Download | Introduction | Formatting the data | First example dataset: data from mouse Recombinant Inbred Strains (RIS) | Preparing the workspace | Running the eQTL model: | Computing the FDR Threshold for eQTL identification: | Classifying the eQTLs: | Visualizing the result: | Finding eQTL hotspots: | Example dataset 2: data from a mouse F2 cross
Package: iBMQ 1.53.1
Greg Imholte
iBMQ: integrated Bayesian Modeling of eQTL data
integrated Bayesian Modeling of eQTL data
Authors:
iBMQ_1.53.1.tar.gz
iBMQ_1.53.1.zip(r-4.7)iBMQ_1.53.1.zip(r-4.6)iBMQ_1.53.1.zip(r-4.5)
iBMQ_1.53.1.tar.gz(r-4.7-arm64)iBMQ_1.53.1.tar.gz(r-4.7-x86_64)iBMQ_1.53.1.tar.gz(r-4.6-arm64)iBMQ_1.53.1.tar.gz(r-4.6-x86_64)
iBMQ_1.53.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
iBMQ/json (API)
| # Install 'iBMQ' in R: |
| install.packages('iBMQ', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
Datasets:
- gene - Gene expression from whole eye tissue from n = 68 BXD RIS mice.
- genepos - Gene position data frame
- genotype.liver - A set of 290 SNPs from 60 F2 mice.
- map.liver - SNP position data frame
- phenotype.liver - Gene expression from liver tissue from n = 60 F2 mice.
- PPA.liver - A matrix with Posterior Probabilities of Association
- probe.liver - Gene position data frame
- snp - A set of 1700 SNP from 68 BXD RIS mice.
- snppos - SNP position data frame
On BioConductor:iBMQ-1.53.1(bioc 3.24)iBMQ-1.52.0(bioc 3.23)
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
microarraypreprocessinggeneexpressionsnpgslopenmp
Last updated from:29a65d30cc. Checks:1 ERROR, 7 NOTE, 2 OK, 4 FAIL. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| bioc-checks | ERROR | 151 | ||
| linux-devel-arm64 | NOTE | 186 | ||
| linux-devel-x86_64 | NOTE | 159 | ||
| source / vignettes | OK | 195 | ||
| linux-release-arm64 | NOTE | 158 | ||
| linux-release-x86_64 | NOTE | 189 | ||
| macos-release-arm64 | FAIL | 77 | ||
| macos-release-x86_64 | FAIL | 177 | ||
| macos-oldrel-arm64 | FAIL | 60 | ||
| macos-oldrel-x86_64 | FAIL | 269 | ||
| windows-devel | NOTE | 105 | ||
| windows-release | NOTE | 94 | ||
| windows-oldrel | NOTE | 92 | ||
| wasm-release | OK | 123 |
Exports:calculateThresholdeqtlClassifiereqtlFindereqtlMcmchotspotFinder
Dependencies:BiobaseBiocGenericsclicpp11farvergenericsggplot2gluegtableisobandlabelinglifecycleR6RColorBrewerrlangS7scalesvctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| iBMQ : An Integrated Hierarchical Bayesian Model for Multivariate eQTL Mapping | iBMQ-package iBMQ |
| Calculate PPA significance threshold leading to a desired false discovery rate | calculateThreshold |
| Classifying the eQTLs | eqtlClassifier |
| eqtlFinder | eqtlFinder |
| Bayesian Multiple eQTL mapping using MCMC | eqtlMcmc |
| Gene expression from whole eye tissue from n = 68 BXD RIS mice. | gene |
| Gene position data frame | genepos |
| A set of 290 SNPs from 60 F2 mice. | genotype.liver |
| hotspotFinder | hotspotFinder |
| SNP position data frame | map.liver |
| Gene expression from liver tissue from n = 60 F2 mice. | phenotype.liver |
| A matrix with Posterior Probabilities of Association | PPA.liver |
| Gene position data frame | probe.liver |
| A set of 1700 SNP from 68 BXD RIS mice. | snp |
| SNP position data frame | snppos |