Microarray Analysis Using R/Bioconductor

Reddy Gali, Ph.D.rgali@hms.harvard.edusubmit-c3-bioinformatics@rt.med.harvard.edu

http://catalyst.harvard.edu

Agenda

• Introduction to microarrays• Workflow of a gene expression microarray experiment • Publishing microarray data (MIAME format)• Microarray experimental design• Public microarray databases• Microarray preprocessing - Quality control and Diagnostic

analysis

2

Agenda

3

• Introduction to R/Bioconductor• Installation of R and Bioconductor Packages• General data analysis and strategies• Data analysis using AffylmGUI

Microarray Applications

4

•Analyze and compare patterns of gene expression- before and after an intervention

- between tissue types- between transgenic strains- in neighboring cells (laser capture

microdissection)• Find DNA copy-number variations• SNP detection • Tool for genotyping• High throughput screening tool for drug

discovery • Elucidate gene function (RNAi

microarrays; Silva et al., PNAS 2004)• Investigate interactions between DNA

and protein (ChIP on Chip)

Workflow of Gene Expression

5

Biological question Experimental design

Tissue / sample preparation

Extraction of Total RNA

Microarray hybridization & processing

Image analysis

Probe amplification & labeling

Data analysisExpression measures - Normalization - Statistical Filtering - Clustering - Pathway analysis

Biological Verification

QC

QC

QC

QC

QC

Pitfalls of Microarray Experiment

6

• Gene expression changes detected by microarray analysis cannot be validated by other methods

- Inadequate design

- Data quality is low

- Statistical approach is not adequate - Expression level of gene is below detection limit

- Change in gene expression is small

- Microarray detection probe is not specific or not sensitive

Microarray Processing

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Two color vs Single color

8

Homemade Microarray Affymetrix GeneChip

Tissue

Total RNA

Double-strandedcDNA

Biotin-labeledcRNA

Raw Data Output

Hybridization and Staining

in vitro transcription

cDNA synthesis

normal diseased

First-strand cDNAsynthesis

Cy5 Cy3

MixingHybridization

Cy3 or Cy5labeled cDNA

Tissue

Total RNA

Raw Data Output

normal diseased

Expression Ratio to Absolute Expression Values

Affymetrix probe design

9Lipshutz et al; 1999; Nature Genetics, 21(1):20-24

PMMM

11 Probe pairs / Probe SetMultiple Probe Sets / Gene

Questions usually asked

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• What kind of technology or microarrays I have to use• How many replicates do I need• What is a real replicate• Do I need statistical advice• Should I do technical replicate• Should I do dye swap• Should I pool my samples• How do I analyze my dataset• What software should I use

Design of Microarray Experiment

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• Replicates• Goal, resources, technology, quality, design and

analysis• Two fold change – 3 replicates • Smaller change – 5 replicates• Technical replicates and Biological replicates

• Sample pooling• Amount of sample• Replicates of pooled sample• No way to find variance between samples

MIAME- How to publish

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Minimum Information About a Microarray Experiment (MIAME)- www.mged.org

MIAME – Check list

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• Type of experiment: for example, is it a comparison of normal vs. diseased tissue, a time course, or is it designed to study the effects of a gene knock-out?

• Experimental factors: the parameters or conditions tested, such as time, dose, or genetic variation.

• The number of hybridizations performed in the experiment.

• The type of reference used for the hybridizations, if any.

• Hybridization design: if applicable, a description of the comparisons made in each hybridization, whether to a standard reference sample, or between experimental samples. An accompanying diagram or table may be useful.

• Quality control steps taken: for example, replicates or dye swaps.

MIAME – Check list

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• The origin of the biological sample (for instance, name of the organism, the provider of the sample) and its characteristics: for example, gender, age, developmental stage, strain, or disease state.

• Manipulation of biological samples and protocols used: for example, growth conditions, treatments, separation techniques.

• Protocol for preparing the hybridization extract: for example, the RNA or DNA extraction and purification protocol.

• Labeling protocol(s)

• External controls (spikes)

MIAME – Check list

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• Type of scanning hardware and software used: this information is appropriate for a materials and methods section.

• Type of image analysis software used: specifications should be stated in the materials and methods.

• A description of the measurements produced by the image-analysis software and a description of which measurements were used in the analysis.

• The complete output of the image analysis before data selection and transformation (spot quantitation matrices).

• Data selection and transformation procedures.

• Final gene expression data table(s) used by the authors to make their conclusions after data selection and transformation (gene expression data matrices).

