scholarly journals EasyFlow: User-friendly Workflow for Image-based Droplet Analysis with Multipurpose Modules

2021 ◽  
Author(s):  
Immanuel Sanka ◽  
Simona Bartkova ◽  
Pille Pata ◽  
Karol Makuch ◽  
Olli-Pekka Smolander ◽  
...  
Keyword(s):  
Image Analysis ◽  
Single Cell ◽  
Web Application ◽  
Analysis Pipeline ◽  
Viability Analysis ◽  
Inhibition Concentration ◽  
Droplet Sizes ◽  
The Right ◽  
User Friendly ◽  
Massive Parallelization

Droplet-based experimental platforms allow researchers to perform massive parallelization and high-throughput studies, such as single-cell experiments. Even though there are various options of image analysis software to evaluate the experiment, selecting the right tools require experience and is time consuming. Experts and sophisticated workflow are required to perform the analysis, especially to detect the droplets and analyze their content. There is need for user-friendly droplet analysis pipelines that can be adapted in laboratories with minimum learning curve. Here, we provide a user-friendly workflow for image-based droplet analysis. The workflow comprises of a) CellProfiler-based image-analysis pipeline and b) accompanied with web application that simplifies the analysis and visualization of the droplet-based experiment. We construct necessary modules in CellProfiler (CP) to detect droplets and export the results into our web application. Using the web application, we are able to process and provide basic profiles of the droplet experiment (droplet sizes, droplet signals, sizes-signals plot, and strip plot for each label/condition). We also add a specific module for growth heterogeneity studies in bacteria populations that includes single cell viability analysis and probability distribution of minimum inhibition concentration (MIC) values in population. Our pipeline is usable for both poly- and monodisperse droplet emulsions.

scholarly journals YeastSpotter: accurate and parameter-free web segmentation for microscopy images of yeast cells

2019 ◽  
Vol 35 (21) ◽  
pp. 4525-4527 ◽  
Author(s):  
Alex X Lu ◽  
Taraneh Zarin ◽  
Ian S Hsu ◽  
Alan M Moses
Keyword(s):  
Image Analysis ◽  
Web Application ◽  
Single Cells ◽  
Yeast Cells ◽  
Supplementary Information ◽  
Supplementary Data ◽  
User Friendly ◽  
Microscopy Images

Abstract Summary We introduce YeastSpotter, a web application for the segmentation of yeast microscopy images into single cells. YeastSpotter is user-friendly and generalizable, reducing the computational expertise required for this critical preprocessing step in many image analysis pipelines. Availability and implementation YeastSpotter is available at http://yeastspotter.csb.utoronto.ca/. Code is available at https://github.com/alexxijielu/yeast_segmentation. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Asc-Seurat: analytical single-cell Seurat-based web application

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
W. J. Pereira ◽  
F. M. Almeida ◽  
D. Conde ◽  
K. M. Balmant ◽  
P. M. Triozzi ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Web Application ◽  
Gene Annotation ◽  
Developmental Trajectories ◽  
Cell Types ◽  
Biological Information ◽  
Graphical Interface ◽  
Peripheral Blood Mononuclear ◽  
User Friendly

Abstract Background Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of transcriptomes, arising as a powerful tool for discovering and characterizing cell types and their developmental trajectories. However, scRNA-seq analysis is complex, requiring a continuous, iterative process to refine the data and uncover relevant biological information. A diversity of tools has been developed to address the multiple aspects of scRNA-seq data analysis. However, an easy-to-use web application capable of conducting all critical steps of scRNA-seq data analysis is still lacking. Summary We present Asc-Seurat, a feature-rich workbench, providing an user-friendly and easy-to-install web application encapsulating tools for an all-encompassing and fluid scRNA-seq data analysis. Asc-Seurat implements functions from the Seurat package for quality control, clustering, and genes differential expression. In addition, Asc-Seurat provides a pseudotime module containing dozens of models for the trajectory inference and a functional annotation module that allows recovering gene annotation and detecting gene ontology enriched terms. We showcase Asc-Seurat’s capabilities by analyzing a peripheral blood mononuclear cell dataset. Conclusions Asc-Seurat is a comprehensive workbench providing an accessible graphical interface for scRNA-seq analysis by biologists. Asc-Seurat significantly reduces the time and effort required to analyze and interpret the information in scRNA-seq datasets.

scholarly journals SimpleITK Image-Analysis Notebooks: a Collaborative Environment for Education and Reproducible Research

