down regulated and up regulated using r

Question

[Bioinformatics1]

Is it possible to answer my question?

I am going to draw down-regulated and up-regulated using r or excel.

What parameter showed the RNA Seq samples are down regulated and up regulated ?

It is log2FoldChange values? or logFoldChange?

Thanks

[patelk26]

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.

[Bioinformatics1]

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.

[brunobsouzaa]

You can use the log2FoldChange values but why not include a heatmap on the pipeline?

[Bioinformatics1]

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"

Answer 1

[noahhelton98]

I would definitely recommend analyzing your RNA-seq results in R, and as patelk26 said, those tools output Log2Foldchange values which is usually the standard when trying to determine differential expression of RNA-seq data. When you get the DESeq2 or edgeR results, it creates a dataframe (if you are following bioconductors pipeline for example) that you can easily filter in R for statistical significance and log2FoldChange using the dplyr package (here is a nice cheat sheet I use all the time https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf ) .

I hope this helps, but if you could expand somewhat on your question and give some information on how you got to the step your at/what you have tried/where exactly you are trying to end up I am sure myself and others would be able to provide you with more help

[Bioinformatics1]

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,

Answer 2

[Bioinformatics1]

Is there any pipeline for no replate samples?