experimental design and ms workflows for omics … · experimental design and ms workflows for...

Experimental Design and MS Workflows for Omics Applications

David A. Weil, Ph.D.

Senior Application Scientist

Schaumburg, IL

[email protected]

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Omics Road Show Oct 2014

Acknowledgements: Support Team

AFO Metabolomics Team

Mark Sartain, Sumit Shah, Anne Blackwell, Dan Cuthbertson

Yuqin Dai Santa Clara, CA

Steve Madden Software Product Manager

Rick Reinsdorf National Jewish, Denver, CO

MPP Support Team Global

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Outline

Basics of Experimental Design and Statistics

in “Omics” Research

Agilent’s LCMS Omics Data Analysis Workflow

Targeted, Untargeted, Pathway Directed

Update What’s New in MPP and Profinder

Sample Correlation, Pathways, Metadata, etc

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Statistics

• There are three kinds of lies: lies, damned lies and

statistics- Benjamin Disraeli

• 98% of all statistics are made up – Unknowns

• Statistics is the mathematics of impressions

Ref: David Wishart, Informatics and Statistics for Metabolmoics July 8-9, 2013 at the Canadian

Bioinformatics Workshops


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Applications.. From Food, Fuel, Metabolism


Experimental design is critical for Omics experiments as..

All steps are critical from sample preparation to identification

Variance in a step can lead to false positive conclusions


Why Are Statistics So Important For “Omics”

• Your hypothesis will drive your

experimental design.

• Your statistical design is critical to

answering the question.

• Have a statistical plan before starting.

• Your statistical plan will drive all other

aspects of your experimental design.


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Why Do Statistics in MPP? Statistics in MPP help us:

• Avoid jumping to

conclusions.

• Avoid finding patterns in

random data

• Understand multiple

comparisons

• Consider alternative

explanations.


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What Will Statistics Do and Not Do?

Will not:

• Give you absolute answers. Statistical conclusions give only

probabilities.

• Give you scientific or clinical conclusions. It is your job to

interpret the statistics and draw conclusions in context of the

hypothesis.

Will:

• Help you assess variability.

• Help you extrapolate from a sample to a population.

• Help you uncover relationships between variables.


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Core Definitions For Statistics:

Mean: The center of a population or probability distribution

• Arithmetic Mean: The sum of values divided by N µ=Ʃxi/N

• Geometric Mean: All values multiplied together then raised the to 1/Nth

power. Equivalent to arithmetic mean of log values, what MPP uses.

Confidence Interval: Interval in which the mean is expected to lie with X%

(often 95%) confidence.

Median: The middle value in the dataset. More robust than mean when

outliers are present. The median value is useful to baseline to in

MPP.


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Measures of Variability

Standard Deviation (σ) :

A measure of data dispersion around the mean.

Variance (σ2):

Standard deviation squared. Often partitioned in various sources.

Coefficient of Variation (CV):

A ratio measure of the standard deviation in relation to the mean. Useful for comparing scatter between variables.

Three measurements with the same mean but different standard deviations


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Variables

Experimental Parameters

• Examples:

- Control vs. treated groups

- Lots or batches of raw materials

- Clinical phenotypes

- Drug dose response and time course

- Forced degradation time points

0

50

100

150

200

250

300

350

0 hr 4 hr 8hr 12 hr 24 hr

A

b

u

n

d

a

n

c

e

Time

Time Course and Dose Response

10 nanomolar

100 nanomolar

1000 nanomolar

A B C Phenotype

Nor

mal

ized

Abu

ndan

ce


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Variables Non-Experimental Parameters

• Examples:

- Column changes during experiment

- Significant changes in sample prep

• Change in person doing the preparation

• Storage issues – alteration of temperature, freeze/thaw cycle, etc.

High complexity data is subject to seemingly minor changes in

methods, protocols, sample handling, etc.

Anything that could reasonably affect data should be considered

carefully.


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What Should You Consider When Determining Number of Replicates

• Type of replicate:

- Technical Replicates: Instrument and/or Sample Prep ONLY tell you about the variability in the mean

measurement of a single sample

- Condition Replicates: Biological, Raw Material Lots,etc. Must be independently sampled from population Will tell you about variability in the population

• Statistical Power - Asks how many replicates you need to see the desired effect?

- You must have a hypothesis: You must estimate the effect size or group means

and their variability


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What is Statistical Power?

A measure of confidence in statistical results

Key Terms to know

α : Error in finding something statistically significant when

the null hypothesis is true (set typically to desired p-value

ie. 0.05) -False Positive rate

β: Error in finding something not statistically significant

when the null hypothesis is false – False Negative rate

Power = 1- β

• If your experiment is underpowered you

cannot be confident in the differences you

see (or don’t see)


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Sampling and Sample Preparation

• Sample Preparation is the primary source of non-experimental variance in differential analysis

Starting sample amount

Inconsistent pipetting

Operator/ chemist

Sample degradation and freeze thaw cycles

Mixing and fractionation

• Reduce impact of non-experimental variance

Careful method validation will help estimate variability

Use controls and QCs to account for variability

Normalization will only get you so far


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No universal separation method exits to profile all

the classes of metabolites in a single LC-MS run

Metabolome Small Molecule Extract

Nucleo(s/t)ides Amino Acids Sugars Lipids

Polar/Hydrophilic

Aqueous Normal Phase

HILIC

Mixed Mode

Reverse Phase (with Ion Pairing)

NonPolar/Hydrophobic Reverse Phase

GC-MS


Chromatography Is A Source Of Variability.

• Variability in retention time and peak shape are sources of error

• Each phase and each compound has it’s own inherent variability

• How often will you need to change columns during an experiment? Plan for enough columns!

• MPP can help with alignment and RT correction!

RT 5.197 RT 5.390

D RT 0.193 ~12% height D


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How to avoid technical variability

Use

• Randomization within Sample Plates

• Normalization

• Internal Standards in Sample Prep and Samples

• QA/QC Pooled Samples

• Model Studies to determine measurement variance

• Robust LCMS system key!

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Accounting for Variability – Normalization in MPP

Key Point: Each normalization makes different assumptions about the sample. Violating these assumptions can introduce more error than you hope to correct.

Key Point: Normalizations can be very sensitive to zero values

Key Point: Normalization is not always necessary

Normalization:

External Scalars Osmolality, Protein Content, Cell Count, Etc.

Algorithmic

- Total “Useful” Current and Percentile Shift: Assumes that the sum concentration of all analytes is the same

in every sample.

- Quantile: Assumes that the distribution of intensities in the samples is the same


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Just a brief overview…. Lots of books and online learning..


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Data Acquisition & Analysis --- start with Mass Hunter Targeted Data Acquisition – QQQ

• Known metabolites only

• Higher sensitivity than profiling approach

• Absolute quantitation

• Typically develop methods for tens/hundreds of metabolites

• Project results onto biological pathways for interpretation

Untargeted Data Acquisition – TOF / Q-TOF

• Acquire data and detect all compounds; relative quantitation.

• Targeted data mining: Pathway Directed

− Analyze data with a database of known compounds

− Project results onto pathways

• Untargeted data mining:

− Find all compounds

− Naïve data mining: Discovery based approach

− Track metabolites using mass, or mass spectra and retention time

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LC-QQQ Targeted Metabolomics Workflow

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1

2

3

4

5

• Optimize LC/MRMs for metabolite standards using LC/QQQ

and Optimizer

• Acquire MRM data using LC/QQQ and Study Manager

• Quantitation of metabolites using MassHunter Quantitative

Analysis exporting project into MPP

• Statistical analysis using Mass Profiler Professional

• Choose pathway database and species

• Analyze differential data and visualize the results on pathways


Targeted Metabolomics Quant Software

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• Batch processing of samples

• Easy construction of calibration

curves

• Automate quant analysis and

reporting

• Compound centric review for

manual editing

• Import results into MPP

• Compound identifier included with

results for pathway analysis

MassHunter Quantitative Analysis


Untargeted/Pathway Directed

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Unta

rge

ted

P

ath

wa

y T

arg

ete

d

Feature

Finding

Profinder

Profinder

Identify

ID Browser

Separate &

Detect

LC-TOF/QTOF

LC-TOF/QTOF

Alignment &

Statistics

Mass Profiler

Professional

Mass Profiler

Professional

Pathways

Pathway Architect

Pathway Architect


MassHunter Profinder Software MassHunter Profinder is a productivity

tool for processing multiple samples in

metabolomics, proteomics, intact protein

analyses

• Fast Compound Finding • Untargeted using MFE

• Targeted using Find by Formula

• Visualize, review, and edit results

by compound across many

samples

• Higher quality results based on

cross-sample processing

• Minimizes false positive and

negative results

• Batch Processing


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Group 1

Group 2

Group 3

Group 4

Compound group

level information

Details for a

single compound

Stacked/ove

rlaid EICs

MS

spectra

Pathway Directed – Pathways to PCDL

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Convert pathway metabolite information into Agilent personal compound

database

• Pathway database source - WikiPathways, BioCyc and KEGG

• Select one to many pathways

• Removes redundant metabolites

• Adds compound information – Formula, Compound ID(s), Name, Structure

Can link to METLIN PCDL to add compound information

• Retention time or MS/MS spectra

Increasing Your Confidence in Compound Identification

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Accurate Mass (AM)

AM +

Isotope Pattern

(IP)

AM

+

Retention

Time (RT)

+

IP

MS/MS Library

AM

+

RT

+

MS/MS

Library

Confident compound identification is

crucial for pathway visualization!


Identification With An MS/MS Library Match METLIN Personal Compound Database & Library

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The Only Commercially Available Database and MS/MS Library for Metabolomics

Q-TOF based LC-MS/MS library

Compounds in database - 64092

Compounds with MS/MS - 8040

• Collected in ESI +/- modes

• Selected monoisotopic ion

• Collected at three collision energies

• Spectra reviewed for quality


Identification Without An MS/MS Library Match Molecular Structure Correlator (MSC)

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Confirm proposed structures in

minutes

• Uses accurate mass MS/MS data

• Facilitates compound identification

− Search compounds in a database, i.e.

PCDL or ChemSpider

− Assigns fragment ions to substructures

of a proposed parent structure

− Provides a score to estimate goodness

of match to MS/MS data

Molecular Structure Correlator (MSC)


Mass Profiler Professional (MPP) 13: Statistical Analysis and Visualization Software

Designed for Mass Spectrometry data from multiple platforms

Can Import, Store, and Visualize

Agilent LC/MS Q(TOF), and QQQ Agilent GC/MS Quad, QQQ, and QTOF Agilent ICP/MS and NMR (Craft) Generic file format import

Extensive statistical analyses tools

ANOVA, Clustering, PCA, Fold-change, Volcano plots

Correlation Analysis, including multi-”omic” correlation!

ID Browser for compound identification

Integrated Biology “Omics”

Pathway Architect for biological contextualization

NEW! KEGG Pathways

New Meta Data

Examples of MPP Data Processing

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Pathway Architect: Changing Data Into A Pathway Visualization

Start here Finish here

Pathway Architect 13: Canonical Pathway Data Mapping and Visualization

Browse, filter, and search

Analyze one or two types of omic data

Supports biological pathways from publicly available databases

• WikiPathways

• BioCyc

• KEGG

Supported formats • BioPAX3 –Pathway Commons, Reactome

NCI Nature Pathway

• GPML – PathVisio –custom drawing

Export compound list from pathways

Easy Mining of Complex Pathways for Biological Understanding

Central Carbon Metabolism

New in Pathway Architect 13: KEGG Pathways

• Curated biological

information

available from

KEGG

• Download from

Agilent server

• Integrated directly

in Pathway

Architect

• KEGG license

required

Integrating Genomics and Metabolomics Data Amino Acid Metabolism

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What’s New in MPP 13.0 and Profinder

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MPP 13 Supports Meta Data Analytical Results to Meta Data


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Metadata can be displayed as

• Heat maps

• Colored strips

• Graphical plots

• Discrete plots

• Text

Visualize the significant relationships!

• Maps observable sample information to analytical results

• Provides flexible visualization of metadata (e.g. time points, growth

conditions, patient data etc.) to facilitate interpretation

Correlation Analysis

MPP 13 adds support for

Correlation Analysis.

Researchers can view

relationships between

entities (compounds or

proteins) or samples.

Clicking on a cell of a heat

map, they can quickly view

the specific parameters of

the correlation


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MPP Correlation Analysis Sample to Sample


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pH

9

2

7 Sample correlation Heatmap

indicates sample relationships both

within a particular pH and between

samples of different pH

MPP Correlation Analysis Entity to Entity


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Supported comparisons:

• Within an experiment

• Between experiments

(genes and proteins,

proteins and metabolites

etc.)

Clicking on any square brings

up the regression analysis

What’s New in Profinder B.06.00 SP1 Support for Intact Proteins

Profinder B.06.00 SP1 has

added the Large Molecular

Feature Extraction (LMFE)

algorithm, which enables

profiling of intact proteins using

multiply charged mass spec

data. Now small molecule,

peptide, and intact proteins can

be processed in the same

program!


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Cloud Deployment

GeneSpring MPP was internally

and beta tested using a cloud

configuration including Amazon

Web Services (AWS)

GeneSpring MPP running on Amazon Web

Services (AWS)

Server specifications (e.g.

CPU), many of which are

configurable on request

An upcoming

technical overview

will discuss how to

configure a cloud or

virtual machine

deployment and

advantages like

flexibility and

collaboration


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Customer training courses are bundled with new licenses and

upgrades:

• R2260A Mass Profiler Professional (MPP) Workshop

• R3904A Pathway Architect Workshop

The MPP Supplemental DVD contains:

• Workflow Guides on Metabolomics, Lipidomics, Integrated

Biology and Class Prediction ( )

• Video Tutorials on using MPP including Advanced

Operations and Class Prediction (


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Agilent Solutions for Metabolomics

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– A broad family of products (LC/MS, GC/MS and NMR) to meet your needs

– Agilent TOF technology is most-suited for untargeted metabolomics

• High mass accuracy and mass stability

• Wide dynamic range with good resolution and high isotope ratio fidelity

• Highest sensitivity for low level metabolite detection

– Profinder is the most advanced software for high quality feature finding

– METLIN is the only commercially available MS/MS library for metabolites

– MSC assists ID of MS/MS spectra without library matches

– Software designed to analyze metabolomics data

– Pathway compound ID mapping using automated Agilent BridgeDB

– Ability to visualize metabolomics data in pathways



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