Hi, I'm a newbie in Ref-seq and I'm trying to make a pipeline using HiSat -> HtSeq -> DESeq2. I'm using the data that appears in https://usegalaxy.org/u/jeremy/p/galaxy-rna-seq-analysis-exercise The problem is that when executing DESeq2 it gives me this error and I do not know why. Please, can you help me? Thank you.
Fatal error: An undefined error occurred, please check your input carefully and contact your administrator. estimating size factors estimating dispersions gene-wise dispersion estimates mean-dispersion relationship -- note: fitType='parametric', but the dispersion trend was not well captured by the function: y = a/x + b, and a local regression fit was automatically substituted. specify fitType='local' or 'mean' to avoid this message next time. final dispersion estimates fitting model and testing Warning message: In checkForExperimentalReplicates(object, modelMatrix) : same number of samples and coefficients to fit, estimating dispersion by treating samples as replicates. please read the ?DESeq section on 'Experiments without replicates'. in summary: this analysis only potentially useful for data exploration, accurate differential expression analysis requires replication -- note: fitType='parametric', but the dispersion trend was not well captured by the function: y = a/x + b, and a local regression fit was automatically substituted. specify fitType='local' or 'mean' to avoid this message next time. Error in grid.Call(C_convert, x, as.integer(whatfrom), as.integer(whatto), : Viewport has zero dimension(s) Calls: generateGenericPlots ... makeContent.textrepeltree -> convertHeight -> convertUnit -> grid.Call Warning message: In sparseTest(counts(object, normalized = TRUE), 0.9, 100, 0.1) : the rlog assumes that data is close to a negative binomial distribution, an assumption which is sometimes not compatible with datasets where many genes have many zero counts despite a few very large counts. In this data, for 20% of genes with a sum of normalized counts above 100, it was the case that a single sample's normalized count made up more than 90% of the sum over all samples. the threshold for this warning is 10% of genes. See plotSparsity(dds) for a visualization of this. We recommend instead using the varianceStabilizingTransformation or shifted log (see vignette).