Randomized maps for assessing the reliability of patients clusters in DNA microarray data analyses
Alberto Bertoni and Giorgio Valentini
Artificial Intelligence in Medicine
Clustering algorithms may be applied to the analysis of DNA microarray data to identify novel subgroups that may lead to
new taxonomies of diseases defined at bio-molecular level.
A major problem related to the identification of biologically meaningful clusters is the assessment of their reliability, since
clustering algorithms may find clusters even if no structure is present.
Recently, methods based on random "perturbations" of the data, such as bootstrapping, noise injections techniques and random subspace
methods have been applied to the problem of cluster validity estimation.
In this framework, we propose stability measures that exploits the high dimensionality of DNA microarray data and the redundancy
of information stored in microarray chips.
To this end we
randomly project the original gene expression data into lower dimensional subspaces,
approximately preserving the distance between the examples according to the Johnson-Lindenstrauss (JL) theory.
The stability of the clusters discovered in the original high dimensional space is estimated by comparing them
with the clusters discovered in randomly projected lower dimensional subspaces.
The proposed cluster-stability measures may be applied to validate and to quantitatively assess the reliability of the clusters
obtained by a large class of clustering algorithms.
Results and conclusion:
We tested the effectiveness of our approach with high dimensional synthetic data, whose distribution is a priori known, showing
that the stability measures based on randomized maps correctly predict the number of clusters and the reliability of each
Then we showed how to apply the proposed measures to the analysis of DNA microarray data, whose underlying distribution is
We evaluated the validity of clusters discovered by hierarchical clustering algorithms in diffuse large
B-cell lymphoma (DLBCL) and malignant melanoma patients,
showing that the proposed reliability measures can support bio-medical researchers in the identification of stable clusters of
patients and in the discovery of new subtypes of diseases characterized at bio-molecular level.