 and 2 and (b) Components 2 and 3. Samples are coloured by their tumour subtype. Component 1 discriminates BL vs. the rest, component 2 discriminates EWS vs. the rest, while component 3 further discriminates NB vs. RMS vs. the rest. The combination of all three components enables us to discriminate all classes." width="50%" />
Figure 33: Sample plots from the sPLS-DA performed on the SRBCT gene expression data. Samples are projected into the space spanned by the first three components. The plots represent 95% ellipse confidence intervals around each sample class. The start of each arrow represents the centroid of each class in the space spanned by the components. (a) Components 1 and 2 and (b) Components 2 and 3. Samples are coloured by their tumour subtype. Component 1 discriminates BL vs. the rest, component 2 discriminates EWS vs. the rest, while component 3 further discriminates NB vs. RMS vs. the rest. The combination of all three components enables us to discriminate all classes.
The sample plots are different from PLS-DA (Figure 29) with an overlap of specific classes (i.e. NB + RMS on component 1 and 2), that are then further separated on component 3, thus showing how the genes selected on each component discriminate particular sets of sample groups.
We represent the genes selected with sPLS-DA on the correlation circle plot. Here to increase interpretation, we specify the argument var.names as the first 10 characters of the gene names (Figure 34). We also reduce the size of the font with the argument cex.
Note:
plotvar() as an object to output the coordinates and variable names if the plot is too cluttered.var.name.short <- substr(srbct$gene.name[, 2], 1, 10)
plotVar(splsda.srbct, comp = c(1,2),
var.names = list(var.name.short), cex = 3)
Figure 34: Correlation circle plot representing the genes selected by sPLS-DA performed on the SRBCT gene expression data. Gene names are truncated to the first 10 characters. Only the genes selected by sPLS-DA are shown in components 1 and 2. We observe three groups of genes (positively associated with component 1, and positively or negatively associated with component 2). This graphic should be interpreted in conjunction with the sample plot.
By considering both the correlation circle plot (Figure 34) and the sample plot in Figure 33, we observe that a group of genes with a positive correlation with component 1 (‘EH domain’, ‘proteasome’ etc.) are associated with the BL samples. We also observe two groups of genes either positively or negatively correlated with component 2. These genes are likely to characterise either the NB + RMS classes, or the EWS class. This interpretation can be further examined with the plotLoadings() function.
In this plot, the loading weights of each selected variable on each component are represented (see Module 2). The colours indicate the group in which the expression of the selected gene is maximal based on the mean (method = 'median' is also available for skewed data). For example on component 1:
plotLoadings(splsda.srbct, comp = 1, method = 'mean', contrib = 'max',
name.var = var.name.short)
SRBCT gene expression data. Genes are ranked according to their loading weight (most important at the bottom to least important at the top), represented as a barplot. Colours indicate the class for which a particular gene is maximally expressed, on average, in this particular class. The plot helps to further characterise the gene signature and should be interpreted jointly with the sample plot (Figure 33)." width="75%" />
Figure 35: Loading plot of the genes selected by sPLS-DA on component 1 on the SRBCT gene expression data. Genes are ranked according to their loading weight (most important at the bottom to least important at the top), represented as a barplot. Colours indicate the class for which a particular gene is maximally expressed, on average, in this particular class. The plot helps to further characterise the gene signature and should be interpreted jointly with the sample plot (Figure 33).
Here all genes are associated with BL (on average, their expression levels are higher in this class than in the other classes).
Notes:
ndisplay to only display the top selected genes if the signature is too large.contrib = 'min' to interpret the inverse trend of the signature (i.e. which genes have the smallest expression in which class, here a mix of NB and RMS samples).To complete the visualisation, the CIM in this special case is a simple hierarchical heatmap (see ?cim) representing the expression levels of the genes selected across all three components with respect to each sample. Here we use an Euclidean distance with Complete agglomeration method, and we specify the argument row.sideColors to colour the samples according to their tumour type (Figure 36).
cim(splsda.srbct, row.sideColors = color.mixo(Y))
Figure 36: Clustered Image Map of the genes selected by sPLS-DA on the SRBCT gene expression data across all 3 components. A hierarchical clustering based on the gene expression levels of the selected genes, with samples in rows coloured according to their tumour subtype (using Euclidean distance with Complete agglomeration method). As expected, we observe a separation of all different tumour types, which are characterised by different levels of expression.
The heatmap shows the level of expression of the genes selected by sPLS-DA across all three components, and the overall ability of the gene signature to discriminate the tumour subtypes.
Note:
comp if you wish to visualise a specific set of components in cim().In this section, we artificially create an ‘external’ test set on which we want to predict the class membership to illustrate the prediction process in sPLS-DA (see Extra Reading material). We randomly select 50 samples from the srbct study as part of the training set, and the remainder as part of the test set:
set.seed(33) # For reproducibility with this handbook, remove otherwise
train <- sample(1:nrow(X), 50) # Randomly select 50 samples in training
test <- setdiff(1:nrow(X), train) # Rest is part of the test set
# Store matrices into training and test set:
X.train <- X[train, ]
X.test <- X[test,]
Y.train <- Y[train]
Y.test <- Y[test]
# Check dimensions are OK:
dim(X.train); dim(X.test)
## [1] 50 2308
## [1] 13 2308
Here we assume that the tuning step was performed on the training set only (it is really important to tune only on the training step to avoid overfitting), and that the optimal keepX values are, for example, keepX = c(20,30,40) on three components. The final model on the training data is:
train.splsda.srbct <- splsda(X.train, Y.train, ncomp = 3, keepX = c(20,30,40))
We now apply the trained model on the test set X.test and we specify the prediction distance, for example mahalanobis.dist (see also ?predict.splsda):
predict.splsda.srbct <- predict(train.splsda.srbct, X.test,
dist = "mahalanobis.dist")
The $class output of our object predict.splsda.srbct gives the predicted classes of the test samples.
First we concatenate the prediction for each of the three components (conditionally on the previous component) and the real class - in a real application case you may not know the true class.
# Just the head:
head(data.frame(predict.splsda.srbct$class, Truth = Y.test))
## mahalanobis.dist.comp1 mahalanobis.dist.comp2 mahalanobis.dist.comp3
## EWS.T7 EWS EWS EWS
## EWS.T15 EWS EWS EWS
## EWS.C8 EWS EWS EWS
## EWS.C10 EWS EWS EWS
## BL.C8 BL BL BL
## NB.C6 NB NB NB
## Truth
## EWS.T7 EWS
## EWS.T15 EWS
## EWS.C8 EWS
## EWS.C10 EWS
## BL.C8 BL
## NB.C6 NB
If we only look at the final prediction on component 2, compared to the real class:
# Compare prediction on the second component and change as factor
predict.comp2 <- predict.splsda.srbct$class$mahalanobis.dist[,2]
table(factor(predict.comp2, levels = levels(Y)), Y.test)
## Y.test
## EWS BL NB RMS
## EWS 4 0 0 0
## BL 0 1 0 0
## NB 0 0 1 1
## RMS 0 0 0 6
And on the third compnent:
# Compare prediction on the third component and change as factor
predict.comp3 <- predict.splsda.srbct$class$mahalanobis.dist[,3]
table(factor(predict.comp3, levels = levels(Y)), Y.test)
## Y.test
## EWS BL NB RMS
## EWS 4 0 0 0
## BL 0 1 0 0
## NB 0 0 1 0
## RMS 0 0 0 7
The prediction is better on the third component, compared to a 2-component model.
