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$bulk)[["bulk_counts"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                               
#> gene_1 438 470 492 516 428 470 471 490 474 446
#> gene_2 494 478 457 466 492 493 545 469 508 473
#> gene_3 445 466 482 447 426 441 448 448 479 493
#> gene_4 482 515 487 519 490 463 515 472 489 489
#> gene_5 439 504 431 472 480 441 491 460 474 469
#> gene_6 516 486 508 479 495 472 470 517 483 475
utils::head(SummarizedExperiment::assays(simulation$bulk)[["bulk_tpm"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                                                            
#> gene_1 1045.7492 1016.9426  987.0956  958.7114 1021.5254 950.9426 1034.8296
#> gene_2  921.5852 1030.8216  988.3924 1017.0156  966.7930 851.5821  908.0995
#> gene_3 1110.1693  903.7312 1130.0610 1016.3120  990.0428 998.5708  916.1532
#> gene_4 1019.1105 1011.9936 1088.9689 1030.7578  980.9879 972.7614 1012.2956
#> gene_5  953.0152  995.9850 1009.3354  841.1720 1000.3812 970.8531  936.3996
#> gene_6 1091.2382 1047.3532 1034.2451 1160.0665 1014.6460 988.0679  995.2362
#>                                    
#> gene_1 1020.0372 1016.2667 1010.798
#> gene_2  956.8313  886.1866  897.766
#> gene_3 1111.9337 1079.6585 1024.802
#> gene_4 1084.5834  955.2154 1035.814
#> gene_5  928.4781  930.7928 1018.248
#> gene_6 1079.9140 1022.6324 1094.808

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_1.7.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] SummarizedExperiment_1.35.0 gtable_0.3.5               
#>  [3] xfun_0.43                   bslib_0.7.0                
#>  [5] ggplot2_3.5.1               Biobase_2.65.0             
#>  [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_0.43.0           
#> [21] sparseMatrixStats_1.17.0    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_1.41.0        
#> [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_1.39.0        
#> [37] DelayedArray_0.31.0         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_1.5.0           magrittr_2.0.3             
#> [51] S4Arrays_1.5.0              utf8_1.2.4                 
#> [53] withr_3.0.0                 UCSC.utils_1.1.0           
#> [55] scales_1.3.0                rmarkdown_2.26             
#> [57] XVector_0.45.0              httr_1.4.7                 
#> [59] matrixStats_1.3.0           proxyC_0.4.1               
#> [61] evaluate_0.23               knitr_1.46                 
#> [63] GenomicRanges_1.57.0        IRanges_2.39.0             
#> [65] rlang_1.1.3                 Rcpp_1.0.12                
#> [67] glue_1.7.0                  BiocGenerics_0.51.0        
#> [69] jsonlite_1.8.8              R6_2.5.1                   
#> [71] MatrixGenerics_1.17.0       zlibbioc_1.51.0

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.