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$bulk)[["bulk_counts"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                               
#> gene_1 512 462 480 501 490 463 466 488 515 536
#> gene_2 553 541 560 557 553 565 517 533 523 510
#> gene_3 504 493 474 486 501 479 491 526 437 519
#> gene_4 482 474 460 466 481 507 514 446 498 503
#> gene_5 490 509 528 506 499 471 499 487 515 469
#> gene_6 518 513 526 565 519 537 511 549 524 551
utils::head(SummarizedExperiment::assays(simulation$bulk)[["bulk_tpm"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                                                             
#> gene_1 1032.7062 1002.0424 1036.2670  953.3780 1011.7325  936.0015  997.0924
#> gene_2 1016.5182 1015.5950  940.8245  992.4123  993.0214  956.1491  955.5366
#> gene_3 1019.3454 1006.3544 1070.0030  990.9116 1040.5370  881.5392 1037.9910
#> gene_4  921.9237 1036.1075  937.0216  965.5356  937.9368  984.4215  991.6389
#> gene_5  991.2298 1006.1612  947.8573 1028.1472 1023.7379 1068.4491 1096.1681
#> gene_6 1002.5462  930.5967  991.3344  931.8929  915.2392  949.2703  985.4063
#>                                     
#> gene_1 1033.8302 1042.0093 1001.9939
#> gene_2  933.1581 1008.1181  938.5137
#> gene_3 1083.6039  981.9826  900.8277
#> gene_4  938.6283  991.8480  906.3792
#> gene_5  965.4506 1023.5593 1027.7962
#> gene_6  864.8750 1019.7038  898.2676

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 beta (2024-04-14 r86421)
#> Platform: x86_64-apple-darwin20
#> Running under: macOS Monterey 12.7.1
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib 
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
#> 
#> locale:
#> [1] en_US.UTF-8/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_1.6.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] SummarizedExperiment_1.34.0 gtable_0.3.5               
#>  [3] xfun_0.43                   bslib_0.7.0                
#>  [5] ggplot2_3.5.1               Biobase_2.64.0             
#>  [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_0.42.0           
#> [21] sparseMatrixStats_1.16.0    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_1.40.0        
#> [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_1.38.0        
#> [37] DelayedArray_0.30.0         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_1.4.0           magrittr_2.0.3             
#> [51] S4Arrays_1.4.0              utf8_1.2.4                 
#> [53] withr_3.0.0                 UCSC.utils_1.0.0           
#> [55] scales_1.3.0                rmarkdown_2.26             
#> [57] XVector_0.44.0              httr_1.4.7                 
#> [59] matrixStats_1.3.0           proxyC_0.4.1               
#> [61] evaluate_0.23               knitr_1.46                 
#> [63] GenomicRanges_1.56.0        IRanges_2.38.0             
#> [65] rlang_1.1.3                 Rcpp_1.0.12                
#> [67] glue_1.7.0                  BiocGenerics_0.50.0        
#> [69] jsonlite_1.8.8              R6_2.5.1                   
#> [71] MatrixGenerics_1.16.0       zlibbioc_1.50.0

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.