Next, we look at the output $predict, which gives the predicted dummy scores assigned for each test sample and each class level for a given component (as explained in Extra Reading material). Each column represents a class category:
# On component 3, just the head:
head(predict.splsda.srbct$predict[, , 3])
## EWS BL NB RMS
## EWS.T7 1.26848551 -0.05273773 -0.24070902 0.024961232
## EWS.T15 1.15058424 -0.02222145 -0.11877994 -0.009582845
## EWS.C8 1.25628411 0.05481026 -0.16500118 -0.146093198
## EWS.C10 0.83995956 0.10871106 0.16452934 -0.113199949
## BL.C8 0.02431262 0.90877176 0.01775304 0.049162580
## NB.C6 0.06738230 0.05086884 0.86247360 0.019275265
In PLS-DA and sPLS-DA, the final prediction call is given based on this matrix on which a pre-specified distance (such as mahalanobis.dist here) is applied. From this output, we can understand the link between the dummy matrix \(\boldsymbol Y\), the prediction, and the importance of choosing the prediction distance. More details are provided in Extra Reading material.
As PLS-DA acts as a classifier, we can plot the AUC (Area Under The Curve) ROC (Receiver Operating Characteristics) to complement the sPLS-DA classification performance results. The AUC is calculated from training cross-validation sets and averaged. The ROC curve is displayed in Figure 37. In a multiclass setting, each curve represents one class vs. the others and the AUC is indicated in the legend, and also in the numerical output:
auc.srbct <- auroc(splsda.srbct)
SRBCT gene expression data on component 1 averaged across one-vs.-all comparisons. Numerical outputs include the AUC and a Wilcoxon test p-value for each ‘one vs. other’ class comparisons that are performed per component. This output complements the sPLS-DA performance evaluation but should not be used for tuning (as the prediction process in sPLS-DA is based on prediction distances, not a cutoff that maximises specificity and sensitivity as in ROC). The plot suggests that the sPLS-DA model can distinguish BL subjects from the other groups with a high true positive and low false positive rate, while the model is less well able to distinguish samples from other classes on component 1." width="75%" />
Figure 37: ROC curve and AUC from sPLS-DA on the SRBCT gene expression data on component 1 averaged across one-vs.-all comparisons. Numerical outputs include the AUC and a Wilcoxon test p-value for each ‘one vs. other’ class comparisons that are performed per component. This output complements the sPLS-DA performance evaluation but should not be used for tuning (as the prediction process in sPLS-DA is based on prediction distances, not a cutoff that maximises specificity and sensitivity as in ROC). The plot suggests that the sPLS-DA model can distinguish BL subjects from the other groups with a high true positive and low false positive rate, while the model is less well able to distinguish samples from other classes on component 1.
## $Comp1
## AUC p-value
## EWS vs Other(s) 0.5576 4.493e-01
## BL vs Other(s) 1.0000 5.586e-06
## NB vs Other(s) 0.5180 8.473e-01
## RMS vs Other(s) 0.6814 2.125e-02
##
## $Comp2
## AUC p-value
## EWS vs Other(s) 1.0000 5.135e-11
## BL vs Other(s) 1.0000 5.586e-06
## NB vs Other(s) 0.8382 2.909e-04
## RMS vs Other(s) 0.8419 1.418e-05
##
## $Comp3
## AUC p-value
## EWS vs Other(s) 1 5.135e-11
## BL vs Other(s) 1 5.586e-06
## NB vs Other(s) 1 8.505e-08
## RMS vs Other(s) 1 2.164e-10
The ideal ROC curve should be along the top left corner, indicating a high true positive rate (sensitivity on the y-axis) and a high true negative rate (or low 100 - specificity on the x-axis), with an AUC close to 1. This is the case for BL vs. the others on component 1. The numerical output shows a perfect classification on component 3.
Note:
N-Integration is the framework of having multiple datasets which measure different aspects of the same samples. For example, you may have transcriptomic, genetic and proteomic data for the same set of cells. N-integrative methods are built to use the information in all three of these dataframes simultaenously.
DIABLO is a novel mixOmics framework for the integration of multiple data sets while explaining their relationship with a categorical outcome variable. DIABLO stands for Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies. It can also be referred to as Multiblock (s)PLS-DA.
Human breast cancer is a heterogeneous disease in terms of molecular alterations, cellular composition, and clinical outcome. Breast tumours can be classified into several subtypes, according to their levels of mRNA expression (Sørlie et al. 2001). Here we consider a subset of data generated by The Cancer Genome Atlas Network (Cancer Genome Atlas Network et al. 2012). For the package, data were normalised, and then drastically prefiltered for illustrative purposes.
The data were divided into a training set with a subset of 150 samples from the mRNA, miRNA and proteomics data, and a test set including 70 samples, but only with mRNA and miRNA data (the proteomics data are missing). The aim of this integrative analysis is to identify a highly correlated multi-omics signature discriminating the breast cancer subtypes Basal, Her2 and LumA.
The breast.TCGA (more details can be found in ?breast.TCGA) is a list containing training and test sets of omics data data.train and data.test which include:
$miRNA: A data frame with 150 (70) rows and 184 columns in the training (test) data set for the miRNA expression levels,$mRNA: A data frame with 150 (70) rows and 520 columns in the training (test) data set for the mRNA expression levels,$protein: A data frame with 150 rows and 142 columns in the training data set for the protein abundance (there are no proteomics in the test set),$subtype: A factor indicating the breast cancer subtypes in the training (for 150 samples) and test sets (for 70 samples).This case study covers an interesting scenario where one omic data set is missing in the test set, but because the method generates a set of components per training data set, we can still assess the prediction or performance evaluation using majority or weighted prediction vote.
To illustrate the multiblock sPLS-DA approach, we will integrate the expression levels of miRNA, mRNA and the abundance of proteins while discriminating the subtypes of breast cancer, then predict the subtypes of the samples in the test set.
The input data is first set up as a list of \(Q\) matrices \(\boldsymbol X_1, \dots, \boldsymbol X_Q\) and a factor indicating the class membership of each sample \(\boldsymbol Y\). Each data frame in \(\boldsymbol X\) should be named as we will match these names with the keepX parameter for the sparse method.
library(mixOmics)
data(breast.TCGA)
# Extract training data and name each data frame
# Store as list
X <- list(mRNA = breast.TCGA$data.train$mrna,
miRNA = breast.TCGA$data.train$mirna,
protein = breast.TCGA$data.train$protein)
# Outcome
Y <- breast.TCGA$data.train$subtype
summary(Y)
## Basal Her2 LumA
## 45 30 75
The choice of the design can be motivated by different aspects, including:
Biological apriori knowledge: Should we expect mRNA and miRNA to be highly correlated?
