scholarly journals SingleCellNet: a computational tool to classify single cell RNA-Seq data across platforms and across species

2018 ◽  
Author(s):  
Yuqi Tan ◽  
Patrick Cahan
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Marker Genes ◽  
Rna Seq ◽  
Cell Type ◽  
Computational Tool ◽  
Cell Type Composition ◽  
Type Composition ◽  
Cell Type Specific

Single cell RNA-Seq has emerged as a powerful tool in diverse applications, ranging from determining the cell-type composition of tissues to uncovering the regulators of developmental programs. A near-universal step in the analysis of single cell RNA-Seq data is to hypothesize the identity of each cell. Often, this is achieved by finding cells that express combinations of marker genes that had previously been implicated as being cell-type specific, an approach that is not quantitative and does not explicitly take advantage of other single cell RNA-Seq studies. Here, we describe our tool, SingleCellNet, which addresses these issues and enables the classification of query single cell RNA-Seq data in comparison to reference single cell RNA-Seq data. SingleCellNet compares favorably to other methods, and it is notably able to make sensitive and accurate classifications across platforms and species. We demonstrate how SingleCellNet can be used to classify previously undetermined cells, and how it can be used to assess the outcome of cell fate engineering experiments.

scholarly journals LRcell: detecting the source of differential expression at the sub-cell type level from bulk RNA-seq data

2021 ◽  
Author(s):  
Wenjing Ma ◽  
Sumeet Sharma ◽  
Peng Jin ◽  
Shannon L Gourley ◽  
Zhaohui Qin
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Marker Genes ◽  
Bioconductor Package ◽  
Rna Seq ◽  
Cell Type ◽  
Reference Dataset ◽  
Cell Type Composition ◽  
Type Composition ◽  
Differential Gene

The rapid proliferation of single-cell RNA-sequencing (scRNA-seq) datasets have revealed cell heterogeneity at unprecedented scales. Several deconvolution methods have been developed to decompose bulk experiments to reveal cell type contributions. However, these methods lack power in identifying the accurate cell type composition when having a considerable amount of sub-cell types in the reference dataset. Here, we present LRcell, a R Bioconductor package (http://bioconductor.org/packages/release/bioc/html/LRcell.html) aiming to identify specific sub-cell type(s) that drives the changes observed in a bulk RNA-seq differential gene expression experiment. In addition, LRcell provides pre-embedded marker genes computed from putative single-cell RNA-seq experiments as options to execute the analyses.

scholarly journals JIND: Joint Integration and Discrimination for Automated Single-Cell Annotation

2020 ◽  
Author(s):  
Mohit Goyal ◽  
Guillermo Serrano ◽  
Ilan Shomorony ◽  
Mikel Hernaez ◽  
Idoia Ochoa
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Marker Genes ◽  
Specific Marker ◽  
Rna Seq ◽  
Batch Effects ◽  
Cell Type ◽  
Latent Space ◽  
Cell Type Specific ◽  
Low Dimensional

AbstractSingle-cell RNA-seq is a powerful tool in the study of the cellular composition of different tissues and organisms. A key step in the analysis pipeline is the annotation of cell-types based on the expression of specific marker genes. Since manual annotation is labor-intensive and does not scale to large datasets, several methods for automated cell-type annotation have been proposed based on supervised learning. However, these methods generally require feature extraction and batch alignment prior to classification, and their performance may become unreliable in the presence of cell-types with very similar transcriptomic profiles, such as differentiating cells. We propose JIND, a framework for automated cell-type identification based on neural networks that directly learns a low-dimensional representation (latent code) in which cell-types can be reliably determined. To account for batch effects, JIND performs a novel asymmetric alignment in which the transcriptomic profile of unseen cells is mapped onto the previously learned latent space, hence avoiding the need of retraining the model whenever a new dataset becomes available. JIND also learns cell-type-specific confidence thresholds to identify and reject cells that cannot be reliably classified. We show on datasets with and without batch effects that JIND classifies cells more accurately than previously proposed methods while rejecting only a small proportion of cells. Moreover, JIND batch alignment is parallelizable, being more than five or six times faster than Seurat integration. Availability: https://github.com/mohit1997/JIND.

scholarly journals Strategies for cellular deconvolution in human brain RNA sequencing data

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 750
Author(s):  
Olukayode A. Sosina ◽  
Matthew N. Tran ◽  
Kristen R. Maynard ◽  
Ran Tao ◽  
Margaret A. Taub ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Data ◽  
Moderate Correlation ◽  
Rna Seq ◽  
Cell Type ◽  
Sequencing Data ◽  
Reference Dataset ◽  
The Past ◽  
Cell Type Composition ◽  
Type Composition

Background: Statistical deconvolution strategies have emerged over the past decade to estimate the proportion of various cell populations in homogenate tissue sources like brain using gene expression data. However, no study has been undertaken to assess the extent to which expression-based and DNAm-based cell type composition estimates agree. Results: Using estimated neuronal fractions from DNAm data, from the same brain region (i.e., matched) as our bulk RNA-Seq dataset, as proxies for the true unobserved cell-type fractions (i.e., as the gold standard), we assessed the accuracy (RMSE) and concordance (R2) of four reference-based deconvolution algorithms: Houseman, CIBERSORT, non-negative least squares (NNLS)/MIND, and MuSiC. We did this for two cell-type populations - neurons and non-neurons/glia - using matched single nuclei RNA-Seq and mismatched single cell RNA-Seq reference datasets. With the mismatched single cell RNA-Seq reference dataset, Houseman, MuSiC, and NNLS produced concordant (high correlation; Houseman R2 = 0.51, 95% CI [0.39, 0.65]; MuSiC R2 = 0.56, 95% CI [0.43, 0.69]; NNLS R2 = 0.54, 95% CI [0.32, 0.68]) but biased (high RMSE, >0.35) neuronal fraction estimates. CIBERSORT produced more discordant (moderate correlation; R2 = 0.25, 95% CI [0.15, 0.38]) neuronal fraction estimates, but with less bias (low RSME, 0.09). Using the matched single nuclei RNA-Seq reference dataset did not eliminate bias (MuSiC RMSE = 0.17). Conclusions: Our results together suggest that many existing RNA deconvolution algorithms estimate the RNA composition of homogenate tissue, e.g. the amount of RNA attributable to each cell type, and not the cellular composition, which relates to the underlying fraction of cells.

scholarly journals A novel computational complete deconvolution method using RNA-seq data

2018 ◽  
Author(s):  
Kai Kang ◽  
Qian Meng ◽  
Igor Shats ◽  
David M. Umbach ◽  
Melissa Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Specific Gene Expression ◽  
Cell Type Composition ◽  
Type Composition ◽  
Cell Type Specific

AbstractThe cell type composition of many biological tissues varies widely across samples. Such sample heterogeneity hampers efforts to probe the role of each cell type in the tissue microenvironment. Current approaches that address this issue have drawbacks. Cell sorting or single-cell based experimental techniques disrupt in situ interactions and alter physiological status of cells in tissues. Computational methods are flexible and promising; but they often estimate either sample-specific proportions of each cell type or cell-type-specific gene expression profiles, not both, by requiring the other as input. We introduce a computational Complete Deconvolution method that can estimate both sample-specific proportions of each cell type and cell-type-specific gene expression profiles simultaneously using bulk RNA-Seq data only (CDSeq). We assessed our method’s performance using several synthetic and experimental mixtures of varied but known cell type composition and compared its performance to the performance of two state-of-the art deconvolution methods on the same mixtures. The results showed CDSeq can estimate both sample-specific proportions of each component cell type and cell-typespecificgene expression profiles with high accuracy. CDSeq holds promise for computationally deciphering complex mixtures of cell types, each with differing expression profiles, using RNA-seq data measured in bulk tissue (MATLAB code is available at https://github.com/kkang7/CDSeq_011).

scholarly journals Capybara: A computational tool to measure cell identity and fate transitions

2020 ◽  
Author(s):  
Wenjun Kong ◽  
Yuheng C. Fu ◽  
Samantha A. Morris
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Biological Knowledge ◽  
Rna Seq ◽  
Cell Type ◽  
Computational Tool ◽  
Cell Identity ◽  
Prior Biological Knowledge ◽  
Analytical Tools

SummaryTransitions in cell identity are fundamental to development, reprogramming, and disease. Single-cell technologies enable the dissection of tissue composition on a cell-by-cell basis in complex biological systems. However, highly-sparse single-cell RNA-seq data poses challenges for cell-type identification algorithms based on bulk RNA-seq. Single-cell analytical tools are also limited, where they require prior biological knowledge and typically classify cells in a discrete, categorical manner. Here, we present a computational tool, ‘Capybara,’ designed to measure cell identity as a continuum, at single-cell resolution. This approach enables the classification of discrete cell entities but also identifies cells harboring multiple identities, supporting a metric to quantify cell fate transition dynamics. We benchmark the performance of Capybara against other existing classifiers and demonstrate its efficacy to annotate cells and identify critical transitions within a well-characterized differentiation hierarchy, hematopoiesis. Our application of Capybara to a range of reprogramming strategies reveals previously uncharacterized regional patterning and identifies a putative in vivo correlate for an engineered cell type that has, to date, remained undefined. These findings prioritize interventions to increase the efficiency and fidelity of cell engineering strategies, showcasing the utility of Capybara to dissect cell identity and fate transitions. Capybara code and documentation are available at https://github.com/morris-lab/Capybara.

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 ICTD: A semi-supervised cell type identification and deconvolution method for multi-omics data

2018 ◽  
Author(s):  
Wennan Chang ◽  
Changlin Wan ◽  
Xiaoyu Lu ◽  
Szu-wei Tu ◽  
Yifan Sun ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Training Data ◽  
Marker Genes ◽  
Cell Detection ◽  
Omics Data ◽  
Deconvolution Method ◽  
Cell Type ◽  
Data Set ◽  
Cell Type Specific

AbstractWe developed a novel deconvolution method, namely Inference of Cell Types and Deconvolution (ICTD) that addresses the fundamental issue of identifiability and robustness in current tissue data deconvolution problem. ICTD provides substantially new capabilities for omics data based characterization of a tissue microenvironment, including (1) maximizing the resolution in identifying resident cell and sub types that truly exists in a tissue, (2) identifying the most reliable marker genes for each cell type, which are tissue and data set specific, (3) handling the stability problem with co-linear cell types, (4) co-deconvoluting with available matched multi-omics data, and (5) inferring functional variations specific to one or several cell types. ICTD is empowered by (i) rigorously derived mathematical conditions of identifiable cell type and cell type specific functions in tissue transcriptomics data and (ii) a semi supervised approach to maximize the knowledge transfer of cell type and functional marker genes identified in single cell or bulk cell data in the analysis of tissue data, and (iii) a novel unsupervised approach to minimize the bias brought by training data. Application of ICTD on real and single cell simulated tissue data validated that the method has consistently good performance for tissue data coming from different species, tissue microenvironments, and experimental platforms. Other than the new capabilities, ICTD outperformed other state-of-the-art devolution methods on prediction accuracy, the resolution of identifiable cell, detection of unknown sub cell types, and assessment of cell type specific functions. The premise of ICTD also lies in characterizing cell-cell interactions and discovering cell types and prognostic markers that are predictive of clinical outcomes.

scholarly journals Single-nuclei RNA-seq on human retinal tissue provides improved transcriptome profiling

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Qingnan Liang ◽  
Rachayata Dharmat ◽  
Leah Owen ◽  
Akbar Shakoor ◽  
Yumei Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Transcriptome Profiling ◽  
Cell Types ◽  
Retinal Cell ◽  
Peripheral Retina ◽  
Marker Genes ◽  
Rna Seq ◽  
Cell Type ◽  
Retinal Tissue ◽  
The Individual

AbstractSingle-cell RNA-seq is a powerful tool in decoding the heterogeneity in complex tissues by generating transcriptomic profiles of the individual cell. Here, we report a single-nuclei RNA-seq (snRNA-seq) transcriptomic study on human retinal tissue, which is composed of multiple cell types with distinct functions. Six samples from three healthy donors are profiled and high-quality RNA-seq data is obtained for 5873 single nuclei. All major retinal cell types are observed and marker genes for each cell type are identified. The gene expression of the macular and peripheral retina is compared to each other at cell-type level. Furthermore, our dataset shows an improved power for prioritizing genes associated with human retinal diseases compared to both mouse single-cell RNA-seq and human bulk RNA-seq results. In conclusion, we demonstrate that obtaining single cell transcriptomes from human frozen tissues can provide insight missed by either human bulk RNA-seq or animal models.

