Author

Mohammad Amin Honardoost

Agency for Science, Technology and Research

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· 3

Signature Assays, Organisms & Methods

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Organism:human· 3 Assay:scRNA-seq· 3 Approach:statistical model· 3 Research Focus:cell type annotation· 2 Research Focus:clustering· 2 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Research Focus:dimensionality reduction· 1

Frequent Co-Authors

Bobby RanjaniD· 3 shared Florian SchmidtiD· 3 shared Nirmala Arul Rayan· 3 shared Shyam PrabhakariD· 3 shared Ignasius JoanitoiD· 2 shared Jinyu Park· 2 shared Joanna Hui Juan TaniD· 2 shared Wenjie Sun· 2 shared

Activity by Year

2021-2021 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 43 mutual co-author links

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3 shared papersBioinformatics

Bobby RanjanORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

DUBStepR: correlation-based feature selection for clustering single-cell RNA sequencing data

Bobby Ranjan, Wenjie Sun, Jinyu Park, Kunal Mishra, Florian Schmidt, Ronald Xie, Fatemeh Alipour, Vipul Singhal, Ignasius Joanito, Mohammad Amin Honardoost, Jacy Mei Yun Yong, Ee Tzun Koh, Khai Pang Leong, Nirmala Arul Rayan, Michelle Gek Liang Lim, Shyam Prabhakar

DUBStepR improves single-cell clustering by selecting features using gene-gene correlations and a Density Index, outperforming existing methods and detecting rare cell populations.

Nature Communications · Oct 6, 2021Peer-reviewedBioinformatics

scConsensus: combining supervised and unsupervised clustering for cell type identification in single-cell RNA sequencing data

Bobby Ranjan, Florian Schmidt, Wenjie Sun, Jinyu Park, Mohammad Amin Honardoost, Joanna Hui Juan Tan, Nirmala Arul Rayan, Shyam Prabhakar

scConsensus combines supervised and unsupervised clustering to improve cell type identification in single-cell RNA sequencing data by integrating results and refining clusters with differentially expressed genes.

BMC Bioinformatics · Apr 12, 2021Peer-reviewedBioinformatics

Robust clustering and interpretation ofscRNA-seq data using reference componentanalysis

Florian Schmidt, Bobby Ranjan, Quy Xiao Xuan Lin, Vaidehi Krishnan, Ignasius Joanito, Mohammad Amin Honardoost, Zahid Nawaz, Prasanna Nori Venkatesh, Joanna Hui Juan Tan, Nirmala Arul Rayan, S. Tiong Ong, Shyam Prabhakar

The paper introduces RCA2, a supervised clustering method for scRNA-seq data that is robust to batch effects and supports large-scale analysis.

bioRxiv · Feb 16, 2021 · PreprintBioinformatics