Whenever the available data contains also the sample class (i.e cancer vs. normal), does it make sense to calculate the correlation matrix considering all the samples. Shouldn't be better calculate the correlation values considering each class separated? In the first way we identify only those genes that are correlated across all the conditions, while in the second way we highlight genes that are correlate across a specific condition (cancer or normal). Which is the common approach? Is the second procedure used only when we want discover new biomarkers?
•
written
by
TitoPullo
1 answer
I think you understand the concept of correlation within and between classes correctly. The most common approach is to use correlation across all conditions. Correlation is not a method for discovering new biomarkers, generally. Biomarker discovery is generally done through a process of feature selection such as t-testing or some forms of classification.
Log in to answer this question.
-
WGCNA module of interest, correlate GENES with trait
written by Anders •
-
Complex multifactorial DE analysis with limma/edgeR based on rnaseq data
written by svlachavas
-
correlation analysis of a single variable versus multiple variables
written by pinheirofabiano •
-
gene expression x phenotype correlation
written by demoraesdiogo2017 •
-
Correlation of two ChIP-seq signals?
written by biostart
-
centrality scores in network
written by hennet.lauriane •
-
Correlation of TF Binding with expression data
written by Prakash
-
Limma design and makeContrasts
written by Matina
-
significance of correlation in biological data
written by BrunoGiotti
-
Significance within conditions
written by Assa Yeroslaviz
