@article {aGeleta, title = {Deep Variational Autoencoders for Population Genetics}, year = {Submitted}, abstract = {

Motivation Modern biobanks provide numerous high-resolution genomic sequences of diverse populations. These datasets enable a better understanding of genotype-phenotype interactions with genome-wide association studies (GWAS) and power a new personalized precision medicine with polygenic risk scores (PRS). In order to account for diverse and admixed populations, new algorithmic tools are needed in order to properly capture the genetic composition of populations. Here we explore deep learning techniques, namely variational autoencoders (VAEs), to process genomic data from a population perspective. We hope this work will encourage the adoption of deep neural networks in the population genetics community.

Results In this paper, we show the power of VAEs for a variety of tasks relating to the interpretation, classification, simulation, and compression of genomic data with several worldwide whole genome datasets from both humans and canids and evaluate the performance of the proposed applications with and without ancestry conditioning. The unsupervised setting of autoencoders allows for the detection and learning of granular population structure and inferring of informative latent factors. The learned latent spaces of VAEs are able to capture and represent differentiated Gaussian-like clusters of samples with similar genetic composition on a fine-scale from single nucleotide polymorphisms (SNPs), enabling applications in dimensionality reduction, data simulation, and imputation. These individual genotype sequences can then be decomposed into latent representations and reconstruction errors (residuals) which provide a sparse representation useful for lossless compression. We show that different population groups have differentiated compression ratios and classification accuracies. Additionally, we analyze the entropy of the SNP data, its effect on compression across populations, its relation to historical migrations, and we show how to introduce autoencoders into existing compression pipelines.

}, doi = {https://doi.org/10.1101/2023.09.27.558320}, url = {https://www.biorxiv.org/content/10.1101/2023.09.27.558320v1}, author = {Geleta, Margarita and Mas-Montserrat, Daniel and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @conference {cBarrabes, title = {Adversarial Learning for Feature Shift Detection and Correction}, booktitle = {Neural Information Processing Systems (NeurIPS)}, year = {2023}, month = {12/2023}, address = {New Orleans, USA}, abstract = {

TL;DR: We introduce a framework inspired in adversarial learning to detect and correct features originating a distribution shift between datasets.

Data shift is a phenomenon present in many real-world applications, and while there are multiple methods trying to detect shifts, the task of localizing and correcting the features originating such shifts has not been studied in depth. Feature shifts can occur in many datasets, including in multi-sensor data, where some sensors are malfunctioning, or in tabular and structured data, including biomedical, financial, and survey data, where faulty standardization and data processing pipelines can lead to erroneous features. In this work, we explore using the principles of adversarial learning, where the information from several discriminators trained to distinguish between two distributions is used to both detect the corrupted features and fix them in order to remove the distribution shift between datasets. We show that mainstream supervised classifiers, such as random forest or gradient boosting trees, combined with simple iterative heuristics, can localize and correct feature shifts outperforming current statistical and neural network-based techniques.

}, author = {Barrab{\'e}s, M{\'\i}riam and Mas-Montserrat, Daniel and Geleta, Margarita and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @conference {cMas-Montserrat, title = {Generative Moment Matching Networks for Genotype Simulation}, booktitle = {44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC{\textquoteright}22)}, year = {2022}, abstract = {

The generation of synthetic genomic sequences using neural networks has potential to overcome privacy and data sharing restrictions and to mitigate potential bias within datasets due to under-representation of some population groups. However, there is not a consensus on which architectures, training procedures, and evaluation metrics should be used when simulating single nucleotide polymorphism (SNP) sequences with neural networks. In this paper, we explore the use of Generative Moment Matching Networks (GMMNs) for SNP simulation, we present some architectural and procedural changes to properly train the networks, and we introduce an evaluation scheme to qualitatively and quantitatively asses the quality of the simulated sequences.

}, author = {Mas-Montserrat, Daniel and Perera, Maria and Barrab{\'e}s, M{\'\i}riam and Geleta, Margarita and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @conference {cGeleta21, title = {PixInWav: Residual Steganography for Hiding Pixels in Audio}, booktitle = {ICASSP}, year = {2022}, month = {06/2021}, abstract = {

Steganography comprises the mechanics of hiding data in a host media that may be publicly available. While previous works focused on unimodal setups (e.g., hiding images in images, or hiding audio in audio), PixInWav targets the multimodal case of hiding images in audio. To this end, we propose a novel residual architecture operating on top of short-time discrete cosine transform (STDCT) audio spectrograms. Among our results, we find that the residual audio steganography setup we propose allows independent encoding of the hidden image from the host audio without compromising quality. Accordingly, while previous works require both host and hidden signals to hide a signal, PixInWav can encode images offline --- which can be later hidden, in a residual fashion, into any audio signal. Finally, we test our scheme in a lab setting to transmit images over airwaves from a loudspeaker to a microphone verifying our theoretical insights and obtaining promising results.

