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.

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 Reference Transcriptomes of Porcine Peripheral Immune Cells Created Through Bulk and Single-Cell RNA Sequencing

2021 ◽  
Vol 12 ◽  
Author(s):  
Juber Herrera-Uribe ◽  
Jayne E. Wiarda ◽  
Sathesh K. Sivasankaran ◽  
Lance Daharsh ◽  
Haibo Liu ◽  
...  
Keyword(s):  
T Cells ◽  
Single Cell ◽  
Immune Cells ◽  
Peripheral Blood ◽  
Cell Types ◽  
Cell Populations ◽  
Rna Seq ◽  
Cell Type ◽  
Transcriptomic Data ◽  
Single Cell Rna Sequencing

Pigs are a valuable human biomedical model and an important protein source supporting global food security. The transcriptomes of peripheral blood immune cells in pigs were defined at the bulk cell-type and single cell levels. First, eight cell types were isolated in bulk from peripheral blood mononuclear cells (PBMCs) by cell sorting, representing Myeloid, NK cells and specific populations of T and B-cells. Transcriptomes for each bulk population of cells were generated by RNA-seq with 10,974 expressed genes detected. Pairwise comparisons between cell types revealed specific expression, while enrichment analysis identified 1,885 to 3,591 significantly enriched genes across all 8 cell types. Gene Ontology analysis for the top 25% of significantly enriched genes (SEG) showed high enrichment of biological processes related to the nature of each cell type. Comparison of gene expression indicated highly significant correlations between pig cells and corresponding human PBMC bulk RNA-seq data available in Haemopedia. Second, higher resolution of distinct cell populations was obtained by single-cell RNA-sequencing (scRNA-seq) of PBMC. Seven PBMC samples were partitioned and sequenced that produced 28,810 single cell transcriptomes distributed across 36 clusters and classified into 13 general cell types including plasmacytoid dendritic cells (DC), conventional DCs, monocytes, B-cell, conventional CD4 and CD8 αβ T-cells, NK cells, and γδ T-cells. Signature gene sets from the human Haemopedia data were assessed for relative enrichment in genes expressed in pig cells and integration of pig scRNA-seq with a public human scRNA-seq dataset provided further validation for similarity between human and pig data. The sorted porcine bulk RNAseq dataset informed classification of scRNA-seq PBMC populations; specifically, an integration of the datasets showed that the pig bulk RNAseq data helped define the CD4CD8 double-positive T-cell populations in the scRNA-seq data. Overall, the data provides deep and well-validated transcriptomic data from sorted PBMC populations and the first single-cell transcriptomic data for porcine PBMCs. This resource will be invaluable for annotation of pig genes controlling immunogenetic traits as part of the porcine Functional Annotation of Animal Genomes (FAANG) project, as well as further study of, and development of new reagents for, porcine immunology.

scholarly journals Cell type-specific aging clocks to quantify aging and rejuvenation in regenerative regions of the brain

2022 ◽  
Author(s):  
Matthew T Buckley ◽  
Eric Sun ◽  
Benson M. George ◽  
Ling Liu ◽  
Nicholas Schaum ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Type ◽  
Cell Level ◽  
Transcriptomic Data ◽  
Precise Quantification ◽  
Cell Type Specific ◽  
Tissue Aging ◽  
The Brain

Aging manifests as progressive dysfunction culminating in death. The diversity of cell types is a challenge to the precise quantification of aging and its reversal. Here we develop a suite of 'aging clocks' based on single cell transcriptomic data to characterize cell type-specific aging and rejuvenation strategies. The subventricular zone (SVZ) neurogenic region contains many cell types and provides an excellent system to study cell-level tissue aging and regeneration. We generated 21,458 single-cell transcriptomes from the neurogenic regions of 28 mice, tiling ages from young to old. With these data, we trained a suite of single cell-based regression models (aging clocks) to predict both chronological age (passage of time) and biological age (fitness, in this case the proliferative capacity of the neurogenic region). Both types of clocks perform well on independent cohorts of mice. Genes underlying the single cell-based aging clocks are mostly cell-type specific, but also include a few shared genes in the interferon and lipid metabolism pathways. We used these single cell-based aging clocks to measure transcriptomic rejuvenation, by generating single cell RNA-seq datasets of SVZ neurogenic regions for two interventions - heterochronic parabiosis (young blood) and exercise. Interestingly, the use of aging clocks reveals that both heterochronic parabiosis and exercise reverse transcriptomic aging in the niche, but in different ways across cell types and genes. This study represents the first development of high-resolution aging clocks from single cell transcriptomic data and demonstrates their application to quantify transcriptomic rejuvenation.

