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UMAP guided topological analysis of transcriptomic data for cancer subtyping

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Abstract

Clustering cancer patients into different homogenous subgroups can facilitate the development of subgroup specific therapies. This forms the fundamental principle in personalised medicine. However, the process is complex because of greater variation in the phenotypic and genotypic characteristics of patients involved, even within the same cancer type. Consequently, most of the proposed methods fail to guarantee separability of patients with regard to subtype-specific Kaplan–Meier survival plots. In this study, we propose a novel clustering framework for patient subtyping based on the ideas from algebraic topology and manifold learning. The proposed method is able to discover subtypes that have statistically significant dissimilarity in survival outcome. The methodology is tested on three cancer datasets obtained via The Cancer Genome Atlas and the results are quantified in terms of Restricted Life Expectancy Difference and the \(cox\) log-rank p value. The novelty of our methodology is that it is independent of the notion of similarity used and able to discover subtypes that have significant difference in terms of Kaplan–Meier survival plots even if it uses a single omics profile of patients.

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(Reprinted from https://www.nature.com/articles/srep01236)

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Abbreviations

TCGA:

The Cancer Genome Atlas

CoD:

Curse of Dimensionality

RLED:

Restricted Life Expectancy Difference

RMST:

Restricted Mean Survival Time

UMAP:

Uniform Manifold Approximation and Projection

t-SNE:

t-Distributed Stochastic Neighbourhood Embedding

SNF:

Similarity Network Fusion

RSC:

Robust and Sparse Correlation

PCA:

Principal Component Analysis

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Authors and Affiliations

  1. Department of Computer Sciences, University of Kashmir, Srinagar, JK, India

    Arif Ahmad Rather & Manzoor Ahmad Chachoo

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  1. Arif Ahmad Rather
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  2. Manzoor Ahmad Chachoo
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Correspondence to Arif Ahmad Rather or Manzoor Ahmad Chachoo.

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Rather, A.A., Chachoo, M.A. UMAP guided topological analysis of transcriptomic data for cancer subtyping. Int. j. inf. tecnol. 14, 2855–2865 (2022). https://doi.org/10.1007/s41870-022-01048-y

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