Quality control of Affymetrix arrays. What can go wrong? RNA degradation (before hyb) –3’/5’ Dirty samples –background, % present calls Uneven hybridizations.

Slides:

Advertisements

Similar presentations

NASC Normalisation and Analysis of the Affymetrix Data David J Craigon.

Advertisements

P. J. Munson, National Institutes of Health, Nov. 2001Page 1 A "Consistency" Test for Determining the Significance of Gene Expression Changes on Replicate.

Introduction to Microarray Gene Expression

Microarray Quality Assessment Issues in High-Throughput Data Analysis BIOS Spring 2010 Dr Mark Reimers.

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

Microarray Normalization

Evaluation of Affymetrix array normalization procedures based on spiked cRNAs Andrew Hill Expression Profiling Informatics Genetics Institute/Wyeth-Ayerst.

Zhongxue Chen, Monnie McGee, Qingzhong Liu and Richard Scheuermann

Introduction to Affymetrix Microarrays

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.

Statistical Methods in Microarray Data Analysis Mark Reimers, Genomics and Bioinformatics, Karolinska Institute.

Getting the numbers comparable

Probe Level Analysis of AffymetrixTM Data

DNA Microarray Bioinformatics - #27612 Normalization and Statistical Analysis.

Low-Level Analysis and QC Regional Biases Mark Reimers, NCI.

1 Preprocessing for Affymetrix GeneChip Data 1/18/2011 Copyright © 2011 Dan Nettleton.

Low Level Statistics and Quality Control Javier Cabrera.

Normalization of 2 color arrays Alex Sánchez. Dept. Estadística Universitat de Barcelona.

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.

1 Models and methods for summarizing GeneChip probe set data.

Microarray Data Analysis Data quality assessment and normalization for affymetrix chips.

Microarray Data Analysis Data quality assessment and normalization for affymetrix chips.

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

GeneChips and Microarray Expression Data

Summaries of Affymetrix GeneChip probe level data By Rafael A. Irizarry PH 296 Project, Fall 2003 Group: Kelly Moore, Amanda Shieh, Xin Zhao.

Microarray Preprocessing

Microarray Data Analysis Illumina Gene Expression Data Analysis Yun Lian.

Wfleabase.org/docs/tileMEseq0905.pdf Notes and statistics on base level expression May 2009Don Gilbert Biology Dept., Indiana University

CDNA Microarrays MB206.

Panu Somervuo, March 19, cDNA microarrays.

Probe-Level Data Normalisation: RMA and GC-RMA Sam Robson Images courtesy of Neil Ward, European Application Engineer, Agilent Technologies.

Agenda Introduction to microarrays

Assessing expression data quality in high-density oligonucliotide arrays.

Microarray - Leukemia vs. normal GeneChip System.

Scenario 6 Distinguishing different types of leukemia to target treatment.

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.

Lo w -Level Analysis of Affymetrix Data Mark Reimers National Cancer Institute Bethesda Maryland.

Lawrence Hunter, Ph.D. Director, Computational Bioscience Program University of Colorado School of Medicine

Comparison of Microarray Data Generated from Degraded RNA using Five Different Target Synthesis Methods and Commercial Microarrays Scott Tighe and Tim.

Lecture Topic 5 Pre-processing AFFY data. Probe Level Analysis The Purpose –Calculate an expression value for each probe set (gene) from the PM.

Summarization of Oligonucleotide Expression Arrays BIOS Winter 2010.

Model-based analysis of oligonucleotide arrays, dChip software Statistics and Genomics – Lecture 4 Department of Biostatistics Harvard School of Public.

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:

Pre-processing in DNA microarray experiments Sandrine Dudoit, Robert Gentleman, Rafael Irizarry, and Yee Hwa Yang Bioconductor short course Summer 2002.

ABC D EF GH I JKL. Supplementary figure S1: Exemplary overview of the quality assessment plots generated by Robin. All plots have been generated using.

QC and pre-processing of microarray data Lars Eijssen - BiGCaT Bioinformatics.

t Test Assumptions: Paired/dependent/matched Samples:

Microarray Data Analysis The Bioinformatics side of the bench.