2017 ◽  
Vol 31 (3) ◽  
pp. 290-303 ◽  
Author(s):  
Ziv Yaniv ◽  
Bradley C. Lowekamp ◽  
Hans J. Johnson ◽  
Richard Beare
Keyword(s):  
Image Analysis ◽  
Software Development ◽  
Web Application ◽  
Reproducible Research ◽  
Online Data ◽  
Development Environment ◽  
Data Repositories ◽  
Testing Framework ◽  
Collaborative Environment ◽  
User Friendly

Abstract Modern scientific endeavors increasingly require team collaborations to construct and interpret complex computational workflows. This work describes an image-analysis environment that supports the use of computational tools that facilitate reproducible research and support scientists with varying levels of software development skills. The Jupyter notebook web application is the basis of an environment that enables flexible, well-documented, and reproducible workflows via literate programming. Image-analysis software development is made accessible to scientists with varying levels of programming experience via the use of the SimpleITK toolkit, a simplified interface to the Insight Segmentation and Registration Toolkit. Additional features of the development environment include user friendly data sharing using online data repositories and a testing framework that facilitates code maintenance. SimpleITK provides a large number of examples illustrating educational and research-oriented image analysis workflows for free download from GitHub under an Apache 2.0 license: github.com/InsightSoftwareConsortium/SimpleITK-Notebooks.

scholarly journals DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning

2019 ◽  
Author(s):  
Jean-Baptiste Lugagne ◽  
Haonan Lin ◽  
Mary J. Dunlop
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Image Analysis ◽  
Deep Learning ◽  
Single Cell ◽  
Real Time ◽  
Microfluidic Device ◽  
Time Lapse ◽  
Cell Segmentation ◽  
Analysis Pipeline

AbstractMicroscopy image analysis is a major bottleneck in quantification of single-cell microscopy data, typically requiring human supervision and curation, which limit both accuracy and throughput. To address this, we developed a deep learning-based image analysis pipeline that performs segmentation, tracking, and lineage reconstruction. Our analysis focuses on time-lapse movies of Escherichia coli cells trapped in a “mother machine” microfluidic device, a scalable platform for long-term single-cell analysis that is widely used in the field. While deep learning has been applied to cell segmentation problems before, our approach is fundamentally innovative in that it also uses machine learning to perform cell tracking and lineage reconstruction. With this framework we are able to get high fidelity results (1% error rate), without human supervision. Further, the algorithm is fast, with complete analysis of a typical frame containing ∼150 cells taking <700msec. The framework is not constrained to a particular experimental set up and has the potential to generalize to time-lapse images of other organisms or different experimental configurations. These advances open the door to a myriad of applications including real-time tracking of gene expression and high throughput analysis of strain libraries at single-cell resolution.Author SummaryAutomated microscopy experiments can generate massive data sets, allowing for detailed analysis of cell physiology and properties such as gene expression. In particular, dynamic measurements of gene expression with time-lapse microscopy have proved invaluable for understanding how gene regulatory networks operate. However, image analysis remains a key bottleneck in the analysis pipeline, typically requiring human supervision and a posteriori processing. Recently, machine learning-based approaches have ushered in a new era of rapid, unsupervised image analysis. In this work, we use and repurpose the U-Net deep learning algorithm to develop an image processing pipeline that can not only accurately identify the location of cells in an image, but also track them over time as they grow and divide. As an application, we focus on multi-hour time-lapse movies of bacteria growing in a microfluidic device. Our algorithm is accurate and fast, with error rates near 1% and requiring less than a second to analyze a typical movie frame. This increase in speed and fidelity has the potential to open new experimental avenues, e.g. where images are analyzed on-the-fly so that experimental conditions can be updated in real time.

scholarly journals Asc-Seurat – Analytical single-cell Seurat-based web application

2021 ◽  
Author(s):  
WJ Pereira ◽  
FM Almeida ◽  
KM Balmant ◽  
DC Rodriguez ◽  
PM Triozzi ◽  
...  
Keyword(s):  
Single Cell ◽  
Web Application ◽  
Iterative Process ◽  
Developmental Trajectories ◽  
Cell Types ◽  
Biological Information ◽  
Supplementary Information ◽  
Popular Approach ◽  
Single Cell Rna Sequencing ◽  
User Friendly

AbstractSummarySingle-cell RNA sequencing (scRNA-seq) has become a popular approach for studying the transcriptome, providing a powerful tool for discovering and characterizing cell types and their developmental trajectories. However, scRNA-seq analysis is complex, requiring a continuous, iterative process to refine the data processing and uncover relevant biological information. We present Asc-Seurat, a feature rich workbench, providing a user-friendly and easy-to-install web application encapsulating the necessary tools for an all-encompassing and fluid scRNA-seq data analysis.Availability and implementationAsc-Seurat is available at https://github.com/KirstLab/asc_seurat/ and released under GNU 3 [email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals UBiT2: a client-side web-application for gene expression data analysis

