Tutorial for ROC curve analysis using DE RNA data in R
0 answers
No answers yet.
Log in to answer this question.
More posts like this
-
RNA seq differential expression analysis
written by rrehimi •
-
Pathway analysis of a seurat object with gage in R
written by mfro •
-
How to do functional analysis on differentially expressed gene list from RNA-seq data?
written by Omurice •
-
How to merge miRDeep2 counts for DeSeq2 analysis?
written by MS •
-
How to get output of annotated genes from ChIPseeker tool
written by MS •
-
How to conduct linkage disequilibrium analysis in R?
written by anikcropscience •
-
GOplot - tutorial
written by dimitrischat
-
Differential gene expression using R studio
written by rachana.cdri •
-
tcgabioloink level 1 data
written by ugandhar.ch1 •
-
Microarray differential gene expression analysis using R
written by aharnishi02
What question are you trying to answer with a ROC curve of DE analysis? Do you have some kind of ground truth of the DE status in order to calculate the true positive rate and false positive rate?
I will investigate the diagnostic potential of mRNAs that show at least 2-fold increase compared to control samples, individually or in groups of two or three, by performing ROC curve analyzes.
I just have DE mRNA list as a result of DeSeq2 and I am planning to add DE status according to FDR, log2FC and p-value.
Have you considered a MA plot or volcano plot instead?