Evidence map›Paper›PMID 40471991›Full record

ArticleBriefings in bioinformatics2025

ScInfeR: an efficient method for annotating cell types and sub-types in single-cell RNA-seq, ATAC-seq, and spatial omics.

Asish Kumar Swain, Rajveer Singh Shekhawat, Pankaj Yadav

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Article in Briefings in bioinformatics, 2025. 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 3 papers.

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0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Asish Kumar SwainDepartment of Bioscience and Bioengineering, Indian Institute of Technology (IIT), N.H. 62, Nagaur Road, Karwar, Jodhpur 342030, Rajasthan, India.
Rajveer Singh ShekhawatDepartment of Bioscience and Bioengineering, Indian Institute of Technology (IIT), N.H. 62, Nagaur Road, Karwar, Jodhpur 342030, Rajasthan, India.
Pankaj YadavDepartment of Bioscience and Bioengineering, Indian Institute of Technology (IIT), N.H. 62, Nagaur Road, Karwar, Jodhpur 342030, Rajasthan, India.ORCID 0000-0001-7160-9209

Funding

Ministry of Science and Technology BT/GenomeIndia/2018
6 · The paper itself

Abstract

Cell-type annotation remains a major challenge in single-cell and spatial omics analysis. Most existing methods rely on single-cell RNA sequencing (scRNA-seq) references or predefined marker sets. However, the scarcity of high-quality scRNA-seq references and marker sets makes relying on a single approach prone to bias and limits usability. Furthermore, available methods for cell-type annotation in single-cell ATAC-sequencing (scATAC-seq) and spatial transcriptomics datasets perform poorly. Here, we present ScInfeR, a graph-based cell-type annotation method that combines information from both scRNA-seq references and marker sets. By integrating these two data sources, ScInfeR can accurately annotate broad range of cell-types. It employs a hierarchical framework inspired by message-passing layers in graph neural networks to accurately identify cell subtypes. ScInfeR is highly versatile, supporting cell annotation across scRNA-seq, scATAC-seq, and spatial omics datasets. For scATAC-seq, it effectively utilizes chromatin accessibility data, while for spatial transcriptomics, it incorporates spatial coordinate information. Additionally, ScInfeR supports weighted positive and negative markers, allowing users to define marker importance in cell-type classification. Our extensive benchmarking across multiple atlas-scale scRNA-seq, scATAC-seq, and spatial datasets, evaluating 10 existing tools in over 100 cell-type prediction tasks, demonstrated ScInfeR's superior performance. Noteworthy, it exhibits robustness against batch effects arising in these datasets. To facilitate seamless annotation, we developed ScInfeRDB, an interactive database containing manually curated scRNA-seq references and marker sets for 329 cell-types, covering 2497 gene markers in 28 tissue types from human and plant. ScInfeR is available as an R package, with both the tool and database publicly accessible at https://www.swainasish.in/scinfer.

Indexed as

Chromatin Immunoprecipitation SequencingMolecular Sequence AnnotationRNA-SeqSequence Analysis, RNASingle-Cell AnalysisSoftwareComputational BiologyGene Expression ProfilingHumansSingle-Cell Gene Expression AnalysisTranscriptomecell type annotationscATAC-seqscRNA-seqspatial transcriptomics

Identifiers

PMID40471991
PMCPMC12140018

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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.