A blind search for patterns
description
Transcript of A blind search for patterns
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A blind search for patternsUnravelling low replicate data
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ExSpec Pipeline
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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
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Data Structure and variability
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Data: Structure and variability
The majority of features that are detected are singletons.
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Low Replicate data
“Suck it and see” One off project
Pump priming projects
Medical samples Biopsy
Difficult to access Ecological data
Resampling is difficult
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Methods
Finger printing
PCA
Basic scoring
PDE model
Gradient search
Differential analysis
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PCA
Very simple
Can be highly informative Depends on the data
Used in pipeline Data quality
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Bruno Project Samples :
Human biopsy Replication – biopsy cut into
equal parts
PCA Analysis
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N group Non-cancer biopsy
T group Cancer biopsy
Using PCA clustering we are able to distinguish between healthy and sick patients
PCA Analysis
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PCA reveled profile similarity which correlated with biological evidencePCA
Analysis
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PCA Analysis
Human Urine project• 22 patients sampled• 11 healthy and 11 sick
patients • Sample labels dropped
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PCA Analysis
Ecological Data
Large number of samples without clear replication.
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PCA Analysis
Cluster pattern: Find the features which hold the cluster pattern
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PCA Analysis
Using PCA and profile similarity analysis subset of features of interest were found
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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.
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Basic Scoring : PDE modelling
Multi class problem
Plants Wild type
act ko mutant
Treatments Normal light
High light
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Gradient Analysis
Use rate of change of abuandace to Mine data for spesifc trends
Find features of intrest
Use PDE modelling of rates
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Gradient Analysis
Mining for features which showed rapid increase due to a specific treatment
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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
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Metabolomics and Proteomics Mass Spectrometry Facility @ The University of Exeter
Nick Smirnoff (Director of Mass Spectrometry) [email protected]
Hannah Florance (MS Facility Manager) [email protected]
Venura Perera (Bioinformatics and Mathematical Support) [email protected]
http://biosciences.exeter.ac.uk/facilities/spectrometry/http://bio-massspeclocal.ex.ac.uk/
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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