Gene Expression Omnibus- GEO

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Public Microarray Databases

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• BodyMap - http://bodymap.ims.u-tokyo.ac.jp/• SMD - http://genome-www5.stanford.edu/• RIKEN - http://read.gsc.riken.go.jp/• MGI - http://www.informatics.jax.org/• GEO - http://www.ncbi.nlm.nih.gov/geo/• CIBEX - http://cibex.nig.ac.jp/index.jsp• ArrayExpress - http://www.ebi.ac.uk/microarray-as/ae/

Microarray Platforms

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• Agilent Microarrays 60-mer format

• Codelink Bioarrays 30-mer format

• Affymetrix GeneChips 25-mer format

• Illumina Beadchips

• NimbleGen 60-mer format

RNA quality

• OD 260/280 1.8-2• Electropherograms: degradation, rRNA peaks• Bio-analyzer graphs

Microarray data Mining

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Biological question Experimental design

Microarray experiment

Biological verification/interpretation

Estimation/Testing

Clustering

Classification/PredictionData analysis

Expression quantification

Normalization

Image analysis

Pre-processing

Microarray data Mining

21

CDF / CEL

Quality assessment Background correction

probe level normalization probe set summary

Log ratiosLog intensities

Identify genesClustering etc

Microarrays – Image Inspection

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Microarray: - Visual inspection of the chip Scratches, bubbles, uneven hybridization outlier detection

Diagnostic plots-RNA degradation

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Box Plots of unnormalized data

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Raw vs Normalized data

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Raw Data Normalized Data

Histograms of unnormalized data

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QC stats

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Why Normalize

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• It adjusts the individual hybridization intensities to balance them appropriately so that meaningful biological comparisons can be made.

• Unequal quantities of starting RNA• Differences in labeling or detection efficiencies between the

fluorescent dyes used

• Systematic biases in the measured expression levels. • Sample preparationSample preparation• Variability in hybridizationVariability in hybridization• Spatial effectsSpatial effects• Scanner settingsScanner settings• Experimenter biasExperimenter bias

Data analysis workflow

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Free Software – Data analysis

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• BioconductorBioconductor– is an open source and open development software

project to provide tools for the analysis and comprehension of genomic data.

• TMEV 4.0TMEV 4.0– is an application that allows the viewing of

processed microarray slide representations and the identification of genes and expression patterns of interest.

• dCHIPdCHIP– DNA-Chip Analyzer (dChip) is a software package

for probe-level (e.g. Affymetrix platform) and high-level analysis of gene expression microarrays and SNP microarrays.

R / Bioconductor

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• R and Bioconductor packages• R (http://cran.r-project.org/ )is a comprehensive

statistical environment and programming language for professional data analysis and graphical display.

• Bioconductor (http://www.bioconductor.org/) is an open source and open development software project for the analysis of microarray, sequence and genome data.

• More 300 Bioconductor packages.• http://faculty.ucr.edu/~tgirke/Documents/R_BioC

ond/R_BioCondManual.html

R / Bioconductor - Installation

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OneChannelGUI

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• A graphical interface (GUI) for Bioconductor libraries to be used for quality control, normalization, filtering, statistical validation and

data mining for single channel microarrays • Affymetrix IVT, Human Gene 1.0 ST and exon

arrays are implemented • OneChannelGUI is an add-on Bioconductor

package providing a new set of functions extending the capability of the affylmGUI

package.

TCL and Tk pacakges

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• ActiveTcl is ActiveState's distribution of Tcl. It is most commonly used for rapid prototyping,

scripted applications and GUIs. • Install Tcl - http://www.activestate.com/activetcl/• Tcl/Tk packages, BWidget and Tktable

• Install in C:\Tcl Directory

Installing R/ Active Tcl

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• http://cran.r-project.org/• http://www.activestate.com/activetcl/

Installing AffylmGUI packagesfor Affymetrix data

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• install.packages("affylmGUI",contriburl="http://

bioinf.wehi.edu.au/affylmGUI") • source("http://www.bioconductor.org/biocLite.R") • biocLite("affylmGUI", dependencies=TRUE)• biocLite("affylmGUI")• biocLite("tkrplot")• biocLite("affyPLM")• biocLite("R2HTML")• biocLite("xtable")

• library(affylmGUI)

AffylmGUI Browser

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OneChannelGUI Installation

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• source("http://www.bioconductor.org/biocLite.R")• biocLite("oneChannelGUI")

• biocLite("oneChannelGUI ", dependencies=TRUE)• library(oneChannelGUI)

OneChannelGUI

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Target File creation

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• Create, with excel, Create, with excel, a tab delimited filea tab delimited file named targets.txt named targets.txt• Targets file is made of three columns with the following Targets file is made of three columns with the following

header:header:• Name, FileName, TargetName, FileName, Target• In column In column NameName place a brief name (e.g. c1, c2, etc) place a brief name (e.g. c1, c2, etc)• In column In column FileNameFileName place the name of the place the name of the

corresponding .CEL filecorresponding .CEL file• In column In column TargetTarget place the experimental conditions (e.g. place the experimental conditions (e.g.

