ReviewCell genomics2026
Causal effect estimation from trans-regulatory single-cell CRISPR screens.
Review in Cell genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in single-cell transcriptomics and CRISPR-based genome editing have enabled large-scale perturbation experiments with genome-wide expression readouts. Single-cell CRISPR screens offer the opportunity to move beyond correlation and estimate causal effects of genetic perturbations on gene expression at scale. These approaches promise to substantially deepen insights into cellular functions and disease mechanisms. However, interpreting statistical associations as causal effects requires additional assumptions beyond those needed for standard statistical analyses. In this minireview, we introduce key concepts and principles for causal effect estimation in trans-regulatory single-cell CRISPR studies. We describe a set of assumptions under which estimates from existing statistical methods admit a causal interpretation and provide a concise overview of these approaches. Finally, through an illustrative example, we demonstrate how violations of these assumptions can bias estimated effects.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.