HCS Tools Scripting Integration. Antje Niederlein Technology Development Studio / MPI-CBG Dresden

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1 HCS Tools Scripting Integration Antje Niederlein Technology Development Studio / MPI-CBG Dresden

2 Who we are Screening service for academic laboratories Cells, model organisms RNAi, chemical screens Assay transfer to large scale format Automation» Liqid handling robots, drop dispensers, plate washers, plate readers, high content screening platforms Image Analysis Data Analysis 2 / 20

3 Outline Motivation HCS Tools Scripting Integration Outlook 3 / 20

4 Motivation - Example Project Tuberculosis diffusion Globally diffused (1:3 infected worldwide) Vaccination is highly inefficient Long antibiotic treatment (> 6 months, not very successful) Aim: Establish a high content cell based assay amenable for functional and chemical genomics to identify new regulatory nodes of intracellular survival of M.Tuberculosis 4 / 20

5 Seeding Primary Human monocytes in multititer plates (384 wells) Differentiation to Primary Macrophages Transfection of sirna refractory to liposomic, magnetofection and viral delivery Infection with BCG (weakened version of a bacteria called M.Bovis which is closely related to M.Tuberculosis Collaboration with CELLECTRICON, Sweden Cellaxes platform 384 well electroporator Works on adherent cells Allows transfection on assay plate Parametrization of the electrical pulse Up to 16 conditions in the same plate 5 / 20

6 HCS Data Analysis Cell, image, well based data Data import Meta data integration Quality control Visualization Annotated hit list Normalization Advanced statistics Annotation 6 / 20

7 Our approach Two fold strategy 1) Provide screen mining nodes for KNIME 2) Hide scripting complexity behind user friedly graphical interfaces HCS + Tools + = Higher Throughput! 7 / 20

8 Visualiza*on Plate Viewer - > heatmaps, browse wells, HCS Tools Data Import Image Analysis (Opera, Opere<a, Cell Profiler, Mo@onTracking) Plate Readers (Envision, GeniusPro, MSD SectorImager) Normaliza*on Percent- of- control (POC), Normalized percent inhibi@on (NPI), Z- score, B- score Op@onal: robust sta@s@cs (Median + MAD) Select wells to normalize (controls, samples) Pre- Processing Mutual Informa@on between parameters or different condi@ons Outlier removal 8 / 20

9 HCS Tools Quality Control CV (coefficient of Z, Z, SSMD (strictly standardized mean difference) Screen Mining of screen data from database (TDS internal) Dose Response (IC50) Enrichment Analyzer U*li*es Handle barcodes & wells, join layouts 9 / 20

10 What do we gain? Prototyping of advanced statistics, data mining and visualization Faster development than for 'real nodes' Power of scripting languages compared to Java Keep in mind! 'Real' nodes are faster 'Real' nodes might handle huge data much better 'Real' nodes can provide updates 'Real' nodes are independent of third party software Scripting Integration 10 / 20

11 How does it work? Servers (remote / local) 11 / 20

12 Script Template System Reusability? Local and/or remote template repositories can be given in the preferences» Plain text files, categories, preview» Example template Template tree view Flexibility? RGG (GUI generator) XML based GUI definition language and a Java based GUI engine» Fixed GUI elements 12 / 20

13 Integration Features (compared to R integra@on of KNIME) Be<er editor (undo, redo) Be<er a<ribute name inser@on Preserve column names UI templates + centralized user template repositories Dynamic repain@ng of plots OpenInR nodes for rapid prototyping (Support for ggplot2 and la_ce plots) Faster table conversion (~2x) Flow- variable support in scripts Generic nodes to process arbitrary R data structures 13 / 20

14 Template nodes Templates can be updated Easy to deploy Something in between 14 / 20

15 Dose Response node Martin Stöter, 15 MPI-CBG, 15 / 20

16 Using flow variables in R scripts Dose Response node looks like KNIME, but actually it is R!!! Antje Niederlein 4 October 2011 Martin Stöter, 16 MPI-CBG, 16 / 20

17 Outlook Highest Priority: bugs, small features requests HCS Tools Integrate resized and compressed microscope images Colored parameter profile map (png?) Scripting Integrations Error messages Integrate flow variables into RGG-GUI (or change technology?) 17 / 20

18 Acknowledgements Holger Brandl (Bioinformatics Facility / Software Engineering Faciltiy) Felix Meyenhofer (TDS) TDS Marc Bickle Cordula Andree Rico Barsacchi Sara Christ Milan Esner Annett Lohmann Claudia Möbius Martin Stöter Nadine Tomschke Jan Wagner 18 / 20

19 Thank you for your Attention! 19 / 20