Intrinsic bias in breast cancer gene expression data sets.

ABSTRACT:

Background

While global breast cancer gene expression data sets have considerable commonality in terms of their data content, the populations that they represent and the data collection methods utilized can be quite disparate. We sought to assess the extent and consequence of these systematic differences with respect to identifying clinically significant prognostic groups.

Methods

We ascertained how effectively unsupervised clustering employing randomly generated sets of genes could segregate tumors into prognostic groups using four well-characterized breast cancer data sets.

Results

Using a common set of 5,000 randomly generated lists (70 genes/list), the percentages of clusters with significant differences in metastasis latencies (HR p-value<0.01) was 62%, 15%, 21%

SUBMITTER: Mosley JD 

PROVIDER: S-EPMC2711113 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature