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Transforming, Genome Editing and Phenotyping the Nitrogen-fixing Tropical Cannabaceae Tree Parasponia andersonii

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Data-Driven Tree Transforms and Metrics.

Gal Mishne¹, Ronen Talmon¹, Israel Cohen¹

¹Viterbi Faculty of Electrical Engineering, Technion - Israel Institute of Technology, Haifa 32000, Israel.

IEEE Transactions on Signal and Information Processing Over Networks

|August 18, 2018

Summary

This summary is machine-generated.

This study introduces novel tree-based methods for organizing complex, high-dimensional data. These data-driven transforms and metrics improve the clustering of tumor samples in breast cancer gene expression analysis.

Keywords:

gene expression geometric analysis graph signal processing multiscale representations partition trees

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Area of Science:

Computational Biology
Data Science
Bioinformatics

Background:

High-dimensional data often lacks inherent structure or locality.
Traditional analysis methods fail when data is not on a regular grid or features are arbitrarily ordered.

Purpose of the Study:

To develop data-driven transforms and metrics for organizing complex datasets.
To generalize joint clustering of observations and features for non-disjoint groups.
To enable transfer learning and data integration across datasets.

Main Methods:

Proposed multiscale, data-driven transforms and metrics based on trees.
Utilized an iterative refinement procedure exploiting feature-observation co-dependencies.
Applied to breast cancer gene expression data for clustering tumor samples.

Main Results:

Successfully organized high-dimensional gene expression data.
Learned metrics on genes to cluster tumor samples into distinct subtypes.
Validated joint organization of genes and samples.

Conclusions:

Combining multiple gene expression cohorts improves tumor sample clustering.
The proposed tree-based approach effectively handles complex, high-dimensional biological data.
Enables robust data organization and analysis across different technologies.