1. Introduction to Microarray Analysis
Uma Chandran PhD, MSIS
Department of Biomedical Informatics
10/17/12

2. What is a microarray Probes on surface Arrays can detect
Glass beads, chips, slides
Arrays can detect
mRNA
microRNA
Methylation
SNP
High throughput
10000s of specific probes
Measure global gene expression, SNP calls, LOH, amplification, methylation etc

3. Questions that can be asked
Can measure global changes
Which mRNAs are high in disease versus normal, i.e, out of the 1000s of mRNAs expressed in the cell at any time
Are there single nucleotide polymorphism that are markers for a disease – many studies on for example, autism, schizophrenia
Are there methylation changes in disease versus normal

4. Array DESIGN

5. Affymetrix Insert oligo slide Probes are synthesized on a chip
Probes are oligonculeotides of a specified length
Generally 25 mers
At each x, y location a particular oligonucleotide is synthesized in 1000s of copies at that location

6. Affymetrix
Feature: a location on the array with a particular oligonucleotide sequence
Oligonucleotides are synthesized using a photolithographic manufacturing
process
The oligo on the chip is called the probe and RNA (or DNA) that it hybridizes to
is called the target

7. Affy array design
Probe set

8. Affymetrix

9. Probe design Multiple probe sets/gene Probe sets are selected based on
GenBank
dbEST
RefSeq
Bioinformatics approaches
Design at the time of chip design
However, this may be incorrect as genome builds update

10. Affymetrix data

11. Annotation
The probe set id and sequence are contained in reference files
This id never changes
However, annotations change with genome builds
Many software tools to annotate
Some involve new BLAST of the sequences
Mask out probe sets

12. Affymetrix Chips for Dynamic range Cannot compare genes within chips
Human
HGU95, HGU133A, B, HGU133 set
54K probe sets on the HGU133, 30+ to known genes and ESTs
Control probes like GAPDH
Spike in bacterial probes
Mouse
Rat
Chimpanzee
Plants
Many other species
Dynamic range
Very low ~ 10 units
20K +
Cannot compare genes within chips
For example, a transcript that is expressed at 500 units may not be more abundant than one that is expressed at 200 units
This is due to probe binding affinities etc
However, can compare the same probe across multiple chips
Difficulty in probe design makes it difficult to compare from one version to another

13. Affymetrix workflow
from:

14. Illumina

15. Each bead has one type of oligo and thousands of these oligos/bead
Illumina
Each bead has one type of oligo and thousands of these oligos/bead
Bead is deposited on wells in glass slides. The beads are decoded by a step by proprietary technology

16. Microarray analysis objectives
Data Preprocessing
Data Analysis

17. Analysis questions Treatment Normal Class Comparison Class Discovery
Expression - Which genes/miRs are up or down in tumors v normal, untreated v treated
SNP – Which regions are amplified or deleted
Class Discovery
Within the tumor samples, are there subgroups that have a specific expression profile?
SNP – amplification or deletion common to subgroups?
Class prediction, pathway analysis etc
Integrative analysis
Proteomic and genomic
SNP and expression
Methylation and expression
Insert a picture of two different conditions

18. Challenges in microarray analysis
Different platforms
Ilumina, Affymetrix, Agilent….
Many file types, many data formats
Need to learn platform dependent methods and software required
Analysis
How to get started?
Which methods? Which software? Many freely available tools. Some commercial
Analysis software and methods will depend on platform.
SNP analysis is different from expression
Software used may be very specific to SNP
For example, Excel cannot open large SNP files
How to interpret results

19. Public databases
Many sources for public data – labs, consortia, government
Publications require that data files including raw files be made public
GEO –
Array Express -

20. Hands on #1 Look at GEO Search Data Set with the term Exercise
Exercise Heart Human
Identify Platform by clicking on GSE record
Try restricting by platform such as Affymetrix or Illumina

21. Affy data Normalization method Signal value Probe set Id
Total probesets
Raw files

22. Data pre-processing Affy produces many files - .dat, .cel, .chp etc
Process these to produce data that can be opened in excel or .txt
Illumina produces different file types

23. Data Preprocessing Objective Multiple step
Convert image of thousands of signals to a a signal value for each gene or probe set
Multiple step
Image analysis
Background and noise subtraction
Normalization
Summarized expression value for a probe set or gene
Gene
Gene
Gene 3 75 .
Gene

24. Data Pre-processing Go from .DAT file to feature quantification
The first step where .DAT file is aligned to a grid and the features are quantified is usually performed by Affy’s proprietary algorithm
.DAT CEL file
.CEL file contains the feature quantifications
.CEL file still has probes spread over the chip
Values still need to be summarized to probe set level; for example 90525_at = 250 units
250

