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Microarray Data Analysis of Illumina Data
Using R/Bioconductor
Reddy Gali, Ph.D.
[email protected]
[email protected]
http://catalyst.harvard.edu
Agenda
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Introduction to microarrays
Workflow of a gene expression microarray experiment
Microarray experimental design
Public microarray databases
Microarray preprocessing - Quality control and Diagnostic analysis
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Agenda
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Introduction to R/Bioconductor
Installation of R and Bioconductor Packages
General data analysis and strategies
Data analysis using lumi package
Data analysis using limma package
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Workflow of Gene Expression
Biological question
Experimental design
QC
Tissue / sample preparation
Extraction of Total RNA
QC
Probe amplification & labeling
QC
Microarray hybridization & processing
Image analysis
QC
Data analysis
Biological Verification
Expression measures - Normalization Statistical Filtering - Clustering Pathway analysis
QC
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Pitfalls of Microarray Experiment
• 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
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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 pool my samples
How do I analyze my dataset
What software should I use
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Design of Microarray Experiment
• 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
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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/
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Microarray Platforms
• Agilent Microarrays 60-mer format
• Codelink Bioarrays 30-mer format
• Affymetrix GeneChips 25-mer format
• Illumina Beadchips
• NimbleGen 60-mer format
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Illumina Bead Array Technology
Silica Beads
Each bead is covered with hundreds of thousands of copies of a
specific oligonucleotide
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Some Facts
• Each bead carries copies of probes with, on average, 30
replicates of every bead type per array
• Around 105 copies of a particular DNA sequence of
interest are covalently attached to each bead
• DNA sequences (oligonucleoties) attached to the beads
are 75 base pairs in length, with 25 base pairs used for
decoding and 50 base pairs used for target hybridization
• A pool of different bead types is created, beads of the
same type having the same probe sequence attached
Box Plots of unnormalized data
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Raw vs Normalized data
Raw Data
Normalized Data
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Histograms of unnormalized data
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Why Normalize
• 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.
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Sample preparation
Variability in hybridization
Spatial effects
Scanner settings
Experimenter bias
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Free Software – Data analysis
• Bioconductor
– is an open source and open development software project
to provide tools for the analysis and comprehension of
genomic data.
• TMEV 4.0
– is an application that allows the viewing of processed
microarray slide representations and the identification of
genes and expression patterns of interest.
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R / Bioconductor
• 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_BioCond/
R_BioCondManual.html
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R / Bioconductor - Installation
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Preparing R for analysis
Preparing R for analysis
Preparing R for analysis
Preparing R for analysis
Preparing R for analysis
Analysis using lumi R package
- Loading data into R/Bioconductor
>lumi_data <- lumiR(‘worshop_data.csv')
- Summary of the loaded data
>lumi_data
- Quality control of loaded data
>summary(lumi_data, 'QC')
>density(lumi.Rdata)
>boxplot(lumi.Rdata)
>MAplot(lumi.Rdata)
> plot(lumi.Rdata,
what='sampleRelati
on')
>> plot(lumi.Rdata,
what=‘cv')
>> plot(lumi.Rdata,
what=‘outlier')
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Variance Stabilization
> lumi.Tdata <lumiT(lumi.Rdata)
> lumi.VSdata <plotVST(lumi.Tdata)
Normalization
> lumi.Ndata <- lumiN(lumi.Tdata)
Or Do all the default preprocessing in one step
> lumi.N.Q <- lumiExpresso(lumi.Rdata)
– Background Correction: bgAdjust
– Variance Stabilizing Transform method: vst
– Normalization method: quantile
– Perform all the QC again
> summary(lumi.Ndata, 'QC')
Differential expression
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>design <- model.matrix(~ -1 + factor(c(1, 1, 1,1, 2, 2, 2,2)))
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>colnames(design) = c("control","affected")
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>fit <- lmFit(lumi.Ndata, design)
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>cont.matrix <- makeContrasts(signature = affected - control,levels=design)
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>fit2 <- contrasts.fit(fit, cont.matrix)
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>ebFit <- eBayes(fit2)
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>results <- topTable(ebFit, number=100, sort.by="B", resort.by="M")
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>print(results)
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>write.table(topTable(ebFit, coef=1, adjust="fdr", sort.by="B", number=25000),
file="results.xls", row.names=F, sep="\t")
Thank you
Reddy Gali, Ph.D.
[email protected]
Phone: 617 432 7471
http://catalyst.harvard.edu
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