Accurate integration of multiple heterogeneous single-cell RNA-seq data sets by learning contrastive biological variation.

For most biological and medical applications of single-cell transcriptomics, an integrative study of multiple heterogeneous single-cell RNA sequencing (scRNA-seq) data sets is crucial. However, present approaches are unable to integrate diverse data sets from various biological conditions effectively because of the confounding effects of biological and technical differences. We introduce single-cell integration (scInt), an integration method based on accurate, robust cell-cell similarity construction and unified contrastive biological variation learning from multiple scRNA-seq data sets.

scMTD: a statistical multidimensional imputation method for single-cell RNA-seq data leveraging transcriptome dynamic information.

Background: Single-cell RNA sequencing (scRNA-seq) provides a powerful tool to capture transcriptomes at single-cell resolution. However, dropout events distort the gene expression levels and underlying biological signals, misleading the downstream analysis of scRNA-seq data.

Results: We develop a statistical model-based multidimensional imputation algorithm, scMTD, that identifies local cell neighbors and specific gene co-expression networks based on the pseudo-time of cells, leveraging information on cell-level, gene-level, and transcriptome dynamic to recover scRNA-seq data.

Huperzine A protects against traumatic brain injury through anti-oxidative effects via the Nrf2-ARE pathway.

Objectives: Traumatic brain injury (TBI) is a prominent health problem worldwide and it may lead to cognitive dysfunction, disability, and even death. To date, there is no effective treatment for TBI. Our previous study showed that Huperzine A (HupA) improved cognitive function in a mouse model of TBI.