For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
277
views
6
references
 Top references
cited by
259
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      160
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      EBImage—an R package for image processing with applications to cellular phenotypes

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Summary: EBImage provides general purpose functionality for reading, writing, processing and analysis of images. Furthermore, in the context of microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. This allows the automation of such tasks using the R programming language and use of existing tools in the R environment for signal processing, statistical modeling, machine learning and data visualization.

          Availability: EBImage is free and open source, released under the LGPL license and available from the Bioconductor project ( http://www.bioconductor.org/packages/release/bioc/html/EBImage.html).

          Contact: gregoire.pau@ 123456ebi.ac.uk

          Related collections

          Most cited references6

          • Record: found
          • Abstract: found
          • Article: not found

          Image-based multivariate profiling of drug responses from single cells.

          Lit-Hsin Loo, Lani Wu, Steven J. Altschuler (2007)
          Quantitative analytical approaches for discovering new compound mechanisms are required for summarizing high-throughput, image-based drug screening data. Here we present a multivariate method for classifying untreated and treated human cancer cells based on approximately 300 single-cell phenotypic measurements. This classification provides a score, measuring the magnitude of the drug effect, and a vector, indicating the simultaneous phenotypic changes induced by the drug. These two quantities were used to characterize compound activities and identify dose-dependent multiphasic responses. A systematic survey of profiles extracted from a 100-compound compendium of image data revealed that only 10-15% of the original features were required to detect a compound effect. We report the most informative image features for each compound and fluorescence marker set using a method that will be useful for determining minimal collections of readouts for drug screens. Our approach provides human-interpretable profiles and automatic determination of on- and off-target effects.
          Article 0 comments Cited 102 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            A functional genomic analysis of cell morphology using RNA interference

            AA Kiger, B Baum, S S Jones … (2003)
            Background The diversity of metazoan cell shapes is influenced by the dynamic cytoskeletal network. With the advent of RNA-interference (RNAi) technology, it is now possible to screen systematically for genes controlling specific cell-biological processes, including those required to generate distinct morphologies. Results We adapted existing RNAi technology in Drosophila cell culture for use in high-throughput screens to enable a comprehensive genetic dissection of cell morphogenesis. To identify genes responsible for the characteristic shape of two morphologically distinct cell lines, we performed RNAi screens in each line with a set of double-stranded RNAs (dsRNAs) targeting 994 predicted cell shape regulators. Using automated fluorescence microscopy to visualize actin filaments, microtubules and DNA, we detected morphological phenotypes for 160 genes, one-third of which have not been previously characterized in vivo. Genes with similar phenotypes corresponded to known components of pathways controlling cytoskeletal organization and cell shape, leading us to propose similar functions for previously uncharacterized genes. Furthermore, we were able to uncover genes acting within a specific pathway using a co-RNAi screen to identify dsRNA suppressors of a cell shape change induced by Pten dsRNA. Conclusions Using RNAi, we identified genes that influence cytoskeletal organization and morphology in two distinct cell types. Some genes exhibited similar RNAi phenotypes in both cell types, while others appeared to have cell-type-specific functions, in part reflecting the different mechanisms used to generate a round or a flat cell morphology.
            Article 0 comments Cited 100 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              High-throughput RNAi screening by time-lapse imaging of live human cells.

              Urban Liebel, Holger Erfle, Michael Held … (2006)
              RNA interference (RNAi) is a powerful tool to study gene function in cultured cells. Transfected cell microarrays in principle allow high-throughput phenotypic analysis after gene knockdown by microscopy. But bottlenecks in imaging and data analysis have limited such high-content screens to endpoint assays in fixed cells and determination of global parameters such as viability. Here we have overcome these limitations and developed an automated platform for high-content RNAi screening by time-lapse fluorescence microscopy of live HeLa cells expressing histone-GFP to report on chromosome segregation and structure. We automated all steps, including printing transfection-ready small interfering RNA (siRNA) microarrays, fluorescence imaging and computational phenotyping of digital images, in a high-throughput workflow. We validated this method in a pilot screen assaying cell division and delivered a sensitive, time-resolved phenoprint for each of the 49 endogenous genes we suppressed. This modular platform is scalable and makes the power of time-lapse microscopy available for genome-wide RNAi screens.
              Article 0 comments Cited 95 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Journal ID (hwp): bioinfo
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 1 April 2010
                Publication date (Electronic): 23 March 2010
                Publication date PMC-release: 23 March 2010
                Volume: 26
                Issue: 7
                Pages: 979-981
                Affiliations
                1 EMBL—European Bioinformatics Institute, Cambridge, UK and 2 German Cancer Research Center (DKFZ), Division of Signaling and Functional Genomics, University of Heidelberg, Heidelberg, Germany
                Author notes
                * To whom correspondence should be addressed.

                Associate Editor: Thomas Lengauer

                Article
                Publisher ID: btq046
                DOI: 10.1093/bioinformatics/btq046
                PMC ID: 2844988
                PubMed ID: 20338898
                SO-VID: 1b84e785-e877-4579-b404-e56a41aeaa42
                Copyright © © The Author(s) 2010. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 18 June 2009
                Date revision received : 26 November 2009
                Date accepted : 1 February 2010
                Categories
                Subject: Applications Note
                Subject: Data and Text Mining

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

                Comments

                Comment on this article

                scite_

                Similar content160

                See all similar

                Cited by248

                See all cited by

                Most referenced authors201

                See all reference authors