No abstract

Single-cell ATAC sequencing (scATAC-seq) is a powerful and increasingly popular technique to explore the regulatory landscape of heterogeneous cellular populations. However, the high noise levels, degree of sparsity, and scale of the generated data make its analysis challenging. Here we present PeakVI, a probabilistic framework that leverages deep neural networks to analyze scATAC-seq data. PeakVI fits an informative latent space that preserves biological heterogeneity while correcting batch effects and accounting for technical effects such as library size and region-specific biases. Additionally, PeakVI provides a technique for identifying differential accessibility at a single region resolution, which can be used for cell-type annotation as well as identification of key cis-regulatory elements. We use public datasets to demonstrate that PeakVI is scalable, stable, robust to low-quality data, and outperforms current analysis methods on a range of critical analysis tasks. PeakVI is publicly available and implemented in the scvi-tools framework: https://docs.scvi-tools.org

MotivationMany computational methods aim to identify genetic variants associated with diseases and complex traits. Due to the absence of ground truth data, simulated genotype and phenotype data is needed to benchmark these methods. However, phenotypes are frequently simulated as an additive function of randomly selected variants, neglecting biological complexity such as non-random occurrence of causal SNPs, epistatic effects, heritability and dominance. Including such features would improve benchmarking studies and accelerate the development of methods for genetic analysis.ResultsHere, we describe GEPSi (GWAS Epistatic Phenotype Simulator), a user-friendly python package to simulate phenotype data based on user-supplied genotype data for a population. GEPSi incorporates diverse biological parameters such as heritability, dominance, population stratification and epistatic interactions between SNPs. We demonstrate the use of this package to compare machine learning methods for GWAS analysis.Availability and ImplementationGEPSi is freely available under an Apache 2.0 license, and can be downloaded from https://github.com/clara-parabricks/GEPSi.Supplementary informationSupplementary data are available online.