raw count data- normalization
I got raw count data,which would consist of 32 samples and 20000 genes. With this data, I have to normalize this.
First step: normalized gene expression values per sample.
Second step: gene-wise normalization
Question 1: what is gene-wise normalization?
Question 2: how can I this two step?
I'm beginner, but no one can help me in my workplace. Please help me.
0 answers
No answers yet.
Log in to answer this question.
More posts like this
-
How can I normalize miRNA read count from TCGA
written by Fluke •
-
Question regarding normalization in Seurat
written by ylin575 •
-
Normalization for Unique Molecular Identifier (UMI)
written by JulianC •
-
how can i get normalized gene expression matrix in TCGA?
written by ghmdsr •
-
Normalization of gene expression data
written by bioinfonerd •
-
TMM normalization using tweeDEseq
written by ghmdsr •
-
Tutorial: raw count data- normalization
written by ghmdsr •
-
TMM - Normalized FPKM values for RNASeq data ?
written by gangireddy
-
How to use normalized expression values as input for limma?
written by Line Heylen •
-
What is the best way to quantile normalize expression matrix ?
written by jack
I recommend reading the documentation of DESeq2, a framework for differential RNA-seq analysis. It also includes a count normalization approach, using the
estimateSizeFactors()function, which will give you a per-sample size factor that you can use to divide each sample by. There is a nice StatQuest video about this here. Other normalization approaches include TPM or FPKM normalization, depending on what you want to do with your data. Please give some details.