Hi all,

I am using DESEq for a DGE analysis.

I have STRANDED RNA-Seq data for 4 developmental stages with no replicates but I know that to have a more reliable DGE I should have replicates. So I got (from another lab member) UNSTRANDED RNA-Seq data with 3 replicates per stage.

So my data situation at the moment is:

STAGE 1     stranded
STAGE 1.1  unstranded
STAGE 1.2  unstranded
STAGE 1.3  unstranded
STAGE 2     stranded
STAGE 2.1  unstranded
STAGE 2.2  unstranded
STAGE 2.3  unstranded
STAGE 3     stranded
STAGE 3.1  unstranded
STAGE 3.2  unstranded
STAGE 3.3  unstranded
STAGE 4     stranded
STAGE 4.1  unstranded
STAGE 4.2  unstranded
STAGE 4.3  unstranded

Before doing a DGE, I thought to test the correlation between these samples, just to show that similar samples “cluster” together. If so, I thought to use the unstranded data for my DGE analysis to reach the final number of 4 replicates per stage.

I mapped the raw reads to the genome using TOPHAT (v2.0.9) (fr-unstranded for unstranded data and fr-secondstrand for stranded data), used htseq-count (HTSeq 0.5.4p5) to get the raw reads counts for both the data. For the stranded data I used the option -s yes and for the unstranded data I used -s no. I then used DESeq (v1.14.0) to include metadata and for normalization, and I removed the genes that always have a 0 value. I then calcualted the correlation which was really low.

I tried to use the option -s reverse for the stranded data and still got really low correlation. So I reran htseq-count on the stranded data selecting the option -s no and in this way I got a very similar number of total counts between the unstranded and stranded data, around 4-5M counts each stage (while both cases before the stranded ones were double in number).

I included the metadata


> Design
            condition
ADULT        ADULT
ADULT1       ADULT
ADULT2       ADULT
ADULT3       ADULT
JUV            JUV
JUV1           JUV
JUV2           JUV
JUV3           JUV
COMP          COMP
COMP1         COMP
COMP2         COMP
COMP3         COMP
PRECOMP    PRECOMP
PRECOMP1   PRECOMP
PRECOMP2   PRECOMP
PRECOMP3   PRECOMP

and estimated the new size factors, normalized and calculated the new correlation. Pearson performed pretty well, confirmed by both a PCA and correlogram. So my initial idea was to do a DGE "treating" the stranded data as unstranded, having 4 replicates per stage. Though, I'd still like to figure out a way to use the stranded counts since I am not sure if I am losing some information running htseq-count using -s no on the stranded data.


What I had in mind was using unstranded data to estimate the level of variation to get a threshold for DE detection but still use the stranded data as expression values. Not sure if I can do that though given one is stranded and the other is not.


I would like to hear from you if you have any thoughts about this.


Let me know if you need any further details to better understand the issue.


Thanks in advance,

Federico

Federico Gaiti
Ph.D. Candidate
School of Biological Sciences
University of Queensland
St Lucia QLD 4072
Australia
f.gaiti@uq.edu.au


	[[alternative HTML version deleted]]

