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Dissecting gene expression heterogeneity: generalized Pearson correlation squares and the K-lines clustering

Jingyi Jessica Li¹, Heather J Zhou¹, Peter J Bickel²

¹Department of Statistics, University of California, Los Angeles.

Journal of the American Statistical Association

|December 19, 2024

Summary

This summary is machine-generated.

This study introduces generalized Pearson correlation squares to analyze complex linear relationships in gene expression data. The new method, including a K-lines clustering algorithm, effectively dissects heterogeneous data patterns.

Keywords:

asymptotic distribution mixture of linear dependences specified and unspecified generalized Pearson correlation squares

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Area of Science:

Statistics
Bioinformatics
Computational Biology

Background:

Gene expression data often exhibits complex, heterogeneous relationships.
Existing correlation measures may not fully capture mixtures of linear dependencies.

Purpose of the Study:

To generalize the squared Pearson correlation for analyzing mixtures of linear dependencies.
To develop a robust method for dissecting heterogeneous relationships in gene expression data.
To enable data-adaptive clustering and efficient statistical inference.

Main Methods:

Generalization of the squared Pearson correlation to capture mixtures of linear dependences.
Development of a K-lines clustering algorithm for data-adaptive identification of distinct linear patterns.
Derivation of asymptotic distributions for population-level parameter inference.

Main Results:

The generalized Pearson correlation squares effectively capture mixtures of linear dependences.
The K-lines clustering algorithm successfully identifies distinct linear patterns in data.
Simulation studies and gene expression data analysis confirm the method's effectiveness and power advantage.

Conclusions:

The generalized Pearson correlation squares provide a powerful tool for analyzing complex relationships in high-dimensional data.
The K-lines clustering algorithm facilitates the discovery of interpretable structures within heterogeneous datasets.
The R package gR2 implements these novel estimation and inference procedures.