On 2020-08-01 15:52 -0400, Matthew McCormack wrote: | On 8/1/20 1:13 PM, Jeff Newmiller wrote: | | On August 1, 2020 4:01:08 AM PDT, Anas Jamshed wrote: | | | I performed this in GEO2R and find | | | R script there and Runs R script
Anas, how did you come up with this script at all by reading the article? How can you be sure that limma::lmFit/limma::eBayes procedure was the one Jia et al. used in their article? The three author emails are listed on page 1 of the article. | | | After running this no genes are | | | found plz help me | | | | https://www.bioconductor.org/help/ | | As with the previous post, I agree | that Bioconductor will be a better | place to ask this question. | | As a quick thought you also might try | to adjust the p-value in the last | line: This is the "distribution" of possible log2 Fold Change in tT: > tab <- table(signif(tT$logFC, 1)) > tab[as.character(sort( + as.numeric(names(tab)), + decreasing=F))] -0.5 -0.4 -0.3 -0.2 -0.1 -0.09 0.1 1 25 158 376 185 7 49 0.2 0.3 0.4 0.5 0.6 0.7 250 140 42 11 4 2 ... knowing full well “regulated” is supposed to be abs(logFC)>1, we can instead select above .5 there to get the few up-regulated ones ... > rownames(tT) <- NULL > subset(x=tT, + subset= + adj.P.Val < .01 & + abs(logFC) > .5, + select=adj.P.Val:Gene.symbol) adj.P.Val P.Value t 4 7.457501e-05 5.894525e-09 7.075753 5 7.457501e-05 7.877860e-09 6.993182 9 1.170092e-04 1.926078e-08 6.738920 29 2.565179e-04 1.432599e-07 6.168230 42 3.202947e-04 2.511181e-07 6.008168 60 4.039665e-04 4.433103e-07 5.845695 343 1.043185e-03 6.555444e-06 5.066127 475 1.391091e-03 1.208538e-05 4.885755 B logFC Gene.symbol 4 10.264385 0.6225559 REG1A 5 9.996103 0.6630585 TNMD 9 9.168329 0.5138611 NKAIN4 29 7.306904 0.5538644 C1QB 42 6.785641 0.5530439 ISG20 60 6.257651 0.5082288 GZMH 343 3.755608 0.5543619 MIR155///MIR155HG 475 3.188253 0.7264114 CXCL13 ... none of which are in the network of important proteins in figure 5 on page 6. Best, Rasmus
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