25. Data Pre-processing – Step 1
Image processing
Usually done using proprietary software
Affy: convert .dat file to .cel file
May perform noise subtraction, background
Illumina: Bead Studio software to convert bead level data to next level of data

26. Data Preprocessing – Step 2
Normalization
Bring all the experiments up to the same scale
Multi-step process depending on technology
Summarized expression value for a probe set or gene
Affy: .cel to .chp; need .cdf file which describes the file layout
Ilumina: normalization option and background subtraction option using Bead Studio
Gene
Gene
Gene 3 75 .
Gene

27. .CEL +.CDF to .CHP
In going from .CEL to .CHP file to generate signal values, the multiple probes within a probe set are “averaged” to produce a single value for that gene/transcript

28. Normalization Corrects for variation in hybridization etc
Important for all high throughput platforms
Assumption that no global change in gene expression
Without normalization
Intensity value for gene will be lower on Chip B
Many genes will appear to be downregulated when in reality they are not
Treated Control
Gene
Gene
Gene .
Gene 50 75 32
250

29. How to normalize? After normalization from .cel
Many methods – Affy MAS5.0
Median scaling – median intensity for all chips should be the same
Known genes, house keeping, invariant genes
Quantile - RMA
Normalization method may differ depending on platform
Illumina – cubic spline
Affymetrix
Choose method
.cel to .chp file
Which method to choose?
Know the biology
After normalization from .cel
.chp file
.txt file A B
Before (down)
After (no change)

30. Normalization

31. Affy data Normalization method Signal value Probe set Id
Total probesets
Raw files

32. Workflows Affy Illumina
.dat file > .cel file > .chp file > .txt file
Affy software needed for .dat > cel
The rest of the steps can be carried out by other tools
Illumina
Through Bead Studio
Bkg subtraction > normalization with various options > background normalization > .txt file
Need bead studio to carry out these steps and raw files not necessarily given
normalization
cdf file

33. Illumina Does not have .DAT, .CEL, .CDF and .CHP files
There is no chip definition or chip layout as in Affy
However, the identity of each bead has to be decoded vial proprietary software

34. Illumina Data preprocessing Signal normalization
Raw files are .txt files
Probe id

35. Affy v Illumina Affy Illumina 25mer Probe synthesized on chips
Multiple probes/probeset
May have multiple probes/transcript
.dat, .cel, .cdf, .chp file types
Normalization methods such as quantile
Txt output can be used for downstream data analysis
Annotations can be updated
Illumina
Longer oligo
Bead technology
Single probe
May have multiple probes/transcript
Image file processed by Bead Studio
Several normalization methods
Txt output can be used for downstream data analysis
Annotations can be updated

37. Hands on #2 -Data analysis
Import data into BRB
Which files to import
.cel file if performing normalization through BRB
Or mport already normalized file as .txt file for further analysis

38. Steps in analysis - Import
Affy
Import all files into Affy tools such as Expression console
Normalize and generate signal values using Affy MAS5.0
Assess QC using GAPDH, B-actin and control probes for spike in and hybridization
Then, import into other tools such as BRB for analysis
Illumina
Depending on background subtraction/normalization, may have generated negative values
Check QC metrics, such as did chip pass?
Remove negative values
Import into tools such as BRB

39. Step in Data analysis – Normalization
Import raw data into a tool
Has data been normalized?
If not, which method to use? What is available for a particular platform
If not available in tools, is R code or package available
After normalization, check distribution
Are there any batch effects?
Is the data log transformed?
If not, should you log transform? When? After or before normalization?
Are there missing or negative values in data?
What should be done? Impute? Remove rows

40. Steps in Data analysis – update Annotations
Very important step
Annotations updated
Annotations provided may often be incorrect
Multiple probe sets for each gene

41. BRB – Array tools Website Excel plug in; R and fortran
Import, choose correct format
For Affy:
.cel files
Process using GCRMA or MAS5.0
Or directly from processed files
Attaches annotation
Create experiment labels

42. Class Discovery Objective? Methods
Can data tell us which classes are similar?
Are there subgroups?
Do T-ALL, T-LL, B-ALL fall into distinct groups?
Methods
Hierarchical clustering
K-means, SOM etc
These are Unsupervised Methods
Class Ids are not known to the algorithm
For example, does not know which one is cancer or non cancer
Do the expression values differentiate, does it discover new classes

43. Multidimensional scaling - MDS

44. Class comparison – differential expression analysis
What genes are up regulated between control and test or multiple test conditions
Normal v tumor
Treated v untreated
Fold change
Not sufficient, need statistics
Statistics
t test, non-parametric, fdr,

45. Class comparison Many analysis methods
May produce different results
Different underlying statistics and methods
t test
t test with permutations
SAM
Emperical bayesian
Depends on underlying assumptions about data
High throughput data with many rows and few samples
What is the distribution
Variance from gene to gene
Save raw data files to try different methods and compare results

