Finding most co-expressed genes of input gene based on Pearson correlation

Question

[kozhaki.seq]

I have a matrix of microarray (around 30000 genes X 800 samples) as a back-end data set. In the workflow, if someone input a gene of their interest, I need to find it's highly co-expressed genes based on back-end data set. For that, I need to calculate pairwise Pearson correlation of input gene against data set (in this case, 30000 pairs). I have tried psych package before and for this pairwise calculation, there might be some other better method. Also,I assume this process take a little long time. Can any experienced person can suggest on this? Thanks

[Sam]

So you want a faster method for calculating the correlation matrix? Although you don't need WGCNA, I do believe the people in WGCNA has a faster implementation for the calculation of correlation. you can have a look

[kozhaki.seq]

Yes, since my matrix dimension is high, I am concern about the time..Yes, I will look into it @Sam

Answer 1

[Zhilong Jia]

amap::Dist, a R function, can calculate the distance or correlation in parallel. See manual http://www.inside-r.org/packages/cran/amap/docs/Dist