IKAP—Identifying K mAjor cell Population groups in single-cell RNA-sequencing analysis
Author(s) -
Yun-Ching Chen,
Abhilash Suresh,
Chingiz Underbayev,
Clare Sun,
Komudi Singh,
Fayaz Seifuddin,
Adrian Wiestner,
Mehdi Pirooznia
Publication year - 2019
Publication title -
gigascience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.947
H-Index - 54
ISSN - 2047-217X
DOI - 10.1093/gigascience/giz121
Subject(s) - cell , cluster analysis , biology , population , computational biology , cell type , bioinformatics , genetics , computer science , medicine , artificial intelligence , environmental health
In single-cell RNA-sequencing analysis, clustering cells into groups and differentiating cell groups by differentially expressed (DE) genes are 2 separate steps for investigating cell identity. However, the ability to differentiate between cell groups could be affected by clustering. This interdependency often creates a bottleneck in the analysis pipeline, requiring researchers to repeat these 2 steps multiple times by setting different clustering parameters to identify a set of cell groups that are more differentiated and biologically relevant.
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