Imagehts analysis of microscopy images of perturbed cell populations
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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 analysis of microscopy images of perturbed cell populations

ImageHTSAnalysis of microscopy images of perturbed cell populations


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

-

feature 1


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