ImageHTS Analysis of microscopy images of perturbed cell populations

1. ImageHTSAnalysis of microscopy images of perturbed cell populations

2. 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

3. Input data • Microscopy images of cells • Perturbed cells (siRNA, drugs) • CellHTS setup, images organized in (plate, sample, well) CD3EAP

4. 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)

5. 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')

6. 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 ...

7. 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/

8. 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]])

10. 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')

13. CellHTS report

17. Query website

18. 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

19. 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