Hello.. I have RNA Seq data from about 20 different samples which are of differnet stages. The experiment was not properly designed for expression profiling but i wanted to extract some meaningful and correct information from the analysis I have the data set up like this.. Stage 1 : Day1 (Sample 1), day 10 (Sample 2) , day 20 (Sample 3) - total reads combining 3 samples (100 mil) Stage 2 : Day 3 (Sample 4) (30 million) Stage 3 : male - day 1, 2, 10 (Sample 5,6,7) (400 million) Stage 4 : female - day 1,2,10 (Sample 8,9,10) (250 million) Stage 5 : specific tissue (Sample 11) (50 million) The total reads for 5 diff stages varying from 30 million to 400 million. There is no reference genome for this so i assembled them using trinity by combining all the reads. i have around 300k transcripts. Now i have done two diff experiments.. 1) Mapping the reads back separetely for all samples to assembled transcriptome (300k ) and used edgeR to call differential expression. I used the downstream processing pipeline mentioned in trinity which uses edgeR. I considered samples separately and got all vs all comparisons. I wanted to get expression profiling of those different stages. But with edgeR, i might not get good profiling as it is at sample level as you see that for one stage i have one sample where as for one stage i have max of 6 samples. 2) b) Combine the raw reads stage wise in to fastq files like for Stage1 : 100 mil , Stage 2 : 30 mil , Stage3 : 400mil etc and run edgeR with the stages. But my question here is as i have huge variation in number of reads, do you think edgeR can handle well to give correct FPKM values and correct profiling of these samples? Any suggestions on how i should proceed further in this analysis?? [[alternative HTML version deleted]]