scholarly journals VPAC: Variational projection for accurate clustering of single-cell transcriptomic data

2019 ◽  
Author(s):  
Shengquan Chen ◽  
Kui Hua ◽  
Hongfei Cui ◽  
Rui Jiang
Keyword(s):  
Single Cell ◽  
Scale Up ◽  
Mixture Distribution ◽  
Cell Types ◽  
Gaussian Mixture ◽  
Specific Cell ◽  
Transcriptomic Data ◽  
Cell Clustering ◽  
Dataset Size ◽  
User Friendly

AbstractBackgroundSingle-cell RNA-sequencing (scRNA-seq) technologies have advanced rapidly in recent years and enabled the quantitative characterization at a microscopic resolution. With the exponential growth of the number of cells profiled in individual scRNA-seq experiments, the demand for identifying putative cell types from the data has become a great challenge that appeals for novel computational methods. Although a variety of algorithms have recently been proposed for single-cell clustering, such limitations as low accuracy, inferior robustness, and inadequate stability greatly impede the scope of applications of these methods.ResultsWe propose a novel model-based algorithm, named VPAC, for accurate clustering of single-cell transcriptomic data through variational projection, which assumes that single-cell samples follow a Gaussian mixture distribution in a latent space. Through comprehensive validation experiments, we demonstrate that VPAC can not only be applied to datasets of discrete counts and normalized continuous data, but also scale up well to various data dimensionality, different dataset size and different data sparsity. We further illustrate the ability of VPAC to detect genes with strong unique signatures of a specific cell type, which may shed light on the studies in system biology. We have released a user-friendly python package of VPAC in Github (https://github.com/ShengquanChen/VPAC). Users can directly import our VPAC class and conduct clustering without tedious installation of dependency packages.ConclusionsVPAC enables highly accurate clustering of single-cell transcriptomic data via a statistical model. We expect to see wide applications of our method to not only transcriptome studies for fully understanding the cell identity and functionality, but also the clustering of more general data.

scholarly journals Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data

2021 ◽  
Author(s):  
Yifan Zhao ◽  
Huiyu Cai ◽  
Zuobai Zhang ◽  
Jian Tang ◽  
Yue Li
Keyword(s):  
Single Cell ◽  
Topic Model ◽  
Cell Types ◽  
Gene Signature ◽  
Batch Effects ◽  
Cell Type ◽  
Transcriptomic Data ◽  
Variational Autoencoder ◽  
Single Cell Rna Sequencing ◽  
Cell Type Specific

Abstract The advent of single-cell RNA sequencing (scRNA-seq) technologies has revolutionized transcriptomic studies. However, integrative analysis of scRNA-seq data remains a challenge largely due to batch effects. We present single-cell Embedded Topic Model (scETM), an unsupervised deep generative model that recapitulates known cell types by inferring the latent cell topic mixtures via a variational autoencoder. scETM is scalable to over 10^6 cells and enables effective knowledge transfer across datasets. scETM also offers high interpretability and allows the incorporation of prior pathway knowledge into the gene embeddings. The scETM-inferred topics show enrichment in cell-type-specific and disease-related pathways.

scholarly journals scReClassify: post hoc cell type classification of single-cell rNA-seq data

BMC Genomics ◽  
2019 ◽  
Vol 20 (S9) ◽  
Author(s):  
Taiyun Kim ◽  
Kitty Lo ◽  
Thomas A. Geddes ◽  
Hani Jieun Kim ◽  
Jean Yee Hwa Yang ◽  
...  
Keyword(s):  
Single Cell ◽  
Supervised Learning ◽  
Current Knowledge ◽  
Cell Types ◽  
Computational Techniques ◽  
Biological Knowledge ◽  
Marker Genes ◽  
Cell Type ◽  
Post Hoc ◽  
Type Classification

