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Data preprocessing methods for selective sweep detection using convolutional neural networks.

Hanqing Zhao¹, Nikolaos Alachiotis¹

¹University of Twente, Drienerlolaan 5, Enschede, 7522 NB, Overijssel, the Netherlands.

Methods (San Diego, Calif.)

|November 16, 2024

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New data rearrangement algorithms improve Convolutional Neural Networks (CNNs) for detecting positive selection in genomic data. Sorting genomic data columns boosts CNN performance, even with complex population histories.

Area of Science:

Genomics
Computational Biology
Population Genetics

Background:

Identifying positive selection is crucial for understanding evolution.
Convolutional Neural Networks (CNNs) show promise in detecting selective sweeps, outperforming traditional methods.
Preprocessing genomic data by rearranging image pixels is common but its effectiveness, especially with confounding factors, is understudied.

Purpose of the Study:

To introduce novel pixel rearrangement algorithms for enhancing CNN-based selective sweep detection.
To evaluate the performance of these algorithms across various simulated demographic scenarios.
To compare the efficacy of different rearrangement strategies and default preprocessing methods.

Main Methods:

Development and application of a suite of pixel rearrangement algorithms for genomic data matrices.

Keywords:

Convolutional neural networks Data preprocessing Population genetics Selective sweep

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Assessment of four distinct CNN models for selective sweep detection using rearranged and default data.

Simulation of datasets incorporating confounding factors such as population bottlenecks, migration, and recombination hotspots.

Main Results:

Judicious application of rearrangement algorithms significantly enhanced CNN classification accuracy for selective sweep detection.
Sorting columns within genomic matrices yielded superior CNN performance compared to sequence rearrangement.
Rearrangement algorithms demonstrated greater robustness to demographic model misspecification than default preprocessing.

Conclusions:

Data rearrangement techniques offer a valuable preprocessing step for improving CNN performance in detecting positive selection.
Column sorting is a particularly effective rearrangement strategy.
These methods provide a more robust approach to selective sweep detection, especially in complex population genetic settings.