aggregateBioVar

aggregateBioVar is a statistical model and a software tool for single-cell RNA-sequencing (scRNA-seq) data analysis. The motivation behind this tool is to address the challenge of modeling different layers of biological variation in scRNA-seq studies, mainly when collecting data from multiple biological replicates. The tool proposes a statistical model for scRNA-seq gene counts and a simple method for estimating model parameters. Through a simulation study and comparisons with various differential expression testing methods on scRNA-seq datasets, the authors demonstrate that failing to account for additional biological variation in scRNA-seq studies can lead to inflated false discovery rates (FDRs) in statistical tests.

Topic

RNA-Seq;Gene expression;Cell biology;Gene transcripts;Microarray experiment

Detail

  • Operation: Differential gene expression profiling;Aggregation;Expression profile comparison;Genotyping

  • Software interface: Library

  • Language: R

  • License: The GNU General Public License v3.0

  • Cost: Free

  • Version name: 1.12.0

  • Credit: The National Institutes of Health, the Parker B. Francis Fellowship Program, the Cystic Fibrosis Foundation University of Iowa Research Development Program (Bioinformatics Core), a Pilot Grant from the University of Iowa Center for Gene Therapy, a Pilot Grant from the University of Iowa Environmental Health Sciences Research Center.

  • Input: -

  • Output: -

  • Contact: Jason Ratcliff jason-ratcliff@uiowa.edu

  • Collection: -

  • Maturity: Stable

Publications

  • Differential gene expression analysis for multi-subject single-cell RNA-sequencing studies with aggregateBioVar.
  • Thurman AL, et al. Differential gene expression analysis for multi-subject single-cell RNA-sequencing studies with aggregateBioVar. Differential gene expression analysis for multi-subject single-cell RNA-sequencing studies with aggregateBioVar. 2021; 37:3243-3251. doi: 10.1093/bioinformatics/btab337
  • https://doi.org/10.1093/BIOINFORMATICS/BTAB337
  • PMID: 33970215
  • PMC: PMC8504643

Download and documentation

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