科研之友
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摘要
Background: For gene expression data obtained from a time-course microarray experiment, Liu et al. [1] 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. [2,3]. In this note we point out several inaccuracies in Liu et al. [1] 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. [1]. Results: Application of ORIOGEN to the well-known breast cancer cell line data of Lobenhofer et al. [4] 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. [1] using Matlab. Also, based on a data simulated according to the model and parameters of simulation 1 (sigma(2) = 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. [1], ORIOGEN always maintained the desired false positive rate. According to Figure three in Liu et al. [1] their algorithm