Hi,
I have raw data from [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array that I want to process using the Expresso function for Affymetrix microarrays.
My samples include tumor tissues and matched adjacent tissues.
I am planning to use the RMA method which includes RMA+Quantiles+pmonly+median polishment, but it would be great if you share your experience with me. Which methods would you prefer to combine according to your statistical experience in this field?
Background correction Options:
- Affymetrix MicroArray Suite (MAS)
- Robust Multiarray Analysis (RMA)
- None
Normalization Options:
- Quantiles
- Lowless
- Cubic Spline (Qspline)
- Invariant set
Probe match correction Options:
- Perfect-Match (PM) only ["pmonly"]
- Subtract with Mismatch (MM) ["subtractmm"]
- Affymetrix MicroArray Suite (MAS) ["mas"]
Values presentation Options:
- Average Difference ["avgdiff"]
- Li & Wong (2001) outlier removal ["liwong"]
- John Tukey median polishment ["medianpolish"]
Thank you in advance,
Sevcan
I appreciate Sevcan Atay for the valuable topic. Would be interested to know as well.
I appreciate Sevcan Atay for the valuable topic. Would be interested to know as well.
RMA is always a good idea even if normalization is not at all the most important question in gene expression profiling (as could be shown in https://www.researchgate.net/publication/276981219). The strategy of selecting significant genes is much more of interest. For example, don't use Absent genes, don't use Fold Change allone. In one of my last contributions (https://www.researchgate.net/publication/342626427) I decided to average 4 differently normalized RMA Signals (I used RMAExpress were you can use MedianPolish and PLM as summarization). So I selected with and without background subtraction with both summarizations to get 4 Signals for each probe set. This could be an option.
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