A Normalized Tree Index for identification of correlated clinical parameters in microarray experiments

Area of Science:

• Genomics and Bioinformatics
• Biomedical Data Analysis
• Cancer Research

Background:

• Gene expression measurements offer insights into complex diseases like cancer.
• Combining gene expression profiles with clinical data is promising for understanding biological mechanisms.
• Traditional statistical methods struggle with correlating high-dimensional data and clinical parameters.

Purpose of the Study:

• To introduce a novel index, the Normalized Tree Index (NTI), for correlating high-dimensional microarray data with nominal clinical parameters.
• To provide a method for assessing the significance of identified correlations using empirical p-values.

Main Methods:

• Hierarchical agglomerative clustering is applied to microarray data.
• The Normalized Tree Index (NTI) is computed to measure the correlation between the cluster tree and nominal clinical labels.
• Significance is determined via empirical p-values.

Main Results:

• The NTI successfully identified significant correlations between clinical parameters and microarray data in breast cancer datasets.
• Some findings confirmed existing knowledge, while others suggested novel risk factors.
• The results provide a foundation for generating new hypotheses in cancer research.

Conclusions:

• The Normalized Tree Index (NTI) is a valuable tool for biomedical data analysis.
• It enables the detection of correlations between high-dimensional data and nominal labels.
• The NTI provides a p-value to quantify the significance of detected correlations.