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$bulk)[["bulk_counts"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                               
#> gene_1 499 508 476 476 528 489 507 506 491 477
#> gene_2 476 537 485 497 511 494 521 478 521 502
#> gene_3 481 547 545 529 513 525 484 459 516 501
#> gene_4 488 497 499 488 500 509 493 531 541 495
#> gene_5 500 484 486 473 512 523 550 533 516 492
#> gene_6 529 561 534 535 496 533 468 528 489 529
utils::head(SummarizedExperiment::assays(simulation$bulk)[["bulk_tpm"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                                                             
#> gene_1  947.7640  943.2131  914.9015  999.3596  922.6701  994.2621  880.2635
#> gene_2  924.6762  979.6290  984.7765 1011.9153  943.8900  895.7023 1060.2005
#> gene_3 1027.1098  943.0247  983.6329  952.3262  926.9179  943.6404  973.8691
#> gene_4 1026.4207 1116.6366 1041.2650  995.9628 1075.1566 1015.3612 1064.9941
#> gene_5 1080.0519  893.1556  963.2688 1040.0354 1093.7194 1015.1840  970.7325
#> gene_6  955.4295  810.0188  844.1762  979.0908  879.5309  917.7866 1003.5746
#>                                     
#> gene_1  858.7787  981.2729  946.8566
#> gene_2  982.2477  937.9480  976.5180
#> gene_3 1004.6163  947.9308  914.5340
#> gene_4 1013.7783 1045.8459 1002.2870
#> gene_5  986.2305  951.0888 1047.6556
#> gene_6  935.1481  898.0657  954.2087

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 4.4.0 Patched (2024-04-24 r86482)
#> Platform: aarch64-apple-darwin20
#> Running under: macOS Ventura 13.6.6
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/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_1.5.4
#> 
#> loaded via a namespace (and not attached):
#>  [1] SummarizedExperiment_1.33.3 gtable_0.3.5               
#>  [3] xfun_0.43                   bslib_0.7.0                
#>  [5] ggplot2_3.5.1               Biobase_2.63.1             
#>  [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_0.41.7           
#> [21] sparseMatrixStats_1.15.1    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_1.39.14       
#> [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_1.37.1        
#> [37] DelayedArray_0.29.9         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_1.3.7           magrittr_2.0.3             
#> [51] S4Arrays_1.3.7              utf8_1.2.4                 
#> [53] withr_3.0.0                 UCSC.utils_0.99.7          
#> [55] scales_1.3.0                rmarkdown_2.26             
#> [57] XVector_0.43.1              httr_1.4.7                 
#> [59] matrixStats_1.3.0           proxyC_0.4.1               
#> [61] evaluate_0.23               knitr_1.46                 
#> [63] GenomicRanges_1.55.4        IRanges_2.37.1             
#> [65] rlang_1.1.3                 Rcpp_1.0.12                
#> [67] glue_1.7.0                  BiocGenerics_0.49.1        
#> [69] jsonlite_1.8.8              R6_2.5.1                   
#> [71] MatrixGenerics_1.15.1       zlibbioc_1.49.3

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.