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Description

The analysis of the screen was conducted using software built on the language and statistical environment R and its add-on packages from the CRAN and Bioconductor repositories.

Installation

The analysis software is platform-independant and can be used on Windows, Mac or UNIX machines.
  • A recent version of R (>=2.8.0) is needed. R can be downladed from the CRAN repository. The add-ons packages e1071, lattice, pamr, grid and hwriter are required and can be installed with the following command:
    echo "install.packages(c('e1071', 'lattice', 'pamr', 'grid', 'hwriter'), repos='http://cran.r-project.org')" | R --vanilla
  • Recent versions of ImageMagick and GTK+ are needed to install EBImage.
  • The packages geneplotter, splots, biomaRt and GO.db from Bioconductor are required and can be installed using the command:
    echo "source('http://bioconductor.org/biocLite.R') ; biocLite() ; biocLite(c('geneplotter', 'splots', 'biomaRt', 'splots', 'GO.db'))" | R --vanilla
  • The image processing package EBImage (the version 2.7.11 was used in this study) and the analysis software cellmorph_1.1.tar.gz are required and can be installed using the commands:
    R CMD INSTALL EBImage_2.7.11.tar.gz
    R CMD INSTALL cellmorph_1.1.tar.gz
  • The raw microscope TIFF images should be downloaded here.

Usage

  • Directory structure.
    The current working directory should contain the following subdirectories:
    • source contains (or links to) the raw original TIFF images
    • view contains the calibrated and segmented JPEG images
    • data contains internal R objects about image segmentation and cell features
    • out contains the final HTML report

  • Cell segmentation from raw images.
    This step performs the nucleus segmentation, channel calibration and cell segmentation, using the raw original TIFF images from the source directory and fills the view and data directories. The R script of this step is the following:
    library('cellmorph')
    root='.'
    for (i in 1:68) segmentPlate(i)
    Please note that this step takes time but can be easily parallelized using a computer cluster.

  • Phenotypic profiles extraction from segmented images.
    This step performs the cell feature extraction and cell classification of segmented wells and computes the phenotypic profile of each well. The R script of this step is the following:
    library('cellmorph')
    root='.'
    for (i in 1:68) extractFeatures(i,run=1)
    aggregateFeatures()
    The output of this step is the file data/GeneDescriptors.rda which contains the phenotypic profiles of all the wells.

  • Computation of phenoprints, phenotypic map and global report.
    The following step produces an HTML output report which contains quantitative information about all the previous steps (cell segmentation report, cell classification report, quality control, phenoprint results, hits distribution, clustering information, phenotypic map). The HTML report is located in out/report.html. The R script of this step is:
    library('cellmorph')
    experiment9()
    generate.report()
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