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Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

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Radiomic clustering using graph network techniques coupled with unbalanced optimal transport.

Jung Hun Oh¹, Aditya Apte¹, Harini Veeraraghavan¹

¹Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Computational and Structural Biotechnology Journal

|December 8, 2025

Summary

This study introduces a novel network model and clustering algorithm to identify patient subgroups from radiomic data in head and neck squamous cell carcinoma (HNSCC) and non-small cell lung cancer (NSCLC). The findings reveal distinct radiophenotypes linked to survival outcomes and tumor-immune interactions.

Keywords:

CIBERSORT Network analysis Optimal transport Radiomics Sample clustering

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Area of Science:

Oncology
Radiology
Bioinformatics
Machine Learning

Background:

High-dimensional radiomic datasets often require subgroup identification for effective machine learning application.
Limited dataset sizes pose challenges for traditional machine learning methods in cancer research.

Purpose of the Study:

To develop and validate a novel approach for identifying radiomic subgroups in cancer datasets.
To investigate the prognostic value and immune cell associations of identified radiophenotypes in head and neck squamous cell carcinoma (HNSCC) and non-small cell lung cancer (NSCLC).

Main Methods:

A regularized network model with an extended Bayesian information criterion was used to identify sub-networks.
A graph network-based k-means clustering algorithm with unbalanced optimal transport was developed for sample grouping.
Survival analysis (Kaplan-Meier) and CIBERSORT analysis were performed on identified subgroups using radiomic features from CT scans and RNA-Seq data.

Main Results:

The proposed method identified high- and low-risk groups in HNSCC, showing significant differences in progression-free survival (p=0.0202).
In NSCLC, distinct high- and low-risk groups were identified with significant differences in overall survival (p=0.0007).
Significant differences in immune cell abundance (neutrophils in HNSCC; resting dendritic cells and activated mast cells in NSCLC) were observed between risk groups.

Conclusions:

Radiomic characteristics can effectively identify radiophenotypes with distinct prognoses.
The identified radiophenotypes may be associated with varying tumor-immune interactions, offering potential for personalized cancer treatment strategies.