Package: multistateQTL 1.1.0

Amelia Dunstone

multistateQTL: Toolkit for the analysis of multi-state QTL data

A collection of tools for doing various analyses of multi-state QTL data, with a focus on visualization and interpretation. The package 'multistateQTL' contains functions which can remove or impute missing data, identify significant associations, as well as categorise features into global, multi-state or unique. The analysis results are stored in a 'QTLExperiment' object, which is based on the 'SummarisedExperiment' framework.

Authors:Christina Del Azodi [aut], Davis McCarthy [ctb], Amelia Dunstone [cre, ctb]

multistateQTL_1.1.0.tar.gz
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multistateQTL.pdf |multistateQTL.html✨
multistateQTL/json (API)
NEWS

# Install 'multistateQTL' in R:

install.packages('multistateQTL', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/dunstone-a/multistateqtl/issues

On BioConductor:multistateQTL-1.1.0(bioc 3.20)multistateQTL-1.0.0(bioc 3.19)

bioconductor-package

17 exports 1.00 score 107 dependencies

Last updated 2 months agofrom:c5015dd8f2

Exports:callSignificance callSignificance<-getComplete getSignificant getTopHits plotCompareStates plotPairwiseSharing plotQTLClusters plotSimulationParams plotUpSet qtleEstimate qtleParams qtleSimulate replaceNAs runPairwiseSharing runTestMetrics simPerformance

Dependencies:abind ashr askpass assertthat backports Biobase BiocGenerics bit bit64 checkmate circlize cli clue cluster codetools collapse colorspace ComplexHeatmap cpp11 crayon curl data.table DelayedArray digest doParallel dplyr etrunct fansi farver fitdistrplus foreach generics GenomeInfoDb GenomeInfoDbData GenomicRanges GetoptLong ggplot2 GlobalOptions glue gridExtra gtable hms httr invgamma IRanges irlba isoband iterators jsonlite labeling lattice lifecycle magrittr mashr MASS Matrix MatrixGenerics matrixStats mgcv mime mixsqp munsell mvtnorm nlme openssl pillar pkgconfig plyr png prettyunits progress purrr QTLExperiment R6 RColorBrewer Rcpp RcppArmadillo RcppGSL rjson rlang rmeta S4Arrays S4Vectors scales shape softImpute SparseArray SQUAREM stringi stringr SummarizedExperiment survival sys tibble tidyr tidyselect truncnorm tzdb UCSC.utils utf8 vctrs viridis viridisLite vroom withr XVector zlibbioc

multistateQTL: Orchestrating multi-state QTL analysis in R

Christina B Azodi, Davis McCarthy, Amelia Dunstone

Rendered frommultiStateQTL.Rmdusingknitr::rmarkdownon Jun 30 2024.

Last update: 2024-04-21
Started: 2022-11-09

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Call associations as significant in each state.	callSignificance callSignificance,QTLExperiment-method callSignificance<-
Filter QTLExperiment based on missing data	getComplete
Filter to only QTLs significant in at least one state	getSignificant
Filter QTLExperiment to keep only top hits	getTopHits
Compare QTL between two states	plotCompareStates
Heatmap of pairwise QTL sharing with state-level annotations	plotPairwiseSharing
Heatmap of QTL across states	plotQTLClusters
Distribution of estimated simulation parameters	plotSimulationParams
Upset plot of QTL sharing between states with state-level annotations	plotUpSet
Estimate parameters from real data for simulating multi-state QTL summary statistics	qtleEstimate
Default qtle simulation parameters	qtleParams
Create simulated multistateQTL data for testing purposes	qtleSimulate
Return multistateQTL object with NAs filled in	replaceNAs
Compute the proportion of (significant) QTL shared by pairs of conditions	runPairwiseSharing
Classify multi-state QTL	runTestMetrics
Performance metrics for multistateQTL simulations	simPerformance