The use of interventional procedures for cancer pain. A brief review.

CONTEXT: Pain is a common experience in people living with cancer. Concerns around opioid prescribing have seen a move toward a multi-modality management approach, which includes interventional pain procedures. PURPOSE: In this paper we discuss the interventional pain procedures used to treat cancer pain at two major tertiary centers in Australia. METHODS AND RESULTS: This expert review provides practical insights on cancer pain management from healthcare providers in different specialties. These insights can be used to guide the management of a wide range of cancer pain types. CONCLUSIONS: Furthermore, this review identifies the need for a systematic and comprehensive approach to the management of cancer pain that is broader than that of a single specialty. With recent advances in pain management procedures, an interdisciplinary approach is essential in order to provide an up to date, patient tailored approach to pain management. This review will help inform the development of a cancer pain intervention registry.

Impact of similarity metrics on single-cell RNA-seq data clustering.

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.