I would like to know if it is possible to use DESeq2 to analyze NimbleGene microarrays. I have a dataset of several conditions with enriched (IP) and control (Input) sample for several knock-outs. We would like to try and identify enriched probes (= this is what equals the gene name on the microarray) both within an IP-Input comparison, but also between two conditions.
a short example - These is one of the comparison I would like to run:
dataset:
file sample.id condition assay sample.target 561534A07_532.pair 561534A07_532 WT Input Y0003_FK2 561534A07_635.pair 561534A07_635 WT IP Y0003_FK2 561500A01_532.pair 561500A01_532 WT IP Y0003_FK2_2 561500A01_635.pair 561500A01_635 WT Input Y0003_FK2_2 561565A01_532.pair 561565A01_532 cdc48 Input MJK503_FK2_1 561565A01_635.pair 561565A01_635 cdc48 IP MJK503_FK2_1 561565A10_532.pair 561565A10_532 cdc48 IP MJK503_FK2_2 561565A10_635.pair 561565A10_635 cdc48 Input MJK503_FK2_2
My first comparison would be between the IP and Input of the WT and of the cdc48 sets separately, But I would also like to try and identify enriched regions/probes between samples. this would be based on ~condition for the design formula.
But Is it also possible to calculate the ratio of ratios and compare the two data sets with each other?
What I would like to do is something like (IP of cdc48 / Input of cdc48) / (IP of WT / Input of WT)
should this be something like that:
dds <- DESeqDataSet(se, design= ~ assay + condition + assay:condition) dds <- DESeq(dds, test="LRT", reduced= ~ assay + condition)
Is it possible to do something like that with DESeq2? I know the probe intensities are not counts, but maybe it is possible to read them into DESeq2 with the tximport package which than could convert them into counts.
thanks, Assa
