A function allowing the identification of differentially expressed genes.

This function executes in a docker edgeR for the idnetification of differentially expressed genes in single-cells RNAseq

anovaLike(
  group = c("sudo", "docker"),
  file,
  sep,
  cluster.file,
  ref.cluster,
  logFC.threshold = 1,
  FDR.threshold,
  logCPM.threshold = 4,
  plot = c(TRUE, FALSE)
)

Arguments

group,

a character string. Two options: sudo or docker, depending to which group the user belongs

file,

a character string indicating the counts table file with the path of the file.

sep,

separator used in count file, e.g. '\t', ','

cluster.file,

a character string indicating the _clustering.output.txt file of interest, generated by bootstrapSimlar or bootStrapTsne. IMPORTANT this file miust be located in the same folder where counts.table is placed

ref.cluster,

a number indicating the cluster to be used a reference for anova-like comparison with the other clusters.

logFC.threshold,

minimal logFC present in at least one of the comparisons with respect to reference covariate

FDR.threshold,

minimal FDR present in at least one of the comparisons with respect to reference covariate

logCPM.threshold,

minimal average abundance

plot,

boolean, TRUE a plot of differentially expressed genes is generated

Value

Three tab delimited files file with prefix DE\_, filtered\_DE\_, logFC\_filtered\_DE\_ followed by the counts table name, the count table, reordered on the basis of cluster positions, has the extension \_reordered.txt

Author

Raffaele Calogero, raffaele.calogero [at] unito [dot] it, University of Torino, Italy

Examples

if (FALSE) {
    #running deDetection
system("wget http://130.192.119.59/public/annotated_setPace_10000_noC5_clustering.output.txt")
system("wget http://130.192.119.59/public/annotated_setPace_10000_noC5.txt.zip")
unzip("annotated_setPace_10000_noC5.txt.zip")
anovaLike(group="docker", file=paste(getwd(),"annotated_setPace_10000_noC5.txt",sep="/"),
       sep="\t", cluster.file="annotated_setPace_10000_noC5_clustering.output.txt", ref.cluster=3,
       logFC.threshold=1, FDR.threshold=0.05, logCPM.threshold=4, plot=TRUE)

}