control, treatment, etc)control, treatment, etc)• Place targets.txt and CEL files into a folder (directory)Place targets.txt and CEL files into a folder (directory)

Target File

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Working with OnechannelGUI

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Working with OnechannelGUI

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A

Click on “File” to start a new projectClick on “File” to start a new project

B

Select working directory that has the .CEL files and targets.txt file

Select working directory that has the .CEL files and targets.txt file

Click on “New” to start a new projectClick on “New” to start a new project

C

D

Selected 3’IVT arraysSelected 3’IVT arrays

Working with OnechannelGUI

Working with OnechannelGUI

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NormalizationNormalization FilteringFiltering StatisticalStatisticalanalysisanalysis

AnnotationAnnotationBiological Biological KnowledgeKnowledgeextractionextraction

QualityQualitycontrolcontrol

Quality Control plots

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Click on Quality Control menuClick on Quality Control menu

QC plots/reports

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• > library(affyQCReport)> library(affyQCReport)> QCReport(mydata, file=“reddy.pdf”)> QCReport(mydata, file=“reddy.pdf”)

• Work with your data set• Plot various QC plots and come up with what

arrays are not of good quality• Plot RNA degradation plot• Download affyQCreport package and create a

QC report for the dataset you are working

Working with OnechannelGUI

48

NormalizationNormalization FilteringFiltering StatisticalStatisticalanalysisanalysis

AnnotationAnnotationBiological Biological KnowledgeKnowledgeextractionextraction

QualityQualitycontrolcontrol

Probe set summary

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Click on probe set menuand select the probe set summary and normalization option.

Click on probe set menuand select the probe set summary and normalization option.

A

B

Normalization

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Exercise 4

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• Calculate probe set summaries with GCRMA and RMA– With GCRMA and RMA– Export and save the normalized values

Working with OnechannelGUI

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NormalizationNormalization FilteringFiltering StatisticalStatisticalanalysisanalysis

AnnotationAnnotationBiological Biological KnowledgeKnowledgeextractionextraction

QualityQualitycontrolcontrol

Filtering - OnechannelGUI

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Signal features:Signal features: Percent intensities greater of a user defined Percent intensities greater of a user defined

valuevalue Interquantile range (IQR) greater of a Interquantile range (IQR) greater of a

defined valuedefined value

Annotation features:Annotation features:• Specific gene features (i.e. GO term, Specific gene features (i.e. GO term,

presence of transcriptional regulative presence of transcriptional regulative elements in promoters, etc.)elements in promoters, etc.)

• Using Ingenuity pathway Using Ingenuity pathway knowledge baseknowledge base

Filtering

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• Perform IQR filter at 0.25 followed by an intensity Perform IQR filter at 0.25 followed by an intensity filter at 50% of the arrays with and intensity over filter at 50% of the arrays with and intensity over 100.100.

• Export the data as tab delimited file.Export the data as tab delimited file.-Question:-Question:

How many probe sets are left after the first How many probe sets are left after the first and the second filter?and the second filter?

• Using transcription factors from Ingenuity create a Using transcription factors from Ingenuity create a file containing only the entrez genes without header file containing only the entrez genes without header and use it to filter the data set. Save the data setand use it to filter the data set. Save the data set

Linear Modeling (Limma)

Differential Expression

Computer contrasts builds differential expression

Computer contrasts builds differential expression

MA and Volcano plots

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Expression values

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AffyIDAffyID

Gene Symbol

Gene Symbol

Gene Description

Gene Description

Log2 FCLog2 FC

Average intensity

Average intensity

T statisticsT statistics

P-valuesP-values

Log-odd statistics

Log-odd statistics

Differential Expression

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• Use the “Table of Genes Ranked in order of Use the “Table of Genes Ranked in order of Differential Expression” and filter the genes and Differential Expression” and filter the genes and export the normalized expression valuesexport the normalized expression values

• Plot differentially expressed genes with raw p-value Plot differentially expressed genes with raw p-value ≤ ≤ 0.05 and an absolute fold change ≥ 1 for the two 0.05 and an absolute fold change ≥ 1 for the two

contrasts.contrasts.

• Using "Venn Diagram between probe set lists“, Using "Venn Diagram between probe set lists“, evaluate the level of overlap between the two sets. evaluate the level of overlap between the two sets.

Hint: make two sets from two contrastsHint: make two sets from two contrasts

60http://catalyst.harvard.edu

Reddy Gali, Ph.D.rgali@hms.harvard.eduPhone: 617 432 7471

Thank you