dreamlet

DOI: 10.18129/B9.bioc.dreamlet  

Cohort-scale differential expression analysis of single cell data using linear (mixed) models

Bioconductor version: Release (3.18)

Recent advances in single cell/nucleus transcriptomic technology has enabled collection of cohort-scale datasets to study cell type specific gene expression differences associated disease state, stimulus, and genetic regulation. The scale of these data, complex study designs, and low read count per cell mean that characterizing cell type specific molecular mechanisms requires a user-frieldly, purpose-build analytical framework. We have developed the dreamlet package that applies a pseudobulk approach and fits a regression model for each gene and cell cluster to test differential expression across individuals associated with a trait of interest. Use of precision-weighted linear mixed models enables accounting for repeated measures study designs, high dimensional batch effects, and varying sequencing depth or observed cells per biosample.

Author: Gabriel Hoffman [aut, cre]

Maintainer: Gabriel Hoffman <gabriel.hoffman at mssm.edu>

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

Installation

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

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

BiocManager::install("dreamlet")

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("dreamlet")

 

HTML R Script Dreamlet analysis of single cell RNA-seq
HTML R Script Loading large-scale H5AD datasets
HTML mashr analysis following dreamlet
HTML R Script Modeling continuous cell-level covariates
HTML R Script Testing non-linear effects
PDF   Reference Manual
Text   NEWS

Details

biocViews BatchEffect, DifferentialExpression, Epigenetics, FunctionalGenomics, GeneExpression, GeneRegulation, GeneSetEnrichment, ImmunoOncology, Normalization, Preprocessing, QualityControl, RNASeq, Regression, Sequencing, SingleCell, Software, Transcriptomics
Version 1.0.0
In Bioconductor since BioC 3.18 (R-4.3) (< 6 months)
License Artistic-2.0
Depends R (>= 4.3.0), variancePartition(>= 1.31.18), ggplot2
Imports edgeR, SummarizedExperiment, SingleCellExperiment, DelayedMatrixStats, sparseMatrixStats, MatrixGenerics, Matrix, methods, purrr, GSEABase, data.table, zenith(>= 1.1.2), mashr (>= 0.2.52), ashr, dplyr, BiocParallel, S4Vectors, IRanges, limma, tidyr, BiocGenerics, DelayedArray, gtools, reshape2, ggrepel, scattermore, Rcpp, lme4 (>= 1.1-33), MASS, Rdpack, utils, stats
LinkingTo Rcpp, beachmat
Suggests BiocStyle, knitr, pander, rmarkdown, muscat, ExperimentHub, RUnit, scater, scuttle
SystemRequirements C++11
Enhances
URL https://DiseaseNeurogenomics.github.io/dreamlet
BugReports https://github.com/DiseaseNeurogenomics/dreamlet/issues
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report  

Package Archives

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

Source Package dreamlet_1.0.0.tar.gz
Windows Binary dreamlet_1.0.0.zip
macOS Binary (x86_64)
macOS Binary (arm64) dreamlet_1.0.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/dreamlet
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/dreamlet
Bioc Package Browser https://code.bioconductor.org/browse/dreamlet/
Package Short Url https://bioconductor.org/packages/dreamlet/
Package Downloads Report Download Stats
Old Source Packages for BioC 3.18 Source Archive

Documentation »

Bioconductor

R / CRAN packages and documentation

Support »

Please read the posting guide. Post questions about Bioconductor to one of the following locations: