A response to information criterion-based clustering with order-restricted candidate profiles in short time-course...

For gene expression data obtained from a time-course microarray experiment, Liu et al. developed a new algorithm for clustering genes with similar expression profiles over time. Performance of their proposal was compared with three other methods including the order-restricted inference based methodology of Peddada et al. In this note we point out several inaccuracies in Liu et al. and conclude that the order-restricted inference based methodology of Peddada et al. (programmed in the software ORIOGEN) indeed operates at the desired nominal Type 1 error level, an important feature of a statistical decision rule, while being computationally substantially faster than indicated by Liu et al.

Application of ORIOGEN to the well-known breast cancer cell line data of Lobenhofer et al. revealed that ORIOGEN software took only 21 minutes to run (using 100,000 bootstraps with p = 0.0025), substantially faster than the 72 hours found by Liu et al. using Matlab. Also, based on a data simulated according to the model and parameters of simulation 1 (sigma2 = 1, M = 5) in [1] we found that ORIOGEN took less than 30 seconds to run in stark contrast to Liu et al. who reported that their implementation of the same algorithm in R took 2979.29 seconds. Furthermore, for the simulation studies reported in [1], unlike the claims made by Liu et al., ORIOGEN always maintained the desired false positive rate. According to Figure three in Liu et al. their algorithm had a false positive rate ranging approximately from 0.20 to 0.70 for the scenarios that they simulated.

Our comparisons of run times indicate that the implementations of ORIOGEN's algorithm in Matlab and R by Liu et al. is inefficient compared to the publicly available JAVA implementation. Our results on the false positive rate of ORIOGEN suggest some error in Figure three of Liu et al., perhaps due to a programming error.