stephenslab / susieR

R package for "sum of single effects" regression.
https://stephenslab.github.io/susieR
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Number of CSs #228

Open Xi-Cao opened 7 months ago

Xi-Cao commented 7 months ago

Hi there, I'm confused that how the CSs were selected. I thought the order min_abs_corr was used to determine the min corr of SNPs incorporated into a CS. 2 捕获 While when I tested in another locus, using this command didn't change the number of CSs it originally found. So why didn't it capture some new CSs with lots of SNPs, that have very small min corr? 3 Thanks, xicao

pcarbo commented 7 months ago

@Xi-Cao min_abs_corr filters out CSs that do not satisfy the minimum absolute correlation ("purity") criterion; it has no effect on the ordering of the CSs.

If the result does not change, it means that all CSs satisfied the previous purity criterion.

Xi-Cao commented 7 months ago

Thanks for your reply. So susieR found 9 CSs in the first example, while some of them didn't meet the purity criterion. In the next example, it actually found only one CS. Did I get this right? I'm confused that why sometimes multiple CSs, each of which contains almost the entire snp with small purity of the region, are found in a region, while some regions just have few CSs. Do the PIP and absolute correlation determine the number of CS, and how?

Xi-Cao commented 7 months ago

By the way, considering it affects the output of CSs, is there a recommended min_abs_corr value or a selected method?

pcarbo commented 6 months ago

The basic idea is that there should be one CS for each independent signal (causal SNP), and different regions will have different numbers of causal SNPs.

Since susie sometimes outputs a CS containing a very large number of SNPs (each with small PIP), the "purity" filter is helpful for removing these CSs that are likely not interesting. Therefore, we recommend setting "min_abs_corr" to a number larger than zero, but we do not have a recommendation as to the exact number. (The default min_abs_corr = 0.5 often works well.)

Xi-Cao commented 6 months ago

I get it, thank you!😊