The paper introduce RNA fingerprinting, an advanced statistical framework designed to map single-cell transcriptional profiles onto comprehensive reference dictionaries of known genetic, chemical, and immunological perturbations. By treating gene expression data with a two-stage process that isolates baseline variation and constructs denoised perturbation signatures, the method accurately infers causal regulators at both the individual cell and group levels. This approach successfully tackles complex biological challenges, including resolving combinatorial effects from dual-gene perturbations, identifying context-specific regulators under stress, and characterizing drug mechanisms across distinct cell types. Ultimately, the framework bridges the gap between observational data and causal mechanisms by organizing complex expression patterns into interpretable probabilistic outputs and credible sets.
References:
Grabski I N, Lee J, Blair J D, et al. Mapping transcriptional responses to cellular perturbations with RNA fingerprinting[J]. Cell, 2026.
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