Distance-to-median {scran} | R Documentation

## Compute the distance-to-median statistic

### Description

Compute the distance-to-median statistic for the CV2 residuals of all genes

### Usage

    DM(mean, cv2, win.size=51)

### Arguments

`mean` | A numeric vector of average counts for each gene.
`cv2` | A numeric vector of squared coefficients of variation for each gene.
`win.size` | An integer scalar specifying the window size for median-based smoothing. This should be odd or will be incremented by 1.

### Details

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 `mean` using `runmed`. 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.

### Value

A numeric vector of DM statistics for all genes.

### Author(s)

Jong Kyoung Kim, with modifications by Aaron Lun

### References

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.

### Examples

    # 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)

[Package scran version 1.16.0 Index]