scholarly journals scGEApp: a Matlab app for feature selection on single-cell RNA sequencing data

2019 ◽  
Author(s):  
James J. Cai
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Single Cell ◽  
Rna Sequencing ◽  
User Interfaces ◽  
Dropout Rate ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Full Spectrum ◽  
Single Cell Rna Sequencing

AbstractMotivationThe recent development of single-cell technologies, especially single-cell RNA sequencing (scRNA-seq), provides an unprecedented level of resolution to the cell type heterogeneity. It also enables the study of gene expression variability across individual cells within a homogenous cell population. Feature selection algorithms have been used to select biologically meaningful genes while controlling for sampling noise. An easy-to-use application for feature selection on scRNA-seq data requires integration of functions for data filtering, normalization, visualization, and enrichment analyses. Graphic user interfaces (GUIs) are desired for such an application.ResultsWe used native Matlab and App Designer to develop scGEApp for feature selection on singlecell gene expression data. We specifically designed a new feature selection algorithm based on the 3D spline fitting of expression mean (μ), coefficient of variance (CV), and dropout rate (rdrop), making scGEApp a unique tool for feature selection on scRNA-seq data. Our method can be applied to single-sample or two-sample scRNA-seq data, identify feature genes, e.g., those with unexpectedly high CV for given μ and rdrop of those genes, or genes with the most feature changes. Users can operate scGEApp through GUIs to use the full spectrum of functions including normalization, batch effect correction, imputation, visualization, feature selection, and downstream analyses with GSEA and GOrilla.Availabilityhttps://github.com/jamesjcai/scGEAppContact:[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals SPsimSeq: semi-parametric simulation of bulk and single cell RNA sequencing data

2019 ◽  
Author(s):  
Alemu Takele Assefa ◽  
Jo Vandesompele ◽  
Olivier Thas
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Empirical Distribution ◽  
Supplementary Information ◽  
Rna Seq ◽  
Sequencing Data ◽  
Actual Distribution ◽  
Wide Range ◽  
Single Cell Rna Sequencing

SummarySPsimSeq is a semi-parametric simulation method for bulk and single cell RNA sequencing data. It simulates data from a good estimate of the actual distribution of a given real RNA-seq dataset. In contrast to existing approaches that assume a particular data distribution, our method constructs an empirical distribution of gene expression data from a given source RNA-seq experiment to faithfully capture the data characteristics of real data. Importantly, our method can be used to simulate a wide range of scenarios, such as single or multiple biological groups, systematic variations (e.g. confounding batch effects), and different sample sizes. It can also be used to simulate different gene expression units resulting from different library preparation protocols, such as read counts or UMI counts.Availability and implementationThe R package and associated documentation is available from https://github.com/CenterForStatistics-UGent/SPsimSeq.Supplementary informationSupplementary data are available at bioRχiv online.

scholarly journals scGEAToolbox: a Matlab toolbox for single-cell RNA sequencing data analysis

2019 ◽  
Author(s):  
James J Cai
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Rna Sequencing ◽  
User Interfaces ◽  
Third Party ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Biomedical Sciences ◽  
Matlab Toolbox ◽  
Single Cell Rna Sequencing

Abstract Motivation Single-cell RNA sequencing (scRNA-seq) technology has revolutionized the way research is done in biomedical sciences. It provides an unprecedented level of resolution across individual cells for studying cell heterogeneity and gene expression variability. Analyzing scRNA-seq data is challenging though, due to the sparsity and high dimensionality of the data. Results I developed scGEAToolbox—a Matlab toolbox for scRNA-seq data analysis. It contains a comprehensive set of functions for data normalization, feature selection, batch correction, imputation, cell clustering, trajectory/pseudotime analysis, and network construction, which can be combined and integrated to building custom workflow. While most of the functions are implemented in native Matlab, wrapper functions are provided to allow users to call the “third-party” tools developed in Matlab or other languages. Furthermore, scGEAToolbox is equipped with sophisticated graphical user interfaces (GUIs) generated with App Designer, making it an easy-to-use application for quick data processing. Availability https://github.com/jamesjcai/scGEAToolbox Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Single-Cell Transcriptome Analysis Reveals Dynamic Cell Populations and Differential Gene Expression Patterns in Control and Aneurysmal Human Aortic Tissue

Circulation ◽  
2020 ◽  
Vol 142 (14) ◽  
pp. 1374-1388
Author(s):  
Yanming Li ◽  
Pingping Ren ◽  
Ashley Dawson ◽  
Hernan G. Vasquez ◽  
Waleed Ageedi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Aortic Wall ◽  
Genome Wide Association ◽  
Aortic Tissue ◽  
Sequencing Data ◽  
Genome Wide ◽  
Single Cell Rna Sequencing ◽  
Differential Gene

Background: Ascending thoracic aortic aneurysm (ATAA) is caused by the progressive weakening and dilatation of the aortic wall and can lead to aortic dissection, rupture, and other life-threatening complications. To improve our understanding of ATAA pathogenesis, we aimed to comprehensively characterize the cellular composition of the ascending aortic wall and to identify molecular alterations in each cell population of human ATAA tissues. Methods: We performed single-cell RNA sequencing analysis of ascending aortic tissues from 11 study participants, including 8 patients with ATAA (4 women and 4 men) and 3 control subjects (2 women and 1 man). Cells extracted from aortic tissue were analyzed and categorized with single-cell RNA sequencing data to perform cluster identification. ATAA-related changes were then examined by comparing the proportions of each cell type and the gene expression profiles between ATAA and control tissues. We also examined which genes may be critical for ATAA by performing the integrative analysis of our single-cell RNA sequencing data with publicly available data from genome-wide association studies. Results: We identified 11 major cell types in human ascending aortic tissue; the high-resolution reclustering of these cells further divided them into 40 subtypes. Multiple subtypes were observed for smooth muscle cells, macrophages, and T lymphocytes, suggesting that these cells have multiple functional populations in the aortic wall. In general, ATAA tissues had fewer nonimmune cells and more immune cells, especially T lymphocytes, than control tissues did. Differential gene expression data suggested the presence of extensive mitochondrial dysfunction in ATAA tissues. In addition, integrative analysis of our single-cell RNA sequencing data with public genome-wide association study data and promoter capture Hi-C data suggested that the erythroblast transformation-specific related gene( ERG ) exerts an important role in maintaining normal aortic wall function. Conclusions: Our study provides a comprehensive evaluation of the cellular composition of the ascending aortic wall and reveals how the gene expression landscape is altered in human ATAA tissue. The information from this study makes important contributions to our understanding of ATAA formation and progression.

schex avoids overplotting for large single-cell RNA-sequencing datasets

2019 ◽  
Vol 36 (7) ◽  
pp. 2291-2292 ◽  
Author(s):  
Saskia Freytag ◽  
Ryan Lister
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Abstract Summary Due to the scale and sparsity of single-cell RNA-sequencing data, traditional plots can obscure vital information. Our R package schex overcomes this by implementing hexagonal binning, which has the additional advantages of improving speed and reducing storage for resulting plots. Availability and implementation schex is freely available from Bioconductor via http://bioconductor.org/packages/release/bioc/html/schex.html and its development version can be accessed on GitHub via https://github.com/SaskiaFreytag/schex. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals PRIME: a probabilistic imputation method to reduce dropout effects in single-cell RNA sequencing

2020 ◽  
Vol 36 (13) ◽  
pp. 4021-4029
Author(s):  
Hyundoo Jeong ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Imputation Method ◽  
Supplementary Information ◽  
Single Cell Sequencing ◽  
Depth Analysis ◽  
Single Cell Rna Sequencing

Abstract Summary Single-cell RNA sequencing technology provides a novel means to analyze the transcriptomic profiles of individual cells. The technique is vulnerable, however, to a type of noise called dropout effects, which lead to zero-inflated distributions in the transcriptome profile and reduce the reliability of the results. Single-cell RNA sequencing data, therefore, need to be carefully processed before in-depth analysis. Here, we describe a novel imputation method that reduces dropout effects in single-cell sequencing. We construct a cell correspondence network and adjust gene expression estimates based on transcriptome profiles for the local subnetwork of cells of the same type. We comprehensively evaluated this method, called PRIME (PRobabilistic IMputation to reduce dropout effects in Expression profiles of single-cell sequencing), on synthetic and eight real single-cell sequencing datasets and verified that it improves the quality of visualization and accuracy of clustering analysis and can discover gene expression patterns hidden by noise. Availability and implementation The source code for the proposed method is freely available at https://github.com/hyundoo/PRIME. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DoubletFinder: Doublet detection in single-cell RNA sequencing data using artificial nearest neighbors

2018 ◽  
Author(s):  
Christopher S. McGinnis ◽  
Lyndsay M. Murrow ◽  
Zev J. Gartner
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
False Negative ◽  
Droplet Microfluidics ◽  
Cell Capture ◽  
Sequencing Data ◽  
Putative Gene ◽  
Detection Tool ◽  
Single Cell Rna Sequencing

SUMMARYSingle-cell RNA sequencing (scRNA-seq) using droplet microfluidics occasionally produces transcriptome data representing more than one cell. These technical artifacts are caused by cell doublets formed during cell capture and occur at a frequency proportional to the total number of sequenced cells. The presence of doublets can lead to spurious biological conclusions, which justifies the practice of sequencing fewer cells to limit doublet formation rates. Here, we present a computational doublet detection tool – DoubletFinder – that identifies doublets based solely on gene expression features. DoubletFinder infers the putative gene expression profile of real doublets by generating artificial doublets from existing scRNA-seq data. Neighborhood detection in gene expression space then identifies sequenced cells with increased probability of being doublets based on their proximity to artificial doublets. DoubletFinder robustly identifies doublets across scRNA-seq datasets with variable numbers of cells and sequencing depth, and predicts false-negative and false-positive doublets defined using conventional barcoding approaches. We anticipate that DoubletFinder will aid in scRNA-seq data analysis and will increase the throughput and accuracy of scRNA-seq experiments.

scholarly journals G2S3: a gene graph-based imputation method for single-cell RNA sequencing data

2020 ◽  
Author(s):  
Weimiao Wu ◽  
Qile Dai ◽  
Yunqing Liu ◽  
Xiting Yan ◽  
Zuoheng Wang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Sequencing Data ◽  
High Data ◽  
Study Gene Expression ◽  
Single Cell Rna Sequencing ◽  
Novel Method

AbstractSingle-cell RNA sequencing provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses. We propose a novel method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and other existing methods to seven single-cell datasets to compare their performance. Our results demonstrated that G2S3 is superior in recovering true expression levels, identifying cell subtypes, improving differential expression analyses, and recovering gene regulatory relationships, especially for mildly expressed genes.

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