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Time warping of evolutionary distant temporal gene expression data based on noise suppression

Time warping of evolutionary distant temporal gene expression data based on noise suppression. Yury Goltsev and Dmitri Papatsenko * Department of Molecular and Cell biology, University of California, Berkeley, USA * BMC Bioinformatics Oct. 2009. Overview. Introduction

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Time warping of evolutionary distant temporal gene expression data based on noise suppression

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  1. Time warping of evolutionary distant temporal gene expression data based on noise suppression VC Lab, Dept. of Computer Science, NTHU, Taiwan YuryGoltsev and Dmitri Papatsenko *Department of Molecular and Cell biology, University of California, Berkeley, USA * BMC Bioinformatics Oct. 2009

  2. Overview • Introduction • Dynamic Time Wraping • Kruskal's algorithm • Orthologous Genes • Alignments of cell cycles • Simulation data • Real data • Conclusion • Reference VC Lab, Dept. of Computer Science, NTHU, Taiwan

  3. Introduction • Dynamic Time Wraping(DTW) VC Lab, Dept. of Computer Science, NTHU, Taiwan *http://dblab.cs.nccu.edu.tw/presentation/DynamicTimeWarping.ppt

  4. Introduction • Dynamic Time Wraping(DTW) VC Lab, Dept. of Computer Science, NTHU, Taiwan *http://www.evanlin.com/document/DataMiniing/Discovering similar time series patterns with fuzzy clustering and DTW methonds.ppt

  5. Dynamic Time Wraping • Kruskal'salgorithm VC Lab, Dept. of Computer Science, NTHU, Taiwan *http://Fmccmb.belozersky.msu.ru/2009/Presentations/20090721MCCMB-Papatsenko.ppt

  6. Introduction • OrthologousGenes • Homology Budding yeast Fission yeast SaccharomycescerevisiaeSchizosaccharomycespombe VC Lab, Dept. of Computer Science, NTHU, Taiwan

  7. Introduction • Cell cycles VC Lab, Dept. of Computer Science, NTHU, Taiwan *Rustici G, et al., Nat Genet. 2004, 36:809-817

  8. Alignments of cell cycles • Periodic patterns (A,B: simulation data) VC Lab, Dept. of Computer Science, NTHU, Taiwan

  9. Alignments of cell cycles • Periodic patterns (real data) • A – Euclidean distance matrix • B – Alignment path • No alignment VC Lab, Dept. of Computer Science, NTHU, Taiwan

  10. Alignments of cell cycles • Periodic patterns (real data) • A – Pearson distance matrix • B – Alignment path VC Lab, Dept. of Computer Science, NTHU, Taiwan

  11. Alignments of cell cycles • Use Pearson distance ratherthan Euclidean distance VC Lab, Dept. of Computer Science, NTHU, Taiwan

  12. Alignments of cell cycles • Remove noise from the data(use Gaussian filter) • A – Pearson distance matrix • B – Alignment path VC Lab, Dept. of Computer Science, NTHU, Taiwan

  13. Alignments of cell cycles • matching cell cycle markers to good valleys VC Lab, Dept. of Computer Science, NTHU, Taiwan

  14. Alignments of cell cycles • matching cell cycle markers to good valleys • G1 phase is longer in S.cerevisiae • G2 is longer in S.pombe. VC Lab, Dept. of Computer Science, NTHU, Taiwan

  15. Conclusion • Desynchronization of gene expression in evolution • Microarray data and low-level data processing VC Lab, Dept. of Computer Science, NTHU, Taiwan

  16. Reference • http://dblab.cs.nccu.edu.tw/presentation/DynamicTimeWarping.ppt • http://www.evanlin.com/document/DataMiniing/Discovering similar time series patterns with fuzzy clustering and DTW methonds.ppt • http://Fmccmb.belozersky.msu.ru/2009/Presentations/20090721MCCMB-Papatsenko.ppt VC Lab, Dept. of Computer Science, NTHU, Taiwan

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