get_pairwise_sharing: Compute the proportion of (significant) signals shared by...

In stephenslab/mashr: Multivariate Adaptive Shrinkage

Compute the proportion of (significant) signals shared by magnitude in each pair of conditions, based on the poterior mean

Description

Compute the proportion of (significant) signals shared by magnitude in each pair of conditions, based on the poterior mean

Usage

get_pairwise_sharing(m, factor = 0.5, lfsr_thresh = 0.05, FUN = identity)

Arguments

m	the mash fit
factor	a number in [0,1] the factor within which effects are considered to be shared
lfsr_thresh	the lfsr threshold for including an effect in the assessment
FUN	a function to be applied to the estimated effect sizes before assessing sharing. The most obvious choice beside the default 'FUN=identity' would be 'FUN=abs' if you want to ignore the sign of the effects when assesing sharing.

Details

For each pair of tissues, first identify the effects that are significant (by lfsr<lfsr_thresh) in at least one of the two tissues. Then compute what fraction of these have an estimated (posterior mean) effect size within a factor 'factor' of one another. The results are returned as an R by R matrix.

Examples

simdata = simple_sims(50,5,1)
data = mash_set_data(simdata$Bhat, simdata$Shat)
m = mash(data, cov_canonical(data))
get_pairwise_sharing(m) # sharing by magnitude (same sign)
get_pairwise_sharing(m, factor=0) # sharing by sign
get_pairwise_sharing(m, FUN=abs) # sharing by magnitude when sign is ignored

stephenslab/mashr documentation built on Oct. 19, 2023, 4:14 p.m.