Analytical aims: As further developed in Singh et al. (2019), a compromise needs to be achieved between a classification and prediction task, and extracting the correlation structure of the data sets. A full design with weights = 1 will favour the latter, but at the expense of classification accuracy, whereas a design with small weights will lead to a highly predictive signature. This pertains to the complexity of the analytical task involved as several constraints are included in the optimisation procedure. For example, here we choose a 0.1 weighted model as we are interested in predicting test samples later in this case study.
design <- matrix(0.1, ncol = length(X), nrow = length(X),
dimnames = list(names(X), names(X)))
diag(design) <- 0
design
## mRNA miRNA protein
## mRNA 0.0 0.1 0.1
## miRNA 0.1 0.0 0.1
## protein 0.1 0.1 0.0
Note however that even with this design, we will still unravel a correlated signature as we require all data sets to explain the same outcome \(\boldsymbol y\), as well as maximising pairs of covariances between data sets.
res1.pls.tcga <- pls(X$mRNA, X$protein, ncomp = 1)
cor(res1.pls.tcga$variates$X, res1.pls.tcga$variates$Y)
res2.pls.tcga <- pls(X$mRNA, X$miRNA, ncomp = 1)
cor(res2.pls.tcga$variates$X, res2.pls.tcga$variates$Y)
res3.pls.tcga <- pls(X$protein, X$miRNA, ncomp = 1)
cor(res3.pls.tcga$variates$X, res3.pls.tcga$variates$Y)
## comp1
## comp1 0.9031761
## comp1
## comp1 0.8456299
## comp1
## comp1 0.7982008
The data sets taken in a pairwise manner are highly correlated, indicating that a design with weights \(\sim 0.8 - 0.9\) could be chosen.
As in the PLS-DA framework presented in Module 3, we first fit a block.plsda model without variable selection to assess the global performance of the model and choose the number of components. We run perf() with 10-fold cross validation repeated 10 times for up to 5 components and with our specified design matrix. Similar to PLS-DA, we obtain the performance of the model with respect to the different prediction distances (Figure 38):
diablo.tcga <- block.plsda(X, Y, ncomp = 5, design = design)
set.seed(123) # For reproducibility, remove for your analyses
perf.diablo.tcga = perf(diablo.tcga, validation = 'Mfold', folds = 10, nrepeat = 10)
#perf.diablo.tcga$error.rate # Lists the different types of error rates
# Plot of the error rates based on weighted vote
plot(perf.diablo.tcga)
Figure 38: Choosing the number of components in block.plsda using perf() with 10 x 10-fold CV function in the breast.TCGA study. Classification error rates (overall and balanced, see Module 2) are represented on the y-axis with respect to the number of components on the x-axis for each prediction distance presented in PLS-DA in Seciton 3.4 and detailed in Extra reading material 3 from Module 3. Bars show the standard deviation across the 10 repeated folds. The plot shows that the error rate reaches a minimum from 2 to 3 dimensions.
The performance plot indicates that two components should be sufficient in the final model, and that the centroids distance might lead to better prediction. A balanced error rate (BER) should be considered for further analysis.
The following outputs the optimal number of components according to the prediction distance and type of error rate (overall or balanced), as well as a prediction weighting scheme illustrated further below.
perf.diablo.tcga$choice.ncomp$WeightedVote
## max.dist centroids.dist mahalanobis.dist
## Overall.ER 3 2 3
## Overall.BER 3 2 4
Thus, here we choose our final ncomp value:
ncomp <- perf.diablo.tcga$choice.ncomp$WeightedVote["Overall.BER", "centroids.dist"]
We then choose the optimal number of variables to select in each data set using the tune.block.splsda function. The function tune() is run with 10-fold cross validation, but repeated only once (nrepeat = 1) for illustrative and computational reasons here. For a thorough tuning process, we advise increasing the nrepeat argument to 10-50, or more.
We choose a keepX grid that is relatively fine at the start, then coarse. If the data sets are easy to classify, the tuning step may indicate the smallest number of variables to separate the sample groups. Hence, we start our grid at the value 5 to avoid a too small signature that may preclude biological interpretation.
# chunk takes about 2 min to run
set.seed(123) # for reproducibility
test.keepX <- list(mRNA = c(5:9, seq(10, 25, 5)),
miRNA = c(5:9, seq(10, 20, 2)),
proteomics = c(seq(5, 25, 5)))
tune.diablo.tcga <- tune.block.splsda(X, Y, ncomp = 2,
test.keepX = test.keepX, design = design,
validation = 'Mfold', folds = 10, nrepeat = 1,
BPPARAM = BiocParallel::SnowParam(workers = 2),
dist = "centroids.dist")
list.keepX <- tune.diablo.tcga$choice.keepX
Note:
?tune.block.splsda.The number of features to select on each component is returned and stored for the final model:
list.keepX
## $mRNA
## [1] 8 25
##
## $miRNA
## [1] 14 5
##
## $protein
## [1] 10 5
Note:
ncomp and keepX parameters (as a list for the latter, as shown below).list.keepX <- list( mRNA = c(8, 25), miRNA = c(14,5), protein = c(10, 5))
The final multiblock sPLS-DA model includes the tuned parameters and is run as:
diablo.tcga <- block.splsda(X, Y, ncomp = ncomp,
keepX = list.keepX, design = design)
## Design matrix has changed to include Y; each block will be
## linked to Y.
#06.tcga # Lists the different functions of interest related to that object
A warning message informs us that the outcome \(\boldsymbol Y\) has been included automatically in the design, so that the covariance between each block’s component and the outcome is maximised, as shown in the final design output:
diablo.tcga$design
## mRNA miRNA protein Y
## mRNA 0.0 0.1 0.1 1
## miRNA 0.1 0.0 0.1 1
## protein 0.1 0.1 0.0 1
## Y 1.0 1.0 1.0 0
The selected variables can be extracted with the function selectVar(), for example in the mRNA block, along with their loading weights (not output here):
# mRNA variables selected on component 1
selectVar(diablo.tcga, block = 'mRNA', comp = 1)
Note:
perf() function, similar to the example given in the PLS-DA analysis (Module 3).plotDiabloplotDiablo() is a diagnostic plot to check whether the correlations between components from each data set were maximised as specified in the design matrix. We specify the dimension to be assessed with the ncomp argument (Figure 39).
plotDiablo(diablo.tcga, ncomp = 1)
breast.TCGA study. Samples are represented based on the specified component (here ncomp = 1) for each data set (mRNA, miRNA and protein). Samples are coloured by breast cancer subtype (Basal, Her2 and LumA) and 95% confidence ellipse plots are represented. The bottom left numbers indicate the correlation coefficients between the first components from each data set. In this example, mRNA expression and protein concentration are highly correlated on the first dimension." width="75%" />
Figure 39: Diagnostic plot from multiblock sPLS-DA applied on the breast.TCGA study. Samples are represented based on the specified component (here ncomp = 1) for each data set (mRNA, miRNA and protein). Samples are coloured by breast cancer subtype (Basal, Her2 and LumA) and 95% confidence ellipse plots are represented. The bottom left numbers indicate the correlation coefficients between the first components from each data set. In this example, mRNA expression and protein concentration are highly correlated on the first dimension.
The plot indicates that the first components from all data sets are highly correlated. The colours and ellipses represent the sample subtypes and indicate the discriminative power of each component to separate the different tumour subtypes. Thus, multiblock sPLS-DA is able to extract a strong correlation structure between data sets, as well as discriminate the breast cancer subtypes on the first component.
plotIndivThe sample plot with the plotIndiv() function projects each sample into the space spanned by the components from each block, resulting in a series of graphs corresponding to each data set (Figure 40). The optional argument blocks can output a specific data set. Ellipse plots are also available (argument ellipse = TRUE).
plotIndiv(diablo.tcga, ind.names = FALSE, legend = TRUE,
title = 'TCGA, DIABLO comp 1 - 2')
breast.TCGA study. The samples are plotted according to their scores on the first 2 components for each data set. Samples are coloured by cancer subtype and are classified into three classes: Basal, Her2 and LumA. The plot shows the degree of agreement between the different data sets and the discriminative ability of each data set." width="75%" />
Figure 40: Sample plot from multiblock sPLS-DA performed on the breast.TCGA study. The samples are plotted according to their scores on the first 2 components for each data set. Samples are coloured by cancer subtype and are classified into three classes: Basal, Her2 and LumA. The plot shows the degree of agreement between the different data sets and the discriminative ability of each data set.
