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.
#> 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:

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$bulk)[["bulk_counts"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                               
#> gene_1 497 493 555 510 514 510 448 465 508 514
#> gene_2 514 474 455 467 501 502 507 495 510 458
#> gene_3 527 552 530 508 528 512 541 521 485 519
#> gene_4 515 533 542 504 527 534 508 499 525 512
#> gene_5 468 484 476 519 471 465 447 439 466 464
#> gene_6 494 512 542 511 476 546 538 535 507 496
utils::head(SummarizedExperiment::assays(simulation$bulk)[["bulk_tpm"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                                                            
#> gene_1  963.5137 941.0241 1043.1307 1027.1367  994.7701  950.9523 1003.3901
#> gene_2 1006.5124 989.0679 1074.4071 1046.7144 1040.3567  980.7472 1087.7254
#> gene_3  950.2018 999.2623 1024.9995  967.7537 1048.0662  962.3478  952.0814
#> gene_4  892.3737 917.0710  982.7849  983.2121  999.6768 1001.4757  993.5846
#> gene_5  968.0412 996.6160  859.9628  958.8703  941.2209  945.4490  878.3073
#> gene_6  868.2238 930.9383  912.4680  984.4850  902.9564 1036.7784  915.3537
#>                                    
#> gene_1 1028.8879  965.0647 1026.620
#> gene_2 1133.5828 1057.3479 1084.746
#> gene_3  975.4868  950.1859 1022.463
#> gene_4 1021.3471 1010.1644 1021.536
#> gene_5  985.1111  974.2961  957.179
#> gene_6  956.9089  955.3062 1034.763

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.
#> 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:

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.
#> 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 RC (2025-04-03 r88103 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] tidyr_1.3.1                 sass_0.4.10                
#>  [3] generics_0.1.3              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_0.35.0        
#> [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] glue_1.8.0                  data.table_1.17.0          
#> [57] Rcpp_1.0.14                 xfun_0.52                  
#> [59] tibble_3.2.1                GenomicRanges_1.61.0       
#> [61] tidyselect_1.2.1            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.