LimmaGUI A Point-and-Click Interface for cDNA Microarray Analysis James Wettenhall and Gordon Smyth Division of Genetics and Bioinformatics Walter and.

Slides:



Advertisements
Similar presentations
Linear Models for Microarray Data
Advertisements

Limma: Linear Models for Microarray Data R user group 21 June 2005 Judith Boer.
The Rice Functional Genomics Program of China cDNA microarray database (RIFGP-CDMD) consists of complete datasets, including the probe sequences, microarray.
Genomic Profiles of Brain Tissue in Humans and Chimpanzees II Naomi Altman Oct 06.
Pre-processing in DNA microarray experiments Sandrine Dudoit PH 296, Section 33 13/09/2001.
A Review of Image Analysis Software for Spotted Microarrays Jess Mar Department of Mathematics University of Queensland CBiS Microarray/Chip.
How to Work With Affymetrix .Cel Files in geWorkbench
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical.
Introduction to Microarray Analysis and Technology Dave Lin - November 5, 2001.
Mathematical Statistics, Centre for Mathematical Sciences
Microarray technology and analysis of gene expression data Hillevi Lindroos.
Introduction to the design of cDNA microarray experiments Statistics 246, Spring 2002 Week 9, Lecture 1 Yee Hwa Yang.
Sandrine Dudoit1 Microarray Experimental Design and Analysis Sandrine Dudoit jointly with Yee Hwa Yang Division of Biostatistics, UC Berkeley
Getting the numbers comparable
Normalization for cDNA Microarray Data Yee Hwa Yang, Sandrine Dudoit, Percy Luu and Terry Speed. SPIE BIOS 2001, San Jose, CA January 22, 2001.
DNA Microarray Bioinformatics - #27612 Normalization and Statistical Analysis.
Preprocessing Methods for Two-Color Microarray Data
Microarray Data Preprocessing and Clustering Analysis
Normalization Class web site: Statistics for Microarrays.
Low-Level Analysis and QC Regional Biases Mark Reimers, NCI.
Figure 1: (A) A microarray may contain thousands of ‘spots’. Each spot contains many copies of the same DNA sequence that uniquely represents a gene from.
‘Gene Shaving’ as a method for identifying distinct sets of genes with similar expression patterns Tim Randolph & Garth Tan Presentation for Stat 593E.
Data Extraction cDNA arrays Affy arrays. Stanford microarray database.
Microarray Analysis Jesse Mecham CS 601R. Microarray Analysis It all comes down to Experimental Design Experimental Design Preprocessing Preprocessing.
ViaLogy Lien Chung Jim Breaux, Ph.D. SoCalBSI 2004 “ Improvements to Microarray Analytical Methods and Development of Differential Expression Toolkit ”
A robust neural networks approach for spatial and intensity-dependent normalization of cDNA microarray data A.L. Tarca, J.E.K. Cooke and J. MacKay Presented.
Corrections and Normalization in microarrays data analysis
1 Normalization Methods for Two-Color Microarray Data 1/13/2009 Copyright © 2009 Dan Nettleton.
(4) Within-Array Normalization PNAS, vol. 101, no. 5, Feb Jianqing Fan, Paul Tam, George Vande Woude, and Yi Ren.
The following slides have been adapted from to be presented at the Follow-up course on Microarray Data Analysis.
(2) Ratio statistics of gene expression levels and applications to microarray data analysis Bioinformatics, Vol. 18, no. 9, 2002 Yidong Chen, Vishnu Kamat,
Practical Issues in Microarray Data Analysis Mark Reimers National Cancer Institute Bethesda Maryland.
DNA microarray technology allows an individual to rapidly and quantitatively measure the expression levels of thousands of genes in a biological sample.
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, Walter and Eliza Hall Institute of Medical Research.
CDNA Microarrays MB206.
Panu Somervuo, March 19, cDNA microarrays.
Randomization issues Two-sample t-test vs paired t-test I made a mistake in creating the dataset, so previous analyses will not be comparable.
Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, Walter and Eliza Hall Institute of Medical Research.
Learning Theory Reza Shadmehr Linear and quadratic decision boundaries Kernel estimates of density Missing data.
A A R H U S U N I V E R S I T E T Faculty of Agricultural Sciences Introduction to analysis of microarray data David Edwards.
Model-based analysis of oligonucleotide arrays, dChip software Statistics and Genomics – Lecture 4 Department of Biostatistics Harvard School of Public.
Statistical Methods for Identifying Differentially Expressed Genes in Replicated cDNA Microarray Experiments Presented by Nan Lin 13 October 2002.
1 Global expression analysis Monday 10/1: Intro* 1 page Project Overview Due Intro to R lab Wednesday 10/3: Stats & FDR - * read the paper! Monday 10/8:
MRNA Expression Experiment Measurement Unit Array Probe Gene Sequence n n n Clinical Sample Anatomy Ontology n 1 Patient 1 n Disease n n ProjectPlatform.
Analysis of GEO datasets using GEO2R Parthav Jailwala CCR Collaborative Bioinformatics Resource CCR/NCI/NIH.
Statistics for Differential Expression Naomi Altman Oct. 06.
A Quantitative Overview to Gene Expression Profiling in Animal Genetics Armidale Animal Breeding Summer Course, UNE, Feb Analysis of (cDNA) Microarray.
1 Example Analysis of a Two-Color Array Experiment Using LIMMA 3/30/2011 Copyright © 2011 Dan Nettleton.
Suppose we have T genes which we measured under two experimental conditions (Ctl and Nic) in n replicated experiments t i * and p i are the t-statistic.
Microarray hybridization Usually comparative – Ratio between two samples Examples – Tumor vs. normal tissue – Drug treatment vs. no treatment – Embryo.
(1) Normalization of cDNA microarray data Methods, Vol. 31, no. 4, December 2003 Gordon K. Smyth and Terry Speed.
Microarray analysis Quantitation of Gene Expression Expression Data to Networks BIO520 BioinformaticsJim Lund Reading: Ch 16.
1 Estimation of Gene-Specific Variance 2/17/2011 Copyright © 2011 Dan Nettleton.
SPH 247 Statistical Analysis of Laboratory Data 1 May 5, 2015 SPH 247 Statistical Analysis of Laboratory Data.
Henrik Bengtsson Mathematical Statistics Centre for Mathematical Sciences Lund University Plate Effects in cDNA Microarray Data.
The microarray data analysis Ana Deckmann Carla Judice Jorge Lepikson Jorge Mondego Leandra Scarpari Marcelo Falsarella Carazzolle Michelle Servais Tais.
A Quantitative Overview to Gene Expression Profiling in Animal Genetics Armidale Animal Breeding Summer Course, UNE, Feb Analysis of (cDNA) Microarray.
Distinguishing active from non active genes: Main principle: DNA hybridization -DNA hybridizes due to base pairing using H-bonds -A/T and C/G and A/U possible.
Statistical Analysis for Expression Experiments Heather Adams BeeSpace Doctoral Forum Thursday May 21, 2009.
Microarray Data Analysis Xuming He Department of Statistics University of Illinois at Urbana-Champaign.
Lecture 2 – Pre-processing and Normalization José Luis Mosquera Computational Lab on Microarrays Data Analysis Special Topics in Computer Science Institute.
Estimation of Gene-Specific Variance
The Simple Linear Regression Model: Specification and Estimation
Inference for the mean vector
Probability & Statistics Probability Theory Mathematical Probability Models Event Relationships Distributions of Random Variables Continuous Random.
DNA Chip Data Interpretation Tools: Genmapp & Dragon View
Pan Du, Simon Lin Robert H. Lurie Comprehensive Cancer Center
Regression Statistics
Normalization for cDNA Microarray Data
Mathematical Foundations of BME
Presentation transcript:

limmaGUI A Point-and-Click Interface for cDNA Microarray Analysis James Wettenhall and Gordon Smyth Division of Genetics and Bioinformatics Walter and Eliza Hall Institute of Medical Research

limma, limmaGUI and affylmGUI limma : linear models for microarrays by Gordon Smyth Also contains many useful functions specifically for cDNA microarrays limmaGUI : A Graphical User Interface for cDNA analysis with limma. affylmGUI : A Graphical User Interface for Affymetrix analysis with limma.

R, G, M and A R f = Red Foreground Intensity R b = Red Background Intensity R = R f - R b G f = Green Foreground Intensity G b = Green Background Intensity G = G f - G b

R G Plot for ApoAI Slide 1

log 2 (R) log 2 (G) Plot for ApoAI Slide 1

M and A Log Ratio : M (“Minus”) = log 2 (R/G) = log 2 R – log 2 G Average Log Intensity : A (“Add”) = log 2 (RG) 1/2 = (1/2)(log 2 R + log 2 G)

M A Plot for ApoAI Slide 1

Normalized M A Plot for ApoAI Slide 1

M and A Have Nicer Distributions

Linear Models in Microarrays Suppose for one gene, we have: RG Array 14 (KO)32 (WT) Array 215 (WT)2 (KO) M 1 = log 2 (R 1 /G 1 ) = log 2 (4/32) = -3 M 2 = log 2 (R 2 /G 2 ) = log 2 (15/2) = 2.9

Linear Models in Microarrays This linear model has one parameter, M KO-WT to be estimated for each gene. This parameter was estimated using a simple (weighted) average. A factor of (-1) was used for the dye-swap.

Linear Models in Microarrays What about confidence statistics? As M 1 is close to -M 2, we are confident in our estimate for M KO-WT so we expect: A low p-value A high B statistic (log-odds of D.E.) A large negative moderated t statistic (because this gene is down-regulated).

Linear Models in Microarrays What makes this a LINEAR model? Let E{ } be the expected value of. We have : E{M 1 } = (1) M KO-WT E{M 2 } = (-1) M KO-WT A linear relationship. The design matrix is :

limma and limmaGUI Documentation is available after installing the package, by typing “help.start()” in R, clicking on “packages” and then clicking on “limma”. Documentation is available online. Example data sets are available online.

Swirl Zebrafish Example Swirl/ Swirl/