Abstract Background Single-cell RNA-sequencing (scRNA-seq) is a fast emerging technology allowing global transcriptome profiling on the single cell level. Cell type identification from scRNA-seq data is a critical task in a variety of research such as developmental biology, cell reprogramming, and cancers. Typically, cell type identification relies on human inspection using a combination of prior biological knowledge (e.g. marker genes and morphology) and computational techniques (e.g. PCA and clustering). Due to the incompleteness of our current knowledge and the subjectivity involved in this process, a small amount of cells may be subject to mislabelling. Results Here, we propose a semi-supervised learning framework, named scReClassify, for ‘post hoc’ cell type identification from scRNA-seq datasets. Starting from an initial cell type annotation with potentially mislabelled cells, scReClassify first performs dimension reduction using PCA and next applies a semi-supervised learning method to learn and subsequently reclassify cells that are likely mislabelled initially to the most probable cell types. By using both simulated and real-world experimental datasets that profiled various tissues and biological systems, we demonstrate that scReClassify is able to accurately identify and reclassify misclassified cells to their correct cell types. Conclusions scReClassify can be used for scRNA-seq data as a post hoc cell type classification tool to fine-tune cell type annotations generated by any cell type classification procedure. It is implemented as an R package and is freely available from https://github.com/SydneyBioX/scReClassify

scholarly journals A Harmonized Atlas of Spinal Cord Cell Types and Their Computational Classification

2020 ◽  
Author(s):  
Daniel E. Russ ◽  
Ryan B. Patterson Cross ◽  
Li Li ◽  
Stephanie C. Koch ◽  
Kaya J.E. Matson ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Single Cell ◽  
Cell Types ◽  
Transcriptomic Data ◽  
Single Cell Sequencing ◽  
Combinatorial Set ◽  
Spinal Cord Cell ◽  
Exciting Potential ◽  
Cell Data

ABSTRACTSingle cell sequencing is transforming many fields of science but the vast amount of data it creates has the potential to both illuminate and obscure underlying biology. To harness the exciting potential of single cell data for the study of the mouse spinal cord, we have created a harmonized atlas of spinal cord transcriptomic cell types that unifies six independent and disparate studies into one common analysis. With the power of this large and diverse dataset, we reveal spinal cord cell type organization, validate a combinatorial set of markers for in-tissue spatial gene expression analysis, and optimize the computational classification of spinal cord cell types based on transcriptomic data. This work provides a comprehensive resource with unprecedented resolution of spinal cord cell types and charts a path forward for how to utilize transcriptomic data to expand our knowledge of spinal cord biology.

scholarly journals Single-Cell Entropy to Quantify the Cellular Order Parameter from Single-Cell RNA-Seq Data

2020 ◽  
Vol 15 (01) ◽  
pp. 35-49
Author(s):  
Jingxin Liu ◽  
You Song ◽  
Jinzhi Lei
Keyword(s):  
Single Cell ◽  
Order Parameter ◽  
Developmental Process ◽  
Cell Types ◽  
Gaussian Mixture ◽  
Cell Type ◽  
A Cell ◽  
Molecular Components ◽  
Different Cell Types ◽  
Type Classification

The cell is the basic functional and biological unit of life, and a complex system that contains a huge number of molecular components. How can we quantify the macroscopic state of a cell from the microscopic information of these molecular components? This is a fundamental question to increase the understanding of the human body. The recent maturation of single-cell RNA sequencing (scRNA-seq) technologies has allowed researchers to gain information on the transcriptomes of individual cells. Although considerable progress has been made in terms of cell-type clustering over the past few years, there is no strong consensus about how to define a cell state from scRNA-seq data. Here, we present single-cell entropy (scEntropy) as an order parameter for cellular transcriptome profiles from scRNA-seq data. scEntropy is a straightforward parameter with which to define the intrinsic transcriptional state of a cell that can provide a quantity to measure the developmental process and to distinguish different cell types. The proposed scEntropy followed by Gaussian mixture model (scEGMM) provides a coherent method of cell-type classification that is simple, includes no parameters or clustering and is comparable to existing machine learning-based methods in benchmarking studies. The results of cell-type classification based on scEGMM are robust and easy to biologically interpret.

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