How do I determine p-value and fold change for TMM normalized data?
Hi,
I have a TMM normalized data for cancer vs no cancer treatment. This data, however, does not contain p-value or fold change. I want to generate plots such as heatmaps and volcano plots, but I cann't do it without their p-values and fold changes. I also do not have the raw counts. Is there a way to calculate p-value and fold change for this normalized data. Any help is greatly appreciated.
Thanks.
1 answer
This discusses the options, by the way, this has been dozens of times before, please use the search function and google, or point out why none of the previous threads was able to help.
Log in to answer this question.
More posts like this
-
Consensus partitioning on merged microarray data
written by chriselhadi •
-
Why do I get a big log fold change but small mean change in b value when plotting differential meth…
written by Christine •
-
Differential gene expression: remove effect of latent variables from log fold change
written by danielcgingerich •
-
Differential expression analysis with LIMMA results
written by leticia •
-
Volcano plot for multiple clusters
written by michelle.piquet •
-
Could you please explain Fold change, % of change, and log2 fold change (L2FC) to a layment?
written by WUSCHEL
-
Volcano plot for mean fluorescence intensity (flow cytometry) data
written by jackarnestad •
-
TMM - Normalized FPKM values for RNASeq data ?
written by gangireddy
-
would it be a good idea to normalize log2 fold changes with loess?
written by tonja.r
-
p-values from a distribution of fold-changes
written by tonja.r
There might be a way to get fold changes from this kind of data in limma.