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$bulk)[["bulk_counts"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                               
#> gene_1 552 537 565 532 481 494 530 505 508 522
#> gene_2 544 494 566 512 489 509 561 524 507 536
#> gene_3 507 548 502 515 571 556 537 498 515 515
#> gene_4 474 529 493 528 536 495 484 494 470 455
#> gene_5 502 528 506 538 525 446 509 544 528 529
#> gene_6 503 536 469 504 534 475 417 478 508 468
utils::head(SummarizedExperiment::assays(simulation$bulk)[["bulk_tpm"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                                                            
#> gene_1  943.0567  981.4934  960.2246 952.5867  926.9356  995.7999 1018.5792
#> gene_2  993.0670  992.9366 1029.8903 998.8267 1046.6983 1012.1126  972.0409
#> gene_3 1035.0774  930.1245  926.8931 976.3936  957.3853  929.8964  958.3748
#> gene_4  876.5641  984.3872  895.4207 978.9390  974.7282  973.1438  994.9129
#> gene_5  994.3982 1003.4192 1026.6003 953.1307  944.2872  958.9390  963.8259
#> gene_6  990.2181  946.3198  928.6903 975.9240  930.3592  894.7487  981.0750
#>                                    
#> gene_1  966.9726  942.5912 984.6801
#> gene_2 1044.7480  969.5817 960.0702
#> gene_3  980.4828 1026.4161 927.0896
#> gene_4 1042.7895 1009.9418 945.4278
#> gene_5  954.5965  940.6112 964.9118
#> gene_6 1000.0781  967.3083 937.1372

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 Under development (unstable) (2024-11-20 r87352)
#> Platform: x86_64-apple-darwin20
#> Running under: macOS Monterey 12.7.6
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRblas.0.dylib 
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.5-x86_64/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_1.9.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] SummarizedExperiment_1.37.0 gtable_0.3.6               
#>  [3] xfun_0.49                   bslib_0.8.0                
#>  [5] ggplot2_3.5.1               Biobase_2.67.0             
#>  [7] lattice_0.22-6              vctrs_0.6.5                
#>  [9] tools_4.5.0                 generics_0.1.3             
#> [11] stats4_4.5.0                parallel_4.5.0             
#> [13] tibble_3.2.1                fansi_1.0.6                
#> [15] pkgconfig_2.0.3             Matrix_1.7-1               
#> [17] data.table_1.16.2           RColorBrewer_1.1-3         
#> [19] S4Vectors_0.45.2            sparseMatrixStats_1.19.0   
#> [21] lifecycle_1.0.4             GenomeInfoDbData_1.2.13    
#> [23] compiler_4.5.0              farver_2.1.2               
#> [25] munsell_0.5.1               codetools_0.2-20           
#> [27] GenomeInfoDb_1.43.1         htmltools_0.5.8.1          
#> [29] sass_0.4.9                  yaml_2.3.10                
#> [31] pillar_1.9.0                crayon_1.5.3               
#> [33] jquerylib_0.1.4             tidyr_1.3.1                
#> [35] BiocParallel_1.41.0         DelayedArray_0.33.2        
#> [37] cachem_1.1.0                abind_1.4-8                
#> [39] tidyselect_1.2.1            digest_0.6.37              
#> [41] dplyr_1.1.4                 purrr_1.0.2                
#> [43] labeling_0.4.3              fastmap_1.2.0              
#> [45] grid_4.5.0                  colorspace_2.1-1           
#> [47] cli_3.6.3                   SparseArray_1.7.2          
#> [49] magrittr_2.0.3              S4Arrays_1.7.1             
#> [51] utf8_1.2.4                  withr_3.0.2                
#> [53] UCSC.utils_1.3.0            scales_1.3.0               
#> [55] rmarkdown_2.29              XVector_0.47.0             
#> [57] httr_1.4.7                  matrixStats_1.4.1          
#> [59] proxyC_0.4.1                evaluate_1.0.1             
#> [61] knitr_1.49                  GenomicRanges_1.59.1       
#> [63] IRanges_2.41.1              rlang_1.1.4                
#> [65] Rcpp_1.0.13-1               glue_1.8.0                 
#> [67] BiocGenerics_0.53.3         jsonlite_1.8.9             
#> [69] R6_2.5.1                    MatrixGenerics_1.19.0      
#> [71] zlibbioc_1.53.0

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.