DESCRIPTION

Package: TRADEtools
Type: Package
Title: Statistical Models for the Distribution of Differential Expression Effects
Version: 0.99.0
Authors@R: c(person("Ajay", "Nadig", email = "ajay.g.nadig@gmail.com",role = c("aut", "cre"),comment = c(ORCID = "0000-0003-1421-4475")))
Description: Single cell CRISPR screens such as Perturb-seq enable transcriptomic profiling of genetic perturbations at scale. However, the data produced by these screens are noisy, and many effects may go undetected. We introduce TRanscriptome-wide Analysis of Differential Expression (TRADE), a statistical model for the distribution of true differential expression effects that accounts for estimation error appropriately. TRADE estimates the “transcriptome-wide impact”, which quantifies the total effect of a perturbation across the transcriptome.  Analyzing multiple large Perturb-seq datasets, we show that many transcriptional effects remain undetected in standard analyses but emerge in aggregate using TRADE. A typical gene perturbation affects an estimated 45 genes, whereas a typical essential gene affects over 500. An advantage of our approach is its ability to compare the transcriptomic effects of genetic perturbations across contexts and dosages despite differences in power. We identify perturbations with cell type-dependent effects and find examples of perturbations where transcriptional responses are not only larger in magnitude, but also qualitatively different, as a function of dosage. Lastly, we expand our analysis to case/control comparison of gene expression for neuropsychiatric conditions, comparing differential expression effects across five diagnoses.  Our framework provides a robust approach to analyze differential expression effects across the whole transcriptome.
License: What license is it under?
Encoding: UTF-8
LazyData: true
Imports: ggplot2, ashr, mashr, doBy
biocViews: DifferentialExpression, Transcriptomics, StatisticalMethod, Software
Suggests: BiocStyle, knitr, rmarkdown
VignetteBuilder: knitr