R package that accompanies our paper ‘Comparison of transformations for single-cell RNA-seq data’ (https://www.nature.com/articles/s41592-023-01814-1).
transformGamPoi provides methods to stabilize the variance of single
cell count data:
- acosh transformation based on the delta method
- shifted logarithm (log(x + c)) with a pseudo-count c, so that it approximates the acosh transformation
- randomized quantile and Pearson residuals
You can install the current development version of transformGamPoi by
typing the following into the R
console:
# install.packages("devtools")
devtools::install_github("const-ae/transformGamPoi")The installation should only take a few seconds and work across all major operating systems (MacOS, Linux, Windows).
Let’s compare the different variance-stabilizing transformations.
We start by loading the transformGamPoi package and setting a seed to
make sure the results are reproducible.
library(transformGamPoi)
set.seed(1)We then load some example data, which we subset to 1000 genes and 500 cells
sce <- TENxPBMCData::TENxPBMCData("pbmc4k")
#> snapshotDate(): 2022-10-31
#> Warning: package 'GenomicRanges' was built under R version 4.2.2
#> Warning: package 'S4Vectors' was built under R version 4.2.2
#> Warning: package 'GenomeInfoDb' was built under R version 4.2.2
#> see ?TENxPBMCData and browseVignettes('TENxPBMCData') for documentation
#> loading from cache
sce_red <- sce[sample(which(rowSums2(counts(sce)) > 0), 1000),
sample(ncol(sce), 500)]We calculate the different variance-stabilizing transformations. We can
either use the generic transformGamPoi() method and specify the
transformation, or we use the low-level functions acosh_transform(),
shifted_log_transform(), and residual_transform() which provide more
settings. All functions return a matrix, which we can for example insert
back into the SingleCellExperiment object:
assay(sce_red, "acosh") <- transformGamPoi(sce_red, transformation = "acosh")
assay(sce_red, "shifted_log") <- shifted_log_transform(sce_red, overdispersion = 0.1)
# For large datasets, we can also do the processing without
# loading the full dataset into memory (on_disk = TRUE)
assay(sce_red, "rand_quant") <- residual_transform(sce_red, "randomized_quantile", on_disk = FALSE)
assay(sce_red, "pearson") <- residual_transform(sce_red, "pearson", clipping = TRUE, on_disk = FALSE)Finally, we compare the variance of the genes after transformation using a scatter plot
par(pch = 20, cex = 1.15)
mus <- rowMeans2(counts(sce_red))
plot(mus, rowVars(assay(sce_red, "acosh")), log = "x", col = "#1b9e77aa", cex = 0.6,
xlab = "Log Gene Means", ylab = "Variance after transformation")
points(mus, rowVars(assay(sce_red, "shifted_log")), col = "#d95f02aa", cex = 0.6)
points(mus, rowVars(assay(sce_red, "pearson")), col = "#7570b3aa", cex = 0.6)
points(mus, rowVars(assay(sce_red, "rand_quant")), col = "#e7298aaa", cex = 0.6)
legend("topleft", legend = c("acosh", "shifted log", "Pearson Resid.", "Rand. Quantile Resid."),
col = c("#1b9e77", "#d95f02", "#7570b3", "#e7298a"), pch = 16)
There are a number of preprocessing methods and packages out there. Of particular interests are
- sctransform by Christoph Hafemeister and the Satija lab. The original developers of the Pearson residual variance-stabilizing transformation approach for single cell data.
- scuttle::logNormCounts()
by Aaron Lun. This is an alternative to the
shifted_log_transform()and plays nicely together with the Bioconductor universe. For more information, I highly recommend to take a look at the normalization section of the OSCA book. - Sanity by Jérémie Breda et al.. This method is not directly concerned with variance stabilization, but still provides an interesting approach for single cell data preprocessing.
sessionInfo()
#> R version 4.2.1 RC (2022-06-17 r82503)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Big Sur ... 10.16
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#>
#> attached base packages:
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#> [8] base
#>
#> other attached packages:
#> [1] TENxPBMCData_1.16.0 HDF5Array_1.26.0
#> [3] rhdf5_2.42.0 DelayedArray_0.24.0
#> [5] Matrix_1.5-3 SingleCellExperiment_1.20.0
#> [7] SummarizedExperiment_1.28.0 Biobase_2.58.0
#> [9] GenomicRanges_1.50.2 GenomeInfoDb_1.34.9
#> [11] IRanges_2.32.0 S4Vectors_0.36.2
#> [13] BiocGenerics_0.44.0 MatrixGenerics_1.10.0
#> [15] matrixStats_0.63.0 transformGamPoi_1.4.0
#>
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#> [45] Biostrings_2.66.0 compiler_4.2.1
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