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$bulk)[["bulk_counts"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                               
#> gene_1 473 490 486 507 474 504 495 431 476 548
#> gene_2 510 460 489 406 444 473 430 434 448 457
#> gene_3 499 496 516 496 511 479 459 464 519 501
#> gene_4 516 513 517 516 520 510 507 502 524 501
#> gene_5 467 473 491 457 492 459 454 438 424 434
#> gene_6 498 528 472 514 614 499 503 500 560 485
head(SummarizedExperiment::assays(simulation$bulk)[["bulk_tpm"]])
#> 6 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]]
#>                                                                             
#> gene_1 1014.5412  962.9520 1143.7252  957.8557 1059.4304 1081.5351 1048.4159
#> gene_2 1026.9029 1093.6139  918.4470  859.5465  971.3649 1068.7237  944.0363
#> gene_3  916.0333  921.7411 1011.4918  970.9023  891.6192  924.4646  998.1671
#> gene_4  869.4863  962.0421 1031.8250 1190.6043  940.5589 1052.8240 1045.1796
#> gene_5  979.6328 1028.6476  997.6582  944.8045  879.8738  950.9376  982.3889
#> gene_6  965.3543  972.8897  949.8896 1018.1812  974.0722 1080.4080 1015.2062
#>                                     
#> gene_1 1043.0077  996.1323  948.2787
#> gene_2  980.0187  960.2888  912.4495
#> gene_3  955.9228  928.8931  913.3788
#> gene_4 1088.7364 1021.7508 1071.1635
#> gene_5  979.1883  931.8840  972.0430
#> gene_6  953.7448  977.2808  992.9875

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: aarch64-apple-darwin20 (64-bit)
#> Running under: macOS Ventura 13.0
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/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] Rcpp_1.0.9                  lattice_0.20-45            
#>  [3] tidyr_1.2.1                 assertthat_0.2.1           
#>  [5] digest_0.6.31               utf8_1.2.2                 
#>  [7] R6_2.5.1                    GenomeInfoDb_1.34.9        
#>  [9] stats4_4.2.2                evaluate_0.20              
#> [11] highr_0.10                  ggplot2_3.4.0              
#> [13] pillar_1.8.1                sparseMatrixStats_1.10.0   
#> [15] zlibbioc_1.44.0             rlang_1.0.6                
#> [17] data.table_1.14.6           jquerylib_0.1.4            
#> [19] S4Vectors_0.36.2            Matrix_1.5-3               
#> [21] rmarkdown_2.20              labeling_0.4.2             
#> [23] BiocParallel_1.32.5         stringr_1.5.0              
#> [25] RCurl_1.98-1.9              munsell_0.5.0              
#> [27] DelayedArray_0.24.0         compiler_4.2.2             
#> [29] xfun_0.36                   pkgconfig_2.0.3            
#> [31] BiocGenerics_0.44.0         htmltools_0.5.4            
#> [33] tidyselect_1.2.0            SummarizedExperiment_1.28.0
#> [35] tibble_3.1.8                GenomeInfoDbData_1.2.9     
#> [37] IRanges_2.32.0              codetools_0.2-18           
#> [39] matrixStats_0.63.0          fansi_1.0.3                
#> [41] dplyr_1.0.10                withr_2.5.0                
#> [43] bitops_1.0-7                grid_4.2.2                 
#> [45] jsonlite_1.8.4              gtable_0.3.1               
#> [47] lifecycle_1.0.3             DBI_1.1.3                  
#> [49] magrittr_2.0.3              scales_1.2.1               
#> [51] RcppParallel_5.1.6          cli_3.6.0                  
#> [53] stringi_1.7.12              cachem_1.0.6               
#> [55] farver_2.1.1                XVector_0.38.0             
#> [57] bslib_0.4.2                 ellipsis_0.3.2             
#> [59] generics_0.1.3              vctrs_0.5.1                
#> [61] RColorBrewer_1.1-3          tools_4.2.2                
#> [63] Biobase_2.58.0              glue_1.6.2                 
#> [65] purrr_1.0.1                 proxyC_0.3.3               
#> [67] MatrixGenerics_1.10.0       parallel_4.2.2             
#> [69] fastmap_1.1.0               yaml_2.3.6                 
#> [71] colorspace_2.0-3            GenomicRanges_1.50.2       
#> [73] knitr_1.41                  sass_0.4.4

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.