huge difference of expression value of different probes for the same gene. Why?

Question

[Raheleh]

Hi Folks, I ploted a heatmap for all probes of PARK7 gene to see how they are expressing in three different groups of tumor data. I expected that all probes for the same gene show a similar pattern. However, surprisingly I saw they have huge difference expression value.

Can anyone explain to me why these probes show such different expression values for the same gene? Thanks!

[GenoMax]

Follow the instructions here: How to add images to a Biostars post

[Raheleh]

Thanks! I followed the instruction but didn't work

[ATpoint]

You pasted a wrong link that redirected to google. The correct link was https://i.ibb.co/rZkJhLh/PARK7-heatmap.png, did the changes now.

[Biological information research group o…]

This can answer from the perspective of alternative splicing, for a specific gene, follow specific probe design in the gene, and because of the different regions of the same gene the use efficiency of alternative splicing sites in the same cell condition is different, will eventually lead to different extents of gene appeared different degree of difference.

Answer 1

[russhh]

At present all I can see are differences in average intensity between the probes. This doesn't quite correspond to 'expression level' as some probes may be more effective / selective than others. Could you subtract off the rowMeans from each row, so that we can compare how the relative intensities for different probes vary across your samples, please.

[Raheleh]

Thank you for your reply. I subtracted off the rowMeans from each row and now it is more meaningful. why we should do this?

[Friederike]

As russhh mentioned, different probes will yield different baseline signals. Some may simply hybridize more easily to their targets or their target sequences may be more abundant because of some bias during the fragmentation of the transcripts etc. That's why you need to look at the relative values for every probe across all samples to get a feeling for whether there might be a systematic reduction associated with individual sample (groups).

You need to remember that, in contrast to RNA-seq, where you're actually sequencing "real" transcripts, microarrays yield a much more indirect measure of transcript levels, which is one of the reasons they've been somewhat overtaken by RNA-seq despite their relative cost efficiency for some applications.

I highly recommend reading up on this if you really want to get to terms with this, e.g. Held et al., Wang et al., or a more recent write-up.