dreamlet

Scalable differential expression analysis of single cell transcriptomics datasets with complex study designs

Bioconductor version: Release (3.20)

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 "4.4") 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")

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

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Details

biocViews	BatchEffect, DifferentialExpression, Epigenetics, FunctionalGenomics, GeneExpression, GeneRegulation, GeneSetEnrichment, ImmunoOncology, Normalization, Preprocessing, QualityControl, RNASeq, Regression, Sequencing, SingleCell, Software, Transcriptomics
Version	1.4.1
In Bioconductor since	BioC 3.18 (R-4.3) (1 year)
License	Artistic-2.0
Depends	R (>= 4.3.0), variancePartition(>= 1.33.11), SingleCellExperiment, ggplot2
Imports	edgeR, SummarizedExperiment, DelayedMatrixStats, sparseMatrixStats, MatrixGenerics, Matrix, methods, purrr, GSEABase, data.table, zenith(>= 1.1.2), mashr (>= 0.2.52), ashr, dplyr, BiocParallel, ggbeeswarm, S4Vectors, IRanges, irlba, limma, metafor, remaCor, broom, tidyr, rlang, BiocGenerics, S4Arrays, SparseArray, DelayedArray, gtools, reshape2, ggrepel, scattermore, Rcpp, lme4 (>= 1.1-33), MASS, Rdpack, utils, stats
System Requirements	C++11
URL	https://DiseaseNeurogenomics.github.io/dreamlet
Bug Reports	https://github.com/DiseaseNeurogenomics/dreamlet/issues

Suggests	BiocStyle, knitr, pander, rmarkdown, muscat, ExperimentHub, RUnit, muscData, scater, scuttle
Linking To	Rcpp, beachmat
Enhances
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report	Build Report

Package Archives

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

Source Package	dreamlet_1.4.1.tar.gz
Windows Binary (x86_64)	dreamlet_1.4.1.zip
macOS Binary (x86_64)	dreamlet_1.4.1.tgz
macOS Binary (arm64)	dreamlet_1.4.1.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