Hello,
We have 2 datasets, one from Illumina HumanHT-12 v4, and the other one is from Affymetrix Human Genome U133 Plus 2.0 Array. We would like to perform a differential gene expression analysis. What would you suggest in quality control, background correction and normalization steps in order to perform such analysis.
Thank you very much!
•
written
by
jth
1 answer
You could try averaging signal among probes for the same gene, consider only genes covered on both arrays, and then use quantile normalization to try and make them look the same.
However, I honestly wouldn't put very much trust in those results. I would strongly recommend looking for a leukemia cell line sample using an Affy array, so the probes at least measure the same thing. For starters, the NCI-60 cell lines are in a lot of GEO datasets, and I'm sure you can find other leukemia Affy datasets as well:
Log in to answer this question.
-
How to retrieve Microarray probe annotations
written by mohammedtoufiq91
-
Expression Profiling by array in Geo Datasets (Normalization)
written by David •
-
Is it correct to merge two raw CEL datasets and then perform quality control, background correction…
written by Sib •
-
Microarray data from different sets but same platform
written by Ahmed.waraky •
-
Illumina HumanHT-12 V3.0 expression beadchip reading data
written by bhargavdave1 •
-
normalization in cross meta-analysis
written by Morteza Hadizadeh •
-
Meta_analysis of different platforms of microarray data
written by j_jamal96 •
-
How to extract only probes that exist on 2 different platform?
written by BioMed •
-
Rna Seq Data In Geo Datasets
written by Bioinformatics_NewComer
-
Analyzing Microarray Data In Triplicates With Lumi/Limma (R)
written by jth
What is the comparison that you want to do for differential expression? What groups are represented on which array platform?
It was one cell line (A leukemia cell line) in illumina and one cell line (Mesenchymal Stem Cells) in affy.
Unfortunately, you are probably stuck since the effects of platform are perfectly confounded with the biological effect of interest.