Clustering at the cellular level and extraction of tumor cell clusters.

<p>(A) t-Distributed stochastic neighbor embedding (tSNE) plot of 1,133,514 cancer cells. The plot is colored according to the clusters determined by the leiden algorithm. (B) tSNE from A colored by <i>EPCAM</i> expression. (C) Extraction of clusters with high <i>EPCAM</i&...

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Bibliographic Details
Main Author:	Shunsuke A. Sakai (13789939) (author)
Other Authors:	Ryosuke Nomura (20746334) (author), Satoi Nagasawa (697597) (author), SungGi Chi (18386476) (author), Ayako Suzuki (380214) (author), Yutaka Suzuki (1379) (author), Mitsuho Imai (8906186) (author), Yoshiaki Nakamura (3981626) (author), Takayuki Yoshino (616028) (author), Shumpei Ishikawa (154679) (author), Katsuya Tsuchihara (392821) (author), Shun-Ichiro Kageyama (2986365) (author), Riu Yamashita (1612) (author)
Published:	2025
Subjects:	Biotechnology Developmental Biology Cancer Plant Biology Virology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified potentially reflect histology detect histological morphology observed tumor vascularization metastatic colorectal cancer xlink "> single skny ): extending identify microenvironment features detects spatial domains tumor microenvironment cancer progression breast cancer spatial extension spatial domains spatial domain trajectory estimation surrounding area stage progression skny facilitates skny algorithm skny ), divided based based toolkit 14 patients
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Clustering at the cellular level and extraction of tumor cell clusters.

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