I got two sets of transcriptomics data (1day & 2 days exposure time) which consist of 20 samples each, with 4 different chemical concentration each and 5 replicates per concentration. Overally there are 40 samples. I used linear regression in deseq2 to examine the effect of different concentration (numerically) and basically found Degs for each data set. There is a batch effect due to different exposure time therefore i did not carry out time series analysis. There are comments that gene expressions which dont follow a linear pattern will be missed out from this analysis and this analysis is very uninteresting (boring). It makes me wonder if i should check out for tools that can do different types of modelling(any suggestion on tools? I use R ) or try to do machine learning like WGCNA to find diferent modules? More suggestion /opinions / criticisms are really appreciated.
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This ends up being the same as a time-series experiment, so have a look at http://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#time-series-experiments In general though people tend to cluster their data and have a look for obvious patterns in expression, which can then be used to make groups of genes.
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