Compute the distance-to-median statistic for the CV2 residuals of all genes
A numeric vector of average counts for each gene.
A numeric vector of squared coefficients of variation for each gene.
An integer scalar specifying the window size for median-based smoothing.
This function will compute the distance-to-median (DM) statistic described by Kolodziejczyk et al. (2015).
Briefly, a median-based trend is fitted to the log-transformed
cv2 against the log-transformed
The DM is defined as the residual from the trend for each gene.
This statistic is a measure of the relative variability of each gene, after accounting for the empirical mean-variance relationship.
Highly variable genes can then be identified as those with high DM values.
A numeric vector of DM statistics for all genes.
Jong Kyoung Kim, with modifications by Aaron Lun
Kolodziejczyk AA, Kim JK, Tsang JCH et al. (2015). Single cell RNA-sequencing of pluripotent states unlocks modular transcriptional variation. Cell Stem Cell 17(4), 471–85.
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# Mocking up some data ngenes <- 1000 ncells <- 100 gene.means <- 2^runif(ngenes, 0, 10) dispersions <- 1/gene.means + 0.2 counts <- matrix(rnbinom(ngenes*ncells, mu=gene.means, size=1/dispersions), nrow=ngenes) # Computing the DM. means <- rowMeans(counts) cv2 <- apply(counts, 1, var)/means^2 dm.stat <- DM(means, cv2) head(dm.stat)
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