Guys, I have microarray data and I ended up at a dead end. I have the DEGs in each treatment, and I know which pathways are affected in each treatment, but turns out it isn't enough due to the complexity of my analysis. The thing is, my dataset is composed of 5 treatments: 1. a positive control, 2. a toxin and 3, 4, 5, major metabolites x,y and z of this toxin, each one in 3 times points (6.12 and 18h of incubation with cells). I was thinking about using an approach to be like "the response of the toxin after 12 h is 23% similar to the response of the metabolite x after 6h".
I tried to use pearson correlation, but it only gives me the R value, which I can't turn into a percentage.
I also have the cosine similarity distance, but it's pretty similar to pearson. I also tried linear regression, binary correlations, but I ran out of ideas. Any suggestion, or paper or, mathematical advice I could use?
Thanks in advance.
Maybe this publication may provide some useful info.
Analyzing time series gene expression data
http://bioinformatics.oxfordjournals.org/content/20/16/2493.short
For time-series experiments I have used STEM, it is quite user-friendly and it has GO support, which works great at least for Arabidopsis thaliana: http://www.cs.cmu.edu/~jernst/stem/
As a suggestion for additional analyses, you could also try to construct gene co-expression networks, compare your treatments and identify significant differences among them. Potentially, that could give you some clues about the direct targets of your toxin.
Co-expression network analyses have been useful for identification of transcription factors regulating biosynthetic pathways in Arabidopsis: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1849962/
It has also been used for microarray experiments in humans: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2847743/
I have not used it, but this software looks quite interesting for this type of analysis:
http://www.ncbi.nlm.nih.gov/pubmed/21756334
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