DOI: 10.5061/dryad.63xsj3v0r
title: Data from: Chromosome-scale inference of hybrid speciation and admixture with convolutional neural networks

description: Inferring the frequency and mode of hybridization among closely related
 organisms is an important step for understanding the process of
 speciation and can help to uncover reticulated patterns of
 phylogeny more generally. Phylogenomic methods to test for the presence of
 hybridization come in many varieties and typically operate by
 leveraging expected patterns of genealogical discordance in the absence of
 hybridization. An important assumption made by these tests is that the
 data (genes or SNPs) are independent given the species tree. However, when
 the data are closely linked, it is especially important to consider their
 non-independence. Recently, deep learning techniques such as convolutional
 neural networks (CNNs) have been used to perform population genetic
 inferences with linked SNPs coded as binary images. Here we use CNNs for
 selecting among candidate hybridization scenarios using the tree topology
 (((P1,P2),P3),Out) and a matrix of pairwise nucleotide divergence
 (dXY) calculated in windows across the genome. Using coalescent
 simulations to train and independently test a neural network showed that
 our method, HyDe-CNN, was able to accurately perform model selection for
 hybridization scenarios across a wide-breath of parameter space. We then
 used HyDe-CNN to test models of admixture in
 Heliconius butterflies, as well as comparing it to a random
 forest classifier trained on introgression-based statistics. Given the
 flexibility of our approach, the dropping cost of long-read sequencing,
 and the continued improvement of CNN architectures, we anticipate that
 inferences of hybridization using deep learning methods like ours will
 help researchers to better understand patterns of admixture in their study
 organisms.
description: CSV Files for HyDe-CNN Tests
 [hyde-cnn_tests.tar.gz] -- This archive contains the the CSV
 files (there are 12, three for each model) with the results of testing the
 trained HyDe-CNN architecture using 10,000 additional simulated data sets
 for each of the four models at each of the branch scaling factors. Each
 CSV file has the parameters used to simulate each image with msprime, the
 predicted best model, the best model weight, and the summary statistics
 calculated for training the random forest classifier.
 Random Forest Classifier Results
 [RF_classifier_results.txt] -- Raw output of the random forest
 classifier trained on introgression-specific summary statistics.
 Trained Models for the HyDe-CNN Architecture
 [trained_models_hyde-cnn.tar.gz] -- This archive contains the
 trained models for all of the neural networks f the HyDe-orCNN
 architecture. Trained Models for the Flagel
 <em>et al</em>. Architecture
 [trained_models_flagel.tar.gz] -- This archive contains the
 trained models for all of the neural networks for the Flagel <em>et
 al</em>. architecture.
 hyde_cnn_*_data_*.npz Nine compressed numpy
 arrays with the input images split into training, validation, and testing
 sets. Each file has the data for all combinations of input type (min,
 mean, min+mean) and branch scaling in coalescent units (0.5, 1.0,
 2.0). HyDe-CNN Code Archive
 [hyde-cnn_code_archive.tar.gz] -- Archived versions of all Python
 and R scripts used to generate, process, and analyze data in the paper.
 All of these scripts are also available on GitHub. <em>Heliconius</em> Chromosome Five VCF and Recombination Map [heliconius_data.tar.gz] -- VCF file containing variants on chromosome five for Heliconius samples as well as the recombination map for simulating chromosome five. Trained Models for <em>Heliconius</em> [trained_models_heliconius.tar.gz] -- This archive contains the trained models for all of the neural networks f the HyDe-orCNN architecture. <em>Heliconius</em> Resampling Results [heliconius_res.tar.gz] -- CSV files with the predicted model weights for all 100 bootstrap replicates for the three different input types (min, mean, min+mean).  heliconius_*_data.npz Compressed arrays with the input images split into training, validation, and testing sets for the <em>Heliconius</em> example. Each file has the data for the different input types (min, mean, min+mean). <em>Heliconius</em> Code Archive [heliconius_code_archive.tar.gz] -- Contains the code for simulating data to train, validate, and test a CNN, as well as a Jupyter Notebook that was used to process the observed data from chromosome five. This code is also on GitHub.