Clustering of High-Dimensional Human Gene Score Data for Target Cell Classification

S. Das; V. Madala; P. Majumder; N. S. Sinha

cs.LGOct 24, 2026

Clustering of High-Dimensional Human Gene Score Data for Target Cell Classification

Authors: S. Das, V. Madala, P. Majumder, N. S. Sinha

Organizations: School of Mathematics, Indian Institute of Science Education and Research Thiruvananthapuram, Kerala, India 695551 · School of Data Sciences, Indian Institute of Science Education and Research Thiruvananthapuram, Kerala, India 695551 · School of Biological Sciences, Indian Institute of Science Education and Research Thiruvananthapuram, Kerala, India 695551

Abstract

The goal is to investigate whether the total human gene-score dataset and the selected human kinase-gene dataset exhibit the same number of clusters when analysed using the same set of 769 features. Two datasets were considered: the complete gene score dataset and a subset containing selected kinase genes. Two missing-data strategies were investigated. In Case I, rows containing missing gene-score values were removed. In Case II, rows with more than 20% missing values were excluded and the other missing values were replaced by the corresponding column mean. For each case, the data were scaled and principal component analysis (PCA) was performed before clustering. Four clustering approaches were considered: K-Means, Hierarchical Clustering, Fuzzy C-Means, and Graph-based Clustering. Across both datasets and both missing-data treatments, the optimal number of clusters was found to be two. In the absence of an optimal number of clusters, the quality of clustering was assessed using the Silhouette score. Relatively high Silhouette scores were obtained in all cases.

Figures & tables

	Gene Score Data ( $16138\times 769$ )		Kinase Data ( $418\times 769$ )
Clustering Method	No. clusters	Silhouette	No. clusters	Silhouette
K-Means	2	0.770	2	0.724
Hierarchical	2	0.788	2	0.825
Fuzzy C-Means	2	0.768	2	0.818
Graph-based	2	0.633	2	0.817

Table 1: Results of various clustering algorithms under complete data analysis (Case I).

	Gene Score Data ( $18073\times 769$ )		Kinase Data ( $498\times 769$ )
Clustering Method	No. clusters	Silhouette	No. clusters	Silhouette
K-Means	2	0.773	2	0.805
Hierarchical	2	0.788	2	0.825
Fuzzy C-Means	2	0.771	2	0.807
Graph-based	2	0.633	2	0.795

Table 2: Results of various clustering algorithms under mean imputation (Case II).

Panel of Figure 1 — Figure 1: K-Means clustering for the mother dataset under complete data analysis: elbow method and silhouette/cluster visualization.

Panel of Figure 2 — Figure 2: K-Means clustering for the kinase dataset under complete data analysis.

Panel of Figure 3 — Figure 3: K-Means clustering for the mother dataset under Case II.

Panel of Figure 4 — Figure 4: K-Means clustering for the kinase dataset under Case II.

Panel of Figure 5 — Figure 5: Hierarchical clustering for the mother dataset under complete data analysis: dendrogram and clustered data.

Panel of Figure 6 — Figure 6: Hierarchical clustering for the kinase dataset under complete data analysis: dendrogram and clustered data.

Panel of Figure 7 — Figure 7: Hierarchical clustering for the mother dataset under Case II.

Panel of Figure 8 — Figure 8: Hierarchical clustering for the kinase dataset under Case II.

Panel of Figure 9 — Figure 9: Fuzzy C-Means clustering under complete data analysis for the mother and kinase datasets.

Panel of Figure 10 — Figure 10: Fuzzy C-Means clustering under Case II for the mother and kinase datasets.

Clustering

Clustering groups data points by shared structure, developing methods to discover, validate, and interpret groups across diverse data and constraints.

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Department of Statistics · Columbia University · New York, NY 10027, USA

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Clustering is widely used for exploratory analysis and scientific discovery, driving insights from market segmentation to biological data analysis, but its outputs can be difficult to interpret, audit, and reproduce as modern datasets become increasingly large and complex. Reliable use of clustering requires understanding which features drive the discovered structure, yet feature-level explanations for clustering remain scarce compared with methods in supervised learning. Furthermore, existing clustering feature importance scores are often tied to specific algorithms and data assumptions. To address these challenges, we propose Cluster LOCO (Leave-One-Covariate-Out), a family of model-agnostic feature importance scores for clustering. Cluster LOCO is built on feature occlusion and clustering generalizability, defined as whether cluster labels learned on one subset of the data can be accurately predicted on held-out samples. For any chosen clustering algorithm, Cluster LOCO quantifies a feature's importance by measuring how much its removal degrades generalizability. We first introduce Cluster LOCO-Split, which relies on data splitting, and then extend it to Cluster LOCO-MP, a minipatch ensemble-based version designed for large-scale data. Across synthetic simulations and an application to cell-type discovery in single-cell transcriptomics, we show that Cluster LOCO more reliably recovers informative features than existing clustering feature importance methods.

Abstract

Clustering

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