Panu Somervuo, March 19, 2007 1 cDNA microarrays.

Slides:



Advertisements
Similar presentations
Pre-processing in DNA microarray experiments Sandrine Dudoit PH 296, Section 33 13/09/2001.
Advertisements

MicroArray Image Analysis Robin Liechti
A Review of Image Analysis Software for Spotted Microarrays Jess Mar Department of Mathematics University of Queensland CBiS Microarray/Chip.
Microarray Normalization
Filtering and Normalization of Microarray Gene Expression Data Waclaw Kusnierczyk Norwegian University of Science and Technology Trondheim, Norway.
Microarray Simultaneously determining the abundance of multiple(100s-10,000s) transcripts.
Introduction to Microarray Analysis and Technology Dave Lin - November 5, 2001.
1 MicroArray -- Data Analysis Cecilia Hansen & Dirk Repsilber Bioinformatics - 10p, October 2001.
Mathematical Statistics, Centre for Mathematical Sciences
Microarray technology and analysis of gene expression data Hillevi Lindroos.
Microarray Data Analysis Stuart M. Brown NYU School of Medicine.
TIGR Spotfinder: a tool for microarray image processing
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.
1 Example of HMMs: copy number data. 2 DNA copy number is the number of copies of a genomic segment present in the cell. Copy numbers are measured in.
Microarray Data Preprocessing and Clustering Analysis
Microarray analysis Golan Yona ( original version by David Lin )
CDNA Microarray Design and Pre-processing By H. Bjørn Nielsen.
Gene Expression Data Analyses (2)
DNA Arrays …DNA systematically arrayed at high density, –virtual genomes for expression studies, RNA hybridization to DNA for expression studies, –comparative.
Microarray Technology Types Normalization Microarray Technology Microarray: –New Technology (first paper: 1995) Allows study of thousands of genes at.
RNA-Seq An alternative to microarray. Steps Grow cells or isolate tissue (brain, liver, muscle) Isolate total RNA Isolate mRNA from total RNA (poly.
Scanning and image analysis Scanning -Dyes -Confocal scanner -CCD scanner Image File Formats Image analysis -Locating the spots -Segmentation -Evaluating.
Gene Expression Data Analyses (1) Trupti Joshi Computer Science Department 317 Engineering Building North (O)
Genomics I: The Transcriptome RNA Expression Analysis Determining genomewide RNA expression levels.
Analysis of microarray data
Filtering and Normalization of Microarray Gene Expression Data Waclaw Kusnierczyk Norwegian University of Science and Technology Trondheim, Norway.
Microarray Preprocessing
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.
CDNA Microarrays Neil Lawrence. Schedule Today: Introduction and Background 18 th AprilIntroduction and Background 25 th AprilcDNA Mircoarrays 2 nd MayNo.
Affymetrix vs. glass slide based arrays
Analysis of Microarray Data 1.Scan the images 2.Quantify intensity of spots 3.Normalization 4.Analysis of data 5.Identification of genes of interest 6.Validation.
Introduction to DNA Microarray Technology Steen Knudsen Uma Chandran.
Lecture 22 Introduction to Microarray
CDNA Microarrays MB206.
Gene Expression Data Qifang Xu. Outline cDNA Microarray Technology cDNA Microarray Technology Data Representation Data Representation Statistical Analysis.
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.
WORKSHOP SPOTTED 2-channel ARRAYS DATA PROCESSING AND QUALITY CONTROL Eugenia Migliavacca and Mauro Delorenzi, ISREC, December 11, 2003.
Analysis of Microarray Data Analysis of images Preprocessing of gene expression data Normalization of data –Subtraction of Background Noise –Global/local.
Agenda Introduction to microarrays
We calculated a t-test for 30,000 genes at once How do we handle results, present data and results Normalization of the data as a mean of removing.
ARK-Genomics: Centre for Comparative and Functional Genomics in Farm Animals Richard Talbot Roslin Institute and R(D)SVS University of Edinburgh Microarrays.
Microarrays and Gene Expression Analysis. 2 Gene Expression Data Microarray experiments Applications Data analysis Gene Expression Databases.
What Is Microarray A new powerful technology for biological exploration Parallel High-throughput Large-scale Genomic scale.
Introduction to Statistical Analysis of Gene Expression Data Feng Hong Beespace meeting April 20, 2005.
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:
Other genomic arrays: Methylation, chIP on chip… UBio Training Courses.
Computational Biology and Bioinformatics Lab. Songhwan Hwang Functional Genomics DNA Microarray Technology.
Gene Expression Analysis. 2 DNA Microarray First introduced in 1987 A microarray is a tool for analyzing gene expression in genomic scale. The microarray.
Henrik Bengtsson Mathematical Statistics Centre for Mathematical Sciences Lund University, Sweden Plate Effects in cDNA Microarray Data.
Microarray Technology. Introduction Introduction –Microarrays are extremely powerful ways to analyze gene expression. –Using a microarray, it is possible.
Statistical Analysis of Microarray Data By H. Bjørn Nielsen.
Microarray hybridization Usually comparative – Ratio between two samples Examples – Tumor vs. normal tissue – Drug treatment vs. no treatment – Embryo.
MICROARRAYS D’EXPRESSIÓ ESTUDI DE REGULADORS DE LA TRANSCRIPCIÓ DE LA FAMILIA trxG M. Corominas:
(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.
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.
From: Duggan et.al. Nature Genetics 21:10-14, 1999 Microarray-Based Assays (The Basics) Each feature or “spot” represents a specific expressed gene (mRNA).
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.
Other uses of DNA microarrays
Microarray: An Introduction
DNA Microarray. Microarray Printing 96-well-plate (PCR Products) 384-well print-plate Microarray.
Getting the numbers comparable
Normalization for cDNA Microarray Data
Microarray Data Analysis
Presentation transcript:

Panu Somervuo, March 19, cDNA microarrays

Panu Somervuo, March 19, cDNA microarrays small slides with several measurement units, spots e.g. 2.5cm-by-7.6cm glass slide with 30,000 spots each spot contains specific nucleotide sequences, probes in hybridization process, labeled (Cy5, Cy3) samples attach to probes comparative genome hybridization (CGH): DNA samples gene expression: RNA samples relative intensity of hybridization can be measured Cy5Cy3

Panu Somervuo, March 19, Data flow biological data, DNA/RNA extraction, fluoresence dye labeling, hybridization  array scanning  image image processing: spot segmentation  datafile data preprocessing and normalization: data analysis1: statistical tests to find differentially expressed genes  gene lists data analysis2: biological interpretations of results

Panu Somervuo, March 19, Image processing segmentation: spot signals are extracted from background intensity information from both spot foreground and background other information like spot size and shape

Panu Somervuo, March 19, Image analysis results file

Panu Somervuo, March 19, Plotting data

Panu Somervuo, March 19, Logarithm of ratio log(Cy5/Cy3) = log(Cy5) – log(Cy3) log2(4/1) = 2 log2(2/1) = 1 log2(1/1) = 0 log2(1/2) = -1 log2(1/4) = -2

Panu Somervuo, March 19, Plotting data scatterplot MA plot (Ratio vs Intensity)

Panu Somervuo, March 19,

10 Normalization goal: to remove the effects of non-biological causes from data (dye-effect, hybridization, scanning, noise) and keep the biological information as well as possible normalization can be based on the behavior of the majority of the spots on the array, or small set of special control spots each normalization method is based on some assumption of the data

Panu Somervuo, March 19, Spot background subtraction how to know if spot signal is real and not just noise? comparison against background signal global versus local background should background subtraction be used or not?

Panu Somervuo, March 19, Normalization can be applied to both single channel and ratio data mean variance

Panu Somervuo, March 19, Mean normalization global mean vs intensity dependent mean Loess/Lowess normalization

Panu Somervuo, March 19, Print tip loess normalization

Panu Somervuo, March 19,

Panu Somervuo, March 19, Control spots (spike-in controls) fold change up 3 log2(3)=1.58 fold change up 10 log2(10)=3.32 fold change down 10 log2(1/10)=-3.32 fold change down 3 log2(1/3)=-1.58

Panu Somervuo, March 19, What is the best normalization method? each method is based on some assumption  each method can fail if utilizing the behavior of majority of the spots, array should represent all genes if utilizing control spots, check if they are reliable lots of methods have been introduced, lots of methods will be introduced…

Panu Somervuo, March 19, Finding differentially expressed genes Manually set fold change cutoff Fold change cutoff based on data Statistical test, p-value

Panu Somervuo, March 19, Limma package in R analysis of microarray data –data import –data plotting –data normalization –statistical tests  differentially expressed genes online help and tutorial available > help(package=limma) > library(limma) > limmaUsersGuide()