Just to make it clear for others: what Mary describes is just one portion
of the section... and I know (and I show later on that document) that there
are other (preferable) ways to assess differential expression...

Mary, there isn't such distinction: rowMeans() is simply estimating for you
the average log-expression for each of the conditions (so, you get the
average log-expression for gene1, gene2, ..., geneN). To get averages, the
normality assumption doesn't play a role.

b


On 15 November 2012 22:12, Mary Ann Allen <Mary.A.Allen@colorado.edu> wrote:

> In the "oligo" manual there is a section that describes how to calculate
> all genes with 2x change or more. This sections uses rowMeans to calculate
> the average of replicates-"rowMeans(e[, index])". However, since the
> expression values in eset are in log2, is rowMeans the correct way to
> calculate averages? I thought rowmeans was for calculating the averages of
> normal numbers, not log2 numbers. If this method is not correct, how should
> the averages of the replicates be calculated?
>
> Thanks,
> Mary A. Allen
>
>
> from the manual
>
> 2.2.4 Assessing dif erential expression
> One simple approach to assess dif erential expression is to flag units
> with log-ratios greater (in absolute value) than 1, i.e. a change greater
> than 2-fold when comparing brain vs. universal reference.
> R> e <- exprs(eset)
> R> index <- which(eset[["Key"]] == "brain")
> R> d <- rowMeans(e[, index])-rowMeans(e[, -index])
> R> a <- rowMeans(e)
> R> sum(abs(d)>1)
> [1] 10043
>
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