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(rpois(3e5, 5), ncol = 300), sparse = TRUE)
tpm <- Matrix::Matrix(matrix(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:

head(SummarizedExperiment::assays(simulation$bulk)[["bulk_counts"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                               
#> gene_1 478 510 523 507 522 526 485 534 486 481
#> gene_2 520 492 509 504 474 457 460 474 497 539
#> gene_3 513 488 539 502 533 524 484 535 485 543
#> gene_4 494 446 454 476 479 499 490 488 446 453
#> gene_5 505 537 489 527 526 485 509 504 524 541
#> gene_6 503 490 520 527 484 523 531 525 512 531
head(SummarizedExperiment::assays(simulation$bulk)[["bulk_tpm"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                                                             
#> gene_1 1013.3233  955.3217  927.4476 1019.4245 1027.0607 1007.4402  960.1933
#> gene_2  884.7496  912.9779 1002.0701  925.0201  999.8513  930.0185 1042.9679
#> gene_3 1050.5618  937.1858  901.5346 1057.1210  937.0628  986.8482 1028.4617
#> gene_4  918.5372  927.5450 1084.7183 1006.8873  944.7870 1016.2195  899.8508
#> gene_5  974.5537  978.6291  960.7363 1101.7041  999.2923 1003.2961  983.1231
#> gene_6 1025.1821 1054.6025 1025.5675  980.9558 1032.8907 1006.5306 1028.7480
#>                                     
#> gene_1  984.7481  943.4918  986.8078
#> gene_2  917.1860  967.8304  934.5880
#> gene_3  979.0803  996.8013 1007.4010
#> gene_4 1019.1448 1016.5002  962.5881
#> gene_5 1016.5614  972.7404 1016.0442
#> gene_6 1052.5242 1066.2708  950.9371

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.

sessionInfo()
#> R version 4.3.0 RC (2023-04-13 r84269 ucrt)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> 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_1.2.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] SummarizedExperiment_1.30.0 gtable_0.3.3               
#>  [3] xfun_0.39                   bslib_0.4.2                
#>  [5] ggplot2_3.4.2               Biobase_2.60.0             
#>  [7] lattice_0.21-8              vctrs_0.6.2                
#>  [9] tools_4.3.0                 bitops_1.0-7               
#> [11] generics_0.1.3              stats4_4.3.0               
#> [13] parallel_4.3.0              tibble_3.2.1               
#> [15] fansi_1.0.4                 highr_0.10                 
#> [17] pkgconfig_2.0.3             Matrix_1.5-4               
#> [19] data.table_1.14.8           RColorBrewer_1.1-3         
#> [21] S4Vectors_0.38.0            sparseMatrixStats_1.12.0   
#> [23] RcppParallel_5.1.7          lifecycle_1.0.3            
#> [25] GenomeInfoDbData_1.2.10     compiler_4.3.0             
#> [27] farver_2.1.1                munsell_0.5.0              
#> [29] codetools_0.2-19            GenomeInfoDb_1.36.0        
#> [31] htmltools_0.5.5             sass_0.4.5                 
#> [33] RCurl_1.98-1.12             yaml_2.3.7                 
#> [35] pillar_1.9.0                jquerylib_0.1.4            
#> [37] tidyr_1.3.0                 BiocParallel_1.34.0        
#> [39] DelayedArray_0.26.0         cachem_1.0.7               
#> [41] tidyselect_1.2.0            digest_0.6.31              
#> [43] dplyr_1.1.2                 purrr_1.0.1                
#> [45] labeling_0.4.2              fastmap_1.1.1              
#> [47] grid_4.3.0                  colorspace_2.1-0           
#> [49] cli_3.6.1                   magrittr_2.0.3             
#> [51] utf8_1.2.3                  withr_2.5.0                
#> [53] scales_1.2.1                rmarkdown_2.21             
#> [55] XVector_0.40.0              matrixStats_0.63.0         
#> [57] proxyC_0.3.3                evaluate_0.20              
#> [59] knitr_1.42                  GenomicRanges_1.52.0       
#> [61] IRanges_2.34.0              rlang_1.1.0                
#> [63] Rcpp_1.0.10                 glue_1.6.2                 
#> [65] BiocGenerics_0.46.0         jsonlite_1.8.4             
#> [67] R6_2.5.1                    MatrixGenerics_1.12.0      
#> [69] zlibbioc_1.46.0

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.