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In the context of multiple testing and discoveries, a popular approach is to use a common threshold leading to a desired false discovery rate (FDR). In the Bayesian paradigm, derivation of the PPA threshold is trivial and can be calculated using a direct posterior probability calculation as described in Newton et al. (2004).
● Data Source:
BioConductor
● Keywords:
● Alias: calculateThreshold
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It is customary to distinguish two kinds of eQTLs: 1) cis-eQTLs (where the eQTL is on the same locus as the expressed gene); and 2) trans-eQTLs (where the eQTL is on a locus other than that of the expressed gene). The eqtlClassifier allows us to classify the eQTLs as either cis-eQTL or trans-eQTL according to their position in the genome.
● Data Source:
BioConductor
● Keywords:
● Alias: eqtlClassifier
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We can calculate how many eQTLs have PPA above the cutoff with the eqtlFinder function.
● Data Source:
BioConductor
● Keywords:
● Alias: eqtlFinder
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Compute the MCMC algorithm to produce Posterior Probability of Association values for eQTL mapping.
● Data Source:
BioConductor
● Keywords:
● Alias: eqtlMcmc
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One main advantage of our method is its increased sensitivity for finding trans-eQTL hotspots (corresponding to situations where a single SNP is linked to the expression of several genes across the genome).
● Data Source:
BioConductor
● Keywords:
● Alias: hotspotFinder
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This method is designed to detect expression QTLs (eQTLs) by incorporating genotypic and gene expression data into a single model while 1) specifically coping with the high dimensionality of eQTL data (large number of genes), 2) borrowing strength from all gene expression data for the mapping procedures, and 3) controlling the number of false positives to a desirable level.
● Data Source:
BioConductor
● Keywords:
● Alias: iBMQ, iBMQ-package
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