Integrate TWAS and Colocalization Analysis for Gene Set Enrichment Analysis


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Documentation for package ‘INTACT’ version 1.0.2

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.em_est Compute gene set enrichment estimates.
.enrich_bootstrap_se Compute bootstrap standard errors for alpha MLEs.
.enrich_res Compute gene set enrichment estimates with standard errors.
.logistic_em A fixed-point mapping for the expectation-maximization algorithm. Used as an argument for fixptfn in the squarem function.
.logistic_em_nopseudo Similar to logistic_em(), but does not use pseudocounts to stablize the algorithm.
.logistic_loglik A log likelihood function for the expectation-maximization algorithm. Used as an argument for objfn in the squarem function.
.pi1_fun Estimate pi1 from TWAS scan z-scores.
expit Transform a gene colocalization probability (GLCP) to a prior to be used in the evidence integration procedure. There are four prior function options, including expit, linear, step, and expit-linear hybrid.
fdr_rst Bayesian FDR control for INTACT output
gene_set_list Simulated gene set list.
hybrid Transform a gene colocalization probability (GLCP) to a prior to be used in the evidence integration procedure. There are four prior function options, including expit, linear, step, and expit-linear hybrid.
intact Compute the posterior probability that a gene may be causal, given a gene's TWAS scan z-score (or Bayes factor) and colocalization probability.
intactGSE Perform gene set enrichment estimation and inference, given TWAS scan z-scores and colocalization probabilities.
linear Transform a gene colocalization probability (GLCP) to a prior to be used in the evidence integration procedure. There are four prior function options, including expit, linear, step, and expit-linear hybrid.
simdat Simulated TWAS and colocalization summary data.
step Transform a gene colocalization probability (GLCP) to a prior to be used in the evidence integration procedure. There are four prior function options, including expit, linear, step, and expit-linear hybrid.