46. Fold change does not take variation into account
low
variability
Differentially expressed gene
medium
variability
Differentially expressed gene.
A low-reliable estimate
high
variability
Differentially expressed gene. Powerful and exact statistical tests must be used
Modified from madB

47. Hypothesis Testing Null hypothesis Alternative hypotheses Normal Tumord
mean1
mean2
Null hypothesis
Alternative hypotheses

48. Statistical power t test
Test hypothesis that the two means are not statistically different
Adding “confidence” to the fold change value
Mean
Standard deviation
Sample size
Calculates statistic
You choose cutoff or threshold
Give me gene list at a cutoff of p <0.05
95% confidence that the mean for that gene between control are treated are different

49. Experimental Design – Very important!!!
Sample size
How many samples in test and control
Will depend on many factors such as whether tissue culture or tissue sample
Power analysis
Replicates
Technical v biological
Biological replicates is more important for more heterogenous samples Need replicates for statistical analysis
To pool or not to pool
Depends on objective
Sample acquistion or extraction
Laser captered or gross dissected
All experimental steps from sample acquisition to hybridization
Microarray experiments are very expensive. So, plan experiments carefully
Not just within your lab, institution but across many datasets is quality of data

50. t tests Results might look like
At a p<0.05, there are 300 genes up and 200 genes downregulated
95% confidence that the means of these genes in the two groups is different
At a p < 0.05, x genes up and y genes down with a fold change of at least 3.0

51. Multiple comparison Microarrays have multiple comparison problem
p <= 0.05 says that 95% confidence means are different; therefore 5% due to chance
5% of is 500
500 genes are picked up by chance
Suppose t tests selects 1000 genes at a p of 0.05
500/1000 ;Approximately 50% of the genes will be false
Very high false discovery rate; need more confidence
How to correct?
Correction for multiple comparison
p value and a corrected p value

52. Corrections for multiple comparisons
Involve corrections to the p value so that the actual p value is higher
Bonferroni
Benjamin-Hochberg
Significance Analysis of Microarrays
Tusher et al. at Stanford

53. Hands on BRB Class comparison Choose comparison
Which tests are available?
P value cutoff
How is multiple correction testing being done?
Stringent p value, fdr
How is the output reported?
Can you figure out how many genes are regulated at different p values and different cutoffs
How to interpret results
Look at gene lists generated by our analysis v those generated in the paper

54. BRB – Class Comparison Output folder Check the .html file
Look at results
P value
Fold change
Annotation
Click on annotation
Cut and paste save into Excel

55. Issues Annotation How to compare between platforms
Multiple probe sets for a gene
Annotation files will get updated
Which one is correct?
Where does it map?
How to report the genes?
How to compare between platforms
Different chips within same platform
Biological annotation

56. Difficult to interpret experimental results

57. Which probe/probe set is correctly aligned to the gene?

58. Probe set errors Types of Probe Error Mismatched Probe SNPs
Cross Hybridization
Intron Probe

59. ESR1 probes in UCSC genome browser

60. How to manipulate Gene lists
Create gene lists
Venn Diagram
Can be done even though study done on different platforms
Compare MAS and RMA
Compare B-ALL v T-LL and T-LL v B-ALL

61. Venn Diagram http://www.pangloss.com/seidel/Protocols/venn.cgi

62. Conclusion GEO has some data analysis features Other analysis
Class prediction
Gene list from class comparison can be used in pathway analysis
HSLS pathway workshops on Ingenuity, DAVID, Pathway Architect
Future:
Integrate expression data with other data such as snp or microRNA
GEO has some data analysis features

63. ESR1 probes in UCSC genome browser

64. Next Gen Sequencing Directly sequence DNA to determine
SNP CN
Expression, mRNA, microRNA
Protein binding sites
Methylation
Initial steps depend not on hybridization but also on base pairing or complementarity and DNA synthesis
Data analysis extremely challenging

65. Next Gen Sequencing Applications
Sequence varation – WGS, Exome Seq
Structural rearrangements – WGS, Exome Seq
Copy number – WGS, Exome Seq
Epigenetic changes such as methylation – Methyl Seq
DNA – protein binding – CHIP Seq
mRNA expression – RNA Seq

66. Next Gen Sequencing

67. Read mapping Alignment
Denovo assembly
Mapping to reference genome
Based on complementarity of a given 35 nucleotide to the entire genome
Computationally intensive
Million of 35 bp reads has to search for alignment against the reference and align spefically to a given regions
Large file sizes
Sequence files in the TB
Aligned file BAM files
Several hundred GB
Reference genome

68. Sequence variation

70. Analysis pipeline- CHIP-Seq