Interpretable deep learning in single-cell omics.

Motivation: Single-cell omics technologies have enabled the quantification of molecular profiles in individual cells at an unparalleled resolution. Deep learning, a rapidly evolving sub-field of machine learning, has instilled a significant interest in single-cell omics research due to its remarkable success in analysing heterogeneous high-dimensional single-cell omics data. Nevertheless, the inherent multi-layer nonlinear architecture of deep learning models often makes them 'black boxes' as the reasoning behind predictions is often unknown and not transparent to the user.

The CD8 T cell tolerance checkpoint triggers a distinct differentiation state defined by protein translation defects.

Peripheral CD8 T cell tolerance is a checkpoint in both autoimmune disease and anti-cancer immunity. Despite its importance, the relationship between tolerance-induced states and other CD8 T cell differentiation states remains unclear. Using flow cytometric phenotyping, single-cell RNA sequencing (scRNA-seq), and chromatin accessibility profiling, we demonstrated that in vivo peripheral tolerance to a self-antigen triggered a fundamentally distinct differentiation state separate from exhaustion, memory, and functional effector cells but analogous to cells defectively primed against tumors.

Evaluation of deep learning-based feature selection for single-cell RNA sequencing data analysis.

Background: Feature selection is an essential task in single-cell RNA-seq (scRNA-seq) data analysis and can be critical for gene dimension reduction and downstream analyses, such as gene marker identification and cell type classification. Most popular methods for feature selection from scRNA-seq data are based on the concept of differential distribution wherein a statistical model is used to detect changes in gene expression among cell types. Recent development of deep learning-based feature selection methods provides an alternative approach compared to traditional differential distribution-based methods in that the importance of a gene is determined by neural networks.

Causal Genetic Loci for a Motivated Behavior Spectrum Harbor Psychiatric Risk Genes.

Behavioral diversity is critical for population fitness. Individual differences in risk-taking are observed across species, but underlying genetic mechanisms and conservation are largely unknown. We examined dark avoidance in larval zebrafish, a motivated behavior reflecting an approach-avoidance conflict.

Moonlighting functions of the ubiquitin-like protein, Hub1/UBL-5.

The faithful splicing of pre-mRNA is critical for accurate gene expression. Dysregulation of pre-mRNA splicing has been associated with several human diseases including cancer. The ubiquitin-like protein Hub1/UBL5 binds to the substrates non-covalently and promotes pre-mRNA splicing.