Literature DB >> 19187010

WebFlow: a software package for high-throughput analysis of flow cytometry data.

Mark M Hammer1, Nikesh Kotecha, Jonathan M Irish, Garry P Nolan, Peter O Krutzik.   

Abstract

Flow cytometry has emerged as a powerful tool for quantitative, single-cell analysis of both surface markers and intracellular antigens, including phosphoproteins and kinase signaling cascades, with the flexibility to process hundreds of samples in multiwell plate format. Quantitative flow cytometric analysis is being applied in many areas of biology, from the study of immunology in animal models or human patients to high-content drug screening of pharmacologically active compounds. However, these experiments generate thousands of data points per sample, each with multiple measured parameters, leading to data management and analysis challenges. We developed WebFlow (http://webflow.stanford.edu), a web server-based software package to manage, analyze, and visualize data from flow cytometry experiments. WebFlow is accessible via standard web browsers and does not require users to install software on their personal computers. The software enables plate-based annotation of large data sets, which provides the basis for exploratory data analysis tools and rapid visualization of multiple different parameters. These tools include custom user-defined statistics to normalize data to other wells or other channels, as well as interactive, user-selectable heat maps for viewing the underlying single-cell data. The web-based approach of WebFlow allows for sharing of data with collaborators or the general public. WebFlow provides a novel platform for quantitative analysis of flow cytometric data from high-throughput drug screening or disease profiling experiments.

Entities:  

Mesh:

Substances:

Year:  2009        PMID: 19187010      PMCID: PMC2956679          DOI: 10.1089/adt.2008.174

Source DB:  PubMed          Journal:  Assay Drug Dev Technol        ISSN: 1540-658X            Impact factor:   1.738


  24 in total

1.  The Stanford Microarray Database.

Authors:  G Sherlock; T Hernandez-Boussard; A Kasarskis; G Binkley; J C Matese; S S Dwight; M Kaloper; S Weng; H Jin; C A Ball; M B Eisen; P T Spellman; P O Brown; D Botstein; J M Cherry
Journal:  Nucleic Acids Res       Date:  2001-01-01       Impact factor: 16.971

2.  Systematic management and analysis of yeast gene expression data.

Authors:  J Aach; W Rindone; G M Church
Journal:  Genome Res       Date:  2000-04       Impact factor: 9.043

3.  Spectral compensation for flow cytometry: visualization artifacts, limitations, and caveats.

Authors:  M Roederer
Journal:  Cytometry       Date:  2001-11-01

4.  yMGV: a database for visualization and data mining of published genome-wide yeast expression data.

Authors:  P Marc; F Devaux; C Jacq
Journal:  Nucleic Acids Res       Date:  2001-07-01       Impact factor: 16.971

Review 5.  Fundamentals of cDNA microarray data analysis.

Authors:  Yuk Fai Leung; Duccio Cavalieri
Journal:  Trends Genet       Date:  2003-11       Impact factor: 11.639

6.  Intracellular phospho-protein staining techniques for flow cytometry: monitoring single cell signaling events.

Authors:  Peter O Krutzik; Garry P Nolan
Journal:  Cytometry A       Date:  2003-10       Impact factor: 4.355

7.  Cell-based screening: a high throughput flow cytometry platform for identification of cell-specific targeting molecules.

Authors:  R A Smith; T D Giorgio
Journal:  Comb Chem High Throughput Screen       Date:  2004-03       Impact factor: 1.339

8.  High-throughput screening with HyperCyt flow cytometry to detect small molecule formylpeptide receptor ligands.

Authors:  Susan M Young; Cristian Bologa; Eric R Prossnitz; Tudor I Oprea; Larry A Sklar; Bruce S Edwards
Journal:  J Biomol Screen       Date:  2005-06

9.  Nine color eleven parameter immunophenotyping using three laser flow cytometry.

Authors:  M Bigos; N Baumgarth; G C Jager; O C Herman; T Nozaki; R T Stovel; D R Parks; L A Herzenberg
Journal:  Cytometry       Date:  1999-05-01

10.  Immunoreactivity of Stat5 phosphorylated on tyrosine as a cell-based measure of Bcr/Abl kinase activity.

Authors:  James W Jacobberger; R Michael Sramkoski; Phyllis S Frisa; Peggy Peng Ye; Megan A Gottlieb; David W Hedley; T Vincent Shankey; Bradley L Smith; Mary Paniagua; Charles L Goolsby
Journal:  Cytometry A       Date:  2003-08       Impact factor: 4.355

View more
  4 in total

Review 1.  Computational analysis of high-throughput flow cytometry data.

Authors:  J Paul Robinson; Bartek Rajwa; Valery Patsekin; Vincent Jo Davisson
Journal:  Expert Opin Drug Discov       Date:  2012-06-18       Impact factor: 6.098

Review 2.  Data-driven approaches used for compound library design, hit triage and bioactivity modeling in high-throughput screening.

Authors:  Shardul Paricharak; Oscar Méndez-Lucio; Aakash Chavan Ravindranath; Andreas Bender; Adriaan P IJzerman; Gerard J P van Westen
Journal:  Brief Bioinform       Date:  2018-03-01       Impact factor: 11.622

3.  K-means quantization for a web-based open-source flow cytometry analysis platform.

Authors:  Nathan Wong; Daehwan Kim; Zachery Robinson; Connie Huang; Irina M Conboy
Journal:  Sci Rep       Date:  2021-03-24       Impact factor: 4.379

4.  Bridging the Divide between Manual Gating and Bioinformatics with the Bioconductor Package flowFlowJo.

Authors:  John J Gosink; Gary D Means; William A Rees; Cheng Su; Hugh A Rand
Journal:  Adv Bioinformatics       Date:  2009-10-07
  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.