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Some cleaning and small correction in computing the g.vars, now g.var… #15

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Some cleaning and small correction in computing the g.vars, now g.var… #15

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38 changes: 18 additions & 20 deletions R/vg.R
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Original file line number Diff line number Diff line change
@@ -1,26 +1,24 @@
####Identify high variability genes using Brennecke's method#####
####Identify high variability genes using loess method#####
findVG<-function(dat, plot.fig=NULL) {
require(Matrix)
require(ggplot2)
if(!is.matrix(dat)){
sf =Matrix::colSums(dat)
}
else{
sf = colSums(dat)
}
library(Matrix)
library(ggplot2)

# Compute the median of gene count for each cell
sf = Matrix::colSums(dat)
sf = sf/median(sf)
tmp.dat <- t( t(dat) / sf)
if(is.matrix(dat)){
g.means <- rowMeans(tmp.dat)
g.vars <- rowMeans(tmp.dat^2) - g.means^2
}
else{
g.means <- Matrix::rowMeans(tmp.dat)
g.vars <- Matrix::rowMeans(tmp.dat^2) - g.means^2
}
dispersion <- log10(g.vars/g.means)

# divide the counts by the median of gene counts per cell
tmp.dat <- t( t(dat) / sf)

# Compute the mean and variance of gene counts for the new normalized count matrix
g.means <- Matrix::rowMeans(tmp.dat)
g.vars <- Matrix::rowMeans(tmp.dat ^ 2) - g.means ^ 2
g.vars <- g.vars * (ncol(tmp.dat) / (ncol(tmp.dat) - 1))

# Compute the dispersion
dispersion <- log10(g.vars/g.means)
g.df = data.frame(g.means, g.vars, dispersion)
#####test samples####
#####test samples####
dispersion = dispersion[!is.na(dispersion)]
###fit normal with 25% to 75%
IQR = quantile(dispersion, c(0.25, 0.75))
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