Question: Negative B-Statistic Values In Limma/Microarray Differential Expression
gravatar for CrazyB
7.1 years ago by
United States
CrazyB240 wrote:

A question concerning the interpretation of microarray expression data -

After running GEO2R, which uses limma-based package for value comparison, I got a table of genes with their

"ID"    "adj.P.Val"    "P.Value"    "t"    "B"    "logFC"    "Gene.symbol"    "Gene.title"

From what I read on google, B-value and the moderated t (and hence P or adj P value) should rank the genes in the same order, which is true for my output table. However, according to other postings on google, only probes with positive B-values are thought to have a "differential expression". In my output, among the 54000 probes, only 85 probes show a "SIGNIFICANT" p-value (<0.05) but with positive B-values (many with p-value <0.0006 also have negative B-values).

How do we interpret those probes with low p-value but negative B-values ? Any suggestion or direction for more information on this issue ? Great many thanks.

ADD COMMENTlink modified 7.1 years ago by Sean Davis26k • written 7.1 years ago by CrazyB240

A p-value of 0.0006 may not be significant with 54000 tests. Are the lowest p-values also the largest B-values?

ADD REPLYlink written 7.1 years ago by Sean Davis26k

Ah, so is it correct that we should or must take into account the total number of tests in limma to gauge the significance ? And yes, the lowest p is 0.00000265 and it does have the largest B (3.055469). But this would mean most of the genes in the array show NO differential expression, right?

ADD REPLYlink written 7.1 years ago by CrazyB240

The output from topTable includes the "adj.P.Val" column. This is the p-value adjusted for multiple testing. By default, the method used is "BH" (Benjamini & Hochberg), also known as "fdr" (false discovery rate). See ?p.adjust.

ADD REPLYlink written 7.1 years ago by Neilfws49k
gravatar for Sean Davis
7.1 years ago by
Sean Davis26k
National Institutes of Health, Bethesda, MD
Sean Davis26k wrote:

The B-statistic is the posterior odds of differential expression. Gordon Smyth has written a bit about it, but the take-home message is that since we do not often know the prior probability of differential expression, the B-statistic is not very useful. Instead, the focus should be on biological significance as measured by fold change and statistical significance as measured by a multiple-test-adjusted significance measure, such as the false discovery rate.

ADD COMMENTlink written 7.1 years ago by Sean Davis26k
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