Author

Smriti Chawla

Indraprastha Institute of Information Technology Delhi

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 5 Genomics· 1 Neuroscience· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 7 Assay:scRNA-seq· 5 Research Focus:cell type annotation· 4 Research Focus:clustering· 4 Organism:human· 4 Organism:mouse· 3 Research Focus:benchmarking· 2 Research Focus:differential expression· 2

Frequent Co-Authors

Debarka SenguptaiD· 5 shared Gaurav AhujaiD· 3 shared Angshul MajumdariD· 2 shared Ludwig GeistlingeriD· 2 shared N BhattacharyaiD· 2 shared Priyadarshini RaiiD· 2 shared Sarita Poonia· 2 shared Vibhor KumariD· 2 shared

Activity by Year

2020-2026 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 22 mutual co-author links

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5 shared papersBioinformatics

Debarka SenguptaORCID

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Author profile →

7 papers

Recent·Most cited

scDiagnostics: systematic assessment of cell type annotation in single-cell transcriptomics data

Anthony-Alexander Christidis, Andrew R. Ghazi, Smriti Chawla, Nitesh Turaga, Robert Gentleman, Ludwig Geistlinger

scDiagnostics assesses cell type annotations in single-cell transcriptomics data to detect misleading or conflicting labels that affect downstream analysis.

bioRxiv · Jul 14, 2026 · PreprintBioinformatics

Spatial and temporal profiling of the complement system uncovered novel functions of the alternative complement pathway in brain development

Yingying Zhang, Brianna R. Watson, Ajitanuj Rattan, Tyrone Lee, Smriti Chawla, Ludwig Geistlinger, Yilin Guan, Minghe Ma, Barbara J. Caldarone, Wen‐Chao Song, Jeffrey R. Moffitt, Michael C. Carroll

The paper uses spatial transcriptomics to study complement system development in the brain, revealing distinct roles of the classical and alternative pathways.

bioRxiv · Nov 22, 2023 · PreprintNeuroscience

Searching match for single-cell open-chromatin profiles in large pools of single-cell transcriptomes and epigenomes for reference supported analysis

Shreya V. Mishra, Neetesh Pandey, Smriti Chawla, Madhu Sharma, Omkar Chandra, Indra Prakash Jha, Debarka Sengupta, Kedar Nath Natarajan, Vibhor Kumar

scEpiSearch enables accurate matching of single-cell open-chromatin profiles to large reference datasets, improving co-embedding and classification of epigenomic states across species and platforms.

Genome Research · Feb 1, 2023Peer-reviewedBioinformatics

Marker-free characterization of single live circulating tumor cell full-length transcriptomes

Sarita Poonia, Anurag Goel, Smriti Chawla, N Bhattacharya, Priyadarshini Rai, Yi Fang Lee, Yoon Sim Yap, Jay A. A. West, Ali Asgar S. Bhagat, Juhi Tayal, Anurag Mehta, Gaurav Ahuja, Angshul Majumdar, Naveen Ramalingam, Debarka Sengupta

The paper introduces unCTC, an R package for unbiased identification and characterization of CTCs from scRNA-Seq data, validated on breast CTCs.

Genome Research · Jan 1, 2023Peer-reviewedBioinformatics

SCellBOW: AI-Driven Tumor Risk Stratification from Single-Cell Transcriptomics Using Phenotype Algebra

N Bhattacharya, Anja Rockstroh, Sanket Suhas Deshpande, Sam Koshy Thomas, Anunay Yadav, Chitrita Goswami, Smriti Chawla, Pierre Solomon, Cynthia Fourgeux, Gaurav Ahuja, Brett G. Hollier, Himanshu Kumar, Antoine Roquilly, Jérémie Poschmann, Melanie Lehman, , Debarka Sengupta

SCellBOW identifies and risk-stratifies tumor cell subpopulations using scRNA-seq data by simulating their impact on disease prognosis.

bioRxiv · Dec 28, 2022 · PreprintBioinformatics

A visual atlas of genes tissue-specific pathological roles

Priyadarshini Rai, Atishay Jain, Neha Jha, Divya Sharma, Shivani Kumar, Abhijit Raj, Apoorva Gupta, Sarita Poonia, Smriti Chawla, Angshul Majumdar, Tanmoy Chakraborty, Gaurav Ahuja, Debarka Sengupta

The paper constructs a visual atlas of gene-pathology relationships by mining PubMed abstracts and using NLP to identify tissue-specific gene roles in disease.

bioRxiv · Jan 8, 2022 · PreprintBioinformatics

UniPath: A uniform approach for pathway and gene-set based analysis of heterogeneity in single-cell epigenome and transcriptome profiles.

Smriti Chawla, Sudhagar Samydurai, Say Li Kong, Zhengwei Wu, Zhenxun Wang, Wai Leong Tam, Debarka Sengupta, Vibhor Kumar

UniPath uses pathway and gene-set enrichment scores to analyze single-cell epigenome and transcriptome data, revealing biologically meaningful co-clustering and aiding in cancer cell regulation inference.

Nucleic Acids Research · Nov 13, 2020Peer-reviewedGenomics