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

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.

Authors

Institutions

Publication Details

Journal
Briefings in Bioinformatics
Published
2025-05-01
DOI
https://doi.org/10.1093/bib/bbaf253
Citations
3
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
2.42

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Pankaj Yadav, Rajveer Singh Shekhawat, Asish Kumar Swain
3 citations
Briefings in Bioinformatics
Single-cell and spatial transcriptomics
2.42
article

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

Pankaj Yadav, Rajveer Singh Shekhawat, Asish Kumar Swain
article en
3 citations

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.

Briefings in BioinformaticsVol. 26(3)
Indian Institute of Technology Jodhpur (IN)
Ministry of Science and Technology, Ministry of Education, India
Life below water
Openalex Percentile: Top 11%
Single-cell and spatial transcriptomics
2.42
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.