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$bulk)[["bulk_counts"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                               
#> gene_1 490 460 488 495 466 498 456 489 463 506
#> gene_2 451 471 484 563 496 480 512 499 463 482
#> gene_3 458 481 531 527 548 505 485 524 494 492
#> gene_4 514 491 519 549 532 532 509 534 504 503
#> gene_5 374 452 493 499 487 486 491 459 452 451
#> gene_6 520 502 511 487 479 479 494 463 483 493
utils::head(SummarizedExperiment::assays(simulation$bulk)[["bulk_tpm"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                                                             
#> gene_1 1005.9876 1085.5772  994.4200 1012.9951 1065.9333 1059.5073 1043.4741
#> gene_2  979.9594  940.5901 1026.5151 1048.3090  952.2728  898.1679  884.9045
#> gene_3 1015.7410  970.4704 1089.2240 1062.7206  966.2197 1027.5792 1030.9974
#> gene_4 1108.3469 1010.8994  926.6860  955.3581 1025.5230  940.4942  967.4069
#> gene_5  938.5714  987.8721  903.2981 1008.4084  889.1928  970.1275  951.8945
#> gene_6 1003.2123  848.2756 1058.2111  953.8348  988.3526  998.2449 1072.4145
#>                                     
#> gene_1 1038.0235 1035.7470 1058.3366
#> gene_2 1122.3779  950.2340  956.2240
#> gene_3 1020.7739 1036.2738 1081.9810
#> gene_4  999.0801 1017.4602  916.1056
#> gene_5  973.7182  973.7451 1040.7308
#> gene_6 1029.3545 1023.2547 1009.9355

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 4.4.0 beta (2024-04-15 r86425 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_1.6.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] SummarizedExperiment_1.34.0 gtable_0.3.5               
#>  [3] xfun_0.43                   bslib_0.7.0                
#>  [5] ggplot2_3.5.1               Biobase_2.64.0             
#>  [7] lattice_0.22-6              vctrs_0.6.5                
#>  [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            
#> [17] Matrix_1.7-0                data.table_1.15.4          
#> [19] RColorBrewer_1.1-3          S4Vectors_0.42.0           
#> [21] sparseMatrixStats_1.16.0    lifecycle_1.0.4            
#> [23] GenomeInfoDbData_1.2.12     compiler_4.4.0             
#> [25] farver_2.1.1                munsell_0.5.1              
#> [27] codetools_0.2-20            GenomeInfoDb_1.40.0        
#> [29] htmltools_0.5.8.1           sass_0.4.9                 
#> [31] yaml_2.3.8                  pillar_1.9.0               
#> [33] crayon_1.5.2                jquerylib_0.1.4            
#> [35] tidyr_1.3.1                 BiocParallel_1.38.0        
#> [37] DelayedArray_0.30.0         cachem_1.0.8               
#> [39] abind_1.4-5                 tidyselect_1.2.1           
#> [41] digest_0.6.35               dplyr_1.1.4                
#> [43] purrr_1.0.2                 labeling_0.4.3             
#> [45] fastmap_1.1.1               grid_4.4.0                 
#> [47] colorspace_2.1-0            cli_3.6.2                  
#> [49] SparseArray_1.4.0           magrittr_2.0.3             
#> [51] S4Arrays_1.4.0              utf8_1.2.4                 
#> [53] withr_3.0.0                 UCSC.utils_1.0.0           
#> [55] scales_1.3.0                rmarkdown_2.26             
#> [57] XVector_0.44.0              httr_1.4.7                 
#> [59] matrixStats_1.3.0           proxyC_0.4.1               
#> [61] evaluate_0.23               knitr_1.46                 
#> [63] GenomicRanges_1.56.0        IRanges_2.38.0             
#> [65] rlang_1.1.3                 Rcpp_1.0.12                
#> [67] glue_1.7.0                  BiocGenerics_0.50.0        
#> [69] jsonlite_1.8.8              R6_2.5.1                   
#> [71] MatrixGenerics_1.16.0       zlibbioc_1.50.0

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.