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I have a set of ~100 samples with microRNA sequencing data obtained using Illumina Genome Analyzer, and another set of ~200 samples with the data obtained using Illumina HiSeq 2000. The total ~300 samples belong to two groups, equally represented in the two data-sets. I am interested in differential expression analysis to compare microRNA expression between the two groups.
I want to combine the GA and HiSeq data (available as either absolute read counts and counts per million reads) to have a larger sample-size for the analyses.
The GA and HiSeq 2000 platforms use the same 'chemistry', and I understand that the main difference between them is that the latter has a higher throughput (processing time), so combining the data obtained with the two different platform seems reasonable.
Can anyone advise if this is indeed so? Further,
(1) Should one use the absolute read count values, or the count per million values?
(2) Should I normalize the data after combining the data-sets? What method will be appropriate?
(3) How should missing values be dealt with? E.g., unlike in the HiSeq data-set, microRNA miR-X may not have been detected in any sample of the GA data-set (and thus missing in it).
Thank you.
I want to combine the GA and HiSeq data (available as either absolute read counts and counts per million reads) to have a larger sample-size for the analyses.
The GA and HiSeq 2000 platforms use the same 'chemistry', and I understand that the main difference between them is that the latter has a higher throughput (processing time), so combining the data obtained with the two different platform seems reasonable.
Can anyone advise if this is indeed so? Further,
(1) Should one use the absolute read count values, or the count per million values?
(2) Should I normalize the data after combining the data-sets? What method will be appropriate?
(3) How should missing values be dealt with? E.g., unlike in the HiSeq data-set, microRNA miR-X may not have been detected in any sample of the GA data-set (and thus missing in it).
Thank you.