Author

Samina Kausar

InsermORCID 0000-0002-5207-7136Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Samina Kausar2 papers · Wellcome/MRC Cambridge Stem Cell Institute

Biology Disciplines

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Bioinformatics· 2 Developmental Biology· 1

Signature Assays, Organisms & Methods

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Approach:experimental assay· 2 Organism:human· 2 Assay:scRNA-seq· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:software / tool· 1

Frequent Co-Authors

Anaı̈s BaudotiD· 2 shared Muhammad AsifiD· 2 shared Alain ChédotaliD· 1 shared Alberto LombardiniiD· 1 shared André LamúriasiD· 1 shared Camille HumbertiD· 1 shared Chantal MissirianiD· 1 shared Christopher De BonoiD· 1 shared

Activity by Year

2022-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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2 shared papersDevelopmental Biology

Anaı̈s BaudotORCID

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3 papers

Recent·Most cited

Multi-modal refinement of the human heart atlas during the first gestational trimester

Christopher De Bono, Yichi Xu, Samina Kausar, Marine Herbane, Camille Humbert, Sevda Rafatov, Chantal Missirian, Mathias Moreno, Weiyang Shi, Yorick Gitton, Alberto Lombardini, Ivo Vanzetta, Séverine Mazaud‐Guittot, Alain Chédotal, Anaı̈s Baudot, , Heather Etchevers

The paper refines a human heart atlas using multi-modal data from the first trimester, identifying novel cell types and spatial features in cardiac development.

Development · Feb 10, 2025Peer-reviewedDevelopmental Biology

scRNAseq_KNIME workflow: A Customizable, Locally Executable, Interactive and Automated KNIME workflow for single-cell RNA seq

Samina Kausar, Muhammad Asif, Anaı̈s Baudot

The paper presents a KNIME workflow for scRNA-seq analysis that is customizable, interactive, and automated, enabling users without programming expertise to perform key analysis steps.

bioRxiv · Jan 14, 2023 · PreprintBioinformatics

DGH-GO: Dissecting the Genetic Heterogeneity of complex diseases using Gene Ontology

Muhammad Asif, Hugo Martiniano, André Lamúrias, Samina Kausar, Francisco M. Couto

DGH-GO is an application that uses Gene Ontology to cluster disease-causing genes and identify genetic heterogeneity in complex diseases like ASD and NDDs.

bioRxiv · Oct 20, 2022 · PreprintBioinformatics