Benchmarking variational AutoEncoders on cancer transcriptomics data

Deep generative models, such as variational autoencoders (VAE), have gained increasing attention in computational biology due to their ability to capture complex data manifolds which subsequently can be used to achieve better performance in downstream tasks, such as cancer type prediction or subtyping of cancer. However, these models are...

scMoC: single-cell multi-omics clustering

Motivation: Single-cell multi-omics assays simultaneously measure different molecular features from the same cell. A key question is how to benefit from the complementary data available and perform cross-modal clustering of cells. Results: We propose Single-Cell Multi-omics Clustering (scMoC), an approach to identify cell clusters from data...