To install the developmental version of the package, run:
To install from Bioconductor:
As complex tissues are typically composed of various cell types,
deconvolution tools have been developed to computationally infer their
cellular composition from bulk RNA sequencing (RNA-seq) data. To
comprehensively assess deconvolution performance, gold-standard datasets
are indispensable. Gold-standard, experimental techniques like flow
cytometry or immunohistochemistry are resource-intensive and cannot be
systematically applied to the numerous cell types and tissues profiled
with high-throughput transcriptomics. The simulation of ‘pseudo-bulk’
data, generated by aggregating single-cell RNA-seq (scRNA-seq)
expression profiles in pre-defined proportions, offers a scalable and
cost-effective alternative. This makes it feasible to create in silico
gold standards that allow fine-grained control of cell-type fractions
not conceivable in an experimental setup. However, at present, no
simulation software for generating pseudo-bulk RNA-seq data
exists.
SimBu was developed to simulate pseudo-bulk samples based on various
simulation scenarios, designed to test specific features of
deconvolution methods. A unique feature of SimBu is the modelling of
cell-type-specific mRNA bias using experimentally-derived or data-driven
scaling factors. Here, we show that SimBu can generate realistic
pseudo-bulk data, recapitulating the biological and statistical features
of real RNA-seq data. Finally, we illustrate the impact of mRNA bias on
the evaluation of deconvolution tools and provide recommendations for
the selection of suitable methods for estimating mRNA content.
This chapter covers all you need to know to quickly simulate some
pseudo-bulk samples!
This package can simulate samples from local or public data. This
vignette will work with artificially generated data as it serves as an
overview for the features implemented in SimBu. For the public data
integration using sfaira (Fischer et al. 2020), please refer to
the “Public Data Integration”
vignette.
We will create some toy data to use for our simulations; two matrices with 300 cells each and 1000 genes/features. One represents raw count data, while the other matrix represents scaled TPM-like data. We will assign these cells to some immune cell types.
counts <- Matrix::Matrix(matrix(stats::rpois(3e5, 5), ncol = 300), sparse = TRUE)
tpm <- Matrix::Matrix(matrix(stats::rpois(3e5, 5), ncol = 300), sparse = TRUE)
tpm <- Matrix::t(1e6 * Matrix::t(tpm) / Matrix::colSums(tpm))
colnames(counts) <- paste0("cell_", rep(1:300))
colnames(tpm) <- paste0("cell_", rep(1:300))
rownames(counts) <- paste0("gene_", rep(1:1000))
rownames(tpm) <- paste0("gene_", rep(1:1000))
annotation <- data.frame(
"ID" = paste0("cell_", rep(1:300)),
"cell_type" = c(
rep("T cells CD4", 50),
rep("T cells CD8", 50),
rep("Macrophages", 100),
rep("NK cells", 10),
rep("B cells", 70),
rep("Monocytes", 20)
)
)
SimBu uses the SummarizedExperiment
class as storage for count data as well as annotation data. Currently it
is possible to store two matrices at the same time: raw counts and
TPM-like data (this can also be some other scaled count matrix, such as
RPKM, but we recommend to use TPMs). These two matrices have to have the
same dimensions and have to contain the same genes and cells. Providing
the raw count data is mandatory!
SimBu scales the matrix that is added via the tpm_matrix
slot by default to 1e6 per cell, if you do not want this, you can switch
it off by setting the scale_tpm
parameter to
FALSE
. Additionally, the cell type annotation of the cells
has to be given in a dataframe, which has to include the two columns
ID
and cell_type
. If additional columns from
this annotation should be transferred to the dataset, simply give the
names of them in the additional_cols
parameter.
To generate a dataset that can be used in SimBu, you can use the
dataset()
method; other methods exist as well, which are
covered in the “Inputs &
Outputs” vignette.
ds <- SimBu::dataset(
annotation = annotation,
count_matrix = counts,
tpm_matrix = tpm,
name = "test_dataset"
)
#> Filtering genes...
#> Created dataset.
SimBu offers basic filtering options for your dataset, which you can
apply during dataset generation:
filter_genes: if TRUE, genes which have expression
values of 0 in all cells will be removed.
variance_cutoff: remove all genes with a expression
variance below the chosen cutoff.
type_abundance_cutoff: remove all cells, which belong to
a cell type that appears less the the given amount.
We are now ready to simulate the first pseudo bulk samples with the created dataset:
simulation <- SimBu::simulate_bulk(
data = ds,
scenario = "random",
scaling_factor = "NONE",
ncells = 100,
nsamples = 10,
BPPARAM = BiocParallel::MulticoreParam(workers = 4), # this will use 4 threads to run the simulation
run_parallel = TRUE
) # multi-threading to TRUE
#> Using parallel generation of simulations.
#> Finished simulation.
ncells
sets the number of cells in each sample, while
nsamples
sets the total amount of simulated samples.
If you want to simulate a specific sequencing depth in your simulations,
you can use the total_read_counts
parameter to do so. Note
that this parameter is only applied on the counts matrix (if supplied),
as TPMs will be scaled to 1e6 by default.
SimBu can add mRNA bias by using different scaling factors to the
simulations using the scaling_factor
parameter. A detailed
explanation can be found in the “Scaling factor”
vignette.
Currently there are 6 scenarios
implemented in the
package:
even: this creates samples, where all existing
cell-types in the dataset appear in the same proportions. So using a
dataset with 3 cell-types, this will simulate samples, where all
cell-type fractions are 1/3. In order to still have a slight variation
between cell type fractions, you can increase the
balance_uniform_mirror_scenario
parameter (default to
0.01). Setting it to 0 will generate simulations with exactly the same
cell type fractions.
random: this scenario will create random cell type
fractions using all present types for each sample. The random sampling
is based on the uniform distribution.
mirror_db: this scenario will mirror the exact fractions
of cell types which are present in the provided dataset. If it consists
of 20% T cells, 30% B cells and 50% NK cells, all simulated samples will
mirror these fractions. Similar to the uniform scenario, you can add a
small variation to these fractions with the
balance_uniform_mirror_scenario
parameter.
weighted: here you need to set two additional parameters
for the simulate_bulk()
function:
weighted_cell_type
sets the cell-type you want to be
over-representing and weighted_amount
sets the fraction of
this cell-type. You could for example use B-cell
and
0.5
to create samples, where 50% are B-cells and the rest
is filled randomly with other cell-types.
pure: this creates simulations of only one single
cell-type. You have to provide the name of this cell-type with the
pure_cell_type
parameter.
custom: here you are able to create your own set of
cell-type fractions. When using this scenario, you additionally need to
provide a dataframe in the custom_scenario_data
parameter,
where each row represents one sample (therefore the number of rows need
to match the nsamples
parameter). Each column has to
represent one cell-type, which also occurs in the dataset and describes
the fraction of this cell-type in a sample. The fractions per sample
need to sum up to 1. An example can be seen here:
pure_scenario_dataframe <- data.frame(
"B cells" = c(0.2, 0.1, 0.5, 0.3),
"T cells" = c(0.3, 0.8, 0.2, 0.5),
"NK cells" = c(0.5, 0.1, 0.3, 0.2),
row.names = c("sample1", "sample2", "sample3", "sample4")
)
pure_scenario_dataframe
#> B.cells T.cells NK.cells
#> sample1 0.2 0.3 0.5
#> sample2 0.1 0.8 0.1
#> sample3 0.5 0.2 0.3
#> sample4 0.3 0.5 0.2
The simulation
object contains three named
entries:
bulk
: a SummarizedExperiment object with the