scholarly journals A single cell brain atlas in human Alzheimer’s disease

2019 ◽  
Author(s):  
Alexandra Grubman ◽  
Gabriel Chew ◽  
John F. Ouyang ◽  
Guizhi Sun ◽  
Xin Yi Choo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Single Cell ◽  
Cell Fate ◽  
Expression Patterns ◽  
Cell Types ◽  
Gene Expression Patterns ◽  
Cell Type ◽  
Web Resource ◽  
Cell Type Specific

AbstractAlzheimer’s disease (AD) is a heterogeneous disease that is largely dependent on the complex cellular microenvironment in the brain. This complexity impedes our understanding of how individual cell types contribute to disease progression and outcome. To characterize the molecular and functional cell diversity in the human AD brain we utilized single nuclei RNA- seq in AD and control patient brains in order to map the landscape of cellular heterogeneity in AD. We detail gene expression changes at the level of cells and cell subclusters, highlighting specific cellular contributions to global gene expression patterns between control and Alzheimer’s patient brains. We observed distinct cellular regulation of APOE which was repressed in oligodendrocyte progenitor cells (OPCs) and astrocyte AD subclusters, and highly enriched in a microglial AD subcluster. In addition, oligodendrocyte and microglia AD subclusters show discordant expression of APOE. Integration of transcription factor regulatory modules with downstream GWAS gene targets revealed subcluster-specific control of AD cell fate transitions. For example, this analysis uncovered that astrocyte diversity in AD was under the control of transcription factor EB (TFEB), a master regulator of lysosomal function and which initiated a regulatory cascade containing multiple AD GWAS genes. These results establish functional links between specific cellular sub-populations in AD, and provide new insights into the coordinated control of AD GWAS genes and their cell-type specific contribution to disease susceptibility. Finally, we created an interactive reference web resource which will facilitate brain and AD researchers to explore the molecular architecture of subtype and AD-specific cell identity, molecular and functional diversity at the single cell level.HighlightsWe generated the first human single cell transcriptome in AD patient brainsOur study unveiled 9 clusters of cell-type specific and common gene expression patterns between control and AD brains, including clusters of genes that present properties of different cell types (i.e. astrocytes and oligodendrocytes)Our analyses also uncovered functionally specialized sub-cellular clusters: 5 microglial clusters, 8 astrocyte clusters, 6 neuronal clusters, 6 oligodendrocyte clusters, 4 OPC and 2 endothelial clusters, each enriched for specific ontological gene categoriesOur analyses found manifold AD GWAS genes specifically associated with one cell-type, and sets of AD GWAS genes co-ordinately and differentially regulated between different brain cell-types in AD sub-cellular clustersWe mapped the regulatory landscape driving transcriptional changes in AD brain, and identified transcription factor networks which we predict to control cell fate transitions between control and AD sub-cellular clustersFinally, we provide an interactive web-resource that allows the user to further visualise and interrogate our dataset.Data resource web interface:http://adsn.ddnetbio.com

scholarly journals Bulk Tissue Cell Type Deconvolution with Multi-Subject Single-Cell Expression Reference

2018 ◽  
Author(s):  
Xuran Wang ◽  
Jihwan Park ◽  
Katalin Susztak ◽  
Nancy R. Zhang ◽  
Mingyao Li
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Tissue Cell ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Cell Type Specific ◽  
Cell Expression

AbstractWe present MuSiC, a method that utilizes cell-type specific gene expression from single-cell RNA sequencing (RNA-seq) data to characterize cell type compositions from bulk RNA-seq data in complex tissues. When applied to pancreatic islet and whole kidney expression data in human, mouse, and rats, MuSiC outperformed existing methods, especially for tissues with closely related cell types. MuSiC enables characterization of cellular heterogeneity of complex tissues for identification of disease mechanisms.

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