ArticleGenome biology2022
scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data.
Article in Genome biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 20 papers.
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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.
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Who cites it
20 citing papers in PubMed.
- scMOG: A graph neural network method for regulatory relationship-preserving single-cell multi-omics integration.Quantitative biology (Beijing, China) · 2026Article
- Semi-supervised disentangled representation learning for single-cell RNA sequencing data.Briefings in bioinformatics · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Patches: A Representation Learning Framework for Decoding Shared and Condition-Specific Transcriptional Programs in Wound Healing.bioRxiv : the preprint server for biology · 2026Article
- Interpretable phenotype decoding from multicondition sequencing data with ALPINE.Genome research · 2025Article
- scRECL: representative ensembles with contrastive learning for scRNA-seq data clustering analysis.Briefings in bioinformatics · 2025Article
- Unveiling the influence of tumor and immune signatures on immune checkpoint therapy in advanced lung cancer.eLife · 2024Article
- scParser: sparse representation learning for scalable single-cell RNA sequencing data analysis.Genome biology · 2024Article
- Interface-guided phenotyping of coding variants in the transcription factor RUNX1.Cell reports · 2024Article
- Building and analyzing metacells in single-cell genomics data.Molecular systems biology · 2024Review
- scCompressSA: dual-channel self-attention based deep autoencoder model for single-cell clustering by compressing gene-gene interactions.BMC genomics · 2024Article
- Integration of scRNA-seq data by disentangled representation learning with condition domain adaptation.BMC bioinformatics · 2024Article
- INSIDER: Interpretable sparse matrix decomposition for RNA expression data analysis.PLoS genetics · 2024Article
- scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data.Nature communications · 2024Article
- Deep neural network learning biological condition information refines gene-expression-based cell subtypes.Briefings in bioinformatics · 2023Article
- Differential variability analysis of single-cell gene expression data.Briefings in bioinformatics · 2023Article
- scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection.Nature communications · 2023Article
- The scINSIGHT Package for Integrating Single-Cell RNA-Seq Data from Different Biological Conditions.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- Author Correction: scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data.Genome biology · 2022Article
- scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data.Genome biology · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
Abstract
The increasing number of scRNA-seq data emphasizes the need for integrative analysis to interpret similarities and differences between single-cell samples. Although different batch effect removal methods have been developed, none are suitable for heterogeneous single-cell samples coming from multiple biological conditions. We propose a method, scINSIGHT, to learn coordinated gene expression patterns that are common among, or specific to, different biological conditions, and identify cellular identities and processes across single-cell samples. We compare scINSIGHT with state-of-the-art methods using simulated and real data, which demonstrate its improved performance. Our results show the applicability of scINSIGHT in diverse biomedical and clinical problems.
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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.