SCHNAPPs - Single Cell sHiNy APPlication(s)

AbstractMotivationThanks to technological advances made in the last few years, we are now able to study transcriptomes from thousands of single cells. These have been applied widely to study various aspects of Biology. Nevertheless, comprehending and inferring meaningful biological insights from these large datasets is still a challenge. Although tools are being developed to deal with the data complexity and data volume, we do not have yet an effective visualizations and comparative analysis tools to realize the full value of these datasets.ResultsIn order to address this gap, we implemented a single cell data visualization portal called Single Cell Viewer (SCV). SCV is an R shiny application that offers users rich visualization and exploratory data analysis options for single cell datasets.AvailabilitySource code for the application is available online at GitHub (http://www.github.com/neuhausi/single-cell-viewer) and there is a hosted exploration application using the same example dataset as this publication at http://periscopeapps.org/[email protected]; [email protected]

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EnImpute: imputing dropout events in single-cell RNA-sequencing data via ensemble learning

Bioinformatics ◽

10.1093/bioinformatics/btz435 ◽

2019 ◽

Vol 35 (22) ◽

pp. 4827-4829 ◽

Cited By ~ 6

Author(s):

Xiao-Fei Zhang ◽

Le Ou-Yang ◽

Shuo Yang ◽

Xing-Ming Zhao ◽

Xiaohua Hu ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Ensemble Learning ◽

R Package ◽

Supplementary Information ◽

Sequencing Data ◽

Single Cell Rna Sequencing ◽

The Individual ◽

Downstream Analysis ◽

Shiny Application

Abstract Summary Imputation of dropout events that may mislead downstream analyses is a key step in analyzing single-cell RNA-sequencing (scRNA-seq) data. We develop EnImpute, an R package that introduces an ensemble learning method for imputing dropout events in scRNA-seq data. EnImpute combines the results obtained from multiple imputation methods to generate a more accurate result. A Shiny application is developed to provide easier implementation and visualization. Experiment results show that EnImpute outperforms the individual state-of-the-art methods in almost all situations. EnImpute is useful for correcting the noisy scRNA-seq data before performing downstream analysis. Availability and implementation The R package and Shiny application are available through Github at https://github.com/Zhangxf-ccnu/EnImpute. Supplementary information Supplementary data are available at Bioinformatics online.

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V-SVA: an R Shiny application for detecting and annotating hidden sources of variation in single-cell RNA-seq data

Bioinformatics ◽

10.1093/bioinformatics/btaa128 ◽

2020 ◽

Vol 36 (11) ◽

pp. 3582-3584

Author(s):

Nathan Lawlor ◽

Eladio J Marquez ◽

Donghyung Lee ◽

Duygu Ucar

Keyword(s):

Single Cell ◽

Gene Annotation ◽

Supplementary Information ◽

Surrogate Variable Analysis ◽

Batch Correction ◽

Surrogate Variable ◽

R Shiny ◽

Sources Of Variation ◽

Shiny Application ◽

Variable Analysis

Abstract Summary Single-cell RNA-sequencing (scRNA-seq) technology enables studying gene expression programs from individual cells. However, these data are subject to diverse sources of variation, including ‘unwanted’ variation that needs to be removed in downstream analyses (e.g. batch effects) and ‘wanted’ or biological sources of variation (e.g. variation associated with a cell type) that needs to be precisely described. Surrogate variable analysis (SVA)-based algorithms, are commonly used for batch correction and more recently for studying ‘wanted’ variation in scRNA-seq data. However, interpreting whether these variables are biologically meaningful or stemming from technical reasons remains a challenge. To facilitate the interpretation of surrogate variables detected by algorithms including IA-SVA, SVA or ZINB-WaVE, we developed an R Shiny application [Visual Surrogate Variable Analysis (V-SVA)] that provides a web-browser interface for the identification and annotation of hidden sources of variation in scRNA-seq data. This interactive framework includes tools for discovery of genes associated with detected sources of variation, gene annotation using publicly available databases and gene sets, and data visualization using dimension reduction methods. Availability and implementation The V-SVA Shiny application is publicly hosted at https://vsva.jax.org/ and the source code is freely available at https://github.com/nlawlor/V-SVA. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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WASP: a versatile, web-accessible single cell RNA-Seq processing platform

BMC Genomics ◽

10.1186/s12864-021-07469-6 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Andreas Hoek ◽

Katharina Maibach ◽

Ebru Özmen ◽

Ana Ivonne Vazquez-Armendariz ◽

Jan Philipp Mengel ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Modular Design ◽

Cellular Heterogeneity ◽

Biological Research ◽

Post Processing ◽

Gene Expression Matrix ◽

R Shiny ◽

Initial Processing ◽

Shiny Application

Abstract Background The technology of single cell RNA sequencing (scRNA-seq) has gained massively in popularity as it allows unprecedented insights into cellular heterogeneity as well as identification and characterization of (sub-)cellular populations. Furthermore, scRNA-seq is almost ubiquitously applicable in medical and biological research. However, these new opportunities are accompanied by additional challenges for researchers regarding data analysis, as advanced technical expertise is required in using bioinformatic software. Results Here we present WASP, a software for the processing of Drop-Seq-based scRNA-Seq data. Our software facilitates the initial processing of raw reads generated with the ddSEQ or 10x protocol and generates demultiplexed gene expression matrices including quality metrics. The processing pipeline is realized as a Snakemake workflow, while an R Shiny application is provided for interactive result visualization. WASP supports comprehensive analysis of gene expression matrices, including detection of differentially expressed genes, clustering of cellular populations and interactive graphical visualization of the results. The R Shiny application can be used with gene expression matrices generated by the WASP pipeline, as well as with externally provided data from other sources. Conclusions With WASP we provide an intuitive and easy-to-use tool to process and explore scRNA-seq data. To the best of our knowledge, it is currently the only freely available software package that combines pre- and post-processing of ddSEQ- and 10x-based data. Due to its modular design, it is possible to use any gene expression matrix with WASP’s post-processing R Shiny application. To simplify usage, WASP is provided as a Docker container. Alternatively, pre-processing can be accomplished via Conda, and a standalone version for Windows is available for post-processing, requiring only a web browser.

