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$bulk)[["bulk_counts"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                               
#> gene_1 502 488 528 504 500 480 511 497 470 544
#> gene_2 531 576 507 484 499 510 559 532 498 532
#> gene_3 504 568 560 476 489 505 542 517 515 492
#> gene_4 523 555 478 478 509 468 465 562 492 496
#> gene_5 480 484 511 435 530 484 479 490 503 487
#> gene_6 527 553 486 523 506 524 580 528 488 521
utils::head(SummarizedExperiment::assays(simulation$bulk)[["bulk_tpm"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                                                             
#> gene_1 1027.3597 1049.7852  934.5624 1003.2201 1063.9139  889.8987 1146.4715
#> gene_2  876.0797  958.5749 1030.3904  911.1914 1008.3851 1026.2874  904.4486
#> gene_3 1010.3846  975.6656 1029.0533  977.8168 1053.3575 1054.4395  976.5724
#> gene_4  989.1262 1014.0460 1026.3382 1005.2361 1005.7735  922.8593 1038.3156
#> gene_5  952.9583 1036.3250  997.9020 1006.6882 1013.7146 1004.0182  958.2728
#> gene_6 1032.1423  890.9122  990.6244 1012.1340  999.2924  975.1258  973.5963
#>                                     
#> gene_1  971.4843  965.6541 1032.6977
#> gene_2  915.2972  935.5393  980.1638
#> gene_3 1092.5942 1007.6334  947.9026
#> gene_4  955.7412  949.3952  983.5728
#> gene_5 1062.5188 1039.1456 1003.9897
#> gene_6 1016.0301  929.8145  934.6203

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 4.4.1 (2024-06-14)
#> Platform: x86_64-apple-darwin20
#> Running under: macOS Monterey 12.7.6
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib 
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
#> 
#> 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_1.8.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] SummarizedExperiment_1.36.0 gtable_0.3.6               
#>  [3] xfun_0.48                   bslib_0.8.0                
#>  [5] ggplot2_3.5.1               Biobase_2.66.0             
#>  [7] lattice_0.22-6              vctrs_0.6.5                
#>  [9] tools_4.4.1                 generics_0.1.3             
#> [11] stats4_4.4.1                parallel_4.4.1             
#> [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_0.44.0           
#> [21] sparseMatrixStats_1.18.0    lifecycle_1.0.4            
#> [23] GenomeInfoDbData_1.2.13     compiler_4.4.1             
#> [25] farver_2.1.2                munsell_0.5.1              
#> [27] codetools_0.2-20            GenomeInfoDb_1.42.0        
#> [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_1.40.0        
#> [37] DelayedArray_0.32.0         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_4.4.1                 
#> [47] colorspace_2.1-1            cli_3.6.3                  
#> [49] SparseArray_1.6.0           magrittr_2.0.3             
#> [51] S4Arrays_1.6.0              utf8_1.2.4                 
#> [53] withr_3.0.2                 UCSC.utils_1.2.0           
#> [55] scales_1.3.0                rmarkdown_2.28             
#> [57] XVector_0.46.0              httr_1.4.7                 
#> [59] matrixStats_1.4.1           proxyC_0.4.1               
#> [61] evaluate_1.0.1              knitr_1.48                 
#> [63] GenomicRanges_1.58.0        IRanges_2.40.0             
#> [65] rlang_1.1.4                 Rcpp_1.0.13                
#> [67] glue_1.8.0                  BiocGenerics_0.52.0        
#> [69] jsonlite_1.8.9              R6_2.5.1                   
#> [71] MatrixGenerics_1.18.0       zlibbioc_1.52.0

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.