Supplemental Figure 1. Levels of nitrate, carbohydrates and metabolites involved in nitrate assimilation, and the medium pH. Starch and the sum of Glucose,

CGH Data BIOS Chromosome Re-arrangements.

Statistical Analyses of High Density Oligonucleotide Arrays Rafael A. Irizarry Department of Biostatistics, JHU (joint work with Bridget Hobbs and Terry.

Oigonucleotide (Affyx) Array Basics Joseph Nevins Holly Dressman Mike West Duke University.

Transcriptome What is it - genome wide transcript abundance How do you obtain it - Arrays + MPSS What do you do with it when you have it - ?

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.

Exploration, Normalization, and Summaries of High Density Oligonucleotide Array Probe Level Data Rafael A. Irizarry Department of Biostatistics, JHU (joint.

AN INTRODUCTION TO GENE EXPRESSION ANALYSIS BY MICROARRAY TECHNIQUE (PART II) DR. AYAT B. AL-GHAFARI MONDAY 10 TH OF MUHARAM 1436.

Arrays How do they work ? What are they ?. WT Dwarf Transgenic Other species Arrays are inverted Northerns: Extract target RNA YFG Label probe + hybridise.

QC analysis Uppsala University Work done by Jonas Almlöf

Microarray - Leukemia vs. normal GeneChip System.

CDNA-Project cDNA project Julia Brettschneider (UCB Statistics)

Correlation of log-transformed signal intensity from two Affymetrix microarray hybridizations using platelet RNA. Plotted are those probesets with an average.

Microarrays 1/31/2018.

Getting the numbers comparable

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

Assessing changes in data – Part 2, Differential Expression with DESeq2

Forecasting - Introduction

Pre-processing AFFY data

Presentation transcript:

Quality control of Affymetrix arrays

What can go wrong? RNA degradation (before hyb) –3’/5’ Dirty samples –background, % present calls Uneven hybridizations (during hyb) –RLE, NUSE Machine problems

If >10%, Affymetrix may provide with new array

Report Type:Expression Report Date: 11:00AM 07/01/2003 ______________________________________________________________________ Filename:030701RE_U133A_PS1.CHP Probe Array Type:HG-U133A Algorithm:Statistical Probe Pair Thr:8 Controls:Antisense ______________________________________________________________________ Alpha1:0.05 Alpha2:0.065 Tau:0.015 Noise (RawQ):1.970 Scale Factor (SF):0.943 TGT Value:200 Norm Factor (NF):1.000 ______________________________________________________________________ Background: Avg: 44.89Std: 0.81Min: 43.10Max: Noise: Avg: 2.27Std: 0.14Min: 2.00Max: 2.80 ___________________________________________________________________ Total Probe Sets:22283 Number Present: % Number Absent: % Number Marginal:3101.4% Average Signal (P):525.7 Average Signal (A):17.8 Average Signal (M):55.8 Average Signal (All):290.3 ______________________________________________________________________ Housekeeping Controls: Probe SetSig(5')Det(5')Sig(M')Det(M')Sig(3')Det(3') Sig(all) Sig(3'/5') HUMISGF3A/M P828.6P1760.2P HUMRGE/M A3.9A40.8A HUMGAPDH/M P7917.8P7937.1P HSAC07/X P8679.7P9096.8P M P135.1A11.6A ______________________________________________________________________ MAS5 report

RNA degradation 3’/5’ ratios of GAPDH AffyRNAdeg in package ‘affy’ –3’/5’ ratios for all genes on the array

RNA degradation

Results show that RNA degradation is reproducible when making technical duplicates

affyPLM Library to work with probes fitPLM() –fits a linear model with probe effect

affyPLM

image(cel.f) –plots weights from fit

Relative Log Expression RLE(affybatch) –the expression value of one array compared to median of all

Normalized Unscaled Standard Errors NUSE(affybatch)

RLE NUSE

Solution Uneven hybridizations: normalization takes care of RNA degradation: outliers problematic High background: the results will not be as clear, but trends can still be seen –Bioinformatics and Computational Biology Soultions Using R and Bioconductor. Gentleman et al. Chapter 3: Quality assessment of Affymetrix GeneChip Data, Bolstad et al.

Machine problems With time, settings change which reflects on the data Roos Jahangir Tafrechi Arnolda de Nooij-van Dalen old vs old new vs old