Supplementary Information
Supplemental data for classification, subtype discovery and prediction of outcome
in pediatric lymphoblastic leukemia by gene expression profiling
Eng-Juh Yeoh, Mary E. Ross, Shelia A. Shurtleff, W. Kent Williams, Divyen Patel, Rami Mahfouz, Fred G. Behm, Susana C. Raimondi, Mary V. Relling, Anami Patel, Cheng Cheng, Dario Campana, Dawn Wilkins, Xiaodong Zhou, Jinyan Li, Huiqing Liu, Ching-Hon Pui, William E. Evans, Clayton Naeve, Limsoon Wong, James R. Downing
Cancer Cell 2002 Mar;1(2):109-10.
- RNA extraction procedure, labeling, hybridization, and data analysis
- Reproducibility of microarray data
- Comparison of microarray data between PB and BM leukemic samples
- Real-time RT-PCR results
- Comparison of real-time RT-PCR and Affymetrix data
- Comparison of Affymetrix and immunophenotype results
- Hierarchical cluster analysis of diagnostic cases using genes that passed the variation filter
- Methods for gene selection
- Chi-square
- Correlation-based Feature Selection (CFS)
- T-statistics
- Wilkins'
- SOM/DAV
- Comparison of genes selected by the different metrics
- Decision tree for the diagnosis of genetic subtypes
- Description of supervised learning algorithms
- Support Vector Machine (SVM)
- Prediction by Collective Likelihood of Emerging Patterns (PCL)
- K-Nearest Neighbors (K-NN)
- Artificial Neural Networks (ANN)
- Table of results using the different algorithms to predict genetic subgroups
- Absence of correlation of expression data for genetic subtypes with stage of B-cell differentiation
- Results for relapse prediction
- Permutations test results
- Results for secondary AML prediction
- FISH analysis
- References
ALL6: Expression of the outcome predictor in acute leukemia 1 (OPAL1) gene is not an independent prognostic factor in patients treated according to COALL or St Jude protocols. (Added December 2006).