This type of graphic allows us to better understand the information extracted from each data set and its discriminative ability. Here we can see that the LumA group can be difficult to classify in the miRNA data.
Note:
block = 'average' that averages the components from all blocks to produce a single plot. The argument block='weighted.average' is a weighted average of the components according to their correlation with the components associated with the outcome.plotArrowIn the arrow plot in Figure 41, the start of the arrow indicates the centroid between all data sets for a given sample and the tip of the arrow the location of that same sample but in each block. Such graphics highlight the agreement between all data sets at the sample level when modelled with multiblock sPLS-DA.
plotArrow(diablo.tcga, ind.names = FALSE, legend = TRUE,
title = 'TCGA, DIABLO comp 1 - 2')
breast.TCGA study. The samples are projected into the space spanned by the first two components for each data set then overlaid across data sets. The start of the arrow indicates the centroid between all data sets for a given sample and the tip of the arrow the location of the same sample in each block. Arrows further from their centroid indicate some disagreement between the data sets. Samples are coloured by cancer subtype (Basal, Her2 and LumA)." width="75%" />
Figure 41: Arrow plot from multiblock sPLS-DA performed on the breast.TCGA study. The samples are projected into the space spanned by the first two components for each data set then overlaid across data sets. The start of the arrow indicates the centroid between all data sets for a given sample and the tip of the arrow the location of the same sample in each block. Arrows further from their centroid indicate some disagreement between the data sets. Samples are coloured by cancer subtype (Basal, Her2 and LumA).
This plot shows that globally, the discrimination of all breast cancer subtypes can be extracted from all data sets, however, there are some dissimilarities at the samples level across data sets (the common information cannot be extracted in the same way across data sets).
The visualisation of the selected variables is crucial to mine their associations in multiblock sPLS-DA. Here we revisit existing outputs presented in Module 2 with further developments for multiple data set integration. All the plots presented provide complementary information for interpreting the results.
plotVarThe correlation circle plot highlights the contribution of each selected variable to each component. Important variables should be close to the large circle (see Module 2). Here, only the variables selected on components 1 and 2 are depicted (across all blocks), see Figure 42. Clusters of points indicate a strong correlation between variables. For better visibility we chose to hide the variable names.
plotVar(diablo.tcga, var.names = FALSE, style = 'graphics', legend = TRUE,
pch = c(16, 17, 15), cex = c(2,2,2),
col = c('darkorchid', 'brown1', 'lightgreen'),
title = 'TCGA, DIABLO comp 1 - 2')
Figure 42: Correlation circle plot from multiblock sPLS-DA performed on the breast.TCGA study. The variable coordinates are defined according to their correlation with the first and second components for each data set. Variable types are indicated with different symbols and colours, and are overlaid on the same plot. The plot highlights the potential associations within and between different variable types when they are important in defining their own component.
The correlation circle plot shows some positive correlations (between selected miRNA and proteins, between selected proteins and mRNA) and negative correlations between mRNAand miRNA on component 1. The correlation structure is less obvious on component 2, but we observe some key selected features (proteins and miRNA) that seem to highly contribute to component 2.
Note:
These results can be further investigated by showing the variable names on this plot (or extracting their coordinates available from the plot saved into an object, see ?plotVar), and looking at various outputs from selectVar() and plotLoadings().
You can choose to only show specific variable type names, e.g. var.names = c(FALSE, FALSE, TRUE) (where each argument is assigned to a data set in \(\boldsymbol X\)). Here for example, the protein names only would be output.
circosPlotThe circos plot represents the correlations between variables of different types, represented on the side quadrants. Several display options are possible, to show within and between connections between blocks, and expression levels of each variable according to each class (argument line = TRUE). The circos plot is built based on a similarity matrix, which was extended to the case of multiple data sets from González et al. (2012) (see also Module 2 and Extra Reading material from that module). A cutoff argument can be further included to visualise correlation coefficients above this threshold in the multi-omics signature (Figure 43). The colours for the blocks and correlation lines can be chosen with color.blocks and color.cor respectively:
circosPlot(diablo.tcga, cutoff = 0.7, line = TRUE,
color.blocks = c('darkorchid', 'brown1', 'lightgreen'),
color.cor = c("chocolate3","grey20"), size.labels = 1.5)
breast.TCGA study. The plot represents the correlations greater than 0.7 between variables of different types, represented on the side quadrants. The internal connecting lines show the positive (negative) correlations. The outer lines show the expression levels of each variable in each sample group (Basal, Her2 and LumA)." width="75%" />
Figure 43: Circos plot from multiblock sPLS-DA performed on the breast.TCGA study. The plot represents the correlations greater than 0.7 between variables of different types, represented on the side quadrants. The internal connecting lines show the positive (negative) correlations. The outer lines show the expression levels of each variable in each sample group (Basal, Her2 and LumA).
The circos plot enables us to visualise cross-correlations between data types, and the nature of these correlations (positive or negative). Here we observe that correlations > 0.7 are between a few mRNAand some Proteins, whereas the majority of strong (negative) correlations are observed between miRNA and mRNAor Proteins. The lines indicating the average expression levels per breast cancer subtype indicate that the selected features are able to discriminate the sample groups.
networkRelevance networks, which are also built on the similarity matrix, can also visualise the correlations between the different types of variables. Each colour represents a type of variable. A threshold can also be set using the argument cutoff (Figure 44). By default the network includes only variables selected on component 1, unless specified in comp.
Note that sometimes the output may not show with Rstudio due to margin issues. We can either use X11() to open a new window, or save the plot as an image using the arguments save and name.save, as we show below. An interactive argument is also available for the cutoff argument, see details in ?network.
# X11() # Opens a new window
network(diablo.tcga, blocks = c(1,2,3),
cutoff = 0.4,
color.node = c('darkorchid', 'brown1', 'lightgreen'),
# To save the plot, uncomment below line
#save = 'png', name.save = 'diablo-network'
)
Figure 44: Relevance network for the variables selected by multiblock sPLS-DA performed on the breast.TCGA study on component 1. Each node represents a selected variable with colours indicating their type. The colour of the edges represent positive or negative correlations. Further tweaking of this plot can be obtained, see the help file ?network.
The relevance network in Figure 44 shows two groups of features of different types. Within each group we observe positive and negative correlations. The visualisation of this plot could be further improved by changing the names of the original features.
