Biology subjects

Lengerich, B.

Publications and source records attributed to Lengerich, B..

3 recordsLinked to original sources

bioRxiv2023

Integrating single-cell RNA-seq datasets with substantial batch effects

Integration of single-cell RNA-sequencing (scRNA-seq) datasets has become a standard part of the analysis, with conditional variational autoencoders (cVAE) being among the most popular approaches. Increasingly, researchers are asking to map cells across challenging cases such as cross-organs, species, or organoids and primary tissue, as well as different scRNA-seq protocols, including single-cell and single-nuclei. Current computational methods struggle to harmonize datasets with such substantial differences, driven by technical or biological variation. Here, we propose to address these challenges for the popular cVAE-based approaches by introducing and comparing a series of regularization constraints. The two commonly used strategies for increasing batch correction in cVAEs, that is Kullback-Leibler divergence (KL) regularization strength tuning and adversarial learning, suffer from substantial loss of biological information. Therefore, we adapt, implement, and assess alternative regularization strategies for cVAEs and investigate how they improve batch effect removal or better preserve biological variation, enabling us to propose an optimal cVAE-based integration strategy for complex systems. We show that using a VampPrior instead of the commonly used Gaussian prior not only improves the preservation of biological variation but also unexpectedly batch correction. Moreover, we show that our implementation of cycle-consistency loss leads to significantly better biological preservation than adversarial learning implemented in the previously proposed GLUE model. Additionally, we do not recommend relying only on the KL regularization strength tuning for increasing batch correction, as it removes both biological and batch information without discriminating between the two. Based on our findings, we propose a new model that combines VampPrior and cycle-consistency loss. We show that using it for datasets with substantial batch effects improves downstream interpretation of cell states and biological conditions. To ease the use of the newly proposed model, we make it available in the scvi-tools package as an external model named sysVI. Moreover, in the future, these regularization techniques could be added to other established cVAE-based models to improve the integration of datasets with substantial batch effects.

bioinformatics↗

bioRxiv2019

Differential Principal Components Reveal Patterns of Differentiation in Case/Control Studies

Dimensionality reduction is an important task in bioinformatics studies. Common unsupervised methods like principal components analysis (PCA) extract axes of variation that are high-variance but do not necessarily differentiate experimental conditions. Methods of supervised discriminant analysis such as partial least squares (PLS-DA) effectively separate conditions, but are hamstrung by inflexibility and overfit to sample labels. We would like a simple method which repurposes the rich literature of component estimation for supervised dimensionality reduction. We propose to address this problem by estimating principal components from a set of difference vectors rather than from the samples. Our method directly utilizes the PCA algorithm as a module, so we can incorporate any PCA variant for improved components estimation. Specifically, Robust PCA, which ameliorates the deleterious effects of noisy samples, improves recovery of components in this framework. We name the resulting method Differential Robust PCA (drPCA). We apply drPCA to several cancer gene expression datasets and find that it more accurately summarizes oncogenic processes than do standard methods such as PCA and PLS-DA. A Python implementation of drPCA and Jupyter notebooks to reproduce experimental results are available at www.github.com/blengerich/drPCA.

bioinformatics↗

bioRxiv2018

Personalized Regression Enables Sample-Specific Pan-Cancer Analysis

In many applications, inter-sample heterogeneity is crucial to understanding the complex biological processes under study. For example, in genomic analysis of cancers, each patient in a cohort may have a different driver mutation, making it difficult or impossible to identify causal mutations from an averaged view of the entire cohort. Unfortunately, many traditional methods for genomic analysis seek to estimate a single model which is shared by all samples in a population, ignoring this inter-sample heterogeneity entirely. In order to better understand patient heterogeneity, it is necessary to develop practical, personalized statistical models. To uncover this inter-sample heterogeneity, we propose a novel regularizer for achieving patient-specific personalized estimation. This regularizer operates by learning two latent distance metrics - one between personalized parameters and one between clinical covariates - and attempting to match the induced distances as closely as possible. Crucially, we do not assume these distance metrics are already known. Instead, we allow the data to dictate the structure of these latent distance metrics. Finally, we apply our method to learn patient-specific, interpretable models for a pan-cancer gene expression dataset containing samples from more than 30 distinct cancer types and find strong evidence of personalization effects between cancer types as well as between individuals. Our analysis uncovers sample-specific aberrations that are overlooked by population level methods, suggesting a promising new path for precision analysis of complex diseases such as cancer.

bioinformatics↗