Microarray Data Analysis Using Bioconductor

library(limma) targets <- readTargets("affy_targets.txt") library(affy) data <- ReadAffy(filenames=targets$FileName) eset <- rma(data) pData(eset) write.exprs(eset, file="(affy_salt_all.txt") design <- model.matrix(~ -1+factor(c(1,1,2,2,3,3))) colnames(design) <- c("group1", "group2", "group3") fit <- lmFit(eset, design) contrast.matrix <- makeContrasts(group2-group1, group3-group2, group3-group1, levels=design) fit2 <- contrasts.fit(fit, contrast.matrix) fit2 <- eBayes(fit2) topTable(fit2, coef=1, adjust="fdr", sort.by="B", number=10) write.table(topTable(fit2, coef=1, adjust="fdr", sort.by="B", number=50000), file="limma_salt_complete.xls", row.names=F, sep="\t")

"ID" "logFC" "AveExpr" "t" "P.Value" "adj.P.Val" "B" "249897_at" 0.602237312171529 5.64509582185098 6.73094814125857 0.00162896558995517 0.876788085929335 -3.5957825791825 "249188_at" 0.44578148174508 7.25973883239111 5.68295138543025 0.00326382351423818 0.876788085929335 -3.65128570595124 .... .... ......... ...... ........... ..........

5.9 years ago by

Sean Davis ♦ 25k

National Institutes of Health, Bethesda, MD

Sean Davis ♦ 25k wrote:

The log2FC are in the column marked logFC and are positive for the two example probesets you have shown.

ADD COMMENT • link written 5.9 years ago by Sean Davis ♦ 25k

well, the file is long enough to attach here I just added to show the output format not to verify the output as positive and nagative

ADD REPLY • link written 5.9 years ago by H@rry • 30

From your code, I am not convinced that anything is wrong. It is certainly possible to have more down-regulated genes than up-regulated.

ADD REPLY • link written 5.9 years ago by Sean Davis ♦ 25k

I agree; the code looks fine.

ADD REPLY • link written 5.9 years ago by Neilfws ♦ 48k

Thannks for confirmation

ADD REPLY • link written 5.9 years ago by H@rry • 30

thanks alot for your reply. I agree that there could be more chances of occurring down-regulated genes in data as compared to up- regulated.

Now the problem is I am using cel files from one of the experiment published by some group and they have demonstrated the results using Affymetrix Microarray Suite 5.0. The analysis is based on two time period i.e 10 and 23 dpi and has 12 .cel file (1,2,3,7,8,9) for 10 dpi and (4,5,6,10,11,12) for 23 dpi. So in their analysis they have shown Upregulation >20 fold at 23 vs. 10 days postinfection and Downregulation <0.05 fold at 23 vs. 10 days postinfection. I am not sure how can I implement my code because I am using this approach for all .cel files (1,2,3,7,8,9) for 10 dpi in one run and (4,5,6,10,11,12) for 23 dpi in another run. Then substraction of 23 values vs 10 values (logfc) for each probeset. I wondering if I am doing the things in right way.

But after analysis I am getting the values like (for 1 probeset only) "247718_at" -2.60385457687538 7.36250717411517 -1.33369969784976 0.246361557614825 0.84824112853189 -4.53046067905237 (23 dpi)

"247718_at" -0.0743259153248443 5.00612574664665 -0.667284645810624 0.53718062033035 0.886783970000752 -4.75126236806355 (10 dpi)

and in article they mentioned like "247718_at" AT5G59310 663.576923 (Upregulation >20 fold at 23 vs. 10 dpi)

I am not sure I can I proceed for such output.

ADD REPLY • link written 5.9 years ago by H@rry • 30

You may want to contact the authors if there are discrepancies between your analysis and theirs. I don't think we can help you much here.

ADD REPLY • link written 5.9 years ago by Sean Davis ♦ 25k

Sure thanks for your reply.

ADD REPLY • link written 5.9 years ago by H@rry • 30

I just wanted to thank you guys one more time for your kind support, I got the desired output as I just changed some strategies to analyse but you guys made me confident in analyzing such data and personally too.

ADD REPLY • link written 5.9 years ago by H@rry • 30

Another upvote for Biostars!

ADD REPLY • link written 5.9 years ago by Sean Davis ♦ 25k

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