Note that the network can be saved in a .gml format to be input into the software Cytoscape, using the R package igraph (Csardi, Nepusz, et al. 2006):
# Not run
library(igraph)
myNetwork <- network(diablo.tcga, blocks = c(1,2,3), cutoff = 0.4)
write.graph(myNetwork$gR, file = "myNetwork.gml", format = "gml")
plotLoadingsplotLoadings() visualises the loading weights of each selected variable on each component and each data set. The colour indicates the class in which the variable has the maximum level of expression (contrib = 'max') or minimum (contrib = 'min'), on average (method = 'mean') or using the median (method = 'median').
plotLoadings(diablo.tcga, comp = 1, contrib = 'max', method = 'median')
breast.TCGA study on component 1. The most important variables (according to the absolute value of their coefficients) are ordered from bottom to top. As this is a supervised analysis, colours indicate the class for which the median expression value is the highest for each feature (variables selected characterise Basal and LumA)." width="75%" />
Figure 45: Loading plot for the variables selected by multiblock sPLS-DA performed on the breast.TCGA study on component 1. The most important variables (according to the absolute value of their coefficients) are ordered from bottom to top. As this is a supervised analysis, colours indicate the class for which the median expression value is the highest for each feature (variables selected characterise Basal and LumA).
The loading plot shows the multi-omics signature selected on component 1, where each panel represents one data type. The importance of each variable is visualised by the length of the bar (i.e. its loading coefficient value). The combination of the sign of the coefficient (positive / negative) and the colours indicate that component 1 discriminates primarily the Basal samples vs. the LumA samples (see the sample plots also). The features selected are highly expressed in one of these two subtypes. One could also plot the second component that discriminates the Her2 samples.
cimDiabloThe cimDiablo() function is a clustered image map specifically implemented to represent the multi-omics molecular signature expression for each sample. It is very similar to a classical hierarchical clustering (Figure 46).
cimDiablo(diablo.tcga, color.blocks = c('darkorchid', 'brown1', 'lightgreen'),
comp = 1, margin=c(8,20), legend.position = "right")
##
## trimming values to [-3, 3] range for cim visualisation. See 'trim' arg in ?cimDiablo
Figure 46: Clustered Image Map for the variables selected by multiblock sPLS-DA performed on the breast.TCGA study on component 1. By default, Euclidean distance and Complete linkage methods are used. The CIM represents samples in rows (indicated by their breast cancer subtype on the left hand side of the plot) and selected features in columns (indicated by their data type at the top of the plot).
According to the CIM, component 1 seems to primarily classify the Basal samples, with a group of overexpressed miRNA and underexpressed mRNAand proteins. A group of LumA samples can also be identified due to the overexpression of the same mRNAand proteins. Her2 samples remain quite mixed with the other LumA samples.
We assess the performance of the model using 10-fold cross-validation repeated 10 times with the function perf(). The method runs a block.splsda() model on the pre-specified arguments input from our final object diablo.tcga but on cross-validated samples. We then assess the accuracy of the prediction on the left out samples. Since the tune() function was used with the centroid.dist argument, we examine the outputs of the perf() function for that same distance:
set.seed(123) # For reproducibility with this handbook, remove otherwise
perf.diablo.tcga <- perf(diablo.tcga, validation = 'Mfold', folds = 10,
nrepeat = 10, dist = 'centroids.dist')
#perf.diablo.tcga # Lists the different outputs
We can extract the (balanced) classification error rates globally or overall with
perf.diablo.tcga$error.rate.per.class, the predicted components associated to \(\boldsymbol Y\), or the stability of the selected features with perf.diablo.tcga$features.
Here we look at the different performance assessment schemes specific to multiple data set integration.
First, we output the performance with the majority vote, that is, since the prediction is based on the components associated to their own data set, we can then weight those predictions across data sets according to a majority vote scheme. Based on the predicted classes, we then extract the classification error rate per class and per component:
# Performance with Majority vote
perf.diablo.tcga$MajorityVote.error.rate
## $centroids.dist
## comp1 comp2
## Basal 0.02888889 0.046666667
## Her2 0.20333333 0.130000000
## LumA 0.05066667 0.009333333
## Overall.ER 0.07466667 0.044666667
## Overall.BER 0.09429630 0.062000000
The output shows that with the exception of the Basal samples, the classification improves with the addition of the second component.
Another prediction scheme is to weight the classification error rate from each data set according to the correlation between the predicted components and the \(\boldsymbol Y\) outcome.
# Performance with Weighted vote
perf.diablo.tcga$WeightedVote.error.rate
## $centroids.dist
## comp1 comp2
## Basal 0.008888889 0.044444444
## Her2 0.136666667 0.110000000
## LumA 0.050666667 0.009333333
## Overall.ER 0.055333333 0.040000000
## Overall.BER 0.065407407 0.054592593
Compared to the previous majority vote output, we can see that the classification accuracy is slightly better on component 2 for the subtype Her2.
An AUC plot per block is plotted using the function auroc(). We have already mentioned in Module 3 for PLS-DA, the interpretation of this output may not be particularly insightful in relation to the performance evaluation of our methods, but can complement the statistical analysis. For example, here for the miRNA data set once we have reached component 2 (Figure 47):
auc.diablo.tcga <- auroc(diablo.tcga, roc.block = "miRNA", roc.comp = 2,
print = FALSE)
Figure 47: ROC and AUC based on multiblock sPLS-DA performed on the breast.TCGA study for the miRNA data set after 2 components. The function calculates the ROC curve and AUC for one class vs. the others. If we set print = TRUE, the Wilcoxon test p-value that assesses the differences between the predicted components from one class vs. the others is output.
Figure 47 shows that the Her2 subtype is the most difficult to classify with multiblock sPLS-DA compared to the other subtypes.
The predict() function associated with a block.splsda() object predicts the class of samples from an external test set. In our specific case, one data set is missing in the test set but the method can still be applied. We need to ensure the names of the blocks correspond exactly to those from the training set:
# Prepare test set data: here one block (proteins) is missing
data.test.tcga <- list(mRNA = breast.TCGA$data.test$mrna,
miRNA = breast.TCGA$data.test$mirna)
predict.diablo.tcga <- predict(diablo.tcga, newdata = data.test.tcga)
# The warning message will inform us that one block is missing
#predict.diablo # List the different outputs
The following output is a confusion matrix that compares the real subtypes with the predicted subtypes for a 2 component model, for the distance of interest centroids.dist and the prediction scheme WeightedVote:
confusion.mat.tcga <- get.confusion_matrix(truth = breast.TCGA$data.test$subtype,
predicted = predict.diablo.tcga$WeightedVote$centroids.dist[,2])
confusion.mat.tcga
## predicted.as.Basal predicted.as.Her2 predicted.as.LumA
## Basal 20 1 0
## Her2 0 13 1
## LumA 0 3 32
From this table, we see that one Basal and one Her2 sample are wrongly predicted as Her2 and Lum A respectively, and 3 LumA samples are wrongly predicted as Her2. The balanced prediction error rate can be obtained as:
get.BER(confusion.mat.tcga)
## [1] 0.06825397
It would be worthwhile at this stage to revisit the chosen design of the multiblock sPLS-DA model to assess the influence of the design on the prediction performance on this test set - even though this back and forth analysis is a biased criterion to choose the design!
We integrate four transcriptomics studies of microarray stem cells (125 samples in total). The original data set from the Stemformatics database1 www.stemformatics.org (Wells et al. 2013) was reduced to fit into the package, and includes a randomly-chosen subset of the expression levels of 400 genes. The aim is to classify three types of human cells: human fibroblasts (Fib) and human induced Pluripotent Stem Cells (hiPSC & hESC).
