microarray data analysis of illumina data using r/bioconductor reddy gali, ph.d....
TRANSCRIPT
Microarray Data Analysis of Illumina Data Using R/Bioconductor
Reddy Gali, [email protected]@rt.med.harvard.edu
http://catalyst.harvard.edu
Agenda
• 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
Workflow of Gene Expression
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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
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• 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
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
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
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
Illumina Bead Array Technology
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Silica Beads
Each bead is covered with hundreds of thousands of copies of a specific oligonucleotide
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
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Raw Data Normalized Data
Histograms of unnormalized data
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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
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.
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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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='sampleRelation')
>> plot(lumi.Rdata, what=‘cv')
>> plot(lumi.Rdata, what=‘outlier')
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
• >design <- model.matrix(~ -1 + factor(c(1, 1, 1,1, 2, 2, 2,2)))
• >colnames(design) = c("control","affected")
• >fit <- lmFit(lumi.Ndata, design)
• >cont.matrix <- makeContrasts(signature = affected - control,levels=design)
• >fit2 <- contrasts.fit(fit, cont.matrix)
• >ebFit <- eBayes(fit2)
• >results <- topTable(ebFit, number=100, sort.by="B", resort.by="M")
• >print(results)
• >write.table(topTable(ebFit, coef=1, adjust="fdr", sort.by="B", number=25000), file="results.xls", row.names=F, sep="\t")
