What I have seen previously in papers is that the authors find genes that are highly correlated with tumor purity.
I'm mainly confused in Paper #1, when author says:
"Spearman correlations were calculated between their expression and tumor purity to generate a ranked list of genes."
If you have a matrix consisting of 50 samples where they are separated into groups of 10 according to the tumor type (5 groups) and 20,000 rows for genes, how do you perform correlation analysis with 50 tumor purity values for the 50 samples to identify the genes that are highly correlated?
•
written
by
newbio17
1 answer
They just computed the correlation between each row of gene expression values (50 values) and the 50 purity values and then creating a ranked list of the results. In R you would do that with cor() and apply().
Log in to answer this question.
-
ssGSEA & WGCNA workflow approach
written by TNF_alpha •
-
single cell RNA seq gene co-expression
written by guillaume.rbt
-
Correct data for methylation data plotting (circos plot)
written by maria2019
-
Is feature selection necessary before dimensional reduction in single-cell analysis?
written by Phoe •
-
Using linear regression to accounting for confounding variables in gene expression data
written by newbio17
-
How to do survival analysis?
written by wenbinm •
-
One-to-Many RNA-Seq Gene Correlation
written by sallehl •
-
Correcting for tumor purity in tumor evolution analysis
written by cbst
-
Tool: Discovering protein-lncRNA interactions from large-scale CLIP-Seq and TCGA Cancer datasets
written by lsp03yjh
-
Co-expression analysis with paired samples
written by agatawesol •
