To install the developmental version of the package, run:
To install from Bioconductor:
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.
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) ) )
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
cell_type. If additional columns from this annotation should be transferred to the dataset, simply give the names of them in the
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.
SimBu offers basic filtering options for your dataset, which you can apply during dataset generation:
filter_genes: if TRUE, genes which have expression values of 0 in all cells will be removed.
variance_cutoff: remove all genes with a expression variance below the chosen cutoff.
type_abundance_cutoff: remove all cells, which belong to a cell type that appears less the the given amount.
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:
even: this creates samples, where all existing cell-types in the dataset appear in the same proportions. So using a dataset with 3 cell-types, this will simulate samples, where all cell-type fractions are 1/3. In order to still have a slight variation between cell type fractions, you can increase the
balance_uniform_mirror_scenario parameter (default to 0.01). Setting it to 0 will generate simulations with exactly the same cell type fractions.
random: this scenario will create random cell type fractions using all present types for each sample. The random sampling is based on the uniform distribution.
mirror_db: this scenario will mirror the exact fractions of cell types which are present in the provided dataset. If it consists of 20% T cells, 30% B cells and 50% NK cells, all simulated samples will mirror these fractions. Similar to the uniform scenario, you can add a small variation to these fractions with the
weighted: here you need to set two additional parameters for the
weighted_cell_type sets the cell-type you want to be over-representing and
weighted_amount sets the fraction of this cell-type. You could for example use
0.5 to create samples, where 50% are B-cells and the rest is filled randomly with other cell-types.
pure: this creates simulations of only one single cell-type. You have to provide the name of this cell-type with the
custom: here you are able to create your own set of cell-type fractions. When using this scenario, you additionally need to provide a dataframe in the
custom_scenario_data parameter, where each row represents one sample (therefore the number of rows need to match the
nsamples parameter). Each column has to represent one cell-type, which also occurs in the dataset and describes the fraction of this cell-type in a sample. The fractions per sample need to sum up to 1. An example can be seen here:
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
simulation object contains three named entries:
bulk: a SummarizedExperiment object with the pseudo-bulk dataset(s) stored in the
assays. They can be accessed like this:
head(SummarizedExperiment::assays(simulation$bulk)[["bulk_counts"]]) #> 6 x 10 sparse Matrix of class "dgCMatrix" #> [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]] #> #> gene_1 543 519 505 532 559 510 561 550 538 521 #> gene_2 522 492 469 498 499 485 502 471 500 485 #> gene_3 501 532 517 490 549 483 547 511 522 522 #> gene_4 552 577 509 524 545 529 500 521 523 527 #> gene_5 454 484 450 500 480 508 491 466 455 454 #> gene_6 515 525 471 552 539 507 482 512 473 502 head(SummarizedExperiment::assays(simulation$bulk)[["bulk_tpm"]]) #> 6 x 10 sparse Matrix of class "dgCMatrix" #> [[ suppressing 10 column names 'random_sample1', 'random_sample2', 'random_sample3' ... ]] #> #> gene_1 1088.7106 973.2851 923.0751 1078.4238 929.7577 1027.9023 1003.3325 #> gene_2 1057.5382 1004.6011 1067.1921 988.1263 855.4264 1058.7784 977.9386 #> gene_3 933.6707 1065.6599 1025.6475 972.3987 1070.4719 1052.8444 995.5539 #> gene_4 955.9610 995.0266 1023.2391 945.3969 976.1144 841.4954 922.0513 #> gene_5 965.6578 1000.2925 916.1971 851.1151 808.1501 925.3223 915.4682 #> gene_6 1084.5659 1024.5275 972.5648 987.2633 1103.7454 1045.4519 1063.8213 #> #> gene_1 1078.4827 965.1898 1060.7188 #> gene_2 915.3888 1063.3222 1050.4351 #> gene_3 972.1793 1033.7919 1026.5390 #> gene_4 967.1775 963.7976 891.5317 #> gene_5 923.3414 899.1277 940.5357 #> gene_6 1027.3938 1007.2298 952.6504
If only a single matrix was given to the dataset initially, only one assay is filled.
cell_fractions: a table where rows represent the simulated samples and columns represent the different simulated cell-types. The entries in the table store the specific cell-type fraction per sample.
scaling_vector: a named list, with the used scaling value for each cell from the single cell dataset.
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:
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
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 Under development (unstable) (2023-11-11 r85510) #> Platform: x86_64-pc-linux-gnu #> Running under: Ubuntu 22.04.3 LTS #> #> Matrix products: default #> BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so #> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 #> #> locale: #>  LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C #>  LC_TIME=en_GB LC_COLLATE=C #>  LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 #>  LC_PAPER=en_US.UTF-8 LC_NAME=C #>  LC_ADDRESS=C LC_TELEPHONE=C #>  LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C #> #> time zone: America/New_York #> tzcode source: system (glibc) #> #> attached base packages: #>  stats graphics grDevices utils datasets methods base #> #> other attached packages: #>  SimBu_1.5.2 #> #> loaded via a namespace (and not attached): #>  SummarizedExperiment_1.33.0 gtable_0.3.4 #>  xfun_0.41 bslib_0.5.1 #>  ggplot2_3.4.4 Biobase_2.63.0 #>  lattice_0.22-5 vctrs_0.6.4 #>  tools_4.4.0 bitops_1.0-7 #>  generics_0.1.3 stats4_4.4.0 #>  parallel_4.4.0 tibble_3.2.1 #>  fansi_1.0.5 highr_0.10 #>  pkgconfig_2.0.3 Matrix_1.6-3 #>  data.table_1.14.8 RColorBrewer_1.1-3 #>  S4Vectors_0.41.1 sparseMatrixStats_1.15.0 #>  RcppParallel_5.1.7 lifecycle_1.0.4 #>  GenomeInfoDbData_1.2.11 farver_2.1.1 #>  compiler_4.4.0 munsell_0.5.0 #>  codetools_0.2-19 GenomeInfoDb_1.39.1 #>  htmltools_0.5.7 sass_0.4.7 #>  RCurl_1.98-1.13 yaml_2.3.7 #>  pillar_1.9.0 crayon_1.5.2 #>  jquerylib_0.1.4 tidyr_1.3.0 #>  BiocParallel_1.37.0 DelayedArray_0.29.0 #>  cachem_1.0.8 abind_1.4-5 #>  tidyselect_1.2.0 digest_0.6.33 #>  dplyr_1.1.3 purrr_1.0.2 #>  labeling_0.4.3 fastmap_1.1.1 #>  grid_4.4.0 colorspace_2.1-0 #>  cli_3.6.1 SparseArray_1.3.1 #>  magrittr_2.0.3 S4Arrays_1.3.0 #>  utf8_1.2.4 withr_2.5.2 #>  scales_1.2.1 rmarkdown_2.25 #>  XVector_0.43.0 matrixStats_1.1.0 #>  proxyC_0.3.4 evaluate_0.23 #>  knitr_1.45 GenomicRanges_1.55.1 #>  IRanges_2.37.0 rlang_1.1.2 #>  Rcpp_1.0.11 glue_1.6.2 #>  BiocGenerics_0.49.1 jsonlite_1.8.7 #>  R6_2.5.1 MatrixGenerics_1.15.0 #>  zlibbioc_1.49.0
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.