makeHeatmap {DExMA} | R Documentation |
It allows to see how the different significant genes are expressed in the different samples. Missing genes appear in gray
makeHeatmap( objectMA, resMA, typeMethod = c("FEM", "REM", "maxP", "minP", "Fisher", "Stouffer"), scaling = c("zscor", "rscale", "swr", "none"), regulation = c("all", "up", "down"), breaks = c(-2, 2), fdrSig = 0.05, numSig = 50 )
objectMA |
A list of list. Each list contains two elements. The first element is the expression matrix (genes in rows and sample in columns) and the second element is a vector of zeros and ones that represents the state of the different samples of the expression matrix. 0 represents one group (controls) and 1 represents the other group (cases). The result of the CreateobjectMA can be used too. |
resMA |
Output generated by the differents functions that performs meta-analysis (metaES, metaPvalue, metaRank or metaAnalysisDE) |
typeMethod |
A character that indicates the method that have been
used to perform meta-analysis. See |
scaling |
Character variable to choose between different scaling approaches. See "Details" for more information. |
regulation |
Character variable that indicates whether we want the heatmap to show all significant genes ("all"), only the up-regulated genes ("up") or only the down-regulated genes("down") |
breaks |
Numeric vector of length 2 that contains the extreme values (minimum and maximum) of the range of values in which the heatmap color scale will be distributed. Default a vector By default a vector of -2 and 2 as extreme values. |
fdrSig |
Adjusted p-value from which a gene is considered significant. Default 0.05 |
numSig |
The number of most significant genes to be represented |
Scaling approaches that can be used are:
"rscale": it applies rescale function of scales package. Values will be between -1 and 1)
"zscor": It calculates a z-score value for each gene, that is, the mean gene expression from each gene is subtracted from each gene expression value and then it is divided by the standard deviation
"swr": it applys scaling relative to reference dataset approach
"none": any scaling approach it is applied.
'NULL'
Juan Antonio Villatoro Garcia, juanantoniovillatorogarcia@gmail.com
Hadley Wickham and Dana Seidel (2020). scales: Scale Functions for Visualization. R package version 1.1.1. https://CRAN.R-project.org/package=scales
Lazar, C, Meganck, S, Taminau, J, and et al. 2013. “Batch Effect Removal Methods for Microarray Gene Expression Data Integration: A Survey,” 469–90.
Raivo Kolde 2019. pheatmap: Pretty Heatmaps. R package version 1.0.12. https://CRAN.R-project.org/package=pheatmap
createObjectMA
, metaAnalysisDE
data(DExMAExampleData) resultsMA <- metaAnalysisDE(maObject, typeMethod="REM") makeHeatmap(objectMA=maObject, resMA=resultsMA, typeMethod="REM", scaling = "zscor", regulation = "all",breaks=c(-2,2), fdrSig = 0.05,numSig=40)