Proteomics DEA
I have proteomics data where most proteins do not follow normal distribution. I am not sure if it would be better to perform a specific differential expression analysis, or if I could log2transform and normalize data (for example, thorugh Quantile method), and after that, performing DEA with limma.
• 1,069 views
•
link
updated
by
ATpoint
•
written
by
frarodmar17 •
•
written
by
frarodmar17 •
0 answers
No answers yet.
Log in to answer this question.
More posts like this
-
Unusual scale-free topology fit in WGCNA after MaxQuant/Perseus preprocessing of LFQ proteomics data
written by Vinesh •
-
Proteomics Data Analysis
written by frarodmar17 •
-
single-cell transcriptomics and bulk proteomics data integration
written by frarodmar17 •
-
limma-trend on GTEx inverse normal transform data
written by Alba Mas •
-
Any strategies to compare gene and protein expression dataset and perform differential expression
written by Vinesh •
-
Filtering proteomics matrix before diff. exp.
written by rpraveenkumarbio •
-
RNASeq differential expression: How to deal with few genes with extremely high expression levels
written by komal.rathi
-
limma applied to unbalanced micro-array experiment
written by ewre
-
Differential expression gene list from TCGA level 3 RNASeq V2 datasets downloaded from UCSC Cancer …
written by zeeniajagga •
-
RNASeq normalization using the expression values of rRNA
written by samuelmiver
limma is a specific differential expression analysis --in my opinion the most generic and useful one for most HT datasets.
But resuts are much worse than Wilcoxon test, because most proteins do not follow normal distribution.
limma does not require normal distribution. It is one of the most extensively used testing framework for any OMICS dataset.