I have counts table with 6 different time points from 8 different human samples (repeats), making total of 48 columns. when I run "runAnalysis" function in CeTF, for comparing two different time points (t1 and t6), I get the error "Error in data.frame(cond = c(rep(conditions[1], nSamples1), rep(conditions[2], : row names supplied are of the wrong length" as shown below.
However, the error is resolved if I divide the counts into just two conditions instead of 6 conditions, such that making nSamples1 and nSamples2= 24, instead of nSamples1 and nSamples2= 8. Can runAnalysis function only work if applied to complete counts table divided into only two conditions? Does it essentially mean that I should rather subset my counts table before running this function?

I am new to this analysis and i am following this link: http://bioconductor.org/packages/release/bioc/vignettes/CeTF/inst/doc/CeTF.html#references

out <- runAnalysis(mat = counts, 
                   conditions=c("t1", "t6"),
                   lfc = 4.5,
                   padj = 0.05,
                   TFs = TFs,
                   nSamples1 = 8,
                   nSamples2= 8,
                   tolType = "mean",
                   diffMethod = "DESeq2", 
                   data.type = "counts")
Error in data.frame(cond = c(rep(conditions[1], nSamples1), rep(conditions[2],  : 
  row names supplied are of the wrong length
########
# no error, when run this 
out1 <- runAnalysis(mat = counts, 
                   conditions=c("t1", "t6"),
                   lfc = 4.5,
                   padj = 0.05,
                   TFs = TFs,
                   nSamples1 = 24,
                   nSamples2= 24,
                   tolType = "mean",
                   diffMethod = "DESeq2", 
                   data.type = "counts")

Thanks for using the package!

The simplest solution to your question is to subset your counting matrix to only the corresponding samples of the two timepoints you want to analyze.

For example:

counts2 <- counts[, c('Sample1_condA', 'Sample2_condA', 'Sample3_condA', 'Sample1_condB', 'Sample2_condB', 'Sample3_condB')]
out1 <- runAnalysis(mat = counts2, 
                    conditions=c("t1", "t6"),
                    lfc = 4.5,
                    padj = 0.05,
                    TFs = TFs,
                    nSamples1 = 3,
                    nSamples2= 3,
                    tolType = "mean",
                    diffMethod = "DESeq2", 
                    data.type = "counts")