There is a biological hierarchy among the three cell types. On one hand, differences between pluripotent (hiPSC and hESC) and non-pluripotent cells (Fib) are well-characterised and are expected to contribute to the main biological variation. On the other hand, hiPSC are genetically reprogrammed to behave like hESC and both cell types are commonly assumed to be alike. However, differences have been reported in the literature (Chin et al. (2009), Newman and Cooper (2010)). We illustrate the use of MINT to address sub-classification problems in a single analysis.
We first load the data from the package and set up the categorical outcome \(\boldsymbol Y\) and the study membership:
library(mixOmics)
data(stemcells)
# The combined data set X
X <- stemcells$gene
dim(X)
## [1] 125 400
# The outcome vector Y:
Y <- stemcells$celltype
length(Y)
## [1] 125
summary(Y)
## Fibroblast hESC hiPSC
## 30 37 58
We then store the vector indicating the sample membership of each independent study:
study <- stemcells$study
# Number of samples per study:
summary(study)
## 1 2 3 4
## 38 51 21 15
# Experimental design
table(Y,study)
## study
## Y 1 2 3 4
## Fibroblast 6 18 3 3
## hESC 20 3 8 6
## hiPSC 12 30 10 6
We first perform a MINT PLS-DA with all variables included in the model and ncomp = 5 components. The perf() function is used to estimate the performance of the model using LOGOCV, and to choose the optimal number of components for our final model (see Fig 48).
mint.plsda.stem <- mint.plsda(X = X, Y = Y, study = study, ncomp = 5)
set.seed(2543) # For reproducible results here, remove for your own analyses
perf.mint.plsda.stem <- perf(mint.plsda.stem)
plot(perf.mint.plsda.stem)
Figure 48: Choosing the number of components in mint.plsda using perf() with LOGOCV in the stemcells study. Classification error rates (overall and balanced, see Module 2) are represented on the y-axis with respect to the number of components on the x-axis for each prediction distance (see Module 3 and Extra Reading material ‘PLS-DA appendix’). The plot shows that the error rate reaches a minimum from 1 component with the BER and centroids distance.
Based on the performance plot (Figure 48), ncomp = 2 seems to achieve the best performance for the centroid distance, and ncomp = 1 for the Mahalanobis distance in terms of BER. Additional numerical outputs such as the BER and overall error rates per component, and the error rates per class and per prediction distance, can be output:
perf.mint.plsda.stem$global.error$BER
# Type also:
# perf.mint.plsda.stem$global.error
## max.dist centroids.dist mahalanobis.dist
## comp1 0.3803556 0.3333333 0.3333333
## comp2 0.3519556 0.3320000 0.3725111
## comp3 0.3499556 0.3384000 0.3232889
## comp4 0.3541111 0.3427778 0.3898000
## comp5 0.3353778 0.3268667 0.3097111
While we may want to focus our interpretation on the first component, we run a final MINT PLS-DA model for ncomp = 2 to obtain 2D graphical outputs (Figure 49):
final.mint.plsda.stem <- mint.plsda(X = X, Y = Y, study = study, ncomp = 2)
#final.mint.plsda.stem # Lists the different functions
plotIndiv(final.mint.plsda.stem, legend = TRUE, title = 'MINT PLS-DA',
subtitle = 'stem cell study', ellipse = T)
stemcells gene expression data. Samples are projected into the space spanned by the first two components. Samples are coloured by their cell types and symbols indicate the study membership. Component 1 discriminates fibroblast vs. the others, while component 2 discriminates some of the hiPSC vs. hESC." width="75%" />
Figure 49: Sample plot from the MINT PLS-DA performed on the stemcells gene expression data. Samples are projected into the space spanned by the first two components. Samples are coloured by their cell types and symbols indicate the study membership. Component 1 discriminates fibroblast vs. the others, while component 2 discriminates some of the hiPSC vs. hESC.
The sample plot (Fig 49) shows that fibroblast are separated on the first component. We observe that while deemed not crucial for an optimal discrimination, the second component seems to help separate hESC and hiPSC further. The effect of study after MINT modelling is not strong.
We can compare this output to a classical PLS-DA to visualise the study effect (Figure 50):
plsda.stem <- plsda(X = X, Y = Y, ncomp = 2)
plotIndiv(plsda.stem, pch = study,
legend = TRUE, title = 'Classic PLS-DA',
legend.title = 'Cell type', legend.title.pch = 'Study')
Figure 50: Sample plot from a classic PLS-DA performed on the stemcells gene expression data that highlights the study effect (indicated by symbols). Samples are projected into the space spanned by the first two components. We still do observe some discrimination between the cell types.
The MINT PLS-DA model shown earlier is built on all 400 genes in \(\boldsymbol X\), many of which may be uninformative to characterise the different classes. Here we aim to identify a small subset of genes that best discriminate the classes.
We can choose the keepX parameter using the tune() function for a MINT object. The function performs LOGOCV for different values of test.keepX provided on each component, and no repeat argument is needed. Based on the mean classification error rate (overall error rate or BER) and a centroids distance, we output the optimal number of variables keepX to be included in the final model.
set.seed(2543) # For a reproducible result here, remove for your own analyses
tune.mint.splsda.stem <- tune(X = X, Y = Y, study = study,
ncomp = 2, test.keepX = seq(1, 100, 1),
method = 'mint.splsda', #Specify the method
measure = 'BER',
dist = "centroids.dist",
nrepeat = 3)
#tune.mint.splsda.stem # Lists the different types of outputs
# Mean error rate per component and per tested keepX value:
#tune.mint.splsda.stem$error.rate[1:5,]
The optimal number of variables to select on each specified component:
tune.mint.splsda.stem$choice.keepX
## comp1 comp2
## 24 1
plot(tune.mint.splsda.stem)
keepX in MINT sPLS-DA performed on the stemcells gene expression data. Each coloured line represents the balanced error rate (y-axis) per component across all tested keepX values (x-axis). The diamond indicates the optimal keepX value on a particular component which achieves the lowest classification error rate as determined with a one-sided \(t-\)test across the studies." width="75%" />
Figure 51: Tuning keepX in MINT sPLS-DA performed on the stemcells gene expression data. Each coloured line represents the balanced error rate (y-axis) per component across all tested keepX values (x-axis). The diamond indicates the optimal keepX value on a particular component which achieves the lowest classification error rate as determined with a one-sided \(t-\)test across the studies.
The tuning plot in Figure 51 indicates the optimal number of variables to select on component 1 (24) and on component 2 (1). In fact, whilst the BER decreases with the addition of component 2, the standard deviation remains large, and thus only one component is optimal. However, the addition of this second component is useful for the graphical outputs, and also to attempt to discriminate the hESC and hiPCS cell types.
