Experimental Design and MS Workflows for Omics Applications. David A. Weil, Ph.D. Senior Application Scientist Schaumburg, IL

Transcription

1 Experimental Design and MS Workflows for Omics Applications David A. Weil, Ph.D. Senior Application Scientist Schaumburg, IL 1

2 Acknowledgements: Support Team AFO Metabolomics Team Mark Sartain, Sumit Shah, Anne Blackwell, Dan Cuthbertson Yuqin Dai Steve Madden Rick Reinsdorf MPP Support Team Santa Clara, CA Software Product Manager National Jewish, Denver, CO Global 2

3 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 3

4 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 4

5 Applications.. From Food, Fuel, Metabolism Page 5

6 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 Page 6

7 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. 7

8 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. 8

9 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. 9

10 Core Definitions For Statistics: Mean: The center of a population or probability distribution Arithmetic Mean: The sum of values divided by N µ=ʃx i /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. 10

11 Measures of Variability Standard Deviation (σ) : A measure of data dispersion around the mean. Three measurements with the same mean but different standard deviations Variance (σ 2 ): 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. 11

12 Normalized Abundance 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 Time Course and Dose Response 350 A b u n d a n c e nanomolar 100 nanomolar 1000 nanomolar A B C Phenotype 0 0 hr 4 hr 8hr 12 hr 24 hr Time 12

13 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. 13

14 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 14

15 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) 15

16 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 16

17 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 Sugars Amino Acids Lipids Polar/Hydrophilic Aqueous Normal Phase HILIC Mixed Mode Reverse Phase (with Ion Pairing) NonPolar/Hydrophobic Reverse Phase GC-MS Page 17

18 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 ~12% height D RT D RT

19 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! 19

20 Accounting for Variability Normalization in MPP 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 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 20

21 Just a brief overview. Lots of books and online learning.. 21

22 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 22

23 LC-QQQ Targeted Metabolomics Workflow 1 Optimize LC/MRMs for metabolite standards using LC/QQQ and Optimizer 2 3 Acquire MRM data using LC/QQQ and Study Manager Quantitation of metabolites using MassHunter Quantitative Analysis exporting project into MPP 4 5 Statistical analysis using Mass Profiler Professional Choose pathway database and species Analyze differential data and visualize the results on pathways 23

24 Targeted Metabolomics Quant Software 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 24

25 Pathway Targeted Untargeted Untargeted/Pathway Directed Separate & Detect Feature Finding Alignment & Statistics Identify Pathways LC-TOF/QTOF Profinder Mass Profiler Professional ID Browser Pathway Architect LC-TOF/QTOF Profinder Mass Profiler Professional Pathway Architect 25

26 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 Compound group level information Details for a single compound Stacked/ove Group 1 rlaid EICs Group 2 Group 3 Group 4 MS spectra 26

27 Pathway Directed Pathways to PCDL 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 27

28 Increasing Your Confidence in Compound Identification 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! 28

29 Identification With An MS/MS Library Match METLIN Personal Compound Database & Library Q-TOF based LC-MS/MS library Compounds in database Compounds with MS/MS Collected in ESI +/- modes Selected monoisotopic ion Collected at three collision energies Spectra reviewed for quality The Only Commercially Available Database and MS/MS Library for Metabolomics 29

30 Identification Without An MS/MS Library Match Molecular Structure Correlator (MSC) 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) 30

31 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

32 Examples of MPP Data Processing 32

33 Pathway Architect: Changing Data Into A Pathway Visualization Start here Finish here

34 Pathway Architect 13: Canonical Pathway Data Mapping and Visualization Central Carbon Metabolism 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

35 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

36 Integrating Genomics and Metabolomics Data Amino Acid Metabolism 37

37 What s New in MPP 13.0 and Profinder 38

38 MPP 13 Supports Meta Data Analytical Results to Meta Data 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 39

39 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 40

40 MPP Correlation Analysis Sample to Sample ph Sample correlation Heatmap indicates sample relationships both within a particular ph and between samples of different ph 41

41 MPP Correlation Analysis Entity to Entity Supported comparisons: Within an experiment Between experiments (genes and proteins, proteins and metabolites etc.) Clicking on any square brings up the regression analysis 42

42 What s New in Profinder B SP1 Support for Intact Proteins Profinder B 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! 43

43 Cloud Deployment GeneSpring MPP was internally and beta tested using a cloud configuration including 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 GeneSpring MPP running on Amazon Web Services (AWS) 44

44 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 ( 45

45 Agilent Solutions for Metabolomics 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 Profinder Wide dynamic is the most range advanced with good software resolution for and high high quality isotope ratio feature fidelity finding METLIN Highest is sensitivity the only commercially for low level metabolite available MS/MS detection 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 46

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