pseudo-bulk dataset(s) stored in the assays
. They can be
accessed like this:utils::head(SummarizedExperiment::assays(simulation$bulk)[["bulk_counts"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#> [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>
#> gene_1 447 517 489 500 466 523 426 466 509 495
#> gene_2 531 506 479 532 527 584 498 534 546 506
#> gene_3 472 445 491 556 436 471 455 474 447 528
#> gene_4 476 476 481 463 503 506 491 476 508 493
#> gene_5 514 412 492 512 484 519 455 508 543 514
#> gene_6 520 569 492 551 546 511 530 544 542 552
utils::head(SummarizedExperiment::assays(simulation$bulk)[["bulk_tpm"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#> [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>
#> gene_1 866.1874 981.0445 976.9384 1025.2350 970.3928 957.6776 907.7802
#> gene_2 966.3444 980.0366 932.7332 1111.4390 968.4383 992.0178 1075.9928
#> gene_3 923.6296 906.5138 990.6548 977.4324 901.9055 978.0750 919.1477
#> gene_4 944.5888 870.1909 947.4791 837.6072 910.6976 958.9851 918.8770
#> gene_5 1099.7801 977.2179 973.4431 1056.1359 1006.4795 976.4724 1063.7510
#> gene_6 982.0899 959.1222 1054.4973 994.7651 1003.7294 885.7927 986.0464
#>
#> gene_1 918.6967 892.4890 928.4991
#> gene_2 1015.8794 985.6254 907.4248
#> gene_3 947.1294 1054.5231 1062.5693
#> gene_4 909.7051 831.4651 832.5745
#> gene_5 1046.5421 966.8061 1023.6006
#> gene_6 957.2819 982.7361 1113.7042
If only a single matrix was given to the dataset initially, only one assay is filled.
cell_fractions
: a table where rows represent the
simulated samples and columns represent the different simulated
cell-types. The entries in the table store the specific cell-type
fraction per sample.
scaling_vector
: a named list, with the used scaling
value for each cell from the single cell dataset.
It is also possible to merge simulations:
simulation2 <- SimBu::simulate_bulk(
data = ds,
scenario = "even",
scaling_factor = "NONE",
ncells = 1000,
nsamples = 10,
BPPARAM = BiocParallel::MulticoreParam(workers = 4),
run_parallel = TRUE
)
#> Using parallel generation of simulations.
#> Finished simulation.
merged_simulations <- SimBu::merge_simulations(list(simulation, simulation2))
Finally here is a barplot of the resulting simulation:
Sometimes, you are only interested in specific cell-types (for
example T cells), but the dataset you are using has too many other
cell-types; you can handle this issue during simulation using the
whitelist
parameter:
simulation <- SimBu::simulate_bulk(
data = ds,
scenario = "random",
scaling_factor = "NONE",
ncells = 1000,
nsamples = 20,
BPPARAM = BiocParallel::MulticoreParam(workers = 4),
run_parallel = TRUE,
whitelist = c("T cells CD4", "T cells CD8")
)
#> Using parallel generation of simulations.
#> Finished simulation.
SimBu::plot_simulation(simulation = simulation)
In the same way, you can also provide a blacklist
parameter, where you name the cell-types you don’t want
to be included in your simulation.
utils::sessionInfo()
#> R version 4.4.0 alpha (2024-03-27 r86216)
#> Platform: aarch64-apple-darwin20
#> Running under: macOS Ventura 13.6.5
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
#>
#> locale:
#> [1] en_US.UTF-8/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] SimBu_1.6.0
#>
#> loaded via a namespace (and not attached):
#> [1] SummarizedExperiment_1.34.0 gtable_0.3.4
#> [3] xfun_0.43 bslib_0.6.2
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#> [9] tools_4.4.0 generics_0.1.3
#> [11] stats4_4.4.0 parallel_4.4.0
#> [13] tibble_3.2.1 fansi_1.0.6
#> [15] highr_0.10 pkgconfig_2.0.3
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#> [21] sparseMatrixStats_1.16.0 RcppParallel_5.1.7
#> [23] lifecycle_1.0.4 GenomeInfoDbData_1.2.12
#> [25] farver_2.1.1 compiler_4.4.0
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