Adamer, M. F. | bioRxiv Science

bioRxiv2022

SIMBSIG: Similarity search and clustering for biobank-scale data

SummaryIn many modern bioinformatics applications, such as statistical genetics, or single-cell analysis, one frequently encounters datasets which are orders of magnitude too large for conventional in-memory analysis. To tackle this challenge, we introduce SIMBSIG, a highly scalable Python package which provides a scikit-learn-like interface for out-of-core, GPU-enabled similarity searches, principal component analysis, and clustering. Due to the PyTorch backend it is highly modular and particularly tailored to many data types with a particular focus on biobank data analysis. AvailabilitySIMBSIG is freely available from PyPI and its source code and documentation can be found on GitHub (https://github.com/BorgwardtLab/simbsig) under a BSD-3 license. Contactmichael.adamer@bsse.ethz.ch

bioinformatics↗

bioRxiv2021

reComBat: Batch effect removal in large-scale, multi-source omics data integration

MotivationWith the steadily increasing abundance of omics data produced all over the world under vastly different experimental conditions residing in public databases, a crucial step in many data-driven bioinformatics applications is that of data integration. The challenge of batch-effect removal for entire databases lies in the large number of batches and biological variation which can result in design matrix singularity. This problem can currently not be solved satisfactorily by any common batch-correction algorithm. ResultsWe present reComBat, a regularized version of the empirical Bayes method to overcome this limitation and benchmark it against popular approaches for the harmonization of public gene expression data (both microarray and bulkRNAsq) of the human opportunistic pathogen Pseudomonas aeruginosa. Batch-effects are successfully mitigated while biologically meaningful gene expression variation is retained. reComBat fills the gap in batch-correction approaches applicable to large-scale, public omics databases and opens up new avenues for data-driven analysis of complex biological processes beyond the scope of a single study. Contactmichael.adamer@bsse.ethz.ch AvailabilityThe code is available at https://github.com/BorgwardtLab/reComBat, all data and evaluation code can be found at https://github.com/BorgwardtLab/batchCorrectionPublicData

bioinformatics↗