Description Usage Arguments Details Value Author(s) References Examples

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

1 |

`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 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`

.
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.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
# 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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