Single-cell RNA sequencing (scRNA-seq) clustering and labelling methods are used to determine precise cellular composition of tissue samples. Automated labelling methods rely on either unsupervised, cluster-based approaches or supervised, cell-based approaches to identify cell types. The high complexity of cancer poses a unique challenge, as tumor microenvironments are often composed of diverse cell subpopulations with unique functional effects that may lead to disease progression, metastasis and treatment resistance. Here, we assess 17 cell-based and 9 cluster-based scRNA-seq labelling algorithms using 8 cancer datasets, providing a comprehensive large-scale assessment of such methods in a cancer-specific context. Using several performance metrics, we show that cell-based methods generally achieved higher performance and were faster compared to cluster-based methods. Cluster-based methods more successfully labelled non-malignant cell types, likely because of a lack of gene signatures for relevant malignant cell subpopulations. Larger cell numbers present in some cell types in training data positively impacted prediction scores for cell-based methods. Finally, we examined which methods performed favorably when trained and tested on separate patient cohorts in scenarios similar to clinical applications, and which were able to accurately label particularly small or under-represented cell populations in the given datasets. We conclude that scPred and SVM show the best overall performances with cancer-specific data and provide further suggestions for algorithm selection. Our analysis pipeline for assessing the performance of cell type labelling algorithms is available in https://github.com/shooshtarilab/scRNAseq-Automated-Cell-Type-Labelling.

MOTIVATION: Current fusion detection tools use diverse calling approaches and provide varying results, making selection of the appropriate tool challenging. Ensemble fusion calling techniques appear promising; however, current options have limited accessibility and function. RESULTS: MetaFusion is a flexible metacalling tool that amalgamates outputs from any number of fusion callers. Individual caller results are standardized by conversion into the new file type Common Fusion Format. Calls are annotated, merged using graph clustering, filtered and ranked to provide a final output of high-confidence candidates. MetaFusion consistently achieves higher precision and recall than individual callers on real and simulated datasets, and reaches up to 100% precision, indicating that ensemble calling is imperative for high-confidence results. MetaFusion uses FusionAnnotator to annotate calls with information from cancer fusion databases and is provided with a Benchmarking Toolkit to calibrate new callers. AVAILABILITY AND IMPLEMENTATION: MetaFusion is freely available at https://github.com/ccmbioinfo/MetaFusion. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Protein interaction networks contain a wealth of biological information, but their large size often hinders cross-organism comparisons. We present OrthoNets, a Cytoscape plugin that displays protein-protein interaction (PPI) networks from two organisms simultaneously, highlighting orthology relationships and aggregating several types of biomedical annotations. OrthoNets also allows PPI networks derived from experiments to be overlaid on networks extracted from public databases, supporting the identification and verification of new interactors. Any newly identified PPIs can be validated by checking whether their orthologs interact in another organism. AVAILABILITY: OrthoNets is freely available at http://wodaklab.org/orthonets/.

BACKGROUND: The Bluejay genome browser has been developed over several years to address the challenges posed by the ever increasing number of data types as well as the increasing volume of data in genome research. Beginning with a browser capable of rendering views of XML-based genomic information and providing scalable vector graphics output, we have now completed version 1.0 of the system with many additional features. Our development efforts were guided by our observation that biologists who use both gene expression profiling and comparative genomics gain functional insights above and beyond those provided by traditional per-gene analyses. RESULTS: Bluejay 1.0 is a genome viewer integrating genome annotation with: (i) gene expression information; and (ii) comparative analysis with an unlimited number of other genomes in the same view. This allows the biologist to see a gene not just in the context of its genome, but also its regulation and its evolution. Bluejay now has rich provision for personalization by users: (i) numerous display customization features; (ii) the availability of waypoints for marking multiple points of interest on a genome and subsequently utilizing them; and (iii) the ability to take user relevance feedback of annotated genes or textual items to offer personalized recommendations. Bluejay 1.0 also embeds the Seahawk browser for the Moby protocol, enabling users to seamlessly invoke hundreds of Web Services on genomic data of interest without any hard-coding. CONCLUSION: Bluejay offers a unique set of customizable genome-browsing features, with the goal of allowing biologists to quickly focus on, analyze, compare, and retrieve related information on the parts of the genomic data they are most interested in. We expect these capabilities of Bluejay to benefit the many biologists who want to answer complex questions using the information available from completely sequenced genomes.

We are interested in ensembles of models built over k data sets. Common approaches are either to combine models by vote averaging, or to build a meta-model on the outputs of the local models. In this paper, we consider the model assignment approach, in which a meta-model selects one of the local statistical models for scoring. We introduce an algorithm called greedy data labeling (GDL) that improves the initial data partition by reallocating some data, so that when each model is built on its local data subset, the resulting hierarchical system has minimal error. We present evidence that model assignment may in certain situations be more natural than traditional ensemble learning, and if enhanced by GDL, it often outperforms traditional ensembles.

Article Free Access Share on Papyrus: a system for data mining over local and wide area clusters and super-clusters Authors: S. Bailey National Center for Data Mining, University of Illinois at Chicago National Center for Data Mining, University of Illinois at ChicagoView Profile , R. Grossman National Center for Data Mining, University of Illinois at Chicago and Magnify, Inc. National Center for Data Mining, University of Illinois at Chicago and Magnify, Inc.View Profile , H. Sivakumar National Center for Data Mining, University of Illinois at Chicago National Center for Data Mining, University of Illinois at ChicagoView Profile , A. Turinsky National Center for Data Mining, University of Illinois at Chicago National Center for Data Mining, University of Illinois at ChicagoView Profile Authors Info & Claims SC '99: Proceedings of the 1999 ACM/IEEE conference on SupercomputingJanuary 1999 Pages 63–eshttps://doi.org/10.1145/331532.331595Online:01 January 1999Publication History 44citation676DownloadsMetricsTotal Citations44Total Downloads676Last 12 Months7Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF