Microarray Data Set

1
Microarray Data Set

• The microarray data set we are dealing with is
  represented as a 2d numerical array.

2
Characteristics of Microarray Data

• High dimensionality of gene space, low
  dimensionality of sample space.
• Thousands to tens of thousands of genes, tens to
  hundreds of samples.
• Features (genes) correlation.
• Genes collaborate to function. Gene correlation
  characterizes how the system works.
• A plethora of domain knowledge.
• Tons of knowledge accumulated about genes in
  question.

3
Microarray Data Analysis

• Analysis from two angles
• sample as object, gene as attribute
• gene as object, sample/condition as attribute

4
Supervised Analysis

• Select training samples (hold out)
• Sort genes (t-test, ranking)
• Select informative genes (top 50 200)
• Cluster based on informative genes

Class 1
Class 2
g1 g2 . . . . . . . g4131 g4132
1 1 1 0 0 0
1 1 1 0 0 0
1 1 1 0 0 0
g1 g2 . . . g4131 g4132
1 1 1 0 0 0
0 0 0 1 1 1
0 0 0 1 1 1
0 0 0 1 1 1
0 0 0 1 1 1
5
Phenotype Structure Mining
samples
4 5 6 7
8 9 10
1 2 3

• An informative gene is a gene which manifests
  samples' phenotype distinction.
• Phenotype structure sample partition
  informative genes.

6
Existing Feature Selection and Extraction
Algorithms

• The characteristic of microarray data set makes
  feature selection a critical process.
• Too many features, too few samples.
• Existing feature selection/extraction algorithms
  include
• Single gene based discriminative scores, such as
  t-test score, S2N, etc.
• Redundancy removal based FSS algorithms.
• General feature selection algorithms. (Relief
  family, Float selection, etc.).
• General feature extraction algorithms PCA, SVD,
  FLD etc. Havent witnessed specific feature
  extraction algorithms.