\ Deep Learning Barcelona Symposium 2022

Presentation from the early stages of the project (January 2021):

}, author = {Geleta, Margarita and Punt{\'\i}, Cristina and McGuinness, Kevin and Pons, Jordi and Cristian Canton-Ferrer and Xavier Gir{\'o}-i-Nieto} } @conference {cGeleta, title = {Deep variational autoencoders for population genetics: applications in classification, imputation, dimensionality reduction, and novel lossless data compression}, booktitle = {American Society of Human Genetics (ASHG)}, year = {2021}, month = {10/2021}, publisher = {ASHG}, organization = {ASHG}, address = {Virtual}, abstract = {

In this study we show the power of variational autoencoders (VAEs) for a variety of tasks relating to the interpretation and compression of genomic data. The unsupervised setting allows for detecting and learning of granular population structure and inferring of new informative latent factors, opening up an avenue for applications in dimensionality reduction, data simulation, population classification, imputation, and lossless genomic data compression. The latent spaces of VAEs are able to capture and represent clearly differentiated Gaussian-like clusters of similar genetic composition on a fine-scale with a relatively small number of SNPs as input. Furthermore, sequences can be decomposed into latent representations and reconstruction errors (residuals) providing a sparse representation that provides a means for efficient lossless compression.

Identifying genetic clusters can be important when performing genome-wide association studies and provides an alternative to self-reported ethnic labels, which are culturally constructed and vary according to the location and individual. A variety of unsupervised dimensionality reduction methods have been explored in the past for such applications, including PCA, MDS, t-SNE, and UMAP. Our proposed VAE can represent the population structure as a Gaussian-distributed continuous multi-dimensional representation and as classification probabilities providing flexible and interpretable population descriptors.

We train our VAE method with several worldwide whole genome datasets from both humans and canids and evaluate the performance of the different proposed applications with networks with and without ancestry conditioning. Our experiments show that different population groups have significantly differentiated compression ratios and classification accuracies. Additionally, we analyze the entropy of the SNP data, noting its effect on compression across populations and connect these patterns to historical migrations and ancestral relationships.

Video from the related BSc thesis at UPC Data Science Engineering (2021):

}, author = {Geleta, Margarita and Mas-Montserrat, Daniel and Bustamante, Carlos and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @mastersthesis {dGeleta21, title = {Unsupervised learning with applications in genomic}, volume = {BSc Data Science Engineering}, year = {2021}, abstract = {

In this study we show the power of variational autoencoders (VAEs) for a variety of tasks relating to the interpretation and compression of genomic data. The unsupervised setting allows for detecting and learning of granular population structure and inferring of new informative latent factors, opening up an avenue for applications in dimensionality reduction, data simulation, population classification, imputation, and lossless genomic data compression. The latent spaces of VAEs are able to capture and represent clearly differentiated Gaussian-like clusters of similar genetic composition on a fine-scale with a relatively small number of Single Nucleotide Polymorphisms (SNPs) as input. Furthermore, sequences can be decomposed into latent representations and reconstruction errors (residuals) providing a sparse representation that provides a means for efficient lossless compression.

Identifying genetic clusters can be important when performing genome-wide association studies and provides an alternative to self-reported ethnic labels, which are culturally constructed and vary according to the location and individual. A variety of unsupervised dimensionality reduction methods have been explored in the past for such applications, including PCA, MDS, t-SNE, and UMAP. Our proposed VAE can represent the population structure as a Gaussian-distributed continuous multi-dimensional representation and as classification probabilities providing flexible and interpretable population descriptors.

We train our VAE method with several worldwide whole genome datasets from both humans and canids, and evaluate the performance of the different proposed applications with networks with and without ancestry conditioning. Our experiments show that different population groups have significantly differentiated compression ratios and classification accuracies. Additionally, we analyze the entropy of the SNP data, noting its effect on compression across populations and connect these patterns to historical migrations and ancestral relationships.

}, author = {Geleta, Margarita}, editor = {Mas-Montserrat, Daniel and Ioannidis, Alexander G. and Xavier Gir{\'o}-i-Nieto} }