Advances in high-throughput sequencing on single-cell gene expressions [single-cell RNA sequencing (scRNA-seq)] have enabled transcriptome profiling on individual cells from complex samples. A common goal in scRNA-seq data analysis is to discover and characterise cell types, typically through clustering methods. The quality of the clustering therefore plays a critical role in biological discovery. While numerous clustering algorithms have been proposed for scRNA-seq data, fundamentally they all rely on a similarity metric for categorising individual cells. Although several studies have compared the performance of various clustering algorithms for scRNA-seq data, currently there is no benchmark of different similarity metrics and their influence on scRNA-seq data clustering. Here, we compared a panel of similarity metrics on clustering a collection of annotated scRNA-seq datasets. Within each dataset, a stratified subsampling procedure was applied and an array of evaluation measures was employed to assess the similarity metrics. This produced a highly reliable and reproducible consensus on their performance assessment. Overall, we found that correlation-based metrics (e.g. Pearson's correlation) outperformed distance-based metrics (e.g. Euclidean distance). To test if the use of correlation-based metrics can benefit the recently published clustering techniques for scRNA-seq data, we modified a state-of-the-art kernel-based clustering algorithm (SIMLR) using Pearson's correlation as a similarity measure and found significant performance improvement over Euclidean distance on scRNA-seq data clustering. These findings demonstrate the importance of similarity metrics in clustering scRNA-seq data and highlight Pearson's correlation as a favourable choice. Further comparison on different scRNA-seq library preparation protocols suggests that they may also affect clustering performance. Finally, the benchmarking framework is available at http://www.maths.usyd.edu.au/u/SMS/bioinformatics/software.html.

Background Single-cell RNA-seq (scRNA-seq) profiling has revealed remarkable variation in transcription, suggesting that expression of many genes at the single-cell level is intrinsically stochastic and noisy. Yet, on the cell population level, a subset of genes traditionally referred to as housekeeping genes (HKGs) are found to be stably expressed in different cell and tissue types. It is therefore critical to question whether stably expressed genes (SEGs) can be identified on the single-cell level, and if so, how can their expression stability be assessed? We have previously proposed a computational framework for ranking expression stability of genes in single cells for scRNA-seq data normalization and integration. In this study, we perform detailed evaluation and characterization of SEGs derived from this framework. Results Here, we show that gene expression stability indices derived from the early human and mouse development scRNA-seq datasets and the "Mouse Atlas" dataset are reproducible and conserved across species. We demonstrate that SEGs identified from single cells based on their stability indices are considerably more stable than HKGs defined previously from cell populations across diverse biological systems. Our analyses indicate that SEGs are inherently more stable at the single-cell level and their characteristics reminiscent of HKGs, suggesting their potential role in sustaining essential functions in individual cells. Conclusions SEGs identified in this study have immediate utility both for understanding variation and stability of single-cell transcriptomes and for practical applications such as scRNA-seq data normalization. Our framework for calculating gene stability index, "scSEGIndex," is incorporated into the scMerge Bioconductor R package (https://sydneybiox.github.io/scMerge/reference/scSEGIndex.html) and can be used for identifying genes with stable expression in scRNA-seq datasets.

The majority of the self-reported allergies were in fact simply accepted adverse effects of the drugs concerned. The patients' reported drug 'allergy' history was generally well respected by anaesthetists and other medical staff. There were 13 incidents, mainly involving morphine, where patients were given a drug to which they had claimed a specific allergy. There were 101 incidents in 89 patients where drugs of the same pharmacological group as that of their allergic drug were used. There were no untoward reactions in 84 patients who had claimed a prior adverse reaction to penicillin who were given cephalosporins. There were no sequelae from any other events. While anaesthetists generally respected patients self-reported 'allergies', more attention needs to be paid to the accurate recording of patients' events and a clear distinction should be made both in medical records and to the patient between true drug allergy and simple adverse drug reactions.

Objective: To determine whether a continuous intravenous infusion of standard amino acids could preserve kidney function after on-pump cardiac surgery.Methods: Adult patients scheduled to receive cardiac surgery lasting longer than 1 hour on-pump were randomized to standard care (n ¼ 36) or an infusion of amino acids initiated immediately after induction of anesthesia (n ¼ 33). The study's primary outcome measurements assessed renal function. These assessments included duration of renal dysfunction, duration and severity of acute kidney injury (AKI), estimated glomerular filtration rate (eGFR) over time, urine output, and use of renal-replacement therapy. Complications and other measures of morbidity were also assessed.