Single-cell RNA sequencing (scRNA-seq) technologies facilitate the characterization of transcriptomic landscapes in diverse species, tissues, and cell types with unprecedented molecular resolution. In order to evaluate various biological hypotheses using high-dimensional single-cell gene expression data, most computational and statistical methods depend on a gene feature selection step to identify genes with high biological variability and reduce computational complexity. Even though many gene selection methods have been developed for scRNA-seq analysis, there lacks a systematic comparison of the assumptions, statistical models, and selection criteria used by these methods. In this article, we summarize and discuss 17 computational methods for selecting gene features in unsupervised analysis of single-cell gene expression data, with unified notations and statistical frameworks. Our discussion provides a useful summary to help practitioners select appropriate methods based on their assumptions and applicability, and to assist method developers in designing new computational tools for unsupervised learning of scRNA-seq data.

Background

Labor epidural analgesia (LEA) may influence gut microbiota. We explored the association between LEA and gut microbiota for both mothers and their newborns.

Methods

In this prospective cohort study, parturients aged 25-35 years with a gestational age of 37-42 weeks and planned vaginal delivery were recruited. Twenty-one parturients received LEA (the LEA group), and 24 did not (the control group). Maternal and neonatal fecal samples were collected, and the gut microbiota profiles were analyzed using the 16S rRNA gene sequencing. The impact of LEA on gut microbiota was assessed using the general liner models.

Results

We showcased the gut microbiota profile from the phyla to species levels based on data on 45 mother-newborn dyads. The results of α- and β-diversity suggested significant changes in gut microbiota between the LEA and control groups. After adjusting for baseline confounders, the administration of LEA had positive correlations with R. ilealis (β = 91.87, adjusted P = 0.007) in mothers; LEA also had negative correlations with A. pittii (β = -449.36, adjusted P = 0.015), P. aeruginosa (β = -192.55, adjusted P = 0.008), or S. maltophilia (β = -142.62, adjusted P = 0.001) in mothers, and with Muribaculaceae (β = -2702.77, adjusted P = 0.003) in neonates.

Conclusion

LEA was associated with changes in maternal and neonatal gut microbiota, and future studies are still required to assess their impact on clinical outcomes and explore the mechanisms.

An in-depth annotation of the newly discovered coronavirus (2019-nCoV) genome has revealed differences between 2019-nCoV and severe acute respiratory syndrome (SARS) or SARS-like coronaviruses. A systematic comparison identified 380 amino acid substitutions between these coronaviruses, which may have caused functional and pathogenic divergence of 2019-nCoV.