Journal
BIOINFORMATICS
Volume 31, Issue 14, Pages 2318-2323Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btv157
Keywords
-
Categories
Funding
- Cancer Research Society Operating Grant (Canada)
- NIH Grant [U54CA151838]
Ask authors/readers for more resources
Motivation: Prior to applying genomic predictors to clinical samples, the genomic data must be properly normalized to ensure that the test set data are comparable to the data upon which the predictor was trained. The most effective normalization methods depend on data from multiple patients. From a biomedical perspective, this implies that predictions for a single patient may change depending on which other patient samples they are normalized with. This test set bias will occur when any cross-sample normalization is used before clinical prediction. Results: We demonstrate that results from existing gene signatures which rely on normalizing test data may be irreproducible when the patient population changes composition or size using a set of curated, publicly available breast cancer microarray experiments. As an alternative, we examine the use of gene signatures that rely on ranks from the data and show why signatures using rank-based features can avoid test set bias while maintaining highly accurate classification, even across platforms.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available