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.
#> Warning in BiocParallel::MulticoreParam(workers = 4): MulticoreParam() not
#> supported on Windows, use SnowParam()
#> 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$bulk)[["bulk_counts"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#> [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>
#> gene_1 491 492 485 490 499 516 538 460 467 525
#> gene_2 521 502 441 476 522 475 497 510 520 466
#> gene_3 481 504 499 500 574 515 509 447 510 503
#> gene_4 462 493 472 460 474 438 499 474 417 438
#> gene_5 499 491 496 479 518 490 537 500 517 496
#> gene_6 469 479 550 496 498 500 486 482 448 512
utils::head(SummarizedExperiment::assays(simulation$bulk)[["bulk_tpm"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#> [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>
#> gene_1 948.4647 886.1494 1015.0512 962.8006 938.3714 900.7010 1015.7844
#> gene_2 984.0274 980.6201 1003.8090 954.4581 899.8970 952.2567 944.7096
#> gene_3 977.8768 1011.2235 929.7885 968.5726 999.6357 951.0266 957.5604
#> gene_4 1030.8498 1039.3798 1009.4310 1004.7134 1054.6991 958.1151 1060.2558
#> gene_5 1001.2696 948.7615 950.4478 983.4961 1005.8776 897.4915 1112.5827
#> gene_6 945.6775 1009.8617 1015.1671 931.7909 1038.2816 983.0703 1019.7259
#>
#> gene_1 1009.2189 993.3148 947.9730
#> gene_2 1046.9548 988.0254 928.7506
#> gene_3 898.5711 965.8238 988.4214
#> gene_4 916.1504 973.5730 987.1413
#> gene_5 1076.8230 961.4825 948.8562
#> gene_6 925.6602 981.9913 1021.9301
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.
#> Warning in BiocParallel::MulticoreParam(workers = 4): MulticoreParam() not
#> supported on Windows, use SnowParam()
#> 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.
#> Warning in BiocParallel::MulticoreParam(workers = 4): MulticoreParam() not
#> supported on Windows, use SnowParam()
#> 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 4.5.0 (2025-04-11 ucrt)
#> Platform: x86_64-w64-mingw32/x64
#> Running under: Windows Server 2022 x64 (build 20348)
#>
#> Matrix products: default
#> LAPACK version 3.12.1
#>
#> locale:
#> [1] LC_COLLATE=C
#> [2] LC_CTYPE=English_United States.utf8
#> [3] LC_MONETARY=English_United States.utf8
#> [4] LC_NUMERIC=C
#> [5] LC_TIME=English_United States.utf8
#>
#> time zone: America/New_York
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] SimBu_1.11.0
#>
#> loaded via a namespace (and not attached):
#> [1] sass_0.4.10 generics_0.1.4
#> [3] tidyr_1.3.1 SparseArray_1.9.0
#> [5] lattice_0.22-7 digest_0.6.37
#> [7] magrittr_2.0.3 RColorBrewer_1.1-3
#> [9] evaluate_1.0.3 sparseMatrixStats_1.21.0
#> [11] grid_4.5.0 fastmap_1.2.0
#> [13] jsonlite_2.0.0 Matrix_1.7-3
#> [15] GenomeInfoDb_1.45.4 proxyC_0.5.2
#> [17] httr_1.4.7 purrr_1.0.4
#> [19] scales_1.4.0 UCSC.utils_1.5.0
#> [21] codetools_0.2-20 jquerylib_0.1.4
#> [23] abind_1.4-8 cli_3.6.5
#> [25] rlang_1.1.6 crayon_1.5.3
#> [27] XVector_0.49.0 Biobase_2.69.0
#> [29] withr_3.0.2 cachem_1.1.0
#> [31] DelayedArray_0.35.1 yaml_2.3.10
#> [33] S4Arrays_1.9.1 tools_4.5.0
#> [35] parallel_4.5.0 BiocParallel_1.43.3
#> [37] dplyr_1.1.4 ggplot2_3.5.2
#> [39] SummarizedExperiment_1.39.0 BiocGenerics_0.55.0
#> [41] vctrs_0.6.5 R6_2.6.1
#> [43] matrixStats_1.5.0 stats4_4.5.0
#> [45] lifecycle_1.0.4 S4Vectors_0.47.0
#> [47] IRanges_2.43.0 pkgconfig_2.0.3
#> [49] gtable_0.3.6 bslib_0.9.0
#> [51] pillar_1.10.2 glue_1.8.0
#> [53] data.table_1.17.4 Rcpp_1.0.14
#> [55] xfun_0.52 tibble_3.2.1
#> [57] GenomicRanges_1.61.0 tidyselect_1.2.1
#> [59] dichromat_2.0-0.1 MatrixGenerics_1.21.0
#> [61] knitr_1.50 farver_2.1.2
#> [63] htmltools_0.5.8.1 labeling_0.4.3
#> [65] rmarkdown_2.29 compiler_4.5.0
Fischer, David S., Leander Dony, Martin König, Abdul Moeed, Luke Zappia, Sophie Tritschler, Olle Holmberg, Hananeh Aliee, and Fabian J. Theis. 2020. “Sfaira Accelerates Data and Model Reuse in Single Cell Genomics.” bioRxiv. https://doi.org/10.1101/2020.12.16.419036.