I'm new in R and I'm using WGCNA package to build a co-regulation network from my RNASeq data. I'm following the WGCNA tutorial but I have a error message when I try to run the goodSamplesGenes function. My RNASeq matrix has 7 conditions with more than 9,000 differentially expressed genes. The experiment was carry out with two biological replicates for each condition and the reads have already been normalized to RPKM. Could anyone help me in this regards?
> gsg = goodSamplesGenes(datExpr0, verbose = 3)
Flagging genes and samples with too many missing values...
Error in goodGenes(datExpr, goodSamples, goodGenes, minFraction = minFraction, :
Too few genes with valid expression levels in the required number of samples.
*datExpr0 it's a data.frame where the genes and the samples are the columns and rows, respectively, like it was done in the WGCNA tutorial.
goodSamplesGenes( datExpr, minFraction = 1/2, minNSamples = ..minNSamples, minNGenes = ..minNGenes, verbose = 1, indent = 0)
expression data. A data frame in which columns are genes and rows ar samples.
minimum fraction of non-missing samples for a gene to be considered good.
minimum number of non-missing samples for a gene to be considered good.
minimum number of good genes for the data set to be considered fit for analysis. If the actual number of good genes falls below this threshold, an error will be issued.
integer level of verbosity. Zero means silent, higher values make the output progressively more and more verbose.
indentation for diagnostic messages. Zero means no indentation, each unit adds two spaces.