scholarly journals CellO: comprehensive and hierarchical cell type classification of human cells with the Cell Ontology

iScience ◽  
2021 ◽  
Vol 24 (1) ◽  
pp. 101913
Author(s):  
Matthew N. Bernstein ◽  
Zhongjie Ma ◽  
Michael Gleicher ◽  
Colin N. Dewey
Keyword(s):  
Human Cells ◽  
Cell Type ◽  
Cell Ontology ◽  
Type Classification

scholarly journals CellO: Comprehensive and hierarchical cell type classification of human cells with the Cell Ontology

2019 ◽  
Author(s):  
Matthew N. Bernstein ◽  
Zhongjie Ma ◽  
Michael Gleicher ◽  
Colin N. Dewey
Keyword(s):  
Single Cell ◽  
Web Application ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Type ◽  
Training Set ◽  
Sequence Read Archive ◽  
Cell Ontology ◽  
Cell Type Specific ◽  
Type Classification

SummaryCell type annotation is a fundamental task in the analysis of single-cell RNA-sequencing data. In this work, we present CellO, a machine learning-based tool for annotating human RNA-seq data with the Cell Ontology. CellO enables accurate and standardized cell type classification by considering the rich hierarchical structure of known cell types, a source of prior knowledge that is not utilized by existing methods. Furthemore, CellO comes pre-trained on a novel, comprehensive dataset of human, healthy, untreated primary samples in the Sequence Read Archive, which to the best of our knowledge, is the most diverse curated collection of primary cell data to date. CellO’s comprehensive training set enables it to run out-of-the-box on diverse cell types and achieves superior or competitive performance when compared to existing state-of-the-art methods. Lastly, CellO’s linear models are easily interpreted, thereby enabling exploration of cell type-specific expression signatures across the ontology. To this end, we also present the CellO Viewer: a web application for exploring CellO’s models across the ontology.HighlightWe present CellO, a tool for hierarchically classifying cell type from single-cell RNA-seq data against the graph-structured Cell OntologyCellO is pre-trained on a comprehensive dataset comprising nearly all bulk RNA-seq primary cell samples in the Sequence Read ArchiveCellO achieves superior or comparable performance with existing methods while featuring a more comprehensive pre-packaged training setCellO is built with easily interpretable models which we expose through a novel web application, the CellO Viewer, for exploring cell type-specific signatures across the Cell OntologyGraphical Abstract

scholarly journals EnClaSC: A novel ensemble approach for accurate and robust cell-type classification of single-cell transcriptomes

2019 ◽  
Author(s):  
Xiaoyang Chen ◽  
Shengquan Chen ◽  
Rui Jiang
Keyword(s):  
Single Cell ◽  
Rapid Development ◽  
Scale Up ◽  
Cell Types ◽  
Cell Type ◽  
Species Classification ◽  
Transcriptomic Data ◽  
General Data ◽  
Type Classification

AbstractBackgroundIn recent years, the rapid development of single-cell RNA-sequencing (scRNA-seq) techniques enables the quantitative characterization of cell types at a single-cell resolution. With the explosive growth of the number of cells profiled in individual scRNA-seq experiments, there is a demand for novel computational methods for classifying newly-generated scRNA-seq data onto annotated labels. Although several methods have recently been proposed for the cell-type classification of single-cell transcriptomic data, such limitations as inadequate accuracy, inferior robustness, and low stability greatly limit their wide applications.ResultsWe propose a novel ensemble approach, named EnClaSC, for accurate and robust cell-type classification of single-cell transcriptomic data. Through comprehensive validation experiments, we demonstrate that EnClaSC can not only be applied to the self-projection within a specific dataset and the cell-type classification across different datasets, but also scale up well to various data dimensionality and different data sparsity. We further illustrate the ability of EnClaSC to effectively make cross-species classification, which may shed light on the studies in correlation of different species. EnClaSC is freely available at https://github.com/xy-chen16/EnClaSC.ConclusionsEnClaSC enables highly accurate and robust cell-type classification of single-cell transcriptomic data via an ensemble learning method. We expect to see wide applications of our method to not only transcriptome studies, but also the classification of more general data.

scholarly journals EnClaSC: a novel ensemble approach for accurate and robust cell-type classification of single-cell transcriptomes

2020 ◽  
Vol 21 (S13) ◽  
Author(s):  
Xiaoyang Chen ◽  
Shengquan Chen ◽  
Rui Jiang
Keyword(s):  
Single Cell ◽  
Rapid Development ◽  
Scale Up ◽  
Cell Types ◽  
Cell Type ◽  
Species Classification ◽  
Transcriptomic Data ◽  
General Data ◽  
Type Classification

