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CRISPR genome engineering and single-cell RNA sequencing have transformedbiological discovery. Single-cell CRISPR screens unite these two technologies,linking genetic perturbations in individual cells to changes in gene expressionand illuminating regulatory networks underlying diseases. Despite theirpromise, single-cell CRISPR screens present substantial statistical challenges.We demonstrate through theoretical and real data analyses that a standardmethod for estimation and inference in single-cell CRISPR screens --"thresholded regression" -- exhibits attenuation bias and a bias-variancetradeoff as a function of an intrinsic, challenging-to-select tuning parameter.To overcome these difficulties, we introduce GLM-EIV ("GLM-basederrors-in-variables"), a new method for single-cell CRISPR screen analysis.GLM-EIV extends the classical errors-in-variables model to responses and noisypredictors that are exponential family-distributed and potentially impacted bythe same set of confounding variables. We develop a computationalinfrastructure to deploy GLM-EIV across hundreds of processors on clouds (e.g.,Microsoft Azure) and high-performance clusters. Leveraging this infrastructure,we apply GLM-EIV to analyze two recent, large-scale, single-cell CRISPR screendatasets, yielding several novel insights.

Exponential family measurement error models for single-cell CRISPR screens