2017 ◽  
Author(s):  
Jean Fan ◽  
David Fan ◽  
Kamil Slowikowski ◽  
Nils Gehlenborg ◽  
Peter Kharchenko
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Single Cell ◽  
Web Application ◽  
Differential Expression Analysis ◽  
Principal Component ◽  
Interactive Visualizations ◽  
Alignment Analysis ◽  
User Friendly ◽  
Client Side

We present a purely client-side web-application, UBiT2 (User-friendly BioInformatics Tools), that provides installation-free, offline alignment, analysis, and visualization of RNA-sequencing as well as qPCR data. Analysis modules were designed with single cell transcriptomic analysis in mind. Using just a browser, users can perform standard analyses such as quality control, filtering, hierarchical clustering, principal component analysis, differential expression analysis, gene set enrichment testing, and more, all with interactive visualizations and exportable publication-quality figures. We apply UBiT2 to recapitulate findings from single cell RNA-seq and Fluidigm Biomark TM multiplex RT-qPCR gene expression datasets. UBiT2 is available at http://pklab.med.harvard.edu/jean/ubit2/index.html with open-source code available at https://github.com/JEFworks/ubit2.

scholarly journals Comparative analysis of web application performance testing tools

2020 ◽  
Vol 17 ◽  
pp. 351-357
Author(s):  
Agata Kołtun ◽  
Beata Pańczyk
Keyword(s):  
Comparative Analysis ◽  
Web Application ◽  
Web Applications ◽  
Performance Testing ◽  
Application Performance ◽  
Advantages And Disadvantages ◽  
Testing Tools ◽  
The Right ◽  
User Friendly

Recent years have brought the rise of importance of quality of developed software. Web applications should be functional, user friendly as also efficient. There are many tools available on the market for testing the performance of web applications. To help you choose the right tool, the article compares three of them: Apache JMeter, LoadNinja and Gatling. They were analyzed in terms of a user-friendly interface, parameterization of the requests and creation of own testing scripts. The research was carried out using a specially prepared application. The summary indicates the most important advantages and disadvantages of the selected tools.

scholarly journals scAgeCom: a murine atlas of age-related changes in intercellular communication inferred with the package scDiffCom

2021 ◽  
Author(s):  
Cyril Lagger ◽  
Eugen Ursu ◽  
Anais Equey ◽  
Roberto A Avelar ◽  
Angela O Pisco ◽  
...  
Keyword(s):  
Single Cell ◽  
Intercellular Communication ◽  
Web Application ◽  
R Package ◽  
Cell Type ◽  
Communication Analysis ◽  
Age Related ◽  
Mouse Tissues ◽  
Transcriptomics Data ◽  
User Friendly

Dysregulation of intercellular communication is a well-established hallmark of aging. To better understand how this process contributes to the aging phenotype, we built scAgeCom, a comprehensive atlas presenting how cell-type to cell-type interactions vary with age in 23 mouse tissues. We first created an R package, scDiffCom, designed to perform differential intercellular communication analysis between two conditions of interest in any mouse or human single-cell RNA-seq dataset. The package relies on its own list of curated ligand-receptor interactions compiled from seven established studies. We applied this tool to single-cell transcriptomics data from the Tabula Muris Senis consortium and the Calico murine aging cell atlas. All the results can be accessed online, using a user-friendly, interactive web application (https://scagecom.org). The most widespread changes we observed include upregulation of immune system processes, inflammation and lipid metabolism, and downregulation of extracellular matrix organization, growth, development and angiogenesis. More specific interpretations are also provided.

scholarly journals ConfocalGN : a minimalistic confocal image simulator.

2016 ◽  
Author(s):  
Serge Dmitrieff ◽  
Francois Nedelec
Keyword(s):  
Image Analysis ◽  
Blood Platelets ◽  
Source Code ◽  
Ground Truth ◽  
Confocal Image ◽  
Analysis Pipeline ◽  
Closed Curves ◽  
Noise Characteristics ◽  
User Friendly ◽  
Microscopy Images

SUMMARY : We developed a user-friendly software to generate synthetic confocal microscopy images from a ground truth specified as a 3D bitmap with pixels of arbitrary size. The software can analyze a real confocal stack to derivate noise parameters and will use them directly to generate new images with similar noise characteristics. Such synthetic images can then be used to assert the quality and robustness of an image analysis pipeline, as well as be used to train machine-learning image analysis procedures. We illustrate the approach with closed curves corresponding to the microtubule ring present in blood platelets. AVAILABILITY AND IMPLEMENTATION : ConfocalGN is written in Malab but does not require any toolbox. The source code is distributed under the GPL 3.0 licence on https://github.com/SergeDmi/ConfocalGN.

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