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dreamlet

This is the development version of dreamlet; for the stable release version, see dreamlet.

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


Bioconductor version: Development (3.19)

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")

# The following initializes usage of Bioc devel
BiocManager::install(version='devel')

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

Details

biocViews BatchEffect, DifferentialExpression, Epigenetics, FunctionalGenomics, GeneExpression, GeneRegulation, GeneSetEnrichment, ImmunoOncology, Normalization, Preprocessing, QualityControl, RNASeq, Regression, Sequencing, SingleCell, Software, Transcriptomics
Version 1.1.18
In Bioconductor since BioC 3.18 (R-4.3) (< 6 months)
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, 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
See More
Suggests BiocStyle, knitr, pander, rmarkdown, muscat, ExperimentHub, RUnit, 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_1.1.18.tar.gz
Windows Binary dreamlet_1.1.18.zip
macOS Binary (x86_64) dreamlet_1.1.18.tgz
macOS Binary (arm64)
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