Simulating Artificial Recombination for a Deep Convolutional Autoencoder

Population structure is an important field of study due to its importance in finding underlying genetics of various diseases.This is why this thesis has looked at a newly presented deep convolutional autoencoder that has been showing promising results when compared to the state-of-the-art method for quantifying genetic similarities within population structure. The main focus was to introduce data augmentation in the form of artificial diploid recombination to this autoencoder in an attempt to increase performance and robustness of the network structure. The training data for the network consist of arrays containing information about single-nucleotide polymorphisms present in an individual. Each instance of augmented data was simulated by randomising cuts based on the distance between the polymorphisms, and then creating a new array by alternating between the arrays of two randomised original data instances. Several networks were then trained using this data augmentation. The performance of the trained networks was compared to networks trained on only original data using several metrics. Both groups of networks had similar performance for most metrics. The main difference was that networks trained on only original data had a low genotype concordance on simulated data. This indicates an underlying risk using the original networks, which can be overcome by introducing the artificial recombination.