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$bulk)[["bulk_counts"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                               
#> gene_1 516 456 475 480 437 528 467 485 479 525
#> gene_2 505 410 489 462 426 551 480 471 494 494
#> gene_3 505 471 505 523 522 481 531 462 493 472
#> gene_4 511 505 497 530 512 493 483 530 461 465
#> gene_5 506 501 509 506 484 452 576 486 539 514
#> gene_6 508 520 519 493 503 497 485 513 485 520
head(SummarizedExperiment::assays(simulation$bulk)[["bulk_tpm"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                                                            
#> gene_1  985.2745 1020.173 1065.6149 1022.0149  811.9487 1041.7848 1010.1016
#> gene_2 1099.1896 1074.816 1078.0002 1058.7923  981.8834 1126.9666 1001.9512
#> gene_3 1024.5542 1020.416 1030.1834 1069.6930 1063.2846  963.4612 1056.2369
#> gene_4  986.1272 1057.623  955.7431 1050.0827  997.3352 1002.8509  992.9933
#> gene_5  992.7706 1008.286 1036.2237  987.0100  988.0751 1106.2628 1006.2926
#> gene_6  903.8217 1011.255  917.9290  891.8412  891.0436  864.1622  982.8831
#>                                     
#> gene_1  999.7789 1063.3882  988.4259
#> gene_2 1031.5284  991.9506 1066.3154
#> gene_3 1015.3942 1032.4804 1056.8731
#> gene_4 1072.4618  985.6665  995.8195
#> gene_5 1032.4437 1096.4056 1101.5217
#> gene_6  972.9980  970.5083  922.2937

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 4.2.2 (2022-10-31 ucrt)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows Server 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    
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] SimBu_1.0.2
#> 
#> loaded via a namespace (and not attached):
#>  [1] SummarizedExperiment_1.28.0 tidyselect_1.2.0           
#>  [3] xfun_0.37                   bslib_0.4.2                
#>  [5] purrr_1.0.1                 lattice_0.20-45            
#>  [7] colorspace_2.1-0            vctrs_0.5.2                
#>  [9] generics_0.1.3              htmltools_0.5.4            
#> [11] stats4_4.2.2                yaml_2.3.7                 
#> [13] utf8_1.2.3                  rlang_1.1.0                
#> [15] jquerylib_0.1.4             pillar_1.8.1               
#> [17] glue_1.6.2                  withr_2.5.0                
#> [19] BiocParallel_1.32.5         RColorBrewer_1.1-3         
#> [21] BiocGenerics_0.44.0         matrixStats_0.63.0         
#> [23] GenomeInfoDbData_1.2.9      lifecycle_1.0.3            
#> [25] zlibbioc_1.44.0             MatrixGenerics_1.10.0      
#> [27] munsell_0.5.0               gtable_0.3.1               
#> [29] proxyC_0.3.3                codetools_0.2-19           
#> [31] evaluate_0.20               labeling_0.4.2             
#> [33] Biobase_2.58.0              knitr_1.42                 
#> [35] IRanges_2.32.0              fastmap_1.1.1              
#> [37] GenomeInfoDb_1.34.9         parallel_4.2.2             
#> [39] fansi_1.0.4                 highr_0.10                 
#> [41] Rcpp_1.0.10                 scales_1.2.1               
#> [43] cachem_1.0.7                DelayedArray_0.24.0        
#> [45] S4Vectors_0.36.2            RcppParallel_5.1.7         
#> [47] jsonlite_1.8.4              XVector_0.38.0             
#> [49] farver_2.1.1                ggplot2_3.4.1              
#> [51] digest_0.6.31               dplyr_1.1.0                
#> [53] GenomicRanges_1.50.2        grid_4.2.2                 
#> [55] cli_3.6.0                   tools_4.2.2                
#> [57] bitops_1.0-7                magrittr_2.0.3             
#> [59] sass_0.4.5                  RCurl_1.98-1.10            
#> [61] tibble_3.2.0                tidyr_1.3.0                
#> [63] pkgconfig_2.0.3             Matrix_1.5-3               
#> [65] data.table_1.14.8           sparseMatrixStats_1.10.0   
#> [67] rmarkdown_2.20              R6_2.5.1                   
#> [69] compiler_4.2.2

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.