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

In Alice-MacQueen/snpdiver: Explore SNP Effects on Multiple Phenotypes in Diversity Panels

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,
  thr.r2 = NA,
  FUN = identity
)

Arguments

m	the mash fit
factor	a number between 0 and 1 - the factor within which effects are considered to be shared.
lfsr_thresh	the lfsr threshold for including an effect in the assessment
thr.r2	Numeric. Value between 0 and 1. The r^2 value above which effects are considered to be linked; in this case, only the SNP with the more significant log10Bayes Factor value in mash is kept.
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 assessing 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

## Not run: 
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

## End(Not run)

Alice-MacQueen/snpdiver documentation built on Dec. 17, 2021, 8:41 a.m.