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

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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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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