Note:
keepX values.Following the tuning results, our final model is as follows (we still choose a model with two components in order to obtain 2D graphics):
final.mint.splsda.stem <- mint.splsda(X = X, Y = Y, study = study, ncomp = 2,
keepX = tune.mint.splsda.stem$choice.keepX)
#mint.splsda.stem.final # Lists useful functions that can be used with a MINT object
The samples can be projected on the global components or alternatively using the partial components from each study (Fig 52).
plotIndiv(final.mint.splsda.stem, study = 'global', legend = TRUE,
title = 'Stem cells, MINT sPLS-DA',
subtitle = 'Global', ellipse = T)
plotIndiv(final.mint.splsda.stem, study = 'all.partial', legend = TRUE,
title = 'Stem cells, MINT sPLS-DA',
subtitle = paste("Study",1:4))
stemcells gene expression data. Samples are projected into the space spanned by the first two components. Samples are coloured by their cell types and symbols indicate study membership. (a) Global components from the model with 95% ellipse confidence intervals around each sample class. (b) Partial components per study show a good agreement across studies. Component 1 discriminates fibroblast vs. the rest, component 2 discriminates further hESC vs. hiPSC." width="50%" />stemcells gene expression data. Samples are projected into the space spanned by the first two components. Samples are coloured by their cell types and symbols indicate study membership. (a) Global components from the model with 95% ellipse confidence intervals around each sample class. (b) Partial components per study show a good agreement across studies. Component 1 discriminates fibroblast vs. the rest, component 2 discriminates further hESC vs. hiPSC." width="50%" />
Figure 52: Sample plots from the MINT sPLS-DA performed on the stemcells gene expression data. Samples are projected into the space spanned by the first two components. Samples are coloured by their cell types and symbols indicate study membership. (a) Global components from the model with 95% ellipse confidence intervals around each sample class. (b) Partial components per study show a good agreement across studies. Component 1 discriminates fibroblast vs. the rest, component 2 discriminates further hESC vs. hiPSC.
The visualisation of the partial components enables us to examine each study individually and check that the model is able to extract a good agreement between studies.
We can examine our molecular signature selected with MINT sPLS-DA. The correlation circle plot, presented in Module 2, highlights the contribution of each selected transcript to each component (close to the large circle), and their correlation (clusters of variables) in Figure 53:
plotVar(final.mint.splsda.stem)
stemcells gene expression data to examine the association of the genes selected on the first two components. We mainly observe two groups of genes, either positively or negatively associated with component 1 along the x-axis. This graphic should be interpreted in conjunction with the sample plot." width="75%" />
Figure 53: Correlation circle plot representing the genes selected by MINT sPLS-DA performed on the stemcells gene expression data to examine the association of the genes selected on the first two components. We mainly observe two groups of genes, either positively or negatively associated with component 1 along the x-axis. This graphic should be interpreted in conjunction with the sample plot.
We observe a subset of genes that are strongly correlated and negatively associated to component 1 (negative values on the x-axis), which are likely to characterise the groups of samples hiPSC and hESC, and a subset of genes positively associated to component 1 that may characterise the fibroblast samples (and are negatively correlated to the previous group of genes).
Note:
var.name argument to show gene name ID, as shown in Module 3 for PLS-DA.The Clustered Image Map represents the expression levels of the gene signature per sample, similar to a PLS-DA object (see Module 3). Here we use the default Euclidean distance and Complete linkage in Figure 54 for a specific component (here 1):
# If facing margin issues, use either X11() or save the plot using the
# arguments save and name.save
cim(final.mint.splsda.stem, comp = 1, margins=c(10,5),
row.sideColors = color.mixo(as.numeric(Y)), row.names = FALSE,
title = "MINT sPLS-DA, component 1")
stemcells gene expression data for component 1 only. A hierarchical clustering based on the gene expression levels of the selected genes on component 1, with samples in rows coloured according to cell type showing a separation of the fibroblast vs. the other cell types." width="75%" />
Figure 54: Clustered Image Map of the genes selected by MINT sPLS-DA on the stemcells gene expression data for component 1 only. A hierarchical clustering based on the gene expression levels of the selected genes on component 1, with samples in rows coloured according to cell type showing a separation of the fibroblast vs. the other cell types.
As expected and observed from the sample plot Figure 52, we observe in the CIM that the expression of the genes selected on component 1 discriminates primarily the fibroblast vs. the other cell types.
Relevance networks can also be plotted for a PLS-DA object, but would only show the association between the selected genes and the cell type (dummy variable in \(\boldsymbol Y\) as an outcome category) as shown in Figure 55. Only the variables selected on component 1 are shown (comp = 1):
# If facing margin issues, use either X11() or save the plot using the
# arguments save and name.save
network(final.mint.splsda.stem, comp = 1,
color.node = c(color.mixo(1), color.mixo(2)),
shape.node = c("rectangle", "circle"))
stemcells gene expression data for component 1 only. Associations between variables from \(\boldsymbol X\) and the dummy matrix \(\boldsymbol Y\) are calculated as detailed in Extra Reading material from Module 2. Edges indicate high or low association between the genes and the different cell types." width="75%" />
Figure 55: Relevance network of the genes selected by MINT sPLS-DA performed on the stemcells gene expression data for component 1 only. Associations between variables from \(\boldsymbol X\) and the dummy matrix \(\boldsymbol Y\) are calculated as detailed in Extra Reading material from Module 2. Edges indicate high or low association between the genes and the different cell types.
The selectVar() function outputs the selected transcripts on the first component along with their loading weight values. We consider variables as important in the model when their absolute loading weight value is high. In addition to this output, we can compare the stability of the selected features across studies using the perf() function, as shown in PLS-DA in Module 3.
# Just a head
head(selectVar(final.mint.plsda.stem, comp = 1)$value)
## value.var
## ENSG00000181449 -0.09764220
## ENSG00000123080 0.09606034
## ENSG00000110721 -0.09595070
## ENSG00000176485 -0.09457383
## ENSG00000184697 -0.09387322
## ENSG00000102935 -0.09370298
The plotLoadings() function displays the coefficient weight of each selected variable in each study and shows the agreement of the gene signature across studies (Figure 56). Colours indicate the class in which the mean expression value of each selected gene is maximal. For component 1, we obtain:
plotLoadings(final.mint.splsda.stem, contrib = "max", method = 'mean', comp=1,
study="all.partial", title="Contribution on comp 1",
subtitle = paste("Study",1:4))
stemcells data, on component 1 per study. Each plot represents one study, and the variables are coloured according to the cell type they are maximally expressed in, on average. The length of the bars indicate the loading coefficient values that define the component. Several genes distinguish between fibroblast and the other cell types, and are consistently overexpressed in these samples across all studies. We observe slightly more variability in whether the expression levels of the other genes are more indicative of hiPSC or hESC cell types." width="75%" />stemcells data, on component 1 per study. Each plot represents one study, and the variables are coloured according to the cell type they are maximally expressed in, on average. The length of the bars indicate the loading coefficient values that define the component. Several genes distinguish between fibroblast and the other cell types, and are consistently overexpressed in these samples across all studies. We observe slightly more variability in whether the expression levels of the other genes are more indicative of hiPSC or hESC cell types." width="75%" />
Figure 56: Loading plots of the genes selected by the MINT sPLS-DA performed on the stemcells data, on component 1 per study. Each plot represents one study, and the variables are coloured according to the cell type they are maximally expressed in, on average. The length of the bars indicate the loading coefficient values that define the component. Several genes distinguish between fibroblast and the other cell types, and are consistently overexpressed in these samples across all studies. We observe slightly more variability in whether the expression levels of the other genes are more indicative of hiPSC or hESC cell types.
Several genes are consistently over-expressed on average in the fibroblast samples in each of the studies, however, we observe a less consistent pattern for the other genes that characterise hiPSC} and hESC. This can be explained as the discrimination between both classes is challenging on component 1 (see sample plot in Figure 52).
