ArticleNature communications2021
Single-cell normalization and association testing unifying CRISPR screen and gene co-expression analyses with Normalisr.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genetic landscape and functional exploration of kidney cancer predisposition in cross-ancestral populations.Nature communications · 2026Pooled it
- Spatiotemporal transcriptomics for tumour microenvironment remodelling in cancer progression and therapy.Clinical and translational medicine · 2026Review
- Single-Cell CRISPR: An Efficient Strategy for Decoding Plant Cis-Regulatory Complexity.Plant biotechnology journal · 2026Review
- Causal effect estimation from trans-regulatory single-cell CRISPR screens.Cell genomics · 2026Review
- A unified framework for selecting and evaluating cell-type-specific gene co-expressions in single-cell data.Briefings in bioinformatics · 2026Article
- Harnessing induced pluripotent stem cells and organoids for disease modeling and precision medicine.Stem cell research & therapy · 2026Review
- Airqtl dissects cell state-specific causal gene regulatory networks with efficient single-cell eQTL mapping.Nature communications · 2025Article
- Endogenous fine-mapping and prioritization of functional regulatory elements in complex genetic loci.Cell genomics · 2025Article
- HNF1A and A1CF coordinate a beta cell transcription-splicing axis that is disrupted in type 2 diabetes.Cell metabolism · 2025Article
- Accelerating crop improvement via integration of transcriptome-based network biology and genome editing.Planta · 2025Review
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Exponential family measurement error models for single-cell CRISPR screens.Biostatistics (Oxford, England) · 2024Article
- Robust differential expression testing for single-cell CRISPR screens at low multiplicity of infection.Genome biology · 2024Article
- Next-generation forward genetic screens: uniting high-throughput perturbations with single-cell analysis.Trends in genetics : TIG · 2024Review
- RENGE infers gene regulatory networks using time-series single-cell RNA-seq data with CRISPR perturbations.Communications biology · 2023Article
- A new Bayesian factor analysis method improves detection of genes and biological processes affected by perturbations in single-cell CRISPR screening.Nature methods · 2023Article
- Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multiomics.Nature methods · 2023Article
- Cell-type-specific co-expression inference from single cell RNA-sequencing data.Nature communications · 2023Article
- Comparison of transformations for single-cell RNA-seq data.Nature methods · 2023Article
- Massively Parallel CRISPR-Based Genetic Perturbation Screening at Single-Cell Resolution.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Review
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Abstract
Single-cell RNA sequencing (scRNA-seq) provides unprecedented technical and statistical potential to study gene regulation but is subject to technical variations and sparsity. Furthermore, statistical association testing remains difficult for scRNA-seq. Here we present Normalisr, a normalization and statistical association testing framework that unifies single-cell differential expression, co-expression, and CRISPR screen analyses with linear models. By systematically detecting and removing nonlinear confounders arising from library size at mean and variance levels, Normalisr achieves high sensitivity, specificity, speed, and generalizability across multiple scRNA-seq protocols and experimental conditions with unbiased p-value estimation. The superior scalability allows us to reconstruct robust gene regulatory networks from trans-effects of guide RNAs in large-scale single cell CRISPRi screens. On conventional scRNA-seq, Normalisr recovers gene-level co-expression networks that recapitulated known gene functions.
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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.