Created
May 7, 2020 03:50
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Function to estimate scaling factors based on ERCC spike ins when using sleuth for differential expression analysis
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| # this is a simple illustration of the principle | |
| # it does not implement e.g. loess fitting to ERCC data as sugessted by Loven et al | |
| # but rather uses the geometric mean/scaling approach as implemented in sleuth/DESeq2 | |
| # on the reduced matrix containing only ERCC spike in quantification | |
| norm_factors_ercc <- function(mat) { | |
| mat <- mat[grep('ERCC', rownames(mat)),] | |
| nz <- apply(mat, 1, function(row) !any(round(row) == 0)) | |
| mat_nz <- mat[nz, , drop = FALSE] | |
| p <- ncol(mat) | |
| geo_means <- exp(apply(mat_nz, 1, function(row) mean(log(row)))) | |
| s <- sweep(mat_nz, 1, geo_means, `/`) | |
| sf <- apply(s, 2, median) | |
| scaling <- exp( (-1 / p) * sum(log(sf))) | |
| sf * scaling | |
| } | |
| # this is the original function | |
| norm_factors <- function(mat) { | |
| nz <- apply(mat, 1, function(row) !any(round(row) == 0)) | |
| mat_nz <- mat[nz, , drop = FALSE] | |
| p <- ncol(mat) | |
| geo_means <- exp(apply(mat_nz, 1, function(row) mean(log(row)))) | |
| s <- sweep(mat_nz, 1, geo_means, `/`) | |
| sf <- apply(s, 2, median) | |
| scaling <- exp( (-1 / p) * sum(log(sf))) | |
| sf * scaling | |
| } | |
| # for demonstration purposes you can use e.g. tximport to load the abundance estimation | |
| kallistoDir <- 'kallisto_results/' # | |
| files <- file.path(s2c$path, 'abundance.h5') | |
| txi <- tximport(files, type = 'kallisto', txOut = T) | |
| mat <- txi$abundance | |
| norm_factors(mat) | |
| norm_factors_ercc(mat) | |
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