Presentation is loading. Please wait.

Presentation is loading. Please wait.

Pan Du, Simon Lin Robert H. Lurie Comprehensive Cancer Center

Published byWilliam Randall Modified over 5 years ago

Similar presentations

Presentation on theme: "Pan Du, Simon Lin Robert H. Lurie Comprehensive Cancer Center"— Presentation transcript:

1 Pan Du, Simon Lin Robert H. Lurie Comprehensive Cancer Center
2/23/2019 Cross-site and Cross-platform Concordance of Microarray Analysis Improved by Variance Stabilization Pan Du, Simon Lin Robert H. Lurie Comprehensive Cancer Center

2 Why Variance Stabilization? How to Stabilize Variance?
2/23/2019 Outline Why Variance Stabilization? How to Stabilize Variance? Illumina Affymetrix Does it work? 2/23/2019

3 Introduction of Microarray Studies
normal cancer A A Array x Array y Array x Array y Biomedical Applications Quality Control Studies (Johnson and Lin, Nature 411:885, 2001) 2/23/2019

4 Evaluation criterion of reproducibility: Concordance
Lab A Lab B Gene list A Gene list B Anything in common? % in common number of genes selected ideal better worse 100 FDA-led Quality Control Study cross-time cross-site cross-platform (Tong et al., Nature Biotech 24:1132, 2006) 2/23/2019

5 General Microarray Analysis Procedure
2/23/2019 General Microarray Analysis Procedure Sample preparation Microarray experiment and data collection Background adjustment Transformation Normalization Gene identification (log2) 2/23/2019

6 Why Variance Stabilization?
x-y plot mean-var plot Ideal raw x log2 (x) log2 (x+offset) 2/23/2019

7 Why do we care? A general assumption of statistical tests to microarray data: variance is independent of intensity Gene A: 7 (normal) → 8 (cancer) Gene B: 13 (normal) → 14 (cancer) 2/23/2019

8 Variance Stabilization: the model
2/23/2019 Variance Stabilization: the model A mathematical model of microarray hybridization (Rocke and Durbin, Bioinformatics 19:996, 2003) 2/23/2019

9 Variance Stabilization: deriving h(y)
2/23/2019 Variance Stabilization: deriving h(y) Asymptotic variance-stabilizing transformation can be achieved by (Tibshirani, JASA, 1988) 2/23/2019

10 VSN (Variance Stabilizing Normalization)
2/23/2019 Huber’s Solution (2002) VSN (Variance Stabilizing Normalization) Estimate the mean and variance from a set of arrays Assume most genes are not differentially expressed Technically challenging because the normalization between arrays has to be considered Practically challenging because usually we have only 2 ~ 6 arrays (Huber et al., Bioinformatics, 2002) 2/23/2019

11 Illumina BeadArray Technology
2/23/2019 Illumina BeadArray Technology Larger than 30 technique replicates are on each array. Beads are randomly assembled and held in these microwells Multiple arrays on the same slide Cost: < $200 2/23/2019

12 Variance Stabilizing Transformation (VST)
2/23/2019 Variance Stabilizing Transformation (VST) Fit the relations between mean and standard deviation Relations between log2 and VST (arcsinh) 2/23/2019 (Lin, Pan, Huber, and Warren, 2007)

13 Variance Stabilization of the Technical Replicates
2/23/2019 Variance Stabilization of the Technical Replicates 2/23/2019

14 Comparison of Log2, VSN and VST
2/23/2019

15 Barnes data: (Barnes, M., et al., 2005)
2/23/2019 Evaluation Data Sets Barnes data: (Barnes, M., et al., 2005) measured a dilution series (two replicates and six dilution ratios) of two human tissues: blood and placenta. MAQC-I: (Shippy, R., et al., 2006) Similar dilution series, conducted at more than one microarray facilities using both Illumina and Affymetrix platforms 2/23/2019

16 Experiment Design 2/23/2019

17 Cross-site concordance evaluation
2/23/2019 Cross-site concordance evaluation MAQC data VST improves the cross-site concordance 2/23/2019

18 Hypothesis: VST also works for Affymetrix arrays
2/23/2019 VST for Affymetrix Hypothesis: VST also works for Affymetrix arrays Treat each pixel as a technical replicate Model the mean and variance the same way 2/23/2019

19 Cross-site concordance for Affymetrix
2/23/2019 Cross-site concordance for Affymetrix 2/23/2019

20 Cross-platform: Affymetrix and Illumina
2/23/2019 Cross-platform: Affymetrix and Illumina Evaluation procedure Comparing sample C and D in the MAQC study The probe ids were first mapped to the Entrez IDs. Legend notation “Current”: RMA (affymetrix), Log2+Quantile (Illumina) “Improved”: VST+RMA (affymetrix); VST+Quantile 2/23/2019

21 Bioconductor lumi package
2/23/2019 Bioconductor lumi package The VST and related algorithms are included in the Bioconduction lumi package Bioconductor: 2/23/2019

22 Robert H. Lurie Comprehensive Cancer Center, Northwestern University
2/23/2019 Acknowledgements Robert H. Lurie Comprehensive Cancer Center, Northwestern University Warren A. Kibbe and other members in the Bioinformatics group Denise Scholtens, Biostatistics European Bioinformatics Institute Wolfgang Huber The Walter and Eliza Hall Institute of Medical Research, Australia Gordon Smyth 2/23/2019

Download ppt "Pan Du, Simon Lin Robert H. Lurie Comprehensive Cancer Center"

Similar presentations

Statistical Issues in the Design of Microarray Experiments Lara Lusa U.O. Statistica Medica e Biometria Istituto Nazionale per lo Studio e la Cura dei.

Statistical Issues in the Design of Microarray Experiments Lara Lusa U.O. Statistica Medica e Biometria Istituto Nazionale per lo Studio e la Cura dei.
P. J. Munson, National Institutes of Health, Nov. 2001Page 1 A Consistency Test for Determining the Significance of Gene Expression Changes on Replicate.

P. J. Munson, National Institutes of Health, Nov. 2001Page 1 A "Consistency" Test for Determining the Significance of Gene Expression Changes on Replicate.
Bias, Variance, and Fit for Three Measures of Expression: AvDiff, Li &Wong’s, and AvLog(PM-BG) Rafael A. Irizarry Department of Biostatistics, JHU (joint.

Bias, Variance, and Fit for Three Measures of Expression: AvDiff, Li &Wong’s, and AvLog(PM-BG) Rafael A. Irizarry Department of Biostatistics, JHU (joint.
Bias, Variance, and Fit for Three Measures of Expression: AvDiff, Li &Wong’s, and AvLog(PM-BG) Rafael A. Irizarry Department of Biostatistics, JHU (joint.

Bias, Variance, and Fit for Three Measures of Expression: AvDiff, Li &Wong’s, and AvLog(PM-BG) Rafael A. Irizarry Department of Biostatistics, JHU (joint.
We processed six samples in triplicate using 11 different array platforms at one or two laboratories. we obtained measures of array signal variability.

We processed six samples in triplicate using 11 different array platforms at one or two laboratories. we obtained measures of array signal variability.
ECS 289A Presentation Jimin Ding Problem & Motivation Two-component Model Estimation for Parameters in above model Define low and high level gene expression.

ECS 289A Presentation Jimin Ding Problem & Motivation Two-component Model Estimation for Parameters in above model Define low and high level gene expression.
Darya Chudova, Alexander Ihler, Kevin K. Lin, Bogi Andersen and Padhraic Smyth BIOINFORMATICS Gene expression Vol. 25 no. 23 2009, pages 3114-3120.

Darya Chudova, Alexander Ihler, Kevin K. Lin, Bogi Andersen and Padhraic Smyth BIOINFORMATICS Gene expression Vol. 25 no , pages
Microarray Quality Assessment Issues in High-Throughput Data Analysis BIOS 691-803 Spring 2010 Dr Mark Reimers.

Microarray Quality Assessment Issues in High-Throughput Data Analysis BIOS Spring 2010 Dr Mark Reimers.
LimmaGUI A Point-and-Click Interface for cDNA Microarray Analysis James Wettenhall and Gordon Smyth Division of Genetics and Bioinformatics Walter and.

LimmaGUI A Point-and-Click Interface for cDNA Microarray Analysis James Wettenhall and Gordon Smyth Division of Genetics and Bioinformatics Walter and.
Gene Expression Index Stat 115 2012. 2 Outline Gene expression index –MAS4, average –MAS5, Tukey Biweight –dChip, model based, multi-array –RMA, model.

Gene Expression Index Stat Outline Gene expression index –MAS4, average –MAS5, Tukey Biweight –dChip, model based, multi-array –RMA, model.
Microarray Normalization

Microarray Normalization
Normalization of microarray data

Normalization of microarray data
Microarray technology and analysis of gene expression data Hillevi Lindroos.

Microarray technology and analysis of gene expression data Hillevi Lindroos.
Development, Implementation and Testing of a DNA Microarray Test Suite Ehsanul Haque Mentors: Dr. Cecilie Boysen Dr. Jim Breaux ViaLogy Corp.

Development, Implementation and Testing of a DNA Microarray Test Suite Ehsanul Haque Mentors: Dr. Cecilie Boysen Dr. Jim Breaux ViaLogy Corp.
Getting the numbers comparable

Getting the numbers comparable
DNA Microarray Bioinformatics - #27612 Normalization and Statistical Analysis.

DNA Microarray Bioinformatics - #27612 Normalization and Statistical Analysis.
More On Preprocessing Javier Cabrera. Outline 1.Transform the data into a scale suitable for analysis. 2.Remove the effects of systematic and obfuscating.

More On Preprocessing Javier Cabrera. Outline 1.Transform the data into a scale suitable for analysis. 2.Remove the effects of systematic and obfuscating.
GCB/CIS 535 Microarray Topics John Tobias November 8th, 2004.

GCB/CIS 535 Microarray Topics John Tobias November 8th, 2004.
Data analytical issues with high-density oligonucleotide arrays A model for gene expression analysis and data quality assessment.

Data analytical issues with high-density oligonucleotide arrays A model for gene expression analysis and data quality assessment.
ViaLogy Lien Chung Jim Breaux, Ph.D. SoCalBSI 2004 “ Improvements to Microarray Analytical Methods and Development of Differential Expression Toolkit ”

ViaLogy Lien Chung Jim Breaux, Ph.D. SoCalBSI 2004 “ Improvements to Microarray Analytical Methods and Development of Differential Expression Toolkit ”

Similar presentations

Ads by Google