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bioc
dreamlet:Scalable differential expression analysis of single cell transcriptomics datasets with complex study designs
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.
Maintained by Gabriel Hoffman. Last updated 7 days ago.
rnaseqgeneexpressiondifferentialexpressionbatcheffectqualitycontrolregressiongenesetenrichmentgeneregulationepigeneticsfunctionalgenomicstranscriptomicsnormalizationsinglecellpreprocessingsequencingimmunooncologysoftwarecpp
12 stars 8.14 score 128 scriptsbioc
multistateQTL:Toolkit for the analysis of multi-state QTL data
A collection of tools for doing various analyses of multi-state QTL data, with a focus on visualization and interpretation. The package 'multistateQTL' contains functions which can remove or impute missing data, identify significant associations, as well as categorise features into global, multi-state or unique. The analysis results are stored in a 'QTLExperiment' object, which is based on the 'SummarisedExperiment' framework.
Maintained by Amelia Dunstone. Last updated 13 days ago.
functionalgenomicsgeneexpressionsequencingvisualizationsnpsoftware
1 stars 4.81 score 9 scripts
hugheylab
limorhyde2:Quantify Rhythmicity and Differential Rhythmicity in Genomic Data
Fit linear models based on periodic splines, moderate model coefficients using multivariate adaptive shrinkage, then compute properties of the moderated curves.
Maintained by Jake Hughey. Last updated 1 years ago.
4.78 score 2 scripts