dreamlet
Scalable differential expression analysis of single cell transcriptomics datasets with complex study designs
Bioconductor version: Release (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("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 | ||
| NEWS | Text |
Details
| biocViews | BatchEffect, DifferentialExpression, Epigenetics, FunctionalGenomics, GeneExpression, GeneRegulation, GeneSetEnrichment, ImmunoOncology, Normalization, Preprocessing, QualityControl, RNASeq, Regression, Sequencing, SingleCell, Software, Transcriptomics |
| Version | 1.2.1 |
| 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, 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_1.2.1.tar.gz |
| Windows Binary (x86_64) | dreamlet_1.2.1.zip (64-bit only) |
| macOS Binary (x86_64) | dreamlet_1.2.1.tgz |
| macOS Binary (arm64) | dreamlet_1.2.1.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 |
| Old Source Packages for BioC 3.19 | Source Archive |