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A blind search for patterns Unravelling low replicate data.

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Presentation on theme: "A blind search for patterns Unravelling low replicate data."— Presentation transcript:

1 A blind search for patterns Unravelling low replicate data

2 ExSpec Pipeline

3 Data: Structure and variability  Structure  Between ,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 Using PCA clustering we are able to distinguish between healthy and sick patients PCA Analysis

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

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

21 Metabolomics and Proteomics Mass Spectrometry The University of Exeter Nick Smirnoff ( Director of Mass Spectrometry ) Hannah Florance ( MS Facility Manager ) Venura Perera ( Bioinformatics and Mathematical Support )

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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