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>

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