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ImageHTS Analysis of microscopy images of perturbed cell populations. ImageHTS. R package Analysis of microscopy images of perturbed cell populations Derived from Oleg's analysis framework Now Generic, works with any screen Extends cellHTS S4 object

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imagehts
ImageHTS
  • R package
  • Analysis of microscopy images of perturbed cell populations
  • Derived from Oleg's analysis framework
  • Now
    • Generic, works with any screen
    • Extends cellHTS S4 object
    • Can display and select cells using Remy's cellPicker
    • Emphasis on web-based distributed tools
  • To be released within 2 months
input data
Input data
  • Microscopy images of cells
  • Perturbed cells (siRNA, drugs)
  • CellHTS setup, images organized in (plate, sample, well)

CD3EAP

pipeline
Pipeline
  • Classic pipeline
    • Segmentation of cells
    • Extraction of cell features
    • Cell classification
    • Summarization of cell features into population phenotypic profiles
    • Normalization
    • Reporting result (cellHTS report, quality score, hit list)
analysis script
Analysis script

library(imageHTS)

path = 'screens/remorpho'

x = parseImageConf('imageconf.txt', path=path)

x = configure(x, 'description.txt', 'plateconf.txt', 'screenlog.txt',

path=path)

unames = getUnames(x)

## segment wells, extract features and summarize features

segmentWells(x, unames, 'segmentationpar.txt')

extractFeatures(x, unames, 'summarizepar.txt')

## learning

readLearnTS(x, 'trainingset.txt')

## predict cell labels

predictCellLabels(x, unames)

profiles = summarizeFeatures(x, unames, 'summarizepar.txt')

analysis configuration
Analysis configuration
  • Reusing cellHTS configuration files
    • plateconf.txt (screen geometry)
    • screenlog.txt (experimenter's screen log)
    • annotation.txt (reagent - target mapping)
  • Each module uses a configuration file

seg.method: ath

nuc.athresh.filter: makeBrush(35, shape='box')/(35*35)

nuc.athresh.t: 0.00424

nuc.morpho.kernel: makeBrush(3, shape='diamond')

nuc.watershed.tolerance: 3

nuc.watershed.neighbourood: 2

nuc.min.density: 0.1

nuc.min.size: 150.0625

nuc.max.size: 2070.25

...

file management
File management
  • Images and intermediate data are huge (~1 TB)
  • Each project can be associated with a master URL where source images and intermediate results are stored
  • If not available locally, imageHTS will get files using this URL
  • Extremely convenient to analyze data from a remote computer
  • Several accessing modes (default, local, server, cache)

neg = getUnames(x, type='neg')

pos = getUnames(x, type='plk1')

xneg = collectCellFeatures(x, neg, access='cache')

yneg = collectCellFeatures(x, pos, access='cache')

/home/gpau/projects/screens/remorpho/

behemoth2

user

http://www.ebi.ac.uk/~gpau/private/aP123GregP/imageHTS/screens/remorpho/

extra tool displayhts
Extra tool: displayHTS
  • Display wells in a web page
  • Use cases: checking controls, tracking

neg = getUnames(x, type='pos', plate=1:2)

displayHTS(x, neg)

profiles = loadHTS(x, 'profiles')

displayHTS(x, profiles[,order(profiles$n.int)[1:10]])

extra tool cellpicker
Extra tool: cellPicker
  • From Remy
  • Web-based client too to display/selecting cell
  • Use cases: tracking outlying cells, building a training set for classification
  • CellPicker is a distributable URL

neg = getUnames(x, type='neg', plate=1:2)

popCellPicker(x, neg, plabel='X')

ongoing screens
Ongoing screens
  • Morphology screen
    • Hit retests on HeLa, U2OS cells, with replicates
    • Kinase screen on HeLa cells
    • Maybe MitocheckODE ?
  • DKFZ (Xian Zhang)
    • Kinase screens on mesenchymal cells
    • Kinase screen on stem cells
ongoing work
Ongoing work

+

  • Easy-to-use, fast, distributed framework
  • Now the software framework is done…
  • Interesting problems
    • Quality score of controls
    • Features selection
    • Features transformation
    • Hits
    • Distances between images
    • With sparse conditions, e.g. n = 300 wells, p = 200 features in a multi-parametric fashion

feature 2

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feature 1

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