Hi Peter and others,

This looks good. In my analyses, I've calculated the matrices of pairwise correlations using only exons/genes/transcripts (whatever the quantification unit is) that are >0 in both samples. I don't know if this matters much, but we should agree on Thursday what's the set of exons/etc that we use to calculate the correlations.

I've done some comparisons of OPS+Pearson vs Spearman, and here are two scatterplots - they give very consistent results. There's one plot of all pairwise correlations, and another one of d-statistics (=median of one sample's correlations against all others). In these plots I've dropped the two expression level outliers, NA18861.4.M_120208_5 and NA19144.4.M_120208_2.
  
Additionally, in case you don't already have these yourself, I just uploaded the correlation matrices of all 667x667 samples, both Spearman and OPS+Pearson correlations in /upload/geuvadis/wp4_rnaseq/main_project/analysis_data/qc/correlation_matrices/ .


best,
Tuuli


Tuuli Lappalainen, PhD
Department of Genetic Medicine and Development
University of Geneva Medical School
CMU / Rue Michel-Servet 1
1211 Geneva 4
Switzerland
Tel. +41-(0)22-3795550
tuuli.lappalainen@unige.ch
On 8/7/12 10:26 AM, P.A.C._t_Hoen@lumc.nl wrote:

Dear all

We are all aware of the pitfalls of Pearson correlations on largely skewed data. Micha mentioned in Barcelona the OPS (optimal power space transformation package) that he developed. It worked very well to determine correlations between samples, I believe. Attached some results.

Best

Peter

 

 

Dr. Peter A.C. 't Hoen

Center for Human and Clinical Genetics

Leiden University Medical Center

Postal zone S4-P

PO Box 9600

2300 RC Leiden

The Netherlands

 

phone: +31-71-5269421

fax: +31-71-5268285

e-mail: p.a.c.hoen@lumc.nl

 



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