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In this presentation……
Part 1 – Gene Expression Microarray Data
Part 2 – Global Expression & Sequence Data Analysis
Part 3 – Proteomic Data Analysis
Examples of Problems
Gene sequence problems: Given a DNA sequence state which sectionsare coding or noncoding regions. Which sections are promoters etc...
Protein Structure problems: Given a DNA or amino acid sequence state what structure the resulting protein takes.
Gene expression problems: Given DNA/gene microarray expression data infer either clinical or biological class labels or genetic machinery that gives rise to the expression data.
Protein expression problems: Study expression of proteins and their function.
Microarray TechnologyBasic idea:
The state of the cell is determined by proteins. A gene codes for a protein which is assembled via mRNA.Measuring amount particular mRNA gives measure ofamount of corresponding protein.Copies of mRNA is expression of a gene.
Microarray technology allows us to measure the expressionof thousands of genes at once.
Measure the expression of thousands of genesunder different experimental conditions and ask what isdifferent and why.
Format
What is whole genome expression analysis?
Clustering algorithm - Hierarchical clustering - K-means clustering - Principal component analysis - Self organizing maps
Beyond clustering - Support vector machines - Automatic discovery of regulatory patterns in promoter region - Bayesian networks analysis
Why Separate Feature selection ?
• most learning algorithms looks for non-linear combinations of features -- can easily find many spurious combinations given small # of records and large # of genes
• We first reduce number of genes by a linear method, e.g. T-values
• Heuristic: select genes from each class• Then apply a favorite machine learning
algorithm
Feature selection approach
• Rank genes by measure; select top 200-500
• T-test for Mean Difference =
• Signal to Noise (S2N) =
• Other: Information-based, biological?• Almost any method works well with a good feature
selection
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2211
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NN
AvgAvg
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21
21
AvgAvg
Heatmap Visualization of selected fieldsALL AML
Heatmap visualizationis done by normalizingeach gene to mean 0,std. 1 to get a picturelike this.
Good correlation overall
AM
L-r
elat
edA
LL
-rel
ated
Possible outliers sample #12
Controlling False Positives
ClassCD37 antigen
178105
41747133
1122
Mean Difference between Classes:T-value = -3.25Significance: p=0.0007
Class Avg Std
1 2287.9 1452.42 4457.5 2010.3
Controlling False Positives with Randomization
Class
178105
41747133
1122
Class
178105
41747133
2112
RandomizedClass
2112Randomize
T-value = -1.1
CD37 antigen Randomization isLess Conservative
Preserves inner structure of data
Controlling false positives with randomization, II
ClassGene
178105
41747133
1122
Class
178105
41747133
2112
RandClass
2112Randomize
500 times
Bottom 1% T-value = -2.08
Select potentially interesting genes at 1%
Gene
Classification
• desired features:– robust in presence of false positives– understandable– return confidence/probability– fast enough
• simplest approaches are most robust
Popular Classification Methods• Decision Trees/Rules
– find smallest gene sets, but also false positives• Neural Nets -
– work well if # of genes is reduced• SVM
– good accuracy, does its own gene selection, hard to understand
• K-nearest neighbor - robust for small # genes• Bayesian nets - simple, robust
Model-building Methodology
• Select gene set (and other parameters) on a train set.
• When the final model is built, evaluate it on the independent test set which should not be used in the model building
Selecting the best gene set
Error Avg
0%5%
10%15%
20%25%30%
1 2 3 4 5 10 20 30 40
Genes per Class
We tested 9 sets, 10 fold x-validation (90 neural nets) Select gene set with lowest average error Heuristically, at least 10 genes overall
Results on the test data
• Evaluation of the 10 genes per class on the test data (34 samples) gives – 33 correct predictions (97% accuracy),– 1 error on sample 66
• Actual Class AML, Net prediction: ALL
• net confidence low
Classification - other applications
• Combining clinical and genetic data
• Outcome / Treatment prediction – Age, Sex, stage of disease, are useful– e.g. if Data from Male, not Ovarian cancer
Classification: Multi-ClassSimilar Approach:
• select top genes most correlated to each class
• select best subset using cross-validation
• build a single model separating all classes
• Advanced:– build separate model for each class vs. rest – choose model making the strongest prediction
Multi-class Data Example
• Brain data, Pomeroy et al 2002, Nature (415), Jan 2002– 42 examples, about 7,000 genes, 5 classes
• Selected top 100 genes most correlated to each class
• Selected best subset by testing 1,2, …, 20 genes subsets, leave-one-out x-validation for each
Brain data results
0
5
10
15
20
25
30
35
40
45
1 2 3 4 5 6 8 10 15 20
AvgError
Number of genes (same from each class)
Optimal set:8 genes per class(4 errorsfrom 42)
Cancer Classification
38 examples of Myeloid and Lymphoblastic leukemias Affymetrix human 6800, (7128 genes including control genes)
34 examples to test classifier
Results: 33/34 correct
d perpendicular distancefrom hyperplane
Test data
d
Nonlinear SVM
Nonlinear SVM does not help when using all genes but does help whenremoving top genes, ranked by Signal to Noise (Golub et al).
Rejections
Golub et al classified 29 test points correctly, rejected 5 of which 2 were errors using 50 genes
Need to introduce concept of rejects to SVM
g1
g2
Normal
Cancer
Reject
Why Feature Selection
• SVMs as stated use all genes/features
• Molecular biologists/oncologists seem to be conviced that only a small subset of genes are responsible for particular biological properties, so they want which genes are are most important in discriminating
• Practical reasons, a clinical device with thousands of genes is not financially practical
•Possible performance improvement
Results with Gene Selection
AML vs ALL: 40 genes 34/34 correct, 0 rejects. 5 genes 31/31 correct, 3 rejects of which 1 is an error.
B vs T cells for AML: 10 genes 33/33 correct, 0 rejects.
d
Test data
d
Test data
The Basic Idea
Use leave-one-out (LOO) bounds for SVMs as a criterion to select features by searching over all possible subsets of n features for the ones that minimizes the bound.
When such a search is impossible because of combinatorial explosion, scale each feature by a real value variable and compute this scaling via gradient descent on the leave-one-out bound. One can then keep the features corresponding to the largest scaling variables.
The rescaling can be done in the input space or in a “Principal Components” space.