Abstract Background In recent years, the rapid development of single-cell RNA-sequencing (scRNA-seq) techniques enables the quantitative characterization of cell types at a single-cell resolution. With the explosive growth of the number of cells profiled in individual scRNA-seq experiments, there is a demand for novel computational methods for classifying newly-generated scRNA-seq data onto annotated labels. Although several methods have recently been proposed for the cell-type classification of single-cell transcriptomic data, such limitations as inadequate accuracy, inferior robustness, and low stability greatly limit their wide applications. Results We propose a novel ensemble approach, named EnClaSC, for accurate and robust cell-type classification of single-cell transcriptomic data. Through comprehensive validation experiments, we demonstrate that EnClaSC can not only be applied to the self-projection within a specific dataset and the cell-type classification across different datasets, but also scale up well to various data dimensionality and different data sparsity. We further illustrate the ability of EnClaSC to effectively make cross-species classification, which may shed light on the studies in correlation of different species. EnClaSC is freely available at https://github.com/xy-chen16/EnClaSC. Conclusions EnClaSC enables highly accurate and robust cell-type classification of single-cell transcriptomic data via an ensemble learning method. We expect to see wide applications of our method to not only transcriptome studies, but also the classification of more general data.

scholarly journals Active feature selection discovers minimal gene-sets for classifying cell-types and disease states in single-cell mRNA-seq data

2021 ◽  
Author(s):  
Xiaoqiao Chen ◽  
Sisi Chen ◽  
Matt Thomson
Keyword(s):  
Feature Selection ◽  
Single Cell ◽  
Cell Types ◽  
Mouse Tissue ◽  
Support Vector ◽  
Cell Type ◽  
Multiple Myeloma Patient ◽  
Gene Sets ◽  
Type Classification

Sequencing costs currently prohibit the application of single cell mRNA-seq for many biological and clinical tasks of interest. Here, we introduce an active learning framework that constructs compressed gene sets that enable high accuracy classification of cell-types and physiological states while analyzing a minimal number of gene transcripts. Our active feature selection procedure constructs gene sets through an iterative cell-type classification task where misclassified cells are examined at each round to identify maximally informative genes through an `active' support vector machine (SVM) classifier. Our active SVM procedure automatically identifies gene sets that enables >90% cell-type classification accuracy in the Tabula Muris mouse tissue survey as well as a ~40 gene set that enables classification of multiple myeloma patient samples with >95% accuracy. Broadly, the discovery of compact but highly informative gene sets might enable drastic reductions in sequencing requirements for applications of single-cell mRNA-seq.

Automatic Grain Type Classification of Snow Micro Penetrometer Signals With Random Forests

2013 ◽  
Vol 51 (6) ◽  
pp. 3328-3335 ◽  
Author(s):  
Scott Havens ◽  
Hans-Peter Marshall ◽  
Christine Pielmeier ◽  
Kelly Elder
Keyword(s):  
Random Forests ◽  
Type Classification

scholarly journals Large-scale unsupervised clustering of Orca vocalizations: a model for describing orca communication systems

2019 ◽  
Author(s):  
Marion Poupard ◽  
Paul Best ◽  
Jan Schlüter ◽  
Helena Symonds ◽  
Paul Spong ◽  
...  
Keyword(s):  
Communication Systems ◽  
Large Scale ◽  
Pitch Contour ◽  
Call Type ◽  
Recording Station ◽  
The Face ◽  
Multichannel Recordings ◽  
Type Classification ◽  
Anthropic Pressure

Killer whales (Orcinus orca) can produce 3 types of signals: clicks, whistles and vocalizations. This study focuses on Orca vocalizations from northern Vancouver Island (Hanson Island) where the NGO Orcalab developed a multi-hydrophone recording station to study Orcas. The acoustic station is composed of 5 hydrophones and extends over 50 km 2 of ocean. Since 2015 we are continuously streaming the hydrophone signals to our laboratory in Toulon, France, yielding nearly 50 TB of synchronous multichannel recordings. In previous work, we trained a Convolutional Neural Network (CNN) to detect Orca vocalizations, using transfer learning from a bird activity dataset. Here, for each detected vocalization, we estimate the pitch contour (fundamental frequency). Finally, we cluster vocalizations by features describing the pitch contour. While preliminary, our results demonstrate a possible route towards automatic Orca call type classification. Furthermore, they can be linked to the presence of particular Orca pods in the area according to the classification of their call types. A large-scale call type classification would allow new insights on phonotactics and ethoacoustics of endangered Orca populations in the face of increasing anthropic pressure.

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