Transcriptome-wide characterization of genetic perturbations

Single cell CRISPR screens such as Perturb-seq enable transcriptomic profiling of cellular perturbations at scale. However, the data produced by these screens are inherently noisy, limiting power to detect true effects with conventional differential expression analyses. Here, we introduce TRanscriptome-wide Analysis of Differential Expression (TRADE), a statistical framework which estimates the transcriptome-wide distribution of true differential expression effects from noisy gene-level measurements. This repository contains data from two large-scale pooled CRISPR screens with single-cell RNA sequencing readout (ie Perturb-seq) experiments in Jurkat and HepG2 cells. Jurkat and HepG2 screens were conducted in parallel. Cells were transduced with a lentiviral library bearing dual sgRNA expression vectors targeting a library consisting primarily of DepMap common essential genes at low infection rate. Cells were selected for sgRNA expression vector by FACS on day 3 post-transduction. On day 7 post-transduction, cells were harvested for single-cell RNA-sequencing on the 10x Genomics Chromium Single-Cell 3′ Gel Beads v3.