Re: positively correlated genes
Gordon K Smyth <[email protected]> Sat, 13 Sep 2014 09:10:18 +1000 (AUS Eastern Standard Time)
| Newsgroups | gmane.science.biology.informatics.conductor |
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| Message-ID | <[email protected]> |
If the logFC is positive, the gene is positively correlated with gene X. If the logFC is negative, the gene is negatively correlated with gene X. You should be using the FDR column rather than the PValue column to judge significance, as is usual with edgeR analyses. Pearson correlation between y and x is equivalent to regressing y on x. Pearson correlation is significant and positive if and only if the regression coefficient of y on x is significantly positive. Situation is similar here. Gordon On Fri, 12 Sep 2014, Sindre Lee wrote: > Can I please ask how to interpret this results? Im used to > Spearman/Pearson correlations and don't quite know how to present or > explain the results obtained this way. I wanted to find genes correlating with gene X. Then I got about 6000 significant genes at p < 0.05. Some with negative some with positive log2FC. Now, what do I do? What does this tell me? Thank you! ________________________________________ From: [email protected] <[email protected]> on behalf of Gordon K Smyth <smyth-QpO9qclcJEu6c6uEtOJ/[email protected]> Sent: 10 September 2014 03:08 To: [email protected] Cc: Bioconductor mailing list Subject: [BioC] positively correlated genes If you are using edgeR's glmFit function or limma's voom and lmFit functions, you can simply add the log-expression values of the gene of interest as a column of the design matrix. Then a standard DE analysis will detect any other genes that are significantly correlated with the gene of interest. Gordon > Date: Tue, 9 Sep 2014 01:41:14 -0700 (PDT) > From: "karthik [guest]" <[email protected]> > To: [email protected], [email protected] > Subject: [BioC] positively correlated genes > > hi > I am interested to find out the genes that are positively and > negatively correlated genes with my genes of interest. (using rnaseq > normalized expression data). Can some one suggest me a better option. > > Thank you > > -- output of sessionInfo(): > > sessionInfo() > R version 3.0.2 (2013-09-25) > Platform: x86_64-w64-mingw32/x64 (64-bit) ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}} _______________________________________________ Bioconductor mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/bioconductor Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor