28 research outputs found
Reordering Hierarchical Tree Based on Bilateral Symmetric Distance
BACKGROUND: In microarray data analysis, hierarchical clustering (HC) is often used to group samples or genes according to their gene expression profiles to study their associations. In a typical HC, nested clustering structures can be quickly identified in a tree. The relationship between objects is lost, however, because clusters rather than individual objects are compared. This results in a tree that is hard to interpret. METHODOLOGY/PRINCIPAL FINDINGS: This study proposes an ordering method, HC-SYM, which minimizes bilateral symmetric distance of two adjacent clusters in a tree so that similar objects in the clusters are located in the cluster boundaries. The performance of HC-SYM was evaluated by both supervised and unsupervised approaches and compared favourably with other ordering methods. CONCLUSIONS/SIGNIFICANCE: The intuitive relationship between objects and flexibility of the HC-SYM method can be very helpful in the exploratory analysis of not only microarray data but also similar high-dimensional data
Contributions to factor analysis of dichotomous variables
multiple factor model, first and second order proportions, generalized least-squares, tetrachoric correlations,
Estimation for the multiple factor model when data are missing
factor analysis, missing data,
Simultaneous factor analysis of dichotomous variables in several groups
group comparisons, invariant measurement parameters, factor means,
The polyserial correlation coefficient
point polyserial correlation, dichotomous variables, polychotomous variables, latent variables,
K-ary Clustering with Optimal Leaf Ordering for Gene Expression Data
A major challenge in gene expression analysis is e#ective data organization and visualization. One of the most popular tools for this task is hierarchical clustering. Hierarchical clustering allows a user to view relationships in scales ranging from single genes to large sets of genes, while at the same time providing a global view of the expression data. However, hierarchical clustering is very sensitive to noise, it usually lacks of a method to actually identify distinct clusters, and produces a large number of possible leaf orderings of the hierarchical clustering tree
Incremental Matrix Reordering for Similarity-Based Dynamic Data Sets
International audienc
An alternative to the methodology for analysis of covariance
confirmatory factor analysis, simultaneous factor analysis, measurement errors,