Description Usage Arguments Details Value Author(s)
Retrieves sQTLs after two-level multiple testing correction and svQTL removal (if requested).
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res.nominal.df |
a data.frame, output of 'sqtl.seeker' with the nominal P-values for each gene/SNP test. |
res.permuted.df |
a data.frame, output of 'sqtl.seeker.p' with the empirical P-values and the MLE estimates for the beta distribution parameters for each gene. |
FDR |
the False Discovery Rate to call an association significant. Default is 0.05. |
method |
the FDR approach. Either |
md.min |
the minimum MD (Maximum Difference) in relative expression required. Maximum difference in relative expression (MD) gives an idea of the effect size of the association. Default is 0.05. |
svQTL.removal |
if TRUE (and column 'pv.svQTL' is present in 'res.df') significant sQTL which are also significant svQTLs are not reported. Default is FALSE. |
FDR.svQTL |
the False Discovery Rate to call a svQTL, that may be removed from the final set of sQTLs. Note that svQTL FDR is computed on the pooled nominal P-values from the svQTL test. |
We consider two levels of multiple testing:
1. Multiple genetic variants are tested per gene.
2. Multiple genes are tested genome-wide.
sQTLseekeR2 uses a permutation approach, implemented and detailed in sqtl.seeker.p
to correct for
the former and false discovery rate (FDR) estimation to control for the latter. Both Benjamini-Hochberg (p.adjust
method)
and Storey (qvalue
method) approaches for FDR can be applied.
If svQTL.removal = TRUE
and svQTLs were tested in 'sqtl.seeker', gene/SNPs with
significant svQTL association (after multiple testing correction and similar FDR threshold)
are removed from the final set of significant sQTLs.
a data.frame with the columns of sqtl.seeker and sqtl.seeker.p outputs with the significant sQTLs.
Diego Garrido-Martín
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