Combined gene family characterization and RNA-Seq to study the response of β-ketoacyl-CoA synthase to abiotic stress in rice (Oryza sativa L.)

Plant Growth Regulation - β-ketoacyl-CoA synthase is a key enzyme in the biosynthesis of over-long-chain fattty acids; thus, it plays a crucial role in plant resistance to stress. Herein, 33...     

Variation in rare earth element (REE), aluminium (Al) and silicon (Si) accumulation among populations of the hyperaccumulator Dicranopteris linearis in southern China

Plant and Soil - Dicranopteris linearis is a rare earth element (REE), aluminium (Al) and silicon (Si) hyperaccumulator plant which occurs in southern China. To date, there have been no studies on...     

The role of exosomal microRNAs in central nervous system diseases

Molecular and Cellular Biochemistry - MicroRNAs (miRNA), endogenous non-coding RNAs approximately 22 nucleotides long, regulate gene expression by mediating translational inhibition or mRNA...     

An in-depth analysis reveals two new genetic variants on 22q11.2 associated with vitiligo in the Chinese Han population

Molecular Biology Reports - Vitiligo is a complex disease in which patchy depigmentation is the result of an autoimmune-induced loss of melanocytes in affected regions. On the basis of a...     

Independent component analysis based gene co-expression network inference (ICAnet) to decipher functional modules for better single-cell clustering and batch integration

With the tremendous increase of publicly available single-cell RNA-sequencing (scRNA-seq) datasets, bioinformatics methods based on gene co-expression network are becoming efficient tools for analyzing scRNA-seq data, improving cell type prediction accuracy and in turn facilitating biological discovery. However, the current methods are mainly based on overall co-expression correlation and overlook co-expression that exists in only a subset of cells, thus fail to discover certain rare cell types and sensitive to batch effect. Here, we developed independent component analysis-based gene co-expression network inference (ICAnet) that decomposed scRNA-seq data into a series of independent gene expression components and inferred co-expression modules, which improved cell clustering and rare cell-type discovery. ICAnet showed efficient performance for cell clustering and batch integration using scRNA-seq datasets spanning multiple cells/tissues/donors/library types. It works stably on datasets produced by different library construction strategies and with different sequencing depths and cell numbers. We demonstrated the capability of ICAnet to discover rare cell types in multiple independent scRNA-seq datasets from different sources. Importantly, the identified modules activated in acute myeloid leukemia scRNA-seq datasets have the potential to serve as new diagnostic markers. Thus, ICAnet is a competitive tool for cell clustering and biological interpretations of single-cell RNA-seq data analysis.