Getting started with SimBu

Alexander Dietrich

Installation

To install the developmental version of the package, run:

install.packages("devtools")
devtools::install_github("omnideconv/SimBu")

To install from Bioconductor:

if (!require("BiocManager", quietly = TRUE)) {
  install.packages("BiocManager")
}

BiocManager::install("SimBu")
library(SimBu)

Introduction

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.

Getting started

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)
  )
)

Creating a dataset

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:

Simulate pseudo bulk datasets

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:

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

Results

The simulation object contains three named entries:

utils::head(SummarizedExperiment::assays(simulation$bulk)[["bulk_counts"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                               
#> gene_1 507 521 470 536 542 531 547 514 528 487
#> gene_2 498 535 498 537 517 540 515 490 538 496
#> gene_3 483 517 526 528 482 487 460 514 475 487
#> gene_4 487 562 522 515 537 488 502 539 546 529
#> gene_5 502 553 520 485 509 499 487 484 478 503
#> gene_6 454 477 471 465 461 456 463 469 468 454
utils::head(SummarizedExperiment::assays(simulation$bulk)[["bulk_tpm"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                                                            
#> gene_1 1049.8114  982.2612 1029.8352 1070.4990 1029.6523 1057.8953 1091.152
#> gene_2  976.1061  996.5289 1080.6844  945.9895 1026.6952  957.2937 1047.738
#> gene_3  980.3291 1004.2761 1014.5505 1003.9212  975.2919  996.5047 1007.828
#> gene_4  973.8404  998.9242  979.2177  986.3947  985.0007 1017.5505 1006.379
#> gene_5 1006.2452 1081.1950 1087.8067 1085.1367 1021.1247  929.0857 1066.901
#> gene_6  971.4748  976.8552  896.6950  969.8555  987.2717  961.2827  931.038
#>                                     
#> gene_1 1031.7355 1028.7983  976.3684
#> gene_2  991.5278 1036.6101  945.1076
#> gene_3  959.3579  948.9001  990.9794
#> gene_4  975.1810  976.3745  990.0470
#> gene_5  969.1312 1095.1139 1005.2026
#> gene_6  928.0087 1048.4262  975.3500

If only a single matrix was given to the dataset initially, only one assay is filled.

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:

SimBu::plot_simulation(simulation = merged_simulations)

More features

Simulate using a whitelist (and blacklist) of cell-types

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 4.5.0 Patched (2025-04-21 r88169)
#> Platform: x86_64-apple-darwin20
#> Running under: macOS Monterey 12.7.6
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRblas.0.dylib 
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.5-x86_64/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] SimBu_1.11.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] sass_0.4.10                 generics_0.1.3             
#>  [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_1.45.0         proxyC_0.4.1               
#> [17] httr_1.4.7                  purrr_1.0.4                
#> [19] scales_1.3.0                UCSC.utils_1.5.0           
#> [21] codetools_0.2-20            jquerylib_0.1.4            
#> [23] abind_1.4-8                 cli_3.6.4                  
#> [25] rlang_1.1.6                 crayon_1.5.3               
#> [27] XVector_0.49.0              Biobase_2.69.0             
#> [29] munsell_0.5.1               withr_3.0.2                
#> [31] cachem_1.1.0                DelayedArray_0.35.1        
#> [33] yaml_2.3.10                 S4Arrays_1.9.0             
#> [35] tools_4.5.0                 parallel_4.5.0             
#> [37] BiocParallel_1.43.0         dplyr_1.1.4                
#> [39] colorspace_2.1-1            ggplot2_3.5.2              
#> [41] GenomeInfoDbData_1.2.14     SummarizedExperiment_1.39.0
#> [43] BiocGenerics_0.55.0         vctrs_0.6.5                
#> [45] R6_2.6.1                    matrixStats_1.5.0          
#> [47] stats4_4.5.0                lifecycle_1.0.4            
#> [49] S4Vectors_0.47.0            IRanges_2.43.0             
#> [51] pkgconfig_2.0.3             gtable_0.3.6               
#> [53] bslib_0.9.0                 pillar_1.10.2              
#> [55] data.table_1.17.0           glue_1.8.0                 
#> [57] Rcpp_1.0.14                 tidyselect_1.2.1           
#> [59] xfun_0.52                   tibble_3.2.1               
#> [61] GenomicRanges_1.61.0        MatrixGenerics_1.21.0      
#> [63] knitr_1.50                  farver_2.1.2               
#> [65] htmltools_0.5.8.1           labeling_0.4.3             
#> [67] rmarkdown_2.29              compiler_4.5.0

References

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.