Unsupervised Analysis of Classical Biomedical Markers: Robustness and Medical Relevance of Patient Clustering Using Bioinformatics Tools

Motivation: It has been proposed that clustering clinical markers, such as blood test results, can be used to stratify patients. However, the robustness of clusters formed with this approach to data pre-processing and clustering algorithm choices has not been evaluated, nor has clustering reproducibility. Here, we made use of the NHANES survey to compare clusters generated with various combinations of pre-processing and clustering algorithms, and tested their reproducibility in two separate samples. Method: Values of 44 biomarkers and 19 health/life style traits were extracted from the National Health and Nutrition Examination Survey (NHANES). The 1999–2002 survey was used for training, while data from the 2003–2006 survey was tested as a validation set. Twelve combinations of pre-processing and clustering algorithms were applied to the training set. The quality of the resulting clusters was evaluated both by considering their properties and by comparative enrichment analysis. Cluster assignments were projected to the validation set (using an artificial neural network) and enrichment in health/life style traits in the resulting clusters was compared to the clusters generated from the original training set. Results: The clusters obtained with different pre-processing and clustering combinations differed both in terms of cluster quality measures and in terms of reproducibility of enrichment with health/life style properties. Z-score normalization, for example, dramatically improved cluster quality and enrichments, as compared to unprocessed data, regardless of the clustering algorithm used. Clustering diabetes patients revealed a group of patients enriched with retinopathies. This coul

Enrichment analysis of biomarker-based patient clusters: A qualitative view.

<p>The NHANES code and description of validated terms found in clusters generated by pre-processing with the Z-score normalization method and clustering algorithm with three algorithms (CLICK, K-means and SOM) (top) or using the CLICK clustering algorithm with four pre-processing procedures (bottom). Raw = no transformation; Norm = transformation to normal; Z-score normalization or Z-score normalization with age-adjustment). All the marked terms were enriched significantly (hyper geometric test, P value<0.05) in both the training and validation sets.</p

Methodology overview.

<p>A test-validation approach was used to test the impact of methodological choices on the clustering of individuals according to their classical blood biomedical marker values. The data from the NHANES 1999–2002 surveys was used as a training set, while the 2003–2006 dataset was used for validation. Various combinations of pre-processing and clustering algorithms were used to define clusters from the training set (black). For pre-processing (top row), transformation to normal of otherwise non-normal variables, Z-score normalized and Z-score normalized-with age adjustment using linear regression, were considered (top block). Each resulting dataset was clustered with three different clustering algorithms (second row): CLICK <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029578#pone.0029578-Sharan1" target="_blank">[18]</a>, K-means <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029578#pone.0029578-Hartigan1" target="_blank">[21]</a> and self-organizing maps <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029578#pone.0029578-Tamayo1" target="_blank">[22]</a>. The resulting clusters were used for enrichment analysis with health/lifestyle traits and for training an artificial neural network (third row). The artificial neural network was subsequently used to assign individuals from the validation set to clusters (third row), using the same pre-processing procedure as used to generate the training set clusters (bottom row). The resulting validation set clusters were also tested for enrichment with the same health/life-style traits as the training set. Enrichments found in both sets were compared.</p

Comparative enrichment analysis of biomarker-based patient clusters: A quantitative analysis.

<p>The number of validated terms found to be enriched in clusters generated with the different pre-processing procedures and clustering algorithms tested in this study. Validated enrichments and validated clusters are defined by the recurrence of statistically significant enrichment in the training and validation datasets. The clusters that were generated from the test dataset using a particular pre-processing clustering combination were subjected to enrichment analysis with 19 health/lifestyle labels (i.e. searching for statistically significant over-representation of patients with the trait in each cluster). An artificial neural network, trained with the cluster assignment of each individual in the training dataset, was used to classify individuals from the validation dataset using the same clinical biomarkers subjected to the same pre-processing algorithm as was the test dataset. The resulting clustering of the validation set was also subjected to enrichment analysis with the same terms as was the training set. An enrichment was deemed to be a validated enrichment if the same label was enriched in the test and validation datasets. A validated cluster was defined as a cluster sharing at least one enriched term between the test and validation sets (i.e. the number of clusters enriched in the training set). The enrichment factor for each pipeline is the average enrichment factor of the three most significant enrichments. <i>K-mns</i> = <i>K-means</i>; <i>NoramTranf</i> = transformation to normal; <i>Z-score</i> = Z-score normalization; <i>AgeAdj</i> = age adjustment followed by Z-score normalization.</p

Selected clusters from the NHANES training set.

<p>(A) The mean and standard deviation of biomarker values are shown for three selected clusters generated with 4152 males 20 years of age or older from the NHANES training set, using the Z-score normalized/CLICK pre-processing/clustering combination. For each cluster, the total number of individuals (top, right) and selected health/lifestyle traits that are significantly enriched in that cluster (top, left) are provided. For each enriched term, the enrichment factor (i.e. the frequency of the term in a cluster divided by its frequency in the entire dataset) is also provided. (B) Comparison of the original values of selected biomarkers in clusters 1, 3 and 14. The values for Hb are enlarged in the top middle section of the figure.</p

The correlation between selected blood markers and age.

<p>Linear regression was calculated for all biomarkers, and least square regression lines (red) were fitted for each marker. r – Pearson correlation coefficient, P – p value, CI- confidence interval of the p-value. (A) Raw data from the training set; (B) training set data after age adjustment. (C) diabetic males, raw data from the training set.</p

Evolutionary conservation of the mature oocyte proteome

The proteome profiles of mature ovulated oocytes of the Cnidaria basal eumetazoan, the starlet sea anemone Nematostella vectensis was compared with published data of mammalian mouse mature oocytes. We identified 1837 proteins in N. vectensis oocytes including known oocyte- and germ-cell-specific markers, proteins associated with RNPs and vitellogenin, a major component of egg yolk proteins. Our findings suggest highly conserved enriched functional pathways in N. vectensis and the mouse mature oocytes. This study provides the first catalog of cnidarian oocyte proteins, revealing highly conserved ancient organization of life processes for over 500 million years of evolution. Significance: The current study provides the first proteomic profile of an oocyte of a cnidarian organism the starlet sea anemone N. vectensis and gives new insights on the ancient origin of an oocyte proteome template. The comparative analysis with a chordate oocyte suggests that the oocyte proteome predates the divergence of the cnidarian and bilaterian lineages. In addition, the data generated in the study will serve as a valuable resource for further developmental and evolutional studies