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SCHNAPPs - Single Cell sHiNy APPlication(s)

10.1101/2020.06.07.127274 ◽

2020 ◽

Author(s):

Bernd Jagla ◽

Vincent Rouilly ◽

Michel Puceat ◽

Milena Hasan

Keyword(s):

Single Cell ◽

Modular Design ◽

Expression Data ◽

Rna Seq ◽

Link Type ◽

Standard Tool ◽

Single Cell Rna Sequencing ◽

The Common ◽

Bench Scientist ◽

Shiny Application

ABSTRACTMotivationSingle-cell RNA-sequencing (scRNAseq) experiments are becoming a standard tool for bench-scientists to explore the cellular diversity present in all tissues. On one hand, the data produced by scRNASeq is technically complex, with analytical workflows that are still very much an active field of bioinformatics research, and on the other hand, a wealth of biological background knowledge is often needed to guide the investigation. Therefore, there is an increasing need to develop applications geared towards bench-scientists to help them abstract the technical challenges of the analysis, so that they can focus on the Science at play. It is also expected that such applications should support closer collaboration between bioinformaticians and bench-scientists by providing reproducible science tools.ResultsWe present SCHNAPPs, a computer program designed to enable bench-scientists to autonomously explore and interpret single cell RNA-seq expression data and associated annotations. The Shiny-based application allows selecting genes and cells of interest, performing quality control, normalization, clustering, and differential expression analyses, applying standard workflows from Seurat (Stuart et al., 2019) or Scran (Lun et al., 2016) packages, and most of the common visualizations. An R-markdown report can be generated that tracks the modifications, and selected visualizations facilitating communication and reproducibility between bench-scientist and bioinformatician. The modular design of the tool allows to easily integrate new visualizations and analyses by bioinformaticians. We still recommend that a data analysis specialist oversees the analysis and interpretation.AvailabilityThe SCHNAPPs application, docker file, and documentation are available on GitHub: https://c3bi-pasteur-fr.github.io/UTechSCB-SCHNAPPs; Example contribution are available at the following GitHub site: https://github.com/baj12/SCHNAPPsContributions.

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FlexDotPlot: a universal and modular dot plot visualization tool for complex multifaceted data

10.1101/2020.04.03.023655 ◽

2020 ◽

Cited By ~ 2

Author(s):

Simon Leonard ◽

Antoine Rolland ◽

Karin Tarte ◽

Frédéric Chalmel ◽

Aurélie Lardenois

Keyword(s):

Single Cell ◽

Source Code ◽

R Package ◽

Quantitative Information ◽

Visualization Tool ◽

Visualization Method ◽

Rna Seq ◽

Shiny App ◽

R Shiny ◽

Shiny Application

AbstractMotivationDot plots are heatmap-like charts that provide a compact way to simultaneously display two quantitative information by means of dots of different sizes and colours. Despite the popularity of this visualization method, particularly in single-cell RNA-seq studies, existing tools used to make dot plots are limited in terms of functionality and usability.ResultsWe developed FlexDotPlot, an R package for generating dot plots from any type of multifaceted data, including single-cell RNA-seq data. FlexDotPlot provides a universal and easy-to-use solution with a high versatility. An interactive R Shiny application is also available in the FlexDotPlot package allowing non-R users to easily generate dot plots with several tunable parameters.Availability and implementationSource code and detailed manual are available at https://github.com/Simon-Leonard/FlexDotPlot. The Shiny app is available as a stand-alone application within the package.

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Electron microscopy of keratinocytes cultured on a collagen substrate used as an allograft for the treatment of chronic wounds

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100130158 ◽

1992 ◽

Vol 50 (2) ◽

pp. 1104-1105

Author(s):

Debby A. Jennings ◽

Michael J. Morykwas ◽

Louis C. Argenta

Keyword(s):

Electron Microscopy ◽

Cell Suspension ◽

Single Cell ◽

Chronic Wounds ◽

Mechanical Stability ◽

Uv Light ◽

Type I ◽

Single Cell Suspension ◽

Collagen Substrate ◽

Dermal Collagen

Grafts of cultured allogenic or autogenic keratlnocytes have proven to be an effective treatment of chronic wounds and burns. This study utilized a collagen substrate for keratinocyte and fibroblast attachment. The substrate provided mechanical stability and augmented graft manipulation onto the wound bed. Graft integrity was confirmed by light and transmission electron microscopy.Bovine Type I dermal collagen sheets (100 μm thick) were crosslinked with 254 nm UV light (13.5 Joules/cm2) to improve mechanical properties and reduce degradation. A single cell suspension of third passage neonatal foreskin fibroblasts were plated onto the collagen. Five days later, a single cell suspension of first passage neonatal foreskin keratinocytes were plated on the opposite side of the collagen. The grafts were cultured for one month.The grafts were fixed in phosphate buffered 4% formaldehyde/1% glutaraldehyde for 24 hours. Graft pieces were then washed in 0.13 M phosphate buffer, post-fixed in 1% osmium tetroxide, dehydrated, and embedded in Polybed 812.

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