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.
#> 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$bulk)[["bulk_counts"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                               
#> gene_1 559 513 541 482 519 492 524 495 498 508
#> gene_2 523 500 502 530 544 485 516 493 462 499
#> gene_3 509 513 481 498 492 528 513 501 509 494
#> gene_4 521 494 511 515 511 515 522 524 568 478
#> gene_5 472 534 488 461 447 466 469 488 481 501
#> gene_6 480 501 523 468 445 438 525 483 497 493
head(SummarizedExperiment::assays(simulation$bulk)[["bulk_tpm"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                                                             
#> gene_1 1002.7900  972.1737  933.1935 1074.4866 1060.6170 1015.0295  951.9207
#> gene_2 1153.8487  986.0551 1077.9588 1066.5824 1019.8683  973.5858  993.5434
#> gene_3  923.4248  970.9872 1015.6614 1027.5902  998.0634 1079.5719  982.5682
#> gene_4 1004.3419  948.2965  958.1434  931.4921 1023.4939  925.2883  998.9796
#> gene_5  959.6135 1031.5055 1066.3722 1013.8103  910.8049  959.9786 1019.4864
#> gene_6  901.4178  961.6959  967.2057 1006.2077  891.7408 1089.0046 1022.0549
#>                                     
#> gene_1 1079.4940 1066.5057  936.8249
#> gene_2  933.6031 1015.2096 1022.2787
#> gene_3 1065.9590 1009.2806 1077.8514
#> gene_4  893.5176  933.3259  972.5926
#> gene_5 1075.8367  965.0093 1011.6570
#> gene_6 1008.7122 1036.4931  970.7816

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.

sessionInfo()
#> R version 4.2.2 (2022-10-31)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Big Sur ... 10.16
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
#> 
#> locale:
#> [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> 
#> 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.