We assess the performance of the MINT sPLS-DA model with the perf() function. Since the previous tuning was conducted with the distance centroids.dist, the same distance is used to assess the performance of the final model. We do not need to specify the argument nrepeat as we use LOGOCV in the function.
set.seed(123) # For reproducible results here, remove for your own study
perf.mint.splsda.stem.final <- perf(final.mint.plsda.stem, dist = 'centroids.dist')
perf.mint.splsda.stem.final$global.error
## $BER
## centroids.dist
## comp1 0.3333333
## comp2 0.3320000
##
## $overall
## centroids.dist
## comp1 0.456
## comp2 0.392
##
## $error.rate.class
## $error.rate.class$centroids.dist
## comp1 comp2
## Fibroblast 0.0000000 0.0000000
## hESC 0.1891892 0.4594595
## hiPSC 0.8620690 0.5517241
The classification error rate per class is particularly insightful to understand which cell types are difficult to classify, hESC and hiPS - whose mixture can be explained for biological reasons.
An AUC plot for the integrated data can be obtained using the function auroc() (Fig 57).
Remember that the AUC incorporates measures of sensitivity and specificity for every possible cut-off of the predicted dummy variables. However, our PLS-based models rely on prediction distances, which can be seen as a determined optimal cut-off. Therefore, the ROC and AUC criteria may not be particularly insightful in relation to the performance evaluation of our supervised multivariate methods, but can complement the statistical analysis (from Rohart, Gautier, Singh, and Lê Cao (2017)).
auroc(final.mint.splsda.stem, roc.comp = 1)
We can also obtain an AUC plot per study by specifying the argument roc.study:
auroc(final.mint.splsda.stem, roc.comp = 1, roc.study = '2')
stemcells gene expression data for global and specific studies, averaged across one-vs-all comparisons. Numerical outputs include the AUC and a Wilcoxon test \(p-\)value for each ‘one vs. other’ class comparison that are performed per component. This output complements the sPLS-DA performance evaluation but should not be used for tuning (as the prediction process in sPLS-DA is based on prediction distances, not a cutoff that maximises specificity and sensitivity as in ROC). The plot suggests that the selected features are more accurate in classifying fibroblasts versus the other cell types, and less accurate in distinguishing hESC versus the other cell types or hiPSC versus the other cell types." width="50%" />stemcells gene expression data for global and specific studies, averaged across one-vs-all comparisons. Numerical outputs include the AUC and a Wilcoxon test \(p-\)value for each ‘one vs. other’ class comparison that are performed per component. This output complements the sPLS-DA performance evaluation but should not be used for tuning (as the prediction process in sPLS-DA is based on prediction distances, not a cutoff that maximises specificity and sensitivity as in ROC). The plot suggests that the selected features are more accurate in classifying fibroblasts versus the other cell types, and less accurate in distinguishing hESC versus the other cell types or hiPSC versus the other cell types." width="50%" />
Figure 57: ROC curve and AUC from the MINT sPLS-DA performed on the stemcells gene expression data for global and specific studies, averaged across one-vs-all comparisons. Numerical outputs include the AUC and a Wilcoxon test \(p-\)value for each ‘one vs. other’ class comparison that are performed per component. This output complements the sPLS-DA performance evaluation but should not be used for tuning (as the prediction process in sPLS-DA is based on prediction distances, not a cutoff that maximises specificity and sensitivity as in ROC). The plot suggests that the selected features are more accurate in classifying fibroblasts versus the other cell types, and less accurate in distinguishing hESC versus the other cell types or hiPSC versus the other cell types.
We use the predict() function to predict the class membership of new test samples from an external study. We provide an example where we set aside a particular study, train the MINT model on the remaining three studies, then predict on the test study. This process exactly reflects the inner workings of the tune() and perf() functions using LOGOCV.
Here during our model training on the three studies only, we assume we have performed the tuning steps described in this case study to choose ncomp and keepX (here set to arbitrary values to avoid overfitting):
# We predict on study 3
indiv.test <- which(study == "3")
# We train on the remaining studies, with pre-tuned parameters
mint.splsda.stem2 <- mint.splsda(X = X[-c(indiv.test), ],
Y = Y[-c(indiv.test)],
study = droplevels(study[-c(indiv.test)]),
ncomp = 1,
keepX = 30)
mint.predict.stem <- predict(mint.splsda.stem2, newdata = X[indiv.test, ],
dist = "centroids.dist",
study.test = factor(study[indiv.test]))
# Store class prediction with a model with 1 comp
indiv.prediction <- mint.predict.stem$class$centroids.dist[, 1]
# The confusion matrix compares the real subtypes with the predicted subtypes
conf.mat <- get.confusion_matrix(truth = Y[indiv.test],
predicted = indiv.prediction)
conf.mat
## predicted.as.Fibroblast predicted.as.hESC predicted.as.hiPSC
## Fibroblast 3 0 0
## hESC 0 4 4
## hiPSC 2 2 6
Here we have considered a trained model with one component, and compared the cell type prediction for the test study 3 with the known cell types. The classification error rate is relatively high, but potentially could be improved with a proper tuning, and a larger number of studies in the training set.
# Prediction error rate
(sum(conf.mat) - sum(diag(conf.mat)))/sum(conf.mat)
## [1] 0.3809524
## [1] '6.35.2'
## R version 4.6.0 alpha (2026-04-08 r89818)
## Platform: aarch64-apple-darwin23
## Running under: macOS Tahoe 26.3.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
##
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/New_York
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] mixOmics_6.35.2 ggplot2_4.0.2 lattice_0.22-9
## [4] MASS_7.3-65 kableExtra_1.4.0 knitr_1.51
## [7] BiocParallel_1.45.0 BiocStyle_2.39.0
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.6 ellipse_0.5.0 xfun_0.57
## [4] bslib_0.10.0 ggrepel_0.9.8 vctrs_0.7.3
## [7] tools_4.6.0 generics_0.1.4 parallel_4.6.0
## [10] tibble_3.3.1 rARPACK_0.11-0 pkgconfig_2.0.3
## [13] Matrix_1.7-5 RColorBrewer_1.1-3 S7_0.2.1-1
## [16] lifecycle_1.0.5 compiler_4.6.0 farver_2.1.2
## [19] stringr_1.6.0 textshaping_1.0.5 tinytex_0.59
## [22] codetools_0.2-20 htmltools_0.5.9 snow_0.4-4
## [25] sass_0.4.10 yaml_2.3.12 pillar_1.11.1
## [28] jquerylib_0.1.4 tidyr_1.3.2 cachem_1.1.0
## [31] magick_2.9.1 RSpectra_0.16-2 tidyselect_1.2.1
## [34] digest_0.6.39 stringi_1.8.7 dplyr_1.2.1
## [37] reshape2_1.4.5 purrr_1.2.2 bookdown_0.46
## [40] labeling_0.4.3 fastmap_1.2.0 grid_4.6.0
## [43] cli_3.6.6 magrittr_2.0.5 dichromat_2.0-0.1
## [46] corpcor_1.6.10 withr_3.0.2 scales_1.4.0
## [49] rmarkdown_2.31 matrixStats_1.5.0 igraph_2.3.0
## [52] otel_0.2.0 gridExtra_2.3 evaluate_1.0.5
## [55] viridisLite_0.4.3 rlang_1.2.0 Rcpp_1.1.1-1
## [58] glue_1.8.1 BiocManager_1.30.27 xml2_1.5.2
## [61] svglite_2.2.2 rstudioapi_0.18.0 jsonlite_2.0.0
## [64] R6_2.6.1 plyr_1.8.9 systemfonts_1.3.2