I am analysing microarray data from GEO for the first time. The data has already been processed and normalized, but the illumina beads have caught multiple transcripts for the same gene (I assume), meaning that some of my genes appear twice (e.g. MCL1 is a gene I am looking at and has two different Illumina ID's associated with it). I am trying to look at total gene expression and gene expression differences between diseased states, so I need to aggregate the data. How can I do this when some genes appear multiple times?
•
written
by
ravelarvargas •
1 answer
The ideal situation would be to obtain the raw data CEL or TXT files (edit: or whatever BEAD arrays use) and then summarise expression over genes using RMA and the median polish.
Given the data that you've got, you can just summarise it yourself as follows:
df <- data.frame(
c("gene1","gene1","gene2","gene2","gene2","gene3"),
c(1,2,3,10,20,30),
c(60,50,40,3,2,1),
c(7,8,9,100,110,120),
c(12,11,10,9,8,7)
)
colnames(df) <- c("gene","sample1","sample2","sample3","sample4")
df
gene sample1 sample2 sample3 sample4
1 gene1 1 60 7 12
2 gene1 2 50 8 11
3 gene2 3 40 9 10
4 gene2 10 3 100 9
5 gene2 20 2 110 8
6 gene3 30 1 120 7
summarise by mean
aggregate(df[,2:ncol(df)], by=df[1], mean)
gene sample1 sample2 sample3 sample4
1 gene1 1.5 55 7.5 11.5
2 gene2 11.0 15 73.0 9.0
3 gene3 30.0 1 120.0 7.0
summarise by median
aggregate(df[,2:ncol(df)], by=df[1], median)
gene sample1 sample2 sample3 sample4
1 gene1 1.5 55 7.5 11.5
2 gene2 10.0 3 100.0 9.0
3 gene3 30.0 1 120.0 7.0
summarise by sum
aggregate(df[,2:ncol(df)], by=df[1], sum)
gene sample1 sample2 sample3 sample4
1 gene1 3 110 15 23
2 gene2 33 45 219 27
3 gene3 30 1 120 7
Kevin
You could just fit your own linear or logistic model to the data with lm() or glm(), and the adjust the p-values with p.adjust(), or use my package: https://bioconductor.org/packages/release/data/experiment/html/RegParallel.html
Better to have the raw data CEL, TXT, GAL, or DAT files, though.
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