Package: INTACT 1.13.0

Jeffrey Okamoto

INTACT: Integrate TWAS and Colocalization Analysis for Gene Set Enrichment Analysis

This package integrates colocalization probabilities from colocalization analysis with transcriptome-wide association study (TWAS) scan summary statistics to implicate genes that may be biologically relevant to a complex trait. The probabilistic framework implemented in this package constrains the TWAS scan z-score-based likelihood using a gene-level colocalization probability. Given gene set annotations, this package can estimate gene set enrichment using posterior probabilities from the TWAS-colocalization integration step.

Authors:Jeffrey Okamoto [aut, cre], Xiaoquan Wen [aut]

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manual.pdf |manual.html✨
card.svg |card.png
INTACT/json (API)
NEWS

# Install 'INTACT' in R:
install.packages('INTACT', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jokamoto97/intact/issues

Datasets:

On BioConductor:INTACT-1.13.0(bioc 3.24)INTACT-1.12.0(bioc 3.23)

bayesiangenesetenrichment

5.53 score 17 stars 20 scripts 277 downloads 9 exports 32 dependencies

Last updated from:05b3bdb6ec. Checks:1 WARNING, 7 NOTE, 2 OK. Indexed: yes.

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bioc-checksWARNING149
linux-devel-x86_64NOTE204
source / vignettesOK195
linux-release-x86_64NOTE158
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Exports:chisq_sumstatexpitfdr_rsthybridintactintactGSElinearmulti_intactstep

Dependencies:bdsmatrixclicpp11dplyrfarvergenericsggplot2gluegtableisobandlabelinglifecyclemagrittrnumDerivpillarpkgconfigpurrrR6RColorBrewerrlangS7scalesSQUAREMstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr

INTACT: Integrate TWAS and Colocalization Analysis for Gene Set Enrichment Analysis

Jeffrey Okamoto, Xiaoquan Wen

Rendered fromINTACT.Rmdusingknitr::rmarkdownon May 30 2026.

Last update: 2024-10-01
Started: 2022-12-13

Readme and manuals

Help Manual

Help pageTopics
Multi-INTACT EM algorithm fixed-point function..bf_em_coloc_pi0
Multi-INTACT EM algorithm log-likelihood function..bf_loglik_coloc
Helper function for EM algorithm to compute weighted sum of Bayes factors..bf_weighted_sum_coloc
Compute gene set enrichment estimates..em_est
Compute bootstrap standard errors for alpha MLEs..enrich_bootstrap_se
Compute gene set enrichment estimates with standard errors..enrich_res
A fixed-point mapping for the expectation-maximization algorithm. Used as an argument for fixptfn in the squarem function..logistic_em
Similar to logistic_em(), but does not use pseudocounts to stablize the algorithm..logistic_em_nopseudo
A log likelihood function for the expectation-maximization algorithm. Used as an argument for objfn in the squarem function..logistic_loglik
Compute gene product relevance probabilities using prior parameter estimates and Bayes factors..multi_em_posteriors
Compute Multi-INTACT prior parameter estimates and gene product relevance probabilities..multi_prior_estimation
Estimate pi1 from TWAS scan z-scores..pi1_fun
A function to compute log Bayes factors from z-statistics using the Wakefield formula.wakefield_bf_z_ln
Compute a gene-level multivariate Wald chi-square statistic using summary-level genetic association and LD data.chisq_sumstat
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.expit
TWAS weights for a simulated gene.exprwt_sumstats
Bayesian FDR control for INTACT outputfdr_rst
Simulated gene set list.gene_set_list
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.hybrid
Compute the posterior probability that a gene may be causal, given a gene's TWAS scan z-score (or Bayes factor) and colocalization probability.intact
Perform gene set enrichment estimation and inference, given TWAS scan z-scores and colocalization probabilities.intactGSE
LD correlation matrix from a simulated data set.ld_sumstats
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.linear
Compute Multi-INTACT prior parameter estimates and gene product relevance probabilities.multi_intact
Simulated TWAS, PWAS, and pairwise colocalization summary data.multi_simdat
PWAS weights for a simulated gene.protwt_sumstats
Simulated TWAS and colocalization summary data.simdat
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.step
TWAS and PWAS z-score for a simulated gene.z_sumstats