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$bulk)[["bulk_counts"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                               
#> gene_1 560 486 505 523 491 483 467 513 587 483
#> gene_2 480 484 489 463 501 482 506 493 449 486
#> gene_3 512 507 498 499 531 491 497 523 512 501
#> gene_4 540 499 499 513 521 445 493 465 510 478
#> gene_5 462 429 502 489 473 465 460 455 498 472
#> gene_6 493 536 531 537 526 503 555 511 494 522
utils::head(SummarizedExperiment::assays(simulation$bulk)[["bulk_tpm"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                                                             
#> gene_1  963.9788  980.3808 1002.9339  976.7506 1043.5362  969.2472  970.1605
#> gene_2 1013.1142 1047.2563  996.6884 1105.1072 1004.6895  956.1521  937.3012
#> gene_3  925.4496  971.6759  967.6298  985.0392  993.1426  973.5429 1070.1399
#> gene_4 1104.0811 1092.6012 1042.0630 1037.3728 1078.9860 1006.2293 1142.3738
#> gene_5 1054.4599 1020.8410 1039.2456  960.9309 1005.3317 1010.4501 1016.7551
#> gene_6 1010.4339  986.7638  943.0839  962.9750  967.8710  984.2772  928.8575
#>                                     
#> gene_1 1046.4281 1047.1471  941.1129
#> gene_2 1033.6745  944.4821 1007.6491
#> gene_3  978.2054  994.9226 1018.7548
#> gene_4 1092.9996 1009.2742 1067.7207
#> gene_5 1014.9645  986.4183 1099.2989
#> gene_6 1002.8816  974.7670  925.0911

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 4.3.3 (2024-02-29)
#> Platform: aarch64-apple-darwin20 (64-bit)
#> Running under: macOS Ventura 13.6.1
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
#> 
#> 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_1.4.3
#> 
#> loaded via a namespace (and not attached):
#>  [1] SummarizedExperiment_1.32.0 gtable_0.3.4               
#>  [3] xfun_0.42                   bslib_0.6.1                
#>  [5] ggplot2_3.5.0               Biobase_2.62.0             
#>  [7] lattice_0.22-5              vctrs_0.6.5                
#>  [9] tools_4.3.3                 generics_0.1.3             
#> [11] stats4_4.3.3                parallel_4.3.3             
#> [13] tibble_3.2.1                fansi_1.0.6                
#> [15] highr_0.10                  pkgconfig_2.0.3            
#> [17] Matrix_1.6-5                data.table_1.15.2          
#> [19] RColorBrewer_1.1-3          S4Vectors_0.40.2           
#> [21] sparseMatrixStats_1.14.0    RcppParallel_5.1.7         
#> [23] lifecycle_1.0.4             GenomeInfoDbData_1.2.11    
#> [25] compiler_4.3.3              farver_2.1.1               
#> [27] munsell_0.5.0               codetools_0.2-19           
#> [29] GenomeInfoDb_1.38.7         htmltools_0.5.7            
#> [31] sass_0.4.8                  yaml_2.3.8                 
#> [33] pillar_1.9.0                crayon_1.5.2               
#> [35] jquerylib_0.1.4             tidyr_1.3.1                
#> [37] BiocParallel_1.36.0         DelayedArray_0.28.0        
#> [39] cachem_1.0.8                abind_1.4-5                
#> [41] tidyselect_1.2.0            digest_0.6.34              
#> [43] dplyr_1.1.4                 purrr_1.0.2                
#> [45] labeling_0.4.3              fastmap_1.1.1              
#> [47] grid_4.3.3                  colorspace_2.1-0           
#> [49] cli_3.6.2                   SparseArray_1.2.4          
#> [51] magrittr_2.0.3              S4Arrays_1.2.1             
#> [53] utf8_1.2.4                  withr_3.0.0                
#> [55] scales_1.3.0                rmarkdown_2.26             
#> [57] XVector_0.42.0              matrixStats_1.2.0          
#> [59] proxyC_0.3.4                evaluate_0.23              
#> [61] knitr_1.45                  GenomicRanges_1.54.1       
#> [63] IRanges_2.36.0              rlang_1.1.3                
#> [65] Rcpp_1.0.12                 glue_1.7.0                 
#> [67] BiocGenerics_0.48.1         jsonlite_1.8.8             
#> [69] R6_2.5.1                    MatrixGenerics_1.14.0      
#> [71] zlibbioc_1.48.0

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.