1 / 26

Mitocheck advances

Mitocheck advances. Gregoire Pau, Wolfgang Huber, EMBL-EBI Cambridge gregoire.pau@ebi.ac.uk. Outline. Model reminder Model results Classification results Comparison with Max algorithm Annotation balance correction Estimating the 'real' number of hits Digging new phenotypes.

bozica
Download Presentation

Mitocheck advances

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Mitocheck advances Gregoire Pau, Wolfgang Huber, EMBL-EBI Cambridge gregoire.pau@ebi.ac.uk

  2. Outline • Model reminder • Model results • Classification results • Comparison with Max algorithm • Annotation balance correction • Estimating the 'real' number of hits • Digging new phenotypes

  3. Cell populations time courses

  4. Model • Temporal dynamics of cell state change on population average level • Non-linear ODE model

  5. Parameters • 11 parameters • 7 kinetic parameters [cells/h] • 4 initial conditions • Robust estimation • Population level • Least square Levenberg-Marquardt fitting • u is the cell proportion that can undergo mitosis • Biological significance

  6. Fitted examples

  7. Expected parameters for known phenotypes • Wild type: high k3 & low k2 • Mitotic arrest: low k3 & low k5 • Mitotic shape: high k5 Marginal distributions

  8. LDA parameters projection

  9. Results • Classification • Using SVM classification distance • Spots are scored (and therefore sirnas and genes) • Results are thresholded to a given minimum distance d, to control the number of positive hits • Results • Expressed in terms of sensitivity/specificity (ROC curves) • sirna-based or gene-based • Depends on d • Depends on the manual annotation database used

  10. ROC curves • Using exp7g and {F+FQ} manual annotation DB exp7g max sirna gene

  11. sirna Comparison with Max-model • exp7g = ODE-model with d =-0.56 (1518 hits) • max = Max-algorithm hit list (1528 hits) 700 818 710 exp7g max

  12. Annotation coverage • But {F+FQ} manual annotation DB is unbalanced • Normal, it has been done for validation purposes • [Manual annotation coverage] 700 [0.03] 818 [0.19] 710 [0.12] exp7g [0.12] max [0.16]

  13. New manual annotations • 79 sirna manual annotations (thoroughly done !) : • To assess the local PPV of the exp7g hitlist • To compensate the annotation balance of the exp7g/max part • (PPV = Percentage of true positives among the predicted ones) [9;18] ppv=90 % (9+,1-) [700;709] ppv=100 %(10+,0-) Ranking [1000;1009] ppv=90 % (9+,1-) [1395;1414] ppv=70 % (14+,6-) [1;30] p=73.3 % (22+,8-) [149;158] p=50 % (5+,5-) exp7g max

  14. New annotation coverage • Manual annotation DB {F+FQ+G} • Annotation is more balanced 700 [0.12] 818 [0.21] 710 [0.14] exp7g [0.17] max [0.18]

  15. siRNA annotation PPV • Percentage of true positive among the predicted ones 818 p=94% (174+,11-) 710 p=68% (75+,34-) 700 p=69% (60+,26-) exp7g max [1;30] ppv=73.3 % (22+,8-) Ranking [612;628] ppv=58 % (10+,7-)

  16. New ROC curves sirna gene exp7g max  exp7g and max Results are similar

  17. New results sirna gene exp7g max

  18. New siRNA hits • 700 new hits • Among them, 64 were manually validated 700 818 710 max exp7g 15682 ibsp 216211 c20orf65 34172 megf11 108651 zhx2 124735 ascc3l1 141589 flj38335 141818 loc283985 250852 ensg00000140607 18816 npm3 119899 slc25a5 137772 pigb 118106 gnpda1 28541 rcn3 121185 cox8a 270 pdpk1 133390 prdm13 136527 sf3b3 32974 hdhd3 228749 ensg00000173261 108650 zhx2 241605 zdhhc17 127762 dt1p1a10 227585 ensg00000163632 215883 znf383 148477 tmem24 133913 lsm4 12324 s100a10 108267 c1qr1 240301 ensg00000198050 247065 ensg00000196523 41771 pou4f3 27016 mds032 230884 ensg00000185102 225021 ensg00000080200 130359 hhip 146473 efna1 142894 s100a2 128888 slamf6 247206 ensg00000198069 239835 ensg00000198273 11366 mageb3 145393 g1p3 242652 ensg00000103043 124919 loc51066 32366 mgc11349 127502 kiaa0635 228422 ensg00000173922 248809 ensg00000159882 120192 tubb 11932 psap 10006 id4 242681 ensg00000115109 243837 ensg00000180592 242295 flj45478 227775 ensg00000165831 35764 egln2 37251 c14orf32 13320 itga10 4471 rgr 119914 pold3 248597 ensg00000128422 247699 ensg00000196258 110176 znf583 249377 ensg00000152093

  19. 41771 pou4f3

  20. 41771 pou4f3

  21. Union 700 818 710 2228 max max exp7g exp7g results sirna gene sen=80% spe=90% sen=85% spe=82%

  22. Union ROC sirna gene sen:+20% spe:-4 % exp7g max union Union is much better

  23. Distribution of sirna hits 700 818 710 max exp7g

  24. About the (real) total number of hits

  25. Some chips… • Seem to contain too many hits ! • Saturation is observable on population curves • Constant increase of bi-nucleated cells • There are sometimes PM/M late delays on spots of 8_32 hit 8_32 96_46 weak hit not hit

  26. Digging for new phenotypes • 'Out-of-model' phenotypes • Spots with a high fitting error • Detection of : • Spots that contain motionless cells • Local out-of-focus spots • Artefact spots • Also possible to detect spots with high mitotic activity • Looking for high k1 activity and k3 …

More Related