Time course microarray data provide insight about dynamic biological
processes. While several clustering methods have been proposed for the analysis
of these data structures, comparison and selection of appropriate clustering
methods are seldom discussed. We compared 3 probabilistic based clustering
methods and 3 distance based clustering methods for time course microarray
data. Among probabilistic methods, we considered: smoothing spline clustering
also known as model based functional data analysis (MFDA), functional
clustering models for sparsely sampled data (FCM) and model-based clustering
(MCLUST). Among distance based methods, we considered: weighted gene
co-expression network analysis (WGCNA), clustering with dynamic time warping
distance (DTW) and clustering with autocorrelation based distance (ACF). We
studied these algorithms in both simulated settings and case study data. Our
investigations showed that FCM performed very well when gene curves were short
and sparse. DTW and WGCNA performed well when gene curves were medium or long
(>=10 observations). SSC performed very well when there were clusters of gene
curves similar to one another. Overall, ACF performed poorly in these
applications. In terms of computation time, FCM, SSC and DTW were considerably
slower than MCLUST and WGCNA. WGCNA outperformed MCLUST by generating more
accurate and biological meaningful clustering results. WGCNA and MCLUST are the
best methods among the 6 methods compared, when performance and computation
time are both taken into account. WGCNA outperforms MCLUST, but MCLUST provides
model based inference and uncertainty measure of clustering results
