1. COT 6930 HPC & Bioinformatics Microarray Data Analysis
Xingquan Zhu
Dept. of Computer Science and Engineering

2. Protein structure databases Gene expression database DNA RNA protein
transcription
translation
DNA
RNA
protein
phenotype
Protein
sequence
databases
cDNA
ESTs
UniGene
Genomic
DNA
Databases

3. Outline Gene Expression and Biological Network DNA Microarray
What, Why, and How
DNA Microarray
Microarray Construction
Comparative Hybridization
Data Analysis
Public Databases

4. Gene Expression Gene expression Biologically
Genes are expressed when they are transcribed onto RNA
Amount of mRNA indicates gene activity
No mRNA → gene is off
mRNA present → gene is on & performing function
Biologically
Some genes are always expressed in all tissues
Estimated 10,000 housekeeping / ubiquitous genes
Other genes are selectively on
Depending on tissue, disease, and/or environment
Change in environment → change in gene expression
So organism can respond

5. Biological Network Gene expression does not happen in isolation
Individual genes code for function
Produce mRNA → protein performing function
Sets of genes can form pathways
Gene products can turn on / off other genes
Sets of pathways can form networks
When pathways interact
Biology is a study of networks
Genes
Proteins
Etc…

6. Type of Biological Networks
Genetic network
Interactions between genes, gene products
Gene regulation network
Network of control decisions to turn genes on / off
Subset of genetic network
Metabolic network
Network of interactions between proteins
Synthesize / break down molecules (enzymes, cofactors)

7. An example of Genetic Network

8. Gene Regulation Network

9. An example of Metabolic network

10. Examining Biological Networks – Benefits
Learn about gene function / regulation
Tissue differentiation
Response to environmental factors
Identify / treat diseases
Discover genetic causes of disease
Evaluate effect of drugs
Detect impact of DNA sequence variation (mutations)
Detection of mutations (e.g., SNPs)
Genetic typing

11. Examining Biological Networks – Approach
Measure protein / mRNA in cells
In different tissues (e.g., brain vs. muscle)
Find gene / protein with tissue-specific function
As environment changes
Find genes / proteins responsible for response
In healthy & diseased tissues
Find proteins / genes responsible for disease (if any)
Help identify diseases based on gene expression
In different individuals
Detect DNA sequence variation

12. Examining Biological Networks
Direct approach
Measure protein production / interaction in cell
2D electrophoresis
Mass spectroscopy
Protein microarray
Advantages
Precise results on proteins
Disadvantages
Low throughput (for now)

13. Examining Biological Networks
Indirect approach
Measure mRNA production (gene expression) in cell
Random ESTs
DNA microarray
Advantages
High throughput
Can test large variety of mRNA simultaneously
Disadvantages
RNA level not always correlated with protein level / function
Misses changes at protein level
Results may thus be less precise

14. Outline Gene Expression and Biological Network DNA Microarray
What, Why, and How
DNA Microarray
Microarray Construction
Comparative Hybridization
Data Analysis
Public Databases

15. DNA Microarray Question
How to determine whether a gene is expressed, or how to measure mRNA?

16. DNA Microarray

17. Hybridization to the Chip

18. The Chip is Scanned

19. Images

20. Video: http://www.youtube.com/watch?v=VNsThMNjKhM

21. Oligonucleotide (GeneChip) vs. Spotted Arrays
GeneChip Microarray
A gene is a probe set
A set of (11-16) probes form a probe set
Probe length: 25 bp
Can use small amount of RNA
Efficient hybridization
Spotted Microarray
One probe per gene
Probe length: hundreds to 1k bp
Less expensive

22. GeneChip: Chip->Probeset->Probe pair->Probe
1.28 cm
1.28 cm
Probe set PM MM
Probe cell
Probe Pair PM MM MM

23. GeneChip Array Design 25-mer unique oligo mismatch in the middle
nuclieotide
multiple probes (11~16) for each gene
from Affymetrix Inc.

24. Affymetrix GeneChip
The second technology used in microarray experiments is used by Affymetrix. This technology is based upon growning specific oligo’s on a silicon substrate. Thus these are often called “gene chips”. Multiple variants are placed for each gene, with specific one base varianets as internal controls.
(how many genes on this chip?)
typically no replicates...

25. Affymetrix GeneChip
Here we can see an annotated close-up of an affymetrix chip, with the regions relating to several genes highlighted.

26. DNA Microarray Design & Analysis
Microarray construction
Array design
Choosing probe sequences
Comparative Hybridization (data collection)
Measure relative amount of mRNA
Image processing of scanned images
Spot detection, normalization, quantization
Data Analysis
Statistical test, noise handling (low-level)
Clustering, classification (high-level)

27. cDNA Complementary DNA
Sequences are the complements of the original mRNA sequences
Why don’t we simple capture mRNA
The environment is full of RNA-digesting enzymes
Free RNA is quickly degraded
To prevent the experimental samples from being lost, they are reverse-transcribed back into more stable DNA form

28. cDNA

29. DNA Microarray Construction
Drops (spots) of cDNA fragments as probes
Attach to glass slide / nylon array at known locations
Use mechanical pins & robotics
Use
Label cDNA with fluorescent dyes (fluor)
Measure contrast in intensity
Use laser / CCD scanner

30. DNA Microarray: Automatic Detection

31. DNA Microarray Choice of probe Can use software to help choose probes
Include genes of interest
Examine sequence databases
Avoid redundancy
No duplicate probes
Avoid cross hybridization
Genechip alleviates this problem by using probe pairs PM MM
Can use software to help choose probes
Or simply buy pre-designed arrays
Complete genomes of yeast, Drosophila, C. elegans
33,000+ human genes from GenBank RefSeq on 2 microarrays
Expensive but labor-saving

32. DNA Microarray Design & Analysis
Microarray construction
Spotted cDNA arrays, in situ photolithography…
Array design
Choosing probe sequences
Comparative Hybridization (data collection)
Measure relative amount of mRNA
Image processing of scanned images
Spot detection, normalization, quantization
Data Analysis
Statistical test, noise handling (low-level)
Clustering, classification (high-level)

33. Comparative Hybridization
Goal
Measure relative amount of mRNA expressed
Algorithm
Choose cell populations
mRNA extraction and reverse transcription
Fluorescent labeling of cDNA’s (normalized)
Hybridization to microarray
Scan the hybridized array
Interpret scanned image

34. Comparative Hybridization

35. Comparative Hybridization

36. Comparative Hybridization
Color determined by relative RNA concentrations
Brightness determined by total concentration

37. DNA Microarray Methodology
Anatomy of a Comparative Gene Expression Study
Flash Animation

38. DNA Microarray Design & Analysis
Microarray construction
Spotted cDNA arrays, in situ photolithography…
Array design
Choosing probe sequences
Comparative Hybridization (data collection)
Measure relative amount of mRNA
Image processing of scanned images
Spot detection, normalization, quantization
Data Analysis
Statistical test, noise handling (low-level)
Clustering, classification (high-level)

39. Streamlined Array Analysis
Normalize
Filter
Raw data
•Present/Absent •Minimum value •Fold change
Significance
Classification
Clustering
•Hierarchical CL
•Biclustering
•t-test
•Machine learning
Gene lists
Function
(Genome Ontology)

40. Microarray data
E 1
E 2
E 3
Gene 1
Gene 2
Exp 2
Exp 3
Exp 1
Gene N

41. Microarray data analysis
begin with a data matrix (gene expression values versus samples)
Typically, there are many genes (>> 10,000) and few samples (~ 10)

42. Low-Level Data Analysis
Normalization:
when you have variability in measurements, you need replication and statistics to find real differences
Significance test:
It’s not just the genes with 2 fold increase, but those with a significant p-value across replicates

43. Sources of Variability in Raw Data
Biological variability
Sample preparation
Probe labeling
RNA extraction
Experimental condition
temperature, time, mixing, etc.
Scanning
laser and detector, chemistry of the flourescent label
Image analysis
identifying and quantifying each spot on the array

44. Data Normalization
Can control for many of the experimental sources of variability (systematic, not random or gene specific)
Bring each image to the same average brightness
Can use simple math or fancy:
divide by the mean (whole chip or by sectors)
LOESS (locally weighted regression)
No sure biological standards

45. Scatter plots
One of the most common visualization method for microarray data.
Useful to compare gene expression values from two microarray experiments (e.g. control, experimental)
Each dot corresponds to a gene expression value
Most dots fall along a line
Outliers represent up-regulated or down-regulated genes
Page 193

46. Scatter plot analysis of microarray data
expression level
high
low up
down

47. Differential Gene Expression in Different Tissue and Cell Types
Brain
Astrocyte
Fibroblast
We are interested in outliers
The major goal of scatter plot is to identify genes that are differentially regulated between different experimental conditions.

