introduction to proteomics ii · 12/22/2015 · center for mass spectrometry and proteomics |...

Center for Mass Spectrometry and Proteomics | Phone | (612)625-2280 | (612)625-2279

INTRODUCTION TO PROTEOMICS II


Outline

• Overview • Workflow • Challenges • Experimental design • Tradeoffs • Statistics • Case studies

Terminology

• Differential expression • Targeted vs. Discovery • Dynamic range • Inference • Bias • Variability • Normalization • Blocking • Randomization


‘Omics Technologies

Copyright © by International & American Associations for Dental Research

Mass Spectrometry-Based ‘Omics


Raltelaar, et al., Nature Reviews Genetics 14, 35-48 (January 2013) | doi:10.1038/nrg3356

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Adapted from Walther T, Mann M. JCB 2010;190:491-500

Typical Proteomics Pipeline

Sample Prep

Mass Spec

Data Analysis


After a Decade - Potential Unmet

• Biological – Diverse – Ever changing – Dynamic Range

• Complex Workflow – Sample Preparation – Mass Spectrometry – Data Analysis

Bantscheff et al., 2011

Proteins in Sample

Proteins Identified

Proteins Quantified


Mass Spec + Inference

• The “Inference Problem” • If we don’t find protein, it doesn’t mean it’s

not there • Identification and quantification requires

statistics – which requires experimental design


Experimental Design

• Experimental design is the part of statistics that you carry out before you can carry out an experiment

• Proper planning can save many headaches • You should design your experiments with a

particular biological question in mind


Adapted from Walther T, Mann M. JCB 2010;190:491-500

Typical Proteomics Pipeline

Sample Prep

Mass Spec

Data Analysis

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Design Choices

• Discovery vs. Targeted • Workflow Technology • Tradeoffs • Statistical Rigor


Nature Biotechnology ISSN: 1087-0156

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http://www.sepscience.com/Sectors/Proteomics/Articles/460-/Advances-in-Clinical-Proteomics--A-Workshop-Report

Number Analytes vs. Number Samples

(Targeted)


Mallick & Kuster, Nature Biotechnology, 28, 695–709. (2010) doi:10.1038/nbt.1658

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Sample Prep

Mass Spec

Data Analysis

Patient Group A Patient Group B

Prepare Samples

Peptides

Preprocess

Quantify Identify

Analyze Statistically

MS1 & MS2

Spectra

Differentially Abundant

Peptides/Proteins

HPLC-MS/MS

Population Biological

Sample Handling

Instrument

Sources of Bias and Variability

Typical Discovery Experiment



Why Variance Matters


Avoiding Bias & Variability in Results

• Appropriate number of biological samples • Appropriate number of technical Replicates • Normalization • Blocking • Randomization Choices depend on workflow / technology choices


Institute of Biostatistics and Analyses Masaryk University

Normalization

Randomization

http://www.iba.muni.cz/


Before you start …

Consult experts & statisticians

Plan, Plan, Plan!!!!

introduction to proteomics ii · 12/22/2015 · center for mass spectrometry and proteomics |...

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