NDLI: SCITUNA: single-cell data integration tool using network alignment

Content Provider	Springer Nature : BioMed Central
Author	Houdjedj, Aissa Marouf, Yacine Myradov, Mekan Doğan, Süleyman Onur Erten, Burak Onur Tastan, Oznur Erten, Cesim Kazan, Hilal
Abstract	Background As single-cell genomics experiments increase in complexity and scale, the need to integrate multiple datasets has grown. Such integration enhances cellular feature identification by leveraging larger data volumes. However, batch effects-technical variations arising from differences in labs, times, or protocols-pose a significant challenge. Despite numerous proposed batch correction methods, many still have limitations, such as outputting only dimension-reduced data, relying on computationally intensive models, or resulting in overcorrection for batches with diverse cell type composition. Results We introduce a novel method for batch effect correction named SCITUNA, a Single-Cell data Integration Tool Using Network Alignment. We perform evaluations on 39 individual batches from four real datasets and a simulated dataset, which include both scRNA-seq and scATAC-seq datasets, spanning multiple organisms and tissues. A thorough comparison of existing batch correction methods using 13 metrics reveals that SCITUNA outperforms current approaches and is successful at preserving biological signals present in the original data. In particular, SCITUNA shows a better performance than the current methods in all the comparisons except for the multiple batch integration of the lung dataset where the difference is 0.004. Conclusion SCITUNA effectively removes batch effects while retaining the biological signals present in the data. Our extensive experiments reveal that SCITUNA will be a valuable tool for diverse integration tasks.
Related Links	https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-025-06087-3.pdf
Ending Page	24
Page Count	24
Starting Page	1
File Format	HTM / HTML
ISSN	14712105
DOI	10.1186/s12859-025-06087-3
Journal	BMC Bioinformatics
Issue Number	1
Volume Number	26
Language	English
Publisher	BioMed Central
Publisher Date	2025-03-27
Access Restriction	Open
Subject Keyword	Bioinformatics Microarrays Computational Biology Computer Appl. in Life Sciences Algorithms Single-cell data integration Batch effect Rare cell types Iterative correction Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biochemistry Computer Science Applications Applied Mathematics Structural Biology
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	3.6/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

SSBER: removing batch effect for single-cell RNA sequencing data

Analysis of single-cell RNA sequencing data based on autoencoders

Correction: Single-cell multi-omics integration for unpaired data by a siamese network with graph-based contrastive loss

Systems biology intertwines with single cell and AI

scInterpreter: a knowledge-regularized generative model for interpretably integrating scRNA-seq data

DNAAlignEditor: DNA alignment editor tool

A flexible network-based imputing-and-fusing approach towards the identification of cell types from single-cell RNA-seq data

Influence of batch effect correction methods on drug induced differential gene expression profiles

Orthogonal multimodality integration and clustering in single-cell data

SCITUNA: single-cell data integration tool using network alignment

Similar Documents

SSBER: removing batch effect for single-cell RNA sequencing data

Analysis of single-cell RNA sequencing data based on autoencoders

Correction: Single-cell multi-omics integration for unpaired data by a siamese network with graph-based contrastive loss

Systems biology intertwines with single cell and AI

scInterpreter: a knowledge-regularized generative model for interpretably integrating scRNA-seq data

DNAAlignEditor: DNA alignment editor tool

A flexible network-based imputing-and-fusing approach towards the identification of cell types from single-cell RNA-seq data

Influence of batch effect correction methods on drug induced differential gene expression profiles

Orthogonal multimodality integration and clustering in single-cell data

SCITUNA: single-cell data integration tool using network alignment