  CRISPR genome engineering and single-cell RNA sequencing have accelerated
biological discovery. Single-cell CRISPR screens unite these two technologies,
linking genetic perturbations in individual cells to changes in gene expression
and illuminating regulatory networks underlying diseases. Despite their
promise, single-cell CRISPR screens present substantial statistical challenges.
We demonstrate through theoretical and real data analyses that a standard
method for estimation and inference in single-cell CRISPR screens --
"thresholded regression" -- exhibits attenuation bias and a bias-variance
tradeoff as a function of an intrinsic, challenging-to-select tuning parameter.
To overcome these difficulties, we introduce GLM-EIV ("GLM-based
errors-in-variables"), a new method for single-cell CRISPR screen analysis.
GLM-EIV extends the classical errors-in-variables model to responses and noisy
predictors that are exponential family-distributed and potentially impacted by
the same set of confounding variables. We develop a computational
infrastructure 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 screen
datasets, yielding several novel insights.
