Dimensionality reduction and data integration for scRNA-seq data based on integrative hierarchical Poisson factorisation

Abstract

Single-cell RNA sequencing (scRNA-seq) data sets consist of high-dimensional, sparse and noisy feature vectors, and pose a challenge for classic methods for dimensionality reduction. Such problems are compounded when dealing with composite data sets formed by different batches. We introduce Integrative Hierarchical Poisson Factorisation (IHPF), an extension of HPF that makes use of a noise ratio hyper-parameter to tune the variability attributed to batches vs. biological sources (cell phenotypes). We exemplify the application of IHPF under different data integration scenarios with varying alignments of batches and cell diversity, and show that IHPF produces latent factors that can be advantageously applied for cell clustering and visualisation. In addition, the extracted factors have a dual block structure in both cell and gene spaces with enhanced biological interpretability.