48. DNA Microarray Design & Analysis
Microarray construction
Spotted cDNA arrays, in situ photolithography…
Array design
Choosing probe sequences
Comparative Hybridization (data collection)
Measure relative amount of mRNA
Image processing of scanned images
Spot detection, normalization, quantization
Data Analysis
Statistical test, noise handling (low-level)
Clustering, classification (high-level)

49. Higher Level Data Analysis
Computational tasks:
Clustering
Classification
Statistical validation
Data visualization
Pattern detection
Biological problems:
Discovery of common sequences in co-regulated genes
Meta-studies using data from multiple experiments
Linkage between gene expression data and gene sequence/function/metabolic pathways databases

50. Microarray data
E 1
E 2
E 3
Gene 1
Gene 2
Exp 2
Exp 3
Exp 1
Gene N

51. Why care about “clustering” ?
Gene 1
Gene 2
Gene N E1 E2 E3
Gene N
Gene 1
Gene 2
Discover functional relation
Similar expression functionally
related
Assign function to unknown gene
Find which gene controls which
other genes

52. Types of Clustering Methods
Hierarchical
Link similar genes, build up to a tree of all
K-mean Clustering
Self Organizing Maps (SOM)
Split all genes into similar sub-groups
Finds its own groups (machine learning)
Bi-Clustering

53. Some distance measures
Given vectors x = (x1, …, xn), y = (y1, …, yn)
Euclidean distance:
Manhattan distance:
Correlation
distance:

54. Finding a Centroid
We use the following equation to find the n dimensional centroid point amid k n dimensional points:
Let’s find the midpoint between 3 2D points, say: (2,4) (5,2) (8,9)

55. Hierarchical Clustering
Treat each example as a cluster
While (clusters >1)
Merge two clusters with the least distance
Update cluster centroid
Clusters--
Endwhile
Easy
No need to specify the number of clusters beforehand
Trouble to interpret
“tree” structure
Hard to interpret the relation between nodes, e.g. one group of gene repress another group, they are anti-correlated and far away from each other

56. K-means Algorithm Choose k initial center points randomly
Cluster data using Euclidean distance (or other distance metric)
Calculate new center points for each cluster using only points within the cluster
Re-Cluster all data using the new center points
This step could cause data points to be placed in a different cluster
Repeat steps 3 & 4 until the center points have moved such that in step 4 no data points are moved from one cluster to another or some other convergence criteria is met

57. An example with k=2
We Pick k=2 centers at random
We cluster our data around these center points

58. K-means example with k=2
We recalculate centers based on our current clusters

59. K-means example with k=2
We re-cluster our data around our new center points

60. K-means example with k=2
We repeat the last two steps until no more data points are moved into a different cluster

61. Cluster Quality
Since any data can be clustered, how do we know our clusters are meaningful?
The size (diameter) of the cluster vs. The inter-cluster distance
Distance between the members of a cluster and the cluster’s center
Diameter of the smallest sphere

62. Cluster Quality Continued
distance=5
size=5
distance=20
Quality of cluster assessed by ratio of distance to nearest cluster and cluster diameter
size=5

63. Cluster Quality Continued
Quality can be assessed simply by looking at the diameter of a cluster
A cluster can be formed even when there is no similarity between clustered patterns. This occurs because the algorithm forces k clusters to be created.

64. k-means comments Strength Weakness Easy
Relatively efficient: O(tkn), where n is # objects, k is # clusters, and t is # iterations. Normally, k, t << n.
Weakness
Sensitive to the initial seeds
Applicable only when mean is defined, then what about categorical data?
Need to specify k, the number of clusters, in advance
Unable to handle noisy data and outliers
Not suitable to discover clusters with non-convex shapes

65. A Problem of K-means Sensitive to outliers When mean is not meaningful
Outlier: objects with extremely large values
May substantially distort the distribution of the data
When mean is not meaningful
K-medoids: the most centrally located object in a cluster + + 1 2 3 4 5 6 7 8 9 10

66. A Problem K-means: Differing Density
Original Points
K-means (3 Clusters)

67. Clusters with non-convex shapes
Original Points
K-means (2 Clusters)

68. A parallel k-means package
Parallel K-Means Data Clustering

69. Other clustering methods
Self Organizing Maps (SOM)
Determine its own groups by using neural networks
Bi-clustering
Simultaneously merge columns and rows into clusters
Group of genes
Group of examples

70. Two-way clustering
of genes (y-axis)
and cell lines (x-axis)

71. Outline Gene Expression and Biological Network DNA Microarray
What, Why, and How
DNA Microarray
Microarray Construction
Comparative Hybridization
Data Analysis
Public Databases

72. Public Databases
Gene Expression data is an essential aspect of annotating the genome
Publication and data exchange for microarray experiments
Data mining/Meta-studies
Common data format - XML
MIAME (Minimal Information About a Microarray Experiment)

73. GEO at the NCBI

74. Array Express at EMBL

75. Array Express at EMBL

76. Outline Gene Expression and Biological Network DNA Microarray
What, Why, and How
DNA Microarray
Microarray Construction
Comparative Hybridization
Data Analysis
Public Databases