Journal of Medical Statistics and Informatics

Journal of Medical Statistics and Informatics

ISSN 2053-7662
Original Research

Determining juvenile cancer types from gene expression using gene contribution and differential analysis

Eric S. Hald1†, Ryan J. Stoner1† and Derrick K. Rollins1,2*

*Correspondence: Derrick K Rollins

These authors contributed equally to this work.

1. Department of Chemical and Biological Engineering, Iowa State University, Ames, Iowa 50011, USA.

Author Affiliations

2. Department of Statistics, Iowa State University, Ames, Iowa 50011, USA.


Background: The usefulness of DNA microarrays is limited to the efficacy of methods of gene importance analysis available, which could have far-reaching implications in diagnosis, discovery, and treatment of the genetic nature of diseases. This article applies a powerful differential method (DM) based on principle component analysis (PCA) to a vast DNA microarray data set containing data from 88 samples of juvenile small round blue cell tumors.

Methods: Using this DM for ranking the most critical genes from microarray data, the top 25 genes in the resulting rank-ordered list were associated with four functional categories: directly cancer-related, protein synthesis, cell cycle control, and neurological function.

Conclusions: The strength of the DM is demonstrated in the ability to tie these functions to previous cancer research. The results show the method's ability to differentiate between different types of similar juvenile cancers and that the method could also be useful in exploring the genetic nature of various diseases.

Keywords: Data mining, microarrays, gene expression data, principle component analysis, bioinformatics

ISSN 2053-7662
Volume 2
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