Joint analysis of three seemingly independent microarray experiments via multivariate mixed-model equations with null residual covariance structure Antonio.

Slides:



Advertisements
Similar presentations
Selective Breeding & cDNA Microarrays
Advertisements

Autocorrelation and Heteroskedasticity
Panel Data Models Prepared by Vera Tabakova, East Carolina University.
Live Animal Evaluation Beef Nick Nelson Blue Mt. Community College ANS 231 Originated by Kenneth Geuns Michigan State University Revised 2009.
Optimal designs for one and two-colour microarrays using mixed models
The STARTS Model David A. Kenny December 15, 2013.
Strip-Plot Designs Sometimes called split-block design
1 Parametric Empirical Bayes Methods for Microarrays 3/7/2011 Copyright © 2011 Dan Nettleton.
Estimating the Effects of Treatment on Outcomes with Confidence Sebastian Galiani Washington University in St. Louis.
Effect Size and Meta-Analysis
Error Component models Ric Scarpa Prepared for the Choice Modelling Workshop 1st and 2nd of May Brisbane Powerhouse, New Farm Brisbane.
GP3xCLI: GenePix Post-Processing Program for Quality Assessment of Raw Microarray Data from CSIRO Livestock Industries ABSTRACT : We present GP3xCLI, an.
A Statistical Framework for the Design of Microarray Experiments and Effective Detection of Differential Gene Expression by Shu-Dong Zhang, Timothy W.
Analysis of Differential Expression T-test ANOVA Non-parametric methods Correlation Regression.
Social Research Methods
Multivariate Analysis of Variance, Part 1 BMTRY 726.
BEEF CATTLE GENETICS By David R. Hawkins Michigan State University.
T-tests and ANOVA Statistical analysis of group differences.
(4) Within-Array Normalization PNAS, vol. 101, no. 5, Feb Jianqing Fan, Paul Tam, George Vande Woude, and Yi Ren.
Classification (Supervised Clustering) Naomi Altman Nov '06.
D. H. “Denny” Crews, Jr. Colorado State University BIF SubCommittee Chair.
DEPARTMENT OF PRIMARY INDUSTRIES 1 Discovering Genes for Beef Production Mike Goddard University of Melbourne and Department of Primary Indusries, Victoria.
Chapter 4-5: Analytical Solutions to OLS
BPS - 3rd Ed. Chapter 211 Inference for Regression.
A Quantitative Overview to Gene Expression Profiling in Animal Genetics Armidale Animal Breeding Summer Course, UNE, Feb Sensitivity A Simple Method.
Chapter 12 Examining Relationships in Quantitative Research Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin.
B AD 6243: Applied Univariate Statistics Repeated Measures ANOVA Professor Laku Chidambaram Price College of Business University of Oklahoma.
MMI Genomics/UCD & MSU Visits – May 2003 Design and Analysis of cDNA Microarray Experiments at CSIRO Livestock Industries Toni Reverter (
Examining Relationships in Quantitative Research
Confirmatory Factor Analysis Psych 818 DeShon. Construct Validity: MTMM ● Assessed via convergent and divergent evidence ● Convergent – Measures of the.
The effect of Marbling on Palatability Buenos Aires September 2008.
2008 ADSA-ASAS Joint Annual Meeting Indianapolis, July 7-11 Genetic Parameters of Saturated and Monounsaturated Fatty Acids Estimated by Test-Day Model.
Winter Injury in American chestnut James Sharpe Rebecca Stern April 20, 2015 Stat 231 James Sharpe Rebecca Stern April 20, 2015 Stat 231.
Chapter Three TWO-VARIABLEREGRESSION MODEL: THE PROBLEM OF ESTIMATION
A Quantitative Overview to Gene Expression Profiling in Animal Genetics Antonio (Toni) Reverter Bioinformatics Group CSIRO Livestock Industries Queensland.
Chapter 14 Repeated Measures and Two Factor Analysis of Variance
A Quantitative Overview to Gene Expression Profiling in Animal Genetics Armidale Animal Breeding Summer Course, UNE, Feb Analysis of (cDNA) Microarray.
Interbull Meeting – Dublin 2007 Genetic Parameters of Butter Hardness Estimated by Test-Day Model Hélène Soyeurt 1,2, F. Dehareng 3, C. Bertozzi 4 & N.
A Simple Method for Computationally Inferring Microarray Sensitivity Toni Reverter and Brian Dalrymple Bioinformatics Group CSIRO Livestock Industries.
Accuracy of reported births and calving dates of dairy cattle in the United States Poster 1705 ADSA 2001, Indiannapolis H. D. Norman *,1, J. L. Edwards,
A Quantitative Overview to Gene Expression Profiling in Animal Genetics Armidale Animal Breeding Summer Course, UNE, Feb Co-Expression Reverter et.
Design and Analysis of Microarray Experiments at CSIRO Livestock Industries Toni Reverter Bioinformatics Group CSIRO Livestock Industries Queensland Bioscience.
Chapter 13 Repeated-Measures and Two-Factor Analysis of Variance
Introduction to Breeding Livestock Judging and Evaluation
Correlation/Regression - part 2 Consider Example 2.12 in section 2.3. Look at the scatterplot… Example 2.13 shows that the prediction line is given by.
Reverter et al., XV AAABG, Melbourne – July 2003 Intensities versus intensity ratios in the analysis of cDNA microarray data Toni Reverter (
© Department of Statistics 2012 STATS 330 Lecture 22: Slide 1 Stats 330: Lecture 22.
 Milk fat composition varies with the season (summer vs. winter) but also with the milking time (AM vs. PM).  These observations could allow the diversification.
A Quantitative Overview to Gene Expression Profiling in Animal Genetics Armidale Animal Breeding Summer Course, UNE, Feb Analysis of (cDNA) Microarray.
Statistical Analysis for Expression Experiments Heather Adams BeeSpace Doctoral Forum Thursday May 21, 2009.
Chapter 8 Relationships Among Variables. Outline What correlational research investigates Understanding the nature of correlation What the coefficient.
Capture-recapture Models for Open Populations “Single-age Models” 6.13 UF-2015.
Proportional Hazards Model Checking the adequacy of the Cox model: The functional form of a covariate The link function The validity of the proportional.
BPS - 5th Ed. Chapter 231 Inference for Regression.
Direct Variation Equations
Vera Tabakova, East Carolina University
Live Animal Evaluation Beef
Applying meta-analysis to Genotype-Tissue Expression data from multiple tissues to find eQTLs and eGenes Dat Duong, Lisa Gai, Sagi Snir, Eun Yong Kang,
Determining How Costs Behave
Linear Mixed Models in JMP Pro
Quantitative genetics
Spring 2009: Section 5 – Lecture 1
Stats Club Marnie Brennan
12 Inferential Analysis.
Basic Practice of Statistics - 3rd Edition Inference for Regression
OVERVIEW OF LINEAR MODELS
Adequacy of Linear Regression Models
Adequacy of Linear Regression Models
Adequacy of Linear Regression Models
Presentation transcript:

