cot 6930 hpc & bioinformatics microarray data analysis xingquan zhu dept. of computer science...
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COT 6930HPC & Bioinformatics
Microarray Data Analysis
Xingquan Zhu
Dept. of Computer Science and Engineering
DNA RNA
cDNAESTsUniGene
phenotype
GenomicDNADatabases
Protein sequence databases
protein
Protein structure databases
transcription translation
Gene expressiondatabase
Outline
Gene Expression and Biological Network What, Why, and How
DNA Microarray Microarray Construction Comparative Hybridization Data Analysis
Public Databases
Gene Expression
Gene expression 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
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…
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)
An example of Genetic Network
Gene Regulation Network
An example of Metabolic network
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
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
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)
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
Outline
Gene Expression and Biological Network What, Why, and How
DNA Microarray Microarray Construction Comparative Hybridization Data Analysis
Public Databases
DNA Microarray
Question How to determine whether a gene is expressed, or how
to measure mRNA?
DNA Microarray
Hybridization to the Chip
The Chip is Scanned
Images
Video: http://www.youtube.com/watch?v=VNsThMNjKhM
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
Probe set
PM
MM
Probe Pair PM
MM
MMProbe cell
1.28 cm1.
28 c
m
GeneChip: Chip->Probeset->Probe pair->Probe
25-mer unique oligo
mismatch in the middle nuclieotide
multiple probes (11~16) for each gene
from Affymetrix Inc.
GeneChip Array Design
Affymetrix GeneChip
Affymetrix GeneChip
DNA Microarray Design & Analysis
Microarray 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)
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
cDNA
DNA Microarray Construction 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
DNA Microarray: Automatic Detection
DNA Microarray Choice of probe
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
DNA Microarray Design & Analysis
Microarray 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)
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
Comparative Hybridization
Comparative Hybridization
Comparative Hybridization
Color determined by relative RNA concentrations Brightness determined by total concentration
DNA Microarray Methodology
Anatomy of a Comparative Gene Expression Study http://
www.cs.wustl.edu/~jbuhler/research/array/#diagram Flash Animation
http://www.bio.davidson.edu/courses/genomics/chip/chip.html
DNA Microarray Design & Analysis
Microarray 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)
Streamlined Array Analysis
Normalize
normal tumor tumor normal normal tumorID_REF VALUE ABS_CALL VALUE ABS_CALL VALUE ABS_CALL VALUE ABS_CALL VALUE ABS_CALL VALUE ABS_CALL
AFFX-BioB-5_at 210.6 P 234.6 P 362.5 P 389 P 305.6 P 330.5 PAFFX-BioB-M_at 393 P 327.8 P 501.4 P 816.5 P 542 P 440.8 PAFFX-BioB-3_at 264.9 P 164.6 P 244.7 P 379.7 P 261.3 P 303.7 PAFFX-BioC-5_at 738.6 P 676.1 P 737.6 P 1191.2 P 917 P 767.9 PAFFX-BioC-3_at 356.3 P 365.9 P 423.4 P 711.6 P 560.3 P 484.9 PAFFX-BioDn-5_at 566.3 P 442.2 P 649.7 P 834.3 P 599.1 P 606.9 PAFFX-BioDn-3_at 3911.8 P 3703.7 P 4680.9 P 6037.7 P 4653.7 P 4232 PAFFX-CreX-5_at 6433.3 P 5980 P 7734.7 P 10591 P 8162.1 P 8428 PAFFX-CreX-3_at 11917.8 P 9376.7 P 11509.3 P 16814.4 P 13861.8 P 13653.4 PAFFX-DapX-5_at 12.2 A 44.3 M 31.2 A 37.7 P 33.3 A 12.8 AAFFX-DapX-M_at 57.8 M 42.5 A 79 M 48.8 P 39.5 A 39.2 AAFFX-DapX-3_at 29.8 A 6.2 A 23.4 A 28.4 A 3.2 A 7.6 AAFFX-LysX-5_at 15.3 A 16.2 A 15.6 A 16.7 A 3.1 A 3.9 AAFFX-LysX-M_at 33.2 A 12 A 17.7 A 37.3 A 49.2 A 9.1 AAFFX-LysX-3_at 40.7 M 10.7 A 36.2 A 22.1 A 22.8 A 28.2 AAFFX-PheX-5_at 7.8 A 3 A 7.6 A 5.6 A 5 A 6.4 AAFFX-PheX-M_at 4.2 A 4.8 A 6.8 A 6.1 A 3.7 A 5.5 AAFFX-PheX-3_at 54.2 A 39.6 A 19.4 A 16.1 A 44.7 A 31.2 AAFFX-ThrX-5_at 8.2 A 11.2 A 13.2 A 9.5 A 8.5 A 7.5 AAFFX-ThrX-M_at 38.1 A 30.6 A 37.6 A 7.2 A 26.9 A 36.3 AAFFX-ThrX-3_at 15.2 A 5 A 15 A 8.3 A 36.8 A 11.5 AAFFX-TrpnX-5_at 11.2 A 11.8 A 22.2 A 22.1 A 8.9 A 35.6 AAFFX-TrpnX-M_at 9 A 8.1 A 9.1 A 8.7 A 8.1 A 12 AAFFX-TrpnX-3_at 19.8 A 12.8 A 11.8 A 43.2 M 17.4 A 10 AAFFX-HUMISGF3A/M97935_5_at 82.7 P 120.7 P 92.7 P 46.4 P 55.9 P 46.5 PAFFX-HUMISGF3A/M97935_MA_at 397.6 P 416.7 P 244.8 A 181.4 A 197.5 A 192.3 AAFFX-HUMISGF3A/M97935_MB_at 206.2 P 303 P 300.8 P 253.5 P 195.3 P 216 PAFFX-HUMISGF3A/M97935_3_at 663.8 P 723.9 P 812.1 P 666.1 P 629.4 P 754.1 PAFFX-HUMRGE/M10098_5_at 547.6 P 405.9 P 6894.7 P 3496.1 P 1958.5 P 5799.4 PAFFX-HUMRGE/M10098_M_at 239.1 P 175.8 P 3675 P 1348.6 P 695.9 P 2428.2 PAFFX-HUMRGE/M10098_3_at 1236.4 P 721.4 P 9076.1 P 7795.9 P 4237.1 P 7890 PAFFX-HUMGAPDH/M33197_5_at 19508 P 19267.1 P 22892 P 26584 P 29666.6 P 25038.1 PAFFX-HUMGAPDH/M33197_M_at 18996.6 P 20610.4 P 21573.7 P 29936 P 30106.6 P 22380.2 PAFFX-HUMGAPDH/M33197_3_at 18016.4 P 17463.8 P 20921.3 P 26908.3 P 28382.2 P 21885 PAFFX-HSAC07/X00351_5_at 23294.6 P 21783.7 P 18423.3 P 21858.9 P 23517.1 P 19450.3 PAFFX-HSAC07/X00351_M_at 25373.1 P 24922.8 P 22384.2 P 25760.2 P 27718.5 P 21401.6 PAFFX-HSAC07/X00351_3_at 20032.8 P 20251.1 P 20961.7 P 23494.6 P 23381.2 P 21173.3 P
Raw data Filter
ClassificationSignificance Clustering
Gene lists
Function(Genome Ontology)
•Present/Absent•Minimum value•Fold change
•t-test•Machine learning
•Hierarchical CL •Biclustering
Microarray data
Gene 1
Gene 2
Gene N
Exp 1
E 1
Exp 2
E 2
Exp 3
E 3
Microarray data analysis
begin with a data matrix (gene expression values versus samples)
Typically, there are many genes (>> 10,000) and few samples (~ 10)
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
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
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
Page 193
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
Scatter plot analysis of microarray data
expression level high
low
up
down
Brain
Astrocyte Astrocyte
Fibroblast
Differential Gene Expressionin Different Tissue and Cell Types
The major goal of scatter plot is to identify genes that are differentially regulated between different experimental conditions.
We are interested in outliers
DNA Microarray Design & Analysis
Microarray 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)
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
Microarray data
Gene 1
Gene 2
Gene N
Exp 1
E 1
Exp 2
E 2
Exp 3
E 3
Why care about “clustering” ?E1 E2 E3
Gene 1
Gene 2
Gene N
E1 E2 E3
Gene N
Gene 1
Gene 2
•Discover functional relationSimilar expression functionally related
•Assign function to unknown gene
•Find which gene controls which other genes
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
Some distance measures
Given vectors x = (x1, …, xn), y = (y1, …, yn)
Euclidean distance:
Manhattan distance:
Correlation
distance:
n
iiiE yxyxd
1
2)(),(
.),(1
n
iiiM yxyxd
.)()(
))((1),(
1
2
1
2
1
ii
ii
iii
Cyyxx
yyxxyxd
Finding a Centroid
We use the following equation to find the n dimensional centroid point amid k n dimensional points:
),...,2
,1
(),...,,( 11121 k
xnth
k
ndx
k
stxxxxCP
k
ii
k
ii
k
ii
k
Let’s find the midpoint between 3 2D points, say: (2,4) (5,2) (8,9)
)5,5()3
924,
3
852(
CP
Hierarchical Clustering
E1 E2 E3
•Treat each example as a cluster•While (clusters >1)
•Merge two clusters with the least distance•Update cluster centroid•Clusters--
•Endwhile
•EasyNo need to specify the number of clusters beforehand
•Trouble to interpret “tree” structure
K-means Algorithm
1. Choose k initial center points randomly2. Cluster data using Euclidean distance (or other distance
metric)3. Calculate new center points for each cluster using only
points within the cluster4. Re-Cluster all data using the new center points
1. This step could cause data points to be placed in a different cluster
5. 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
An example with k=2
1. We Pick k=2 centers at random
2. We cluster our data around these center points
K-means example with k=2
3. We recalculate centers based on our current clusters
K-means example with k=2
4. We re-cluster our data around our new center points
K-means example with k=2
5. We repeat the last two steps until no more data points are moved into a different cluster
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
Cluster Quality Continued
size=5
size=5distance=2
0
distance=5
Quality of cluster assessed by ratio of distance to nearest cluster and cluster diameter
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.
k-means comments
Strength
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
A Problem of K-means
Sensitive to outliers 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
++
0
1
2
3
4
5
67
8
9
10
0 1 2 3 4 5 6 7 8 9 100
1
2
3
4
5
67
8
9
10
0 1 2 3 4 5 6 7 8 9 10
A Problem K-means: Differing Density
Original Points K-means (3 Clusters)
Clusters with non-convex shapes
Original Points K-means (2 Clusters)
A parallel k-means package
Parallel K-Means Data Clustering http://www.ece.northwestern.edu/~wkliao/Kmeans/
index.html
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
Two-way clusteringof genes (y-axis)and cell lines (x-axis)
Outline
Gene Expression and Biological Network What, Why, and How
DNA Microarray Microarray Construction Comparative Hybridization Data Analysis
Public Databases
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)
GEO at the NCBI
Array Express at EMBL
Array Express at EMBLArray Express at EMBL
Outline
Gene Expression and Biological Network What, Why, and How
DNA Microarray Microarray Construction Comparative Hybridization Data Analysis
Public Databases