Concerted examination of multiple collections of single-cell RNA sequencing (RNA-seq) data promises further biological insights that cannot be uncovered with individual datasets. Here we present scMerge, an algorithm that integrates multiple single-cell RNA-seq datasets using factor analysis of stably expressed genes and pseudoreplicates across datasets. Using a large collection of public datasets, we benchmark scMerge against published methods and demonstrate that it consistently provides improved cell type separation by removing unwanted factors; scMerge can also enhance biological discovery through robust data integration, which we show through the inference of development trajectory in a liver dataset collection.

BACKGROUND:To establish zebrafish as a developmental toxicity model, we used 7 well-characterized compounds to examine several parameters of neurotoxicity during development. METHODS: Embryos were exposed by semistatic immersion from 6 hrs postfertilization (hpf). Teratogenicity was assessed using a modified method previously developed by Phylonix. Dying cells in the brain were assessed by acridine orange staining (these cells are likely to be apoptotic). Motor neurons were assessed by antiacetylated tubulin staining and catecholaminergic neurons were visualized by antityrosine hydroxylase staining. RESULTS: Atrazine, dichlorodiphenyltrichloroethane (DDT), and 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) were primarily teratogenic and not specifically neurotoxic. 2,4-dichlorophenoxyacetic acid (2,4-D), dieldrin, and nonylphenol showed specific neurotoxicity; dieldrin and nonylphenol were specifically toxic to catecholaminergic neurons. Malathion, although not teratogenic, showed some nonspecific toxicity. CONCLUSIONS: Teratogenicity measured in 96-hpf zebrafish is predictive of mammalian teratogenicity and is useful in determining whether a compound causes specific neurotoxicity or general developmental toxicity. Induction of apoptosis or necrosis is an indicator of neurotoxicity. An effect on motor neurons in the caudal third of the embryo correlates with expected defects in motility. Overall, our results showed a strong correlation with mammalian data and suggest that zebrafish is a predictive animal model for neurotoxicity screening. Birth Defects Research (Part A) 76:553-567,

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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.

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