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Related Experiment Video

Updated: Oct 30, 2025

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

PhyliCS: a Python library to explore scCNA data and quantify spatial tumor heterogeneity.

Marilisa Montemurro¹, Elena Grassi^2,3, Carmelo Gabriele Pizzino^2,3

¹Department of Control and Computer Science, Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129, Turin, Italy. marilisa.montemurro@polito.it.

BMC Bioinformatics

|July 4, 2021

Summary

This summary is machine-generated.

PhyliCS analyzes single-cell copy number aberrations to reveal spatial patterns within tumors. This tool helps determine if cancer cell clones are mixed or separated, advancing intra-tumor heterogeneity research.

Keywords:

Algorithms Cancer evolution Clones DNA Intra-tumor heterogeneity Single-cell sequencing

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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Published on: June 26, 2013

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Last Updated: Oct 30, 2025

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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Published on: June 26, 2013

Area of Science:

Oncology
Genomics
Bioinformatics

Background:

Tumors exhibit intra-tumor heterogeneity (ITH) due to distinct cancer cell subpopulations (subclones).
Copy Number Aberrations (CNAs) are key indicators of ITH.
Single-cell DNA (scDNA) sequencing offers high-resolution ITH analysis, but dedicated tools for spatial distribution are needed.

Purpose of the Study:

To introduce PhyliCS, the first tool for analyzing spatial heterogeneity in tumors using multi-sample single-cell CNA data.
To provide a method for distinguishing between spatially intermixed and segregated cancer cell clones.

Main Methods:

PhyliCS processes single-cell CNA profiles from multiple tumor samples.
It computes a Spatial Heterogeneity score to quantify clone distribution.
Includes feature selection, dimensionality reduction, visualization, and clustering for scDNA analysis.

Main Results:

PhyliCS enables the estimation of spatial distribution of cancer subclones within a tumor.
The tool quantifies spatial heterogeneity by differentiating between mixed and segregated clones.

Conclusions:

PhyliCS is a valuable tool for exploring spatial heterogeneity in multi-regional tumor samples.
It effectively leverages single-cell CNA data to understand tumor composition.