Biology subjects

Meng-Papaxanthos, L.

Publications and source records attributed to Meng-Papaxanthos, L..

4 recordsLinked to original sources

bioRxiv2022

LSMMD-MA: Scaling multimodal data integration for single-cell genomics data analysis

MotivationModality matching in single-cell omics data analysis--i.e., matching cells across data sets collected using different types of genomic assays--has become an important problem, because unifying perspectives across different technologies holds the promise of yielding biological and clinical discoveries. However, single-cell dataset sizes can now reach hundreds of thousands to millions of cells, which remains out of reach for most multi-modal computational methods. ResultsWe propose LSMMD-MA, a large-scale Python implementation of the MMD-MA method for multimodal data integration. In LSMMD-MA we reformulate the MMD-MA optimization problem using linear algebra and solve it with KeOps, a CUDA framework for symbolic matrix computation in Python. We show that LSMMD-MA scales to a million cells in each modality, two orders of magnitude greater than existing implementations. AvailabilityLSMMD-MA is freely available at https://github.com/google-research/large_scale_mmdma Contactlpapaxanthos@google.com

bioinformatics↗

bioRxiv2021

Semi-supervised single-cell cross-modality translation using Polarbear

The emergence of single-cell co-assays enables us to learn to translate between single-cell modalities, potentially offering valuable insights from datasets where only one modality is available. However, the sparsity of single-cell measurements and the limited number of cells measured in typical co-assay datasets impedes the power of cross-modality translation. Here, we propose Polarbear, a semi-supervised translation framework to predict cross-modality profiles that is trained using a combination of co-assay data and traditional "single-assay" data. Polarbear uses single-assay and co-assay data to train an autoencoder for each modality and then uses just the co-assay data to train a translator between the embedded representations learned by the autoencoders. With this approach, Polarbear is able to translate between modalities with improved accuracy relative to state-of-the-art translation techniques. As an added benefit of the training procedure, we show that Polarbear also produces a matching of cells across modalities.

bioinformatics↗

bioRxiv2021

networkGWAS: A network-based approach for genome-wide association studies in structured populations

While the search for associations between genetic markers and complex traits has led to the discovery of tens of thousands of trait-related genetic variants, the vast majority of these only explain a small fraction of observed phenotypic variation. One possible strategy to detect stronger associations is to aggregate the effects of several genetic markers and to test entire genes, pathways or (sub)networks of genes for association to a phenotype. The latter, network-based genome-wide association studies, in particular suffers from a vast search space and an inherent multiple testing problem. As a consequence, current approaches are either based on greedy feature selection, thereby risking that they miss relevant associations, or neglect doing a multiple testing correction, which can lead to an abundance of false positive findings. To address the shortcomings of current approaches of network-based genome-wide association studies, we propose networkGWAS, a computationally efficient and statistically sound approach to network-based genome-wide association studies using mixed models and neighborhood aggregation. It allows for population structure correction and for well-calibrated p-values, which are obtained through circular and degree-preserving network permutation schemes. networkGWAS successfully detects known associations on semi-simulated common variants from A. thaliana and on simulated rare variants from H. sapiens, as well as neighborhoods of genes involved in stress-related biological processes on a stress-induced phenotype from S. cerevisiae. It thereby enables the systematic combination of gene-based genome-wide association studies with biological network information. Availabilityhttps://github.com/BorgwardtLab/networkGWAS.git Contactgiulia.muzio@bsse.ethz.ch, karsten.borgwardt@bsse.ethz.ch

genomics↗

bioRxiv2021

Conditional Generative Modeling for De Novo Protein Design with Hierarchical Functions

MotivationProtein design has become increasingly important for medical and biotechnological applications. Because of the complex mechanisms underlying protein formation, the creation of a novel protein requires tedious and time-consuming computational or experimental protocols. At the same time, machine learning has enabled the solving of complex problems by leveraging large amounts of available data, more recently with great improvements on the domain of generative modeling. Yet, generative models have mainly been applied to specific sub-problems of protein design. ResultsHere we approach the problem of general purpose protein design conditioned on functional labels of the hierarchical Gene Ontology. Since a canonical way to evaluate generative models in this domain is missing, we devise an evaluation scheme of several biologically and statistically inspired metrics. We then develop the conditional generative adversarial network ProteoGAN and show that it outperforms several classic and more recent deep learning baselines for protein sequence generation. We further give insights into the model by analysing hyperparameters and ablation baselines. Lastly, we hypothesize that a functionally conditional model could generate proteins with novel functions by combining labels and provide first steps into this direction of research. AvailabilityCode and data is available at https://github.com/timkucera/proteogan Contacttim.kucera@bsse.ethz.ch, mt@visium.ch, lpapaxanthos@google.com

bioinformatics↗