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Filtering on absolute gene expression level in microarrays

Hello all,

I have a normalized microarray dataset (affy_hg_u133_plus_2) of 23100 genes and 582 individuals. I have been in a discussion about filtering my dataset on absolute gene expression levels by looking into whether Y chromosome genes are expressed in the female individuals in my dataset. Y chromosome genes should not be expressed in women thus expression values can be reflective of background noise. The idea is to take the mean Y chromosome gene expression in women and below that it will be background noise and above that we will have the final expressed dataset. I have three questions:

  1. Is this step necessary to my analysis?
  2. If it is would that be the way to go or there are other analysis that I could use?
  3. if we filter as I describe above does it make sense to use the mean? I feel the median would be more appropriate.
affymetrix expression microarrays
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updated by Asaf Asaf • written by mi_kappa • mi_kappa

1 answer

  1. Yes, this type of QC is necessary, I've never analyzed an experiment with zero errors, with that many individuals you'll find some surprises.
  2. You can plot the expression on the Y axis and the Y chromosome genes on the X axis, color according to sex and figure out what the best statistic is, I assume it will be pretty straightforward. If you want to be meticulous you can train a simple naive-Bayes machine that will give a probability for each sample being male or female but that's unnecessary.
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written by Asaf Asaf

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