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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")):


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Dreamlet analysis of single cell RNA-seq 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


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
System Requirements C++11
URL https://DiseaseNeurogenomics.github.io/dreamlet
Bug Reports https://github.com/DiseaseNeurogenomics/dreamlet/issues
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Suggests BiocStyle, knitr, pander, rmarkdown, muscat, ExperimentHub, RUnit, scater, scuttle
Linking To Rcpp, beachmat
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Source Package dreamlet_1.0.0.tar.gz
Windows Binary dreamlet_1.0.0.zip (64-bit only)
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/
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