0. Microarray Data Analysis of Illumina Data Using R/Bioconductor
Reddy Gali, Ph.D.

1. Agenda Introduction to microarrays
Workflow of a gene expression microarray experiment
Microarray experimental design
Public microarray databases
Microarray preprocessing - Quality control and Diagnostic analysis

2. Agenda 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 2

3. Workflow of Gene Expression
Biological question
Experimental design QC
Tissue / sample preparation
Extraction of Total RNA
Probe amplification & labeling
Microarray hybridization & processing
Image analysis
Data analysis
Expression measures - Normalization - Statistical Filtering - Clustering - Pathway analysis
Biological Verification

4. 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 4

5. Questions usually asked
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 5

6. 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 6

7. Gene Expression Omnibus- GEO7

8. Public Microarray Databases
BodyMap -
SMD -
RIKEN -
MGI -
GEO -
CIBEX -
ArrayExpress - 8

9. Microarray Platforms Agilent Microarrays 60-mer format
Codelink Bioarrays 30-mer format
Affymetrix GeneChips 25-mer format
Illumina Beadchips
NimbleGen 60-mer format 9

10. Illumina Bead Array Technology
Silica Beads
Each bead is covered with hundreds of thousands of copies of a specific oligonucleotide 10

11. 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

12. Box Plots of unnormalized data12

13. Raw vs Normalized data
Raw Data
Normalized Data 13

14. Histograms of unnormalized data14

15. 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.
Sample preparation
Variability in hybridization
Spatial effects
Scanner settings
Experimenter bias 15

16. 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. 16

17. R / Bioconductor R and Bioconductor packages
R ( )is a comprehensive statistical environment and programming language for professional data analysis and graphical display.
Bioconductor ( is an open source and open development software project for the analysis of microarray, sequence and genome data.
More 300 Bioconductor packages. 17

18. R / Bioconductor - Installation18

19. Preparing R for analysis

20. Preparing R for analysis

21. Preparing R for analysis

22. Preparing R for analysis

23. Preparing R for analysis

24. 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')

25. >density(lumi.Rdata)

26. >boxplot(lumi.Rdata)

27. >MAplot(lumi.Rdata)

28. >> plot(lumi.Rdata, what='sampleRelation')
>> plot(lumi.Rdata, what=‘cv')
>> plot(lumi.Rdata, what=‘outlier')

29. Variance Stabilization
> lumi.Tdata <- lumiT(lumi.Rdata)
> lumi.VSdata <- plotVST(lumi.Tdata)

30. > lumi.Ndata <- lumiN(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')

31. 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")

32. Thank you http://catalyst.harvard.edu Reddy Gali, Ph.D.
Phone: