Clustering ensemble in scRNA-seq data analysis: Methods, applications and challenges.

With the rapid development of single-cell RNA-sequencing techniques, various computational methods and tools were proposed to analyze these high-throughput data, which led to an accelerated reveal of potential biological information. As one of the core steps of single-cell transcriptome data analysis, clustering plays a crucial role in identifying cell types and interpreting cellular heterogeneity. However, the results generated by different clustering methods showed distinguishing, and those unstable partitions can affect the accuracy of the analysis to a certain extent.

Systematic Identification of Housekeeping Genes Possibly Used as References in by Large-Scale Data Integration.

For accurate gene expression quantification, normalization of gene expression data against reliable reference genes is required. It is known that the expression levels of commonly used reference genes vary considerably under different experimental conditions, and therefore, their use for data normalization is limited. In this study, an unbiased identification of reference genes in was performed based on 145 microarray datasets (2296 gene array samples) covering different developmental stages, different tissues, drug treatments, lifestyle, and various stresses.

Consistent Biomarkers and Related Pathogenesis Underlying Asthma Revealed by Systems Biology Approach.

Asthma is a common chronic airway disease worldwide. Due to its clinical and genetic heterogeneity, the cellular and molecular processes in asthma are highly complex and relatively unknown. To discover novel biomarkers and the molecular mechanisms underlying asthma, several studies have been conducted by focusing on gene expression patterns in epithelium through microarray analysis.