Capturing Best Practice for Microarray Gene Expression Data - - PowerPoint PPT Presentation

Capturing Best Practice for Microarray Gene Expression Data Analysis

Gregory Piatetsky-Shapiro Tom Khabaza Sridhar Ramaswamy

Presented briefly by Joey Mudd

• Microarray devices obtain RNA expression

levels from gene samples

• Data obtained can be used for a variety of

medical purposes: diagnosis, predicting treatment outcome, etc.

• Data produced are typically large and

complex, which makes data mining a useful task

What is Microarray Data?

• Crisp-DM: Cross-Industry

Standard Process model for Data Mining

• Crisp-DM is a way of

standardizing steps taken in a data mining process using high-level structure and terminology

• Useful for describing best

practice

Standardizing Data Mining Process

• Typical number of records is small (<100) due to

difficulty of collecting samples

• Typical number of attributes (genes) is large

(many thousands)

• Can lead to false positives (correlation due to

chance), over-fitting

• Paper suggests reducing number of genes

examined (feature reduction)

Microarray Data Analysis Issues

• Thresholding: Determine appropriate range of values

(authors used min:100, max 16,000 for Affymetrix arrays)

• Normalization: Required for clustering

(authors used mean 0, stddev 1)

• Filtering: Remove attributes that do not vary enough

across samples, such as: MaxValue(G)-MinValue(G)<500, MaxValue(G)/MinValue(G)<5

Data Cleaning and Preparation

Feature Selection

• Because of the large number of attributes/small number
• f samples, feature selection is important
• Use statistical measures to determine “best genes” for

each class

• To avoid under representing some classes, apply

heuristic of selecting equal number of genes from each class

Building Classification Models

• For this data, decision trees work poorly, neural nets

work well

• Feature reduction alone not sufficient
• Test models using a varying number of genes from

each class

• Five-fold sufficient, leave-one-out cross-validation

considered most accurate

Case Study 1

• Leukemia data, 2 classes (AML, ALL), 38 samples

training, 34 samples test (separate samples)

• Filter to reduce number of genes, select top 100 based
• n T-values
• Build neural net models, 10 genes turned out to be best

subset size

• 97% accuracy (33/34 test record correctly classified)

Case Study 2

• Brain data, 5 classes, 42 samples (no separate test set)
• Same preprocessing as Case Study 1
• Select top genes based on Signal to Noise measure, select

equal number of genes per class

• Build neural net models, 12 genes per class (60 total)

gave best results

• Lowest average error rate was 15%.

Case Study 3

• Cluster analysis, with goal of discovering natural classes
• Leukemia data with 3 classes: ALL -> ALL-T and ALL-B
• Same preprocessing as before, also normalize values for

clustering

• Used two clustering methods in Clementine package, both

able to discover natural classes in data, to the authors’ satisfaction

Conclusions

• Ideas presented could be applicable to other domains

where balance between attributes and samples is similar (cheminfomatics or drug design)

• Future work could evaluate cost-sensitive classification

which minimize errors based on cost they inflict

• Principled methodology can lead to good results