Identification of Relevant Genetic Alterations in Cancer using Topological Data Analysis

Rabadán, Raúl; Mohamedi, Yamina; Rubin, Udi; Chu, Tim; Elliott, Oliver; Arnés, Luis; Cal, Santiago; Obaya, Álvaro J.; Levine, Arnold J.; Cámara, Pablo G.

doi:10.1101/2020.01.30.922310

Cited by 7 publications

(8 citation statements)

References 51 publications

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“…Topological data analysis has been applied to complex, high dimensionality biological datasets including gene expression profiles correlated with human cancers and other diseases (5,43,44). To our knowledge, TDA has not been used for plant science datasets outside of shape (45)(46)(47).…”

Section: Topological Shape Reflects the Underlying Biological Feature...mentioning

confidence: 99%

The topological shape of gene expression across the evolution of flowering plants

Palande

Kaste

Roberts

et al. 2022

Preprint

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Since they emerged ~125 million years ago, flowering plants have evolved to dominate the terrestrial landscape and survive in the most inhospitable environments on earth. At their core, these adaptations have been shaped by changes in numerous, interconnected pathways and genes that collectively give rise to emergent biological phenomena. Linking gene expression to morphological outcomes remains a grand challenge in biology, and new approaches are needed to begin to address this gap. Here, we implemented topological data analysis (TDA) to summarize the high dimensionality and noisiness of gene expression data using lens functions that delineate plant tissue and stress responses. Using this framework, we created a topological representation of the shape of gene expression across plant evolution, development, and environment for the phylogenetically diverse flowering plants. The TDA-based Mapper graphs form a well-defined gradient of tissues from leaves to seeds, or from healthy to stressed samples, depending on the lens function. This suggests there are distinct and conserved expression patterns across angiosperms that delineate different tissue types or responses to biotic and abiotic stresses. Genes that correlate with the tissue lens function are enriched in central processes such as photosynthetic, growth and development, housekeeping, or stress responses. Together, our results highlight the power of TDA for analyzing complex biological data and reveal a core expression backbone that defines plant form and function.

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Section: Topological Shape Reflects the Underlying Biological Feature...mentioning

confidence: 99%

The topological shape of gene expression across the evolution of flowering plants

Palande

Kaste

Roberts

et al. 2022

Preprint

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“…We can use TDA to analyze the interactions between genes and identify essential biomarkers. Network nodes are ranked by scoring methods that reflect varying network features 8,9 . The high-scoring nodes are selected as the most essential genes in the network and further analyzed as potential markers.…”

Section: Mainmentioning

confidence: 99%

A Novel Approach to Topological Network Analysis for the Identification of Metrics and Signatures in Non-Small Cell Lung Cancer

Wang

2022

Preprint

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Non-small cell lung cancer (NSCLC), the primary histological form of lung cancer, accounts for about 25% - the highest - of all cancer deaths. As NSCLC is often undetected until symptoms appear in the late stages, it is imperative to discover more effective tumor-associated biomarkers for early diagnosis. Topological data analysis is one of the most powerful methodologies applicable to biological networks. However, current studies fail to consider the biological significance of their quantitative methods and utilize popular scoring metrics without verification, leading to low performance. To extract meaningful insights from genomic data, it is essential to understand the relationship between geometric correlations and biological function mechanisms. Through bioinformatics and network analyses, we propose a novel composite selection index, the C-Index, that best captures significant pathways and interactions in gene networks to identify biomarkers with the highest efficiency and accuracy. Furthermore, we establish a 4-gene biomarker signature that serves as a promising therapeutic target for NSCLC and personalized medicine. We designed a Cascading machine learning model to validate both the C-Index and the biomarkers discovered. The methodology proposed for finding top metrics can be applied to effectively select biomarkers and early diagnose many diseases, revolutionizing the approach to topological network research for all cancers.

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“…21 TDA has been extensively used in biomedical research. [22][23][24][25] This is due to the fact that we now have massive amounts of data, but our ability to analyze and visualize the data still lags behind. By using TDA methods, researches have found a way to understand the structures and the underlying shapes of high-dimensional datasets.…”

Section: Tda and The Mapper Algorithmmentioning

confidence: 99%

Using Topological Data Analysis and RRT to Investigate Protein Conformational Spaces

Afrasiabi

Dehghanpoor

Haspel

2021

Preprint

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Investigating the conformational space of proteins is essential in order to associate their structures with their fundamental functions. Nonetheless, it is a challenging task, both experimentally and computationally. Because of the transient nature of these conformational changes and the fact that they are impermanent, empirical methods have fallen short to capture them. In silico methods, on the other hand, have shown great promise in exploring these conformational pathways. In this article, we provide an extensive evaluation of our previously introduced, robotics inspired conformational search algorithm (RRT* with Monte Carlo). We then identify what intermediate conformations appear the most in our generated conformational pathways using TDA Mapper, a topological data analysis algorithm, and examine how close these intermediate conformations are to existing experimental data.

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Identification of Relevant Genetic Alterations in Cancer using Topological Data Analysis

Cited by 7 publications

References 51 publications

The topological shape of gene expression across the evolution of flowering plants

The topological shape of gene expression across the evolution of flowering plants

A Novel Approach to Topological Network Analysis for the Identification of Metrics and Signatures in Non-Small Cell Lung Cancer

Using Topological Data Analysis and RRT to Investigate Protein Conformational Spaces

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