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A blind search for patterns

A blind search for patterns. Unravelling low replicate data. ExSpec Pipeline. Data: Structure and variability. Structure Between 500-10,000+ features Each feature has an associate ion count for each sample aligned. Data is not normally distributed. Variability

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A blind search for patterns

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  1. A blind search for patterns Unravelling low replicate data

  2. ExSpec Pipeline

  3. Data: Structure and variability • Structure • Between 500-10,000+ features • Each feature has an associate ion count for each sample aligned. • Data is not normally distributed. • Variability • Up to 30% technical variability • Each feature is effected differently

  4. Data Structure and variability

  5. Data: Structure and variability The majority of features that are detected are singletons.

  6. Low Replicate data • “Suck it and see” • One off project • Pump priming projects • Medical samples • Biopsy • Difficult to access • Ecological data • Resampling is difficult

  7. Methods • Finger printing • PCA • Basic scoring • PDE model • Gradient search • Differential analysis

  8. PCA • Very simple • Can be highly informative • Depends on the data • Used in pipeline • Data quality

  9. Bruno Project • Samples : • Human biopsy • Replication – biopsy cut into equal parts PCA Analysis

  10. N group • Non-cancer biopsy • T group • Cancer biopsy PCA Analysis Using PCA clustering we are able to distinguish between healthy and sick patients

  11. PCA Analysis PCA reveled profile similarity which correlated with biological evidence

  12. PCA Analysis • Human Urine project • 22 patients sampled • 11 healthy and 11 sick patients • Sample labels dropped

  13. PCA Analysis Ecological Data Large number of samples without clear replication.

  14. PCA Analysis • Cluster pattern: • Find the features which hold the cluster pattern

  15. PCA Analysis Using PCA and profile similarity analysis subset of features of interest were found

  16. Basic Scoring • Use Z-score to sort data • Use this to pull out important features. • Control – Exp • With two class problem we can use PDE modelling.

  17. Basic Scoring : PDE modelling • Multi class problem • Plants • Wild type • act ko mutant • Treatments • Normal light • High light

  18. Gradient Analysis • Use rate of change of abuandace to • Mine data for spesifc trends • Find features of intrest • Use PDE modelling of rates

  19. Gradient Analysis Mining for features which showed rapid increase due to a specific treatment

  20. Data Provided by: • Ecological data • Dave Hodgson • Nicole Goody • Gradient analysis • John Love • Data scoring • Nicholas Smirnoff • Mike Page • Brno • Ted Hupp • Rob O’Neill • Urine study • Steve Michell • John Mcgrath

  21. Metabolomics and Proteomics Mass Spectrometry Facility @ The University of Exeter http://biosciences.exeter.ac.uk/facilities/spectrometry/ http://bio-massspeclocal.ex.ac.uk/ Nick Smirnoff (Director of Mass Spectrometry) N.Smirnoff@exeter.ac.uk Hannah Florance (MS Facility Manager) H.V.Florance@exeter.ac.uk Venura Perera (Bioinformatics and Mathematical Support) V.Perera@exeter.ac.uk

  22. About me • Background • Applied Maths • Untargeted metabolite profiling • Research interests • Data driven modelling • Small molecule profiling • Gene regulatory network modelling • Application of mathematical methods • Metabolite identification using LC-MS/MS

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