Automated cytometric gating with human-level performance using bivariate segmentation

Recent advances in cytometry have enabled high-throughput data collection with multiple single-cell protein expression measurements. The significant biological and technical variance in cytometry has posed a formidable challenge during the gating process, especially for the initial pre-gates which d...

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Vydané v:	Nature communications Ročník 16; číslo 1; s. 1576 - 15
Hlavní autori:	Chen, Jiong, Ionita, Matei, Feng, Yanbo, Lu, Yinfeng, Orzechowski, Patryk, Garai, Sumita, Hassinger, Kenneth, Bao, Jingxuan, Wen, Junhao, Duong-Tran, Duy, Wagenaar, Joost, McKeague, Michelle L., Painter, Mark M., Mathew, Divij, Pattekar, Ajinkya, Meyer, Nuala J., Wherry, E. John, Greenplate, Allison R., Shen, Li
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Nature Publishing Group UK 12.02.2025 Nature Publishing Group Nature Portfolio
Predmet:	13 13/31 13/56 14 631/114/1305 631/114/1314 631/114/2397 631/250/2520 631/250/38 Algorithms Antibodies Automation Biological activity Bivariate analysis Classification Data collection Datasets Deep learning Flow cytometry Flow Cytometry - methods Gating Human performance Humanities and Social Sciences Humans Image processing Image Processing, Computer-Assisted - methods Image segmentation Immunology Medicine multidisciplinary Protein expression Proteins Science Science (multidisciplinary) Single-Cell Analysis - methods Single-cell protein Software Subpopulations
ISSN:	2041-1723, 2041-1723
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