Question: [Bioc] RNAseq less sensitive than microarrays? Is it a statistical issue?
5.6 years ago by
Lucia Peixoto • 330
Lucia Peixoto • 330 wrote:
Dear All, I have a dataset for which I have two conditions. I have 9 replicates per group for microarrays, 5 per group for RNAseq (which are a subset of the RNA samples used in the microarrays, couldn't sequence all 9), and 8 per group for qPCR (which is an independent set of experiments). Each n is an independent mouse, in and independent day from and independent experiment, so that one experiment with yield n=1 for each of the groups. The correlation between control and treatments within the same day is not better than across days, however. Theoretically they all measure the same biological phenomenon, which is gene expression changes, so I have been doing some comparisons between them to try to get at the truth of what is really being differentially expressed. In particular I have focused in the 5 samples in each of the three groups in which the only difference is whether the RNA was hybridized by microarray or sequenced. To my surprise the gene lists obtained from analyzing differential expression using RNASeq (using either edgeR or DESeq) is considerably smaller than the one obtained from microarray analysis (using locfdr on pairwise t-statistics) at the same FDR. The RNASeq list is included in the microarray list, but there are several differences I have validated by qPCR that the RNASeq analysis is not able to detect at a reasonable FDR. Moreover, there seems to be an unusual bias towards not being able to detect down-regulated genes. I am a little bit puzzled by this, since one of the reasons we are sequencing is that it is supposed to have a better dynamic range. These are the same RNA samples so this apparent lack of sensitivity has to be related to either library prep or statistical analysis. So these are my questions: - can the inability to distinguish down-regulated genes be related to filtering low count reads? (in order to get good separation between groups in an MDS plot I need to filter cpm >0.1) - Is it possible that I need more coverage to improve sensitivity? I am currently sequencing at 50X pair end, that seemed enough. Is there any published study looking at RNASeq sequencing depth and sensitivity in human or mouse genomes? - Are the multiple testing corrections applied in EdgeR and DESeq too stringent thus rendering the overall analysis less sensitive? For the record my count matrices are of counts of transcripts, averaging counts over all exons from the same gene model for all RefSeq genes. I did this because the microarray data is per transcript. In log scale I have on average 0.7 R2 correlation between microarray intensity and RPKM from the same sample. Thanks for the insight! Lucia -- Lucia Peixoto PhD Postdoctoral Research Fellow Laboratory of Dr. Ted Abel Department of Biology School of Arts and Sciences University of Pennsylvania "Think boldly, don't be afraid of making mistakes, don't miss small details, keep your eyes open, and be modest in everything except your aims." Albert Szent-Gyorgyi [[alternative HTML version deleted]]
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