TY  - JOUR
AB  - BACKGROUND:

Measurements on gene level are widely used to gain new insights in complex diseases e.g. cancer. A promising approach to understand basic biological mechanisms is to combine gene expression profiles and classical clinical parameters. However, the computation of a correlation coefficient between high-dimensional data and such parameters is not covered by traditional statistical methods.
METHODS:

We propose a novel index, the Normalized Tree Index (NTI), to compute a correlation coefficient between the clustering result of high-dimensional microarray data and nominal clinical parameters. The NTI detects correlations between hierarchically clustered microarray data and nominal clinical parameters (labels) and gives a measurement of significance in terms of an empiric p-value of the identified correlations. Therefore, the microarray data is clustered by hierarchical agglomerative clustering using standard settings. In a second step, the computed cluster tree is evaluated. For each label, a NTI is computed measuring the correlation between that label and the clustered microarray data.
RESULTS:

The NTI successfully identifies correlated clinical parameters at different levels of significance when applied on two real-world microarray breast cancer data sets. Some of the identified highly correlated labels confirm the actual state of knowledge whereas others help to identify new risk factors and provide a good basis to formulate new hypothesis.
CONCLUSIONS:

The NTI is a valuable tool in the domain of biomedical data analysis. It allows the identification of correlations between high-dimensional data and nominal labels, while at the same time a p-value measures the level of significance of the detected correlations.
DA  - 2011
DO  - 10.1186/1756-0381-4-2
LA  - eng
IS  - 1
PY  - 2011
SN  - 1756-0381
T2  - BioData Mining
TI  - A Normalized Tree Index for identification of correlated clinical parameters in microarray data
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-23000731
Y2  - 2024-11-22T06:00:00
ER  -