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EMBL Systems Microscopy Meeting

EMBL Systems Microscopy Meeting. Bernd Fischer. Definition of Genetic Interactions. Design of Combinatorial knock-down Screen. 1367 x 72 Drosophila genes each targeted by two independent dsRNA designs 1456 plates (=559.104 wells) ~100.000 distinct gene pairs. Imaging.

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EMBL Systems Microscopy Meeting

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  1. EMBL Systems Microscopy Meeting Bernd Fischer

  2. Definition of Genetic Interactions

  3. Design of Combinatorial knock-down Screen • 1367 x 72 Drosophila genes • each targeted by two independent dsRNA designs • 1456 plates (=559.104 wells) • ~100.000 distinct gene pairs

  4. Imaging • Cells grow in incubator for five days • Fixate and stain with DAPI, pH3, and a-Tubulin • Image with automated fluorescence microscope

  5. Image Processing • Segmentation of Nuclei (in DAPI and pH3 channel) • Propagation of Segmentation from nucleus to cell body (region growing in a-Tubulin channel) • Extraction of features (intensity, area, texture, …) per cell, summary per well (mean, sd, quantiles, local cell density).

  6. Quality Control Phenotype reproducibility dsRNA designs

  7. Estimating Genetic Interactions • For many phenotypes, the main effects (single gene) are multiplicative for non interacting genes i, j: • Additive on logarithmic scale • Estimation of main effects (assume that interactions are rare) • Detect Genetic Interactions: Compare to (t-test) effect of control main effect of dsRNA j error term interaction term 0, for non interacting genes ≠0, for interacting genes measurement (nr cells, growth rate, …) main effect of dsRNA i 7 07/11/2014

  8. Interaction Matrix 72 query genes x 10 phenotypes 1367 template genes

  9. Classification of GO categories The interaction profile of each gene is used as a feature vector for machine learning Query genes are regarded as perturbations Trained classifier for 3311 categories (GO, Reactome, Interpro, …) 284 classifier showed good performance by cross validation (precision > 0.5 at recall 0.3)

  10. blue: predicted, but not annotated yellow: annotated and predicted gray: annotation, but not predicted Example APC anaphase promoting complex (APC) Direct inhibition of APC

  11. Matrix Factorization predicted class probability (H) 284 classes 1367 genes PI: n template genes x m interaction features W: n template genes x k classes H: k classes x m interaction features n = 1367 template genes m = 720 (= 72 query genes * 10 phenotypes) k = 284 classes

  12. Matrix Factorization class specific interaction profiles (W) 284 classes 72 query genes x 10 phenotypes Matrix factorization min|| PI - W * H ||

  13. Interactions (Semi-Supervised bi-clustering)

  14. Reconstruction (Semi-Superv. bi-clustering)

  15. Residuals (Semi-Supervised bi-clustering) Further matrix factorization to extract novel complexes/pathways

  16. Modeling Genetic Interaction Data • View 1: Query genes are perturbations. => Cluster template genes to obtain functional information • View 2: phenotypic measurement in 100 000 different combinatorial genetic backgrounds => genotype to phenotype prediction

  17. Network learningidentify the underlying molecular modules and their relation area number of cells phenotype (observed) activity of core modules (e.g. complexes, ‘path-ways’, mRNA or protein expression) (hidden) genetic perturbation state of each single experiment (observed)

  18. EM algorithm E-Step: Compute expected activity levels on hidden nodes, given network structure and parameter => (loopy) belief propagation M-Step: Estimation network structure and regression parameter by maximizing expected likelihood area number of cells phenotype (observed) activity level (hidden) genetic perturbation (observed)

  19. Acknowledgement Thomas Horn Thomas Sandmann Maximilian Billmann Michael Boutros Wolfgang Huber Huber group

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