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

1. 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 Introduction to R/Bioconductor
Installation of R and Bioconductor Packages
General data analysis and strategies
Data analysis using AffylmGUI 2

3. Microarray Applications
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) 3

4. 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

5. 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 5

6. Microarray Processing

7. Two color vs Single color
Homemade Microarray Affymetrix GeneChip
Tissue
Tissue
normal diseased
normal diseased
Total RNA
Total RNA
cDNA synthesis
First-strand cDNA
synthesis
Double-stranded
cDNA
Cy5
Cy3
in vitro transcription
Cy3 or Cy5
labeled cDNA
Biotin-labeled
cRNA
Mixing
Hybridization
Hybridization and Staining
Raw Data Output
Raw Data Output
Expression Ratio to
Absolute Expression Values 7

8. Affymetrix probe designPM MM
11 Probe pairs / Probe Set
Multiple Probe Sets / Gene
Lipshutz et al; 1999; Nature Genetics, 21(1):20-24 8

9. 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 do dye swap
Should I pool my samples
How do I analyze my dataset
What software should I use 9

10. 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 10

11. MIAME- How to publish
Minimum Information About a Microarray Experiment (MIAME)- 11

12. MIAME – Check list
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. 12

13. MIAME – Check list
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) 13

14. MIAME – Check list
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). 14

15. Gene Expression Omnibus- GEO15

16. Public Microarray Databases
BodyMap -
SMD -
RIKEN -
MGI -
GEO -
CIBEX -
ArrayExpress - 16

17. Microarray Platforms Agilent Microarrays 60-mer format
Codelink Bioarrays 30-mer format
Affymetrix GeneChips 25-mer format
Illumina Beadchips
NimbleGen 60-mer format 17

18. RNA quality
OD 260/280  1.8-2
Electropherograms: degradation, rRNA peaks
Bio-analyzer graphs

19. Microarray data Mining
Biological question
Experimental design
Microarray experiment
Biological verification/
interpretation
Estimation/Testing
Clustering
Classification/Prediction
Data analysis
Expression quantification
Normalization
Image analysis
Pre-processing 19

20. Microarray data Mining
CDF / CEL
Quality assessment
Background correction
probe level normalization
probe set summary
Log ratios
Log intensities
Identify genes
Clustering etc 20

21. Microarrays – Image Inspection
Microarray: - Visual inspection of the chip
 Scratches, bubbles, uneven hybridization
 outlier detection 21

22. Diagnostic plots-RNA degradation22

23. Box Plots of unnormalized data23

24. Raw vs Normalized data
Raw Data
Normalized Data 24

25. Histograms of unnormalized data25

26. QC stats 26

27. 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 27

28. Data analysis workflow28

29. 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.
dCHIP
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. 29

30. 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. 30

31. R / Bioconductor - Installation31

32. OneChannelGUI
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. 32

33. TCL and Tk pacakges
ActiveTcl is ActiveState's distribution of Tcl. It is most commonly used for rapid prototyping, scripted applications and GUIs.
Install Tcl -
Tcl/Tk packages, BWidget and Tktable
Install in C:\Tcl Directory 33

34. Installing R/ Active Tcl 34

35. Installing AffylmGUI packages for Affymetrix data
install.packages("affylmGUI",contriburl="
source("
biocLite("affylmGUI", dependencies=TRUE)
biocLite("affylmGUI")
biocLite("tkrplot")
biocLite("affyPLM")
biocLite("R2HTML")
biocLite("xtable")
library(affylmGUI) 35

36. AffylmGUI Browser 36

37. OneChannelGUI Installation
source("
biocLite("oneChannelGUI")
biocLite("oneChannelGUI ", dependencies=TRUE)
library(oneChannelGUI) 37

38. OneChannelGUI 38

39. Target File creation
Create, with excel, a tab delimited file named targets.txt
Targets file is made of three columns with the following header:
Name, FileName, Target
In column Name place a brief name (e.g. c1, c2, etc)
In column FileName place the name of the corresponding .CEL file
In column Target place the experimental conditions (e.g. control, treatment, etc)
Place targets.txt and CEL files into a folder (directory) 39

40. Target File 40

41. Working with OnechannelGUIB 41

42. Working with OnechannelGUI
Click on “File” to start a new project B C
Click on “New” to start a new project
Selected 3’IVT arrays D
Select working directory that has the .CEL files and targets.txt file 42

43. Working with OnechannelGUI

44. Working with OnechannelGUI
Quality
control
Statistical
analysis
Normalization
Filtering
Biological
Knowledge
extraction
Annotation 44

45. Quality Control plots
Click on Quality Control menu 45

46. QC plots/reports 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
> library(affyQCReport)
> QCReport(mydata, file=“reddy.pdf”) 46

47. Working with OnechannelGUI
Quality
control
Statistical
analysis
Normalization
Filtering
Biological
Knowledge
extraction
Annotation 47

48. Probe set summary A Click on probe set menu
and select the probe set summary and normalization option. B 48

49. Normalization 49

50. Exercise 4 Calculate probe set summaries with GCRMA and RMA
Export and save the normalized values 50

51. Working with OnechannelGUI
Quality
control
Statistical
analysis
Normalization
Filtering
Biological
Knowledge
extraction
Annotation 51

52. Filtering - OnechannelGUI
Signal features:
Percent intensities greater of a user defined value
Interquantile range (IQR) greater of a defined value
Annotation features:
Specific gene features (i.e. GO term, presence of transcriptional regulative elements in promoters, etc.)
Using Ingenuity pathway knowledge base 52

53. Filtering
Perform IQR filter at 0.25 followed by an intensity filter at 50% of the arrays with and intensity over 100.
Export the data as tab delimited file.
-Question:
How many probe sets are left after the first and the second filter?
Using transcription factors from Ingenuity create a file containing only the entrez genes without header and use it to filter the data set. Save the data set 53

54. Linear Modeling (Limma)54

55. Differential Expression
Computer contrasts builds differential expression 55

56. MA and Volcano plots 56

57. Expression values P-values Average intensity Gene Description
Gene Symbol
Log2 FC
Log-odd statistics
T statistics
AffyID 57

58. Differential Expression
Use the “Table of Genes Ranked in order of Differential Expression” and filter the genes and export the normalized expression values
Plot differentially expressed genes with raw p-value
≤ 0.05 and an absolute fold change ≥ 1 for the two contrasts.
Using "Venn Diagram between probe set lists“, evaluate the level of overlap between the two sets.
Hint: make two sets from two contrasts 58

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