Hello, Since my data is not replicated, I can not use a regular pipeline.
summa.fit <- decideTests(fit.cont) Error in .ebayes(fit = fit, proportion = proportion, stdev.coef.lim = stdev.coef.lim, : No residual degrees of freedom in linear model fits
What function do you recommend using? a function that does not have p- value?
Thanks,
How are you drawing down-regulated and up-regulated genes? Are you using a differential expression analysis tool like DESeq2 or edgeR? If yes, these tools usually output log2FoldChange values.
I can not use DESeq2, because my data is not replicated. I am using R. I used basspase .illumina, but the graph is not clear. and it does not have pathway, and the graphs show down-regulated and up-regulated. But I have an excel file of the below parameters Gene Status log2(control Count) log2(comparison Count) Mean Count log2FC Std. Err. log2(Fold Change) q Value Significant.
You can use the log2FoldChange values but why not include a heatmap on the pipeline?
I am not sure if I add my data correctly because I see an error in my pipeline: eBayes(fit.cont) Error: evaluation nested too deeply: infinite recursion / options(expressions=)? Please read my comment I wrote in reply to " noahhelton98"