EXPERIMENTAL DESIGN AND DATA TREATMENT IN ......Serge Rezzi,*, Ziad Ramadan,Laurent B. Fay, and, and Sunil Kochhar. Journal of Proteome Research 2007 6 (2), 513-525 Journal of Proteome

EXPERIMENTAL DESIGN AND DATA TREATMENT IN METABOLOMICS

APPLIED TO NUTRITION AND HEALTH

Mélanie Pétéra

PFEM, MetaboHUB-Clermont, Univ Clermont Auvergne, INRA, UNH

Métabolomique - Lille, 15 octobre 2019Mélanie Pétéra


METABOLOMICS TO BETTER UNDERSTAND

NUTRITION-HEALTH RELATIONSHIP

PARTICULARITIES IN DESIGN


A WHOLE TO CONSIDER


TO CONSIDER BEFORE STARTING


A main question clearly defined

Defining priorities

Gathering information

About individualsAbout samples

About experimental context

The goal




Defining priorities




The goal

Technologicalchoices

Knowing what ispossible and what is not


METABOLOMICS AND VARIABILITY

Do not wait to get the data!


BIOLOGICAL AND ANALYTICAL VARIABILITY: BEWARE OF UNFORTUNATE COMBINATION

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t[Comp. 1] /t[Comp. 3]

Colored according to Obs ID (Primary)

R2X[1] = 0.174129 R2X[3] = 0.0819166

Ellipse: Hotelling T2 (0.95)

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[email protected] (PCA-X)

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R2X[1] = 0.174967 R2X[3] = 0.0821061

Ellipse: Hotelling T2 (0.95)

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Signal drift (gradual loss in

sensitivity)




Defining priorities




The goal






Defining priorities




The goal



Confoundingfactors Data

processingparameters

Cofactors




Defining priorities




The goal



Confoundingfactors Data

processingparameters

Cofactors

Involvement in the project design at every level


EXAMPLE – CONFOUNDING EFFECT

Group distribution according to injection sequence

injection numberdistribution per group

Biological groups not mentioned before data analysis

Groups not taken into account in injection sequence randomisation

Unlucky distribution of groups along the injection sequence

Confunding effect:Is a difference due to biological groups or to a remaining signal drift effect?

DATA TREATMENT: WHAT TO CHOOSE?



WORKFLOW OVERVIEW


WORKFLOW OVERVIEW

?

Remaining noise ?

Redundancy ?

Possibility ofidentification ?

Variable selection ?


EXAMPLE 1: REDUNDANCY

REDUNDANCY

Choice of methods thatcan be used anyway

Getting rid of colinearity

Standard methods canstill be penalised

Subtil effectsVariables not linked to

the question of interest

Analyticalredundancy

Biologicalcorrelation

VS

How to make the difference?

Do we want to eliminatebiological correlation?

How to select which variables to keep?

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! !


EXAMPLE 2: FEATURE SELECTION

statisticsvariety of potentially

involved variables

biomarker discovery

mechanism understanding

parsimonious modelsglobal models

JK Nicholson – Imperial College London

identification not mandatory

need of large-scale validation

device-dependant models

biological validity

limited number of variables for clinical use

variable selection issues

limits of identification

A path we follow at PFEM


EXAMPLE 2: FEATURE SELECTION

KNOWLEDGE BASED SIGNATURE OPTIMISATION

Identification of a specific signature of MetS

Morrow et al., 2007

TOOLS FOR DATA ANALYSIS



Tools ChoicesPrinciple and goals

Technical constraints

The forest

The kind of tree needed

The goal


EXAMPLE WITH THE PFEM LANDSCAPE

Graphical User InterfaceErgonomic

Parameter completenessModularity

Data & workflow sharingPossibility of new tools integration

In-house tools to perform new strategies based on research

projects, collaborations…

Enhancement of available tools,new tool development to breakdown current barriers or to undertake new challenges


EXAMPLE OF PFEM TOOL DEVELOPMENT (1)

• What:

a filtered coloured correlation table betweentwo datasets

• Why:

To allow biologist or analyst to have a combined view of metabolomic results and clinical data in a simple way

• How:

A Galaxy tool to plug itdirectly to the output files from W4M workflows


EXAMPLE OF PFEM TOOL DEVELOPMENT (2)

• What:

functionalities dedicated to MBPLSDA

• Why:

To have a single package providing all necessary outputs for efficient evaluationof MBPLSDA models

• How:

R package with a few number of functionsto perform MBPLS and gather relevant indicators

THANK YOU FOR YOUR ATTENTION

Serge Rezzi,*, Ziad Ramadan,Laurent B. Fay, and, and Sunil Kochhar. Journal of Proteome Research 2007 6 (2), 513-525

Mark Haid, Caroline Muschet, Simone Wahl, Werner Römisch-Margl, Cornelia Prehn, Gabriele Möller, and Jerzy Adamski. Journal of Proteome Research 2018 17 (1), 203-211

Wenbin Zhou, Guigang Zeng, Chunming Lyu, Fang Kou, Shen Zhang, Hai Wei. Journal of Sports Science and Medicine 2019 (18), 253 - 263.

Alonso A, Marsal S, Julià A. Front Bioeng Biotechnol. 2015;3:23.

Morrow DA, de Lemos JA. Circulation. 2007;115:949–52.


Documents

EXPERIMENTAL DESIGN AND DATA TREATMENT IN ......Serge Rezzi,*, Ziad Ramadan,Laurent B. Fay, and, and Sunil Kochhar. Journal of Proteome Research 2007 6 (2), 513-525 Journal of Proteome