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MICSQTL

MICSQTL (Multi-omic deconvolution, Integration and Cell-type-specific Quantitative Trait Loci)


Bioconductor version: Release (3.18)

Our pipeline, MICSQTL, utilizes scRNA-seq reference and bulk transcriptomes to estimate cellular composition in the matched bulk proteomes. The expression of genes and proteins at either bulk level or cell type level can be integrated by Angle-based Joint and Individual Variation Explained (AJIVE) framework. Meanwhile, MICSQTL can perform cell-type-specic quantitative trait loci (QTL) mapping to proteins or transcripts based on the input of bulk expression data and the estimated cellular composition per molecule type, without the need for single cell sequencing. We use matched transcriptome-proteome from human brain frontal cortex tissue samples to demonstrate the input and output of our tool.

Author: Yue Pan [aut, cre] , Qian Li [aut], Iain Carmichael [ctb]

Maintainer: Yue Pan <ypan at stjude.org>

Citation (from within R, enter citation("MICSQTL")):

Installation

To install this package, start R (version "4.3") and enter:


if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("MICSQTL")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("MICSQTL")
MICSQTL: Multi-omic deconvolution, Integration and Cell-type-specific Quantitative Trait Loci HTML R Script
Reference Manual PDF
NEWS Text

Details

biocViews CellBasedAssays, Coverage, GeneExpression, Genetics, Proteomics, RNASeq, Sequencing, SingleCell, Software, Visualization
Version 1.0.0
In Bioconductor since BioC 3.18 (R-4.3) (< 6 months)
License GPL-3
Depends R (>= 4.3.0), SummarizedExperiment, stats
Imports TCA, nnls, purrr, TOAST, magrittr, BiocParallel, ggplot2, ggpubr, ggridges, glue, S4Vectors, dirmult
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Suggests testthat (>= 3.0.0), rmarkdown, knitr, BiocStyle
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Package Archives

Follow Installation instructions to use this package in your R session.

Source Package MICSQTL_1.0.0.tar.gz
Windows Binary MICSQTL_1.0.0.zip
macOS Binary (x86_64) MICSQTL_1.0.0.tgz
macOS Binary (arm64) MICSQTL_1.0.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/MICSQTL
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/MICSQTL
Bioc Package Browser https://code.bioconductor.org/browse/MICSQTL/
Package Short Url https://bioconductor.org/packages/MICSQTL/
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