scholarly journals CIPR: a web-based R/shiny app and R package to annotate cell clusters in single cell RNA sequencing experiments

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
H. Atakan Ekiz ◽  
Christopher J. Conley ◽  
W. Zac Stephens ◽  
Ryan M. O’Connell
Keyword(s):  
Data Analysis ◽  
User Interface ◽  
Single Cell ◽  
Graphical User Interface ◽  
R Package ◽  
Web Based ◽  
Cell Clusters ◽  
Shiny App ◽  
Single Cell Rna Sequencing ◽  
R Shiny

Abstract Background Single cell RNA sequencing (scRNAseq) has provided invaluable insights into cellular heterogeneity and functional states in health and disease. During the analysis of scRNAseq data, annotating the biological identity of cell clusters is an important step before downstream analyses and it remains technically challenging. The current solutions for annotating single cell clusters generally lack a graphical user interface, can be computationally intensive or have a limited scope. On the other hand, manually annotating single cell clusters by examining the expression of marker genes can be subjective and labor-intensive. To improve the quality and efficiency of annotating cell clusters in scRNAseq data, we present a web-based R/Shiny app and R package, Cluster Identity PRedictor (CIPR), which provides a graphical user interface to quickly score gene expression profiles of unknown cell clusters against mouse or human references, or a custom dataset provided by the user. CIPR can be easily integrated into the current pipelines to facilitate scRNAseq data analysis. Results CIPR employs multiple approaches for calculating the identity score at the cluster level and can accept inputs generated by popular scRNAseq analysis software. CIPR provides 2 mouse and 5 human reference datasets, and its pipeline allows inter-species comparisons and the ability to upload a custom reference dataset for specialized studies. The option to filter out lowly variable genes and to exclude irrelevant reference cell subsets from the analysis can improve the discriminatory power of CIPR suggesting that it can be tailored to different experimental contexts. Benchmarking CIPR against existing functionally similar software revealed that our algorithm is less computationally demanding, it performs significantly faster and provides accurate predictions for multiple cell clusters in a scRNAseq experiment involving tumor-infiltrating immune cells. Conclusions CIPR facilitates scRNAseq data analysis by annotating unknown cell clusters in an objective and efficient manner. Platform independence owing to Shiny framework and the requirement for a minimal programming experience allows this software to be used by researchers from different backgrounds. CIPR can accurately predict the identity of a variety of cell clusters and can be used in various experimental contexts across a broad spectrum of research areas.

scholarly journals ANALISIS MODEL ANTREAN NON-POISSON DAN UKURAN KINERJA SISTEM BERBASIS GUI WEB INTERAKTIF MENGGUNAKAN R-SHINY (Studi Kasus: Bus di Terminal Penggaron Kota Semarang)

2020 ◽  
Vol 9 (4) ◽  
pp. 444-453
Author(s):  
Devi Wijayanti ◽  
Sugito Sugito ◽  
Hasbi Yasin
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
System Performance ◽  
City Government ◽  
Online Programs ◽  
Web Based ◽  
Bus Stop ◽  
Service Distribution ◽  
R Shiny ◽  
Queue Model

Since September 1, 2018, The Semarang City Government has diverted intercity bus stop within the province from Terboyo Terminal to Penggaron Terminal, resulting in an imbalance of movement and capacity of the Penggaron Terminal which causes queue of bus. Non-Poisson queue is a queue model in which the arrival and service distribution do not have a Poisson distribution or do not have an Exponential distribution. The study was conducted on buses entering the Penggaron Bus Station with the destination of Jepara, Kedungjati, Juwangi, Yogyakarta, Kudus/Pati/Lasem, Pekalongan/Tegal, and Purwokerto/Purworejo. The main goal of this project is to identify the queue model of Non-Poisson and calculate the measure of system performance using the GUI R. One of the programs in R that can create an interactive web-based GUI (Graphical User Interface) is R-Shiny. R-Shiny is a toolkit of R programs that can be used to create online programs. The distribution test obtained using the EasyFit program. The bus queue model of Jepara is (DAGUM/GEV/4):(GD/∞/∞), the queue model of Kedungjati is (GPD/ DAGUM/1):(GD/∞/∞), the queue model of Juwangi is (GEV/ GEV/1):(GD/∞/∞), the queue model of Yogyakarta is (DAGUM/ DAGUM/1) : (GD/∞/∞), the queue model of Kudus/Pati/Lasem is (DAGUM/GEV/1):(GD/∞/∞), the queue model of Pekalongan/Tegal is (GEV/DAGUM/1):(GD/∞/∞), and the queue model of Purwokerto/Purworejo is (GPD/DAGUM/1) : (GD/∞/∞). 

scholarly journals Study of cellular heterogeneity and differential dynamics of autophagy in human embryonic kidney development by single-cell RNA sequencing

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chen Wen-jin ◽  
Pan Xiu-wu ◽  
Chu Jian ◽  
Xu Da ◽  
Chen Jia-xin ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Intercellular Communication ◽  
Wilms Tumor ◽  
R Package ◽  
Cellular Heterogeneity ◽  
Transcriptional Level ◽  
Renal Cells ◽  
Cell Clusters ◽  
Single Cell Rna Sequencing

Abstract Background Autophagy is believed to participate in embryonic development, but whether the expression of autophagy-associated genes undergoes changes during the development of human embryonic kidneys remains unknown. Methods In this work, we identified 36,151 human renal cells from embryonic kidneys of 9–18 gestational weeks in 16 major clusters by single-cell RNA sequencing (scRNA-seq), and detected 1350 autophagy-related genes in all fetal renal cells. The abundance of each cell cluster in Wilms tumor samples from scRNA-seq and GDC TARGET WT datasets was detected by CIBERSORTx. R package Monocle 3 was used to determine differentiation trajectories. Cyclone tool of R package scran was applied to calculate the cell cycle scores. R package SCENIC was used to investigate the transcriptional regulons. The FindMarkers tool from Seurat was used to calculate DEGs. GSVA was used to perform gene set enrichment analyses. CellphoneDB was utilized to analyze intercellular communication. Results It was found that cells in the 13th gestational week showed the lowest transcriptional level in each cluster in all stages. Nephron progenitors could be divided into four subgroups with diverse levels of autophagy corresponding to different SIX2 expressions. SSBpod (podocyte precursors) could differentiate into four types of podocytes (Pod), and autophagy-related regulation was involved in this process. Pseudotime analysis showed that interstitial progenitor cells (IPCs) potentially possessed two primitive directions of differentiation to interstitial cells with different expressions of autophagy. It was found that NPCs, pretubular aggregates and interstitial cell clusters had high abundance in Wilms tumor as compared with para-tumor samples with active intercellular communication. Conclusions All these findings suggest that autophagy may be involved in the development and cellular heterogeneity of early human fetal kidneys. In addition, part of Wilms tumor cancer cells possess the characteristics of some fetal renal cell clusters. Graphical abstract

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

2020 ◽  
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.

scholarly journals PRISMA2020: an R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis

2021 ◽  
Author(s):  
Neal R Haddaway ◽  
Matthew J Page ◽  
Christopher C Pritchard ◽  
Luke A McGuinness
Keyword(s):  
Systematic Review ◽  
Systematic Reviews ◽  
Science Communication ◽  
Review Process ◽  
Evidence Synthesis ◽  
R Package ◽  
Web Based ◽  
Shiny App ◽  
Data Files ◽  
Flow Diagrams

Background Reporting standards, such as PRISMA aim to ensure that the methods and results of systematic reviews are described in sufficient detail to allow full transparency. Flow diagrams in evidence syntheses allow the reader to rapidly understand the core procedures used in a review and examine the attrition of irrelevant records throughout the review process. Recent research suggests that use of flow diagrams in systematic reviews is poor and of low quality and called for standardised templates to facilitate better reporting in flow diagrams. The increasing options for interactivity provided by the Internet gives us an opportunity to support easy-to-use evidence synthesis tools, and here we report on the development of tools for the production of PRISMA 2020-compliant systematic review flow diagrams. Methods and Findings We developed a free-to-use, Open Source R package and web-based Shiny app to allow users to design PRISMA flow diagrams for their own systematic reviews. Our tools allow users to produce standardised visualisations that transparently document the methods and results of a systematic review process in a variety of formats. In addition, we provide the opportunity to produce interactive, web-based flow diagrams (exported as HTML files), that allow readers to click on boxes of the diagram and navigate to further details on methods, results or data files. We provide an interactive example here; https://driscoll.ntu.ac.uk/prisma/. Conclusions We have developed a user-friendly suite of tools for producing PRISMA 2020-compliant flow diagrams for users with coding experience and, importantly, for users without prior experience in coding by making use of Shiny. These free-to-use tools will make it easier to produce clear and PRISMA 2020-compliant systematic review flow diagrams. Significantly, users can also produce interactive flow diagrams for the first time, allowing readers of their reviews to smoothly and swiftly explore and navigate to further details of the methods and results of a review. We believe these tools will increase use of PRISMA flow diagrams, improve the compliance and quality of flow diagrams, and facilitate strong science communication of the methods and results of systematic reviews by making use of interactivity. We encourage the systematic review community to make use of these tools, and provide feedback to streamline and improve their usability and efficiency.

scholarly journals Spectacle: An interactive resource for ocular single-cell RNA sequencing data analysis

2020 ◽  
Vol 200 ◽  
pp. 108204 ◽  
Author(s):  
Andrew P. Voigt ◽  
S. Scott Whitmore ◽  
Nicholas D. Lessing ◽  
Adam P. DeLuca ◽  
Budd A. Tucker ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Rna Sequencing ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Sequencing Data Analysis
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