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>

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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
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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
URL https://DiseaseNeurogenomics.github.io/dreamlet
BugReports https://github.com/DiseaseNeurogenomics/dreamlet/issues
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