Joint analysis of three seemingly independent microarray experiments via multivariate mixed-model equations with null residual covariance structure Antonio Reverter 1, Yong-Hong Wang 1, Keren A Byrne, Siok Hwee Tan 1, Gregory S Harper 1, Heather L. Bruce 2, and Sigrid A Lehnert 1 The Cooperative Research Centre for Cattle and Beef Quality 1 CSIRO Livestock Industries, Queensland Bioscience Precinct 306 Carmody Rd, St Lucia, QLD 4067, Australia 2 Food Science Australia, Tingalpa DC, QLD 4173, Australia ABSTRACT : A bovine microarray of muscle and fat cDNAs was used in three gene expression experiments (EXP1, EXP2, and EXP3). EXP1 (14 slides) contrasts the gene expression profiles of muscle in Brahman steers fed varying quality diets. EXP2 (12 slides) compares the expression profile in muscle tissue between two breeds. EXP3 (22 slides) studies the mechanisms underlying adipogenesis in vitro. This study undertook to jointly analyse these three experiments using multivariate mixed-models with the relaxed assumption of a non-zero correlation amongst gene expressions across experiments while imposing a null residual covariance structure. Equivalent editing criteria were applied across experiments yielding 1,424,322 intensity records analysed by fitting a tetra-variate model with 263,938 equations and 18 (co)variance components. The latter were estimated by restricted maximum likelihood. Correlation estimates for gene expressions ranged from  (for EXP1 and EXP3) to  (for EXP1 and EXP2). These moderate to strong estimates indicate the power to be gained with the joint analysis. The expected percentage of differentially expressed genes, as measured from the gene by treatment interaction, was 1.2% for diet in EXP1, 1.3% for breed in EXP2, and 17.1% and 5.1% for age effect and adipogenic treatment, respectively in EXP3. MEDIUM (4  Animals) LOW (3  Anim, 1 Rep) (pooled 3 Anim) HIGH (pooled 2 Anim) MEDIUM (Pool & Ampl) LOW (Pool & Ampl) EXP1: Rockhampton Diets (LD Muscle) TIME 1TIME 2TIME 3 BREED 2 BREED 1 EXP2: Marbling Breeds (LD Muscle) T 1 T 2 T 3 T 4 T 5 T 6 TREAT 1TREAT 2 EXP3: Adipogenesis (in vitro) EXP1EXP2 EXP3 Tot+AmpRNA AmpRNATotRNA AmpRNA Log(Intensity) =Systematic+1,3441, ,440 Gene+9,2459,2459,2459,245 Gene*Trt+ 27,678 45,096 45, ,102 Error 405, , , ,299 CORRELATION:EXP1 & EXP2 = 0.84 (7,515) EXP1 & EXP3 = 0.56 (8,717) EXP2 & EXP3 = 0.51 (7,316) There is gain to be made from a joint analysis Same Microarray Slide Across the 3 Experiments (48 slides) 4 Variables 1,424,322 Records 263,938 Equations 18 (co)variances REML V(G) V(G*T) V(E) Differentially Expressed % Total Variation Actual N (  95% CI) n/a 546 RESULTS:Jointly Differentially Expressed UpDownBreed 1 Breed 2Treat 1 Treat 2 Diet Restriction Up Down Breed Breed Treat Treat IMPLICATIONS Go beyond standard applications: Pleiotropic Patterns of Co-Regulation Evolution Plasticity MODEL: