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$bulk)[["bulk_counts"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                               
#> gene_1 450 482 483 471 453 464 436 498 499 388
#> gene_2 573 521 496 525 539 507 506 506 513 503
#> gene_3 471 490 507 539 508 501 510 479 527 459
#> gene_4 516 494 538 516 496 516 509 531 501 468
#> gene_5 507 489 506 472 509 465 472 530 461 532
#> gene_6 447 533 517 528 458 529 473 489 485 520
utils::head(SummarizedExperiment::assays(simulation$bulk)[["bulk_tpm"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                                                             
#> gene_1  967.8081 1024.3208  994.5738 1004.5951  996.6018 1041.4286  989.3081
#> gene_2  956.9036  970.1874 1040.8207 1015.3352  963.4246  992.0958  966.8417
#> gene_3  973.1363  937.9478  920.8321  910.2164  934.8774  916.9489  981.4408
#> gene_4  994.0521  983.9864 1029.1127 1138.7234 1092.4974 1076.3314 1137.4457
#> gene_5  974.9884  960.8750  927.7644  953.0952  938.8832  935.4991  972.2291
#> gene_6 1105.4667  896.9438  918.0829 1007.8399 1076.3305  998.2094 1026.1885
#>                                     
#> gene_1 1045.2326 1085.8574 1070.5483
#> gene_2  956.1682  996.6891  973.1312
#> gene_3  981.2296  941.8089  882.6285
#> gene_4 1001.3466 1154.4065 1124.2370
#> gene_5  975.7316  957.0659  963.3046
#> gene_6  996.6564 1056.8176  969.4328

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 Under development (unstable) (2024-10-26 r87273 ucrt)
#> Platform: x86_64-w64-mingw32/x64
#> Running under: Windows Server 2022 x64 (build 20348)
#> 
#> Matrix products: default
#> 
#> 
#> 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_1.9.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] SummarizedExperiment_1.37.0 gtable_0.3.6               
#>  [3] xfun_0.49                   bslib_0.8.0                
#>  [5] ggplot2_3.5.1               Biobase_2.67.0             
#>  [7] lattice_0.22-6              vctrs_0.6.5                
#>  [9] tools_4.5.0                 generics_0.1.3             
#> [11] stats4_4.5.0                parallel_4.5.0             
#> [13] tibble_3.2.1                fansi_1.0.6                
#> [15] highr_0.11                  pkgconfig_2.0.3            
#> [17] Matrix_1.7-1                data.table_1.16.2          
#> [19] RColorBrewer_1.1-3          S4Vectors_0.45.0           
#> [21] sparseMatrixStats_1.19.0    lifecycle_1.0.4            
#> [23] GenomeInfoDbData_1.2.13     compiler_4.5.0             
#> [25] farver_2.1.2                munsell_0.5.1              
#> [27] codetools_0.2-20            GenomeInfoDb_1.43.0        
#> [29] htmltools_0.5.8.1           sass_0.4.9                 
#> [31] yaml_2.3.10                 pillar_1.9.0               
#> [33] crayon_1.5.3                jquerylib_0.1.4            
#> [35] tidyr_1.3.1                 BiocParallel_1.41.0        
#> [37] DelayedArray_0.33.1         cachem_1.1.0               
#> [39] abind_1.4-8                 tidyselect_1.2.1           
#> [41] digest_0.6.37               dplyr_1.1.4                
#> [43] purrr_1.0.2                 labeling_0.4.3             
#> [45] fastmap_1.2.0               grid_4.5.0                 
#> [47] colorspace_2.1-1            cli_3.6.3                  
#> [49] SparseArray_1.7.0           magrittr_2.0.3             
#> [51] S4Arrays_1.7.1              utf8_1.2.4                 
#> [53] withr_3.0.2                 UCSC.utils_1.3.0           
#> [55] scales_1.3.0                rmarkdown_2.28             
#> [57] XVector_0.47.0              httr_1.4.7                 
#> [59] matrixStats_1.4.1           proxyC_0.4.1               
#> [61] evaluate_1.0.1              knitr_1.48                 
#> [63] GenomicRanges_1.59.0        IRanges_2.41.0             
#> [65] rlang_1.1.4                 Rcpp_1.0.13-1              
#> [67] glue_1.8.0                  BiocGenerics_0.53.1        
#> [69] jsonlite_1.8.9              R6_2.5.1                   
#> [71] MatrixGenerics_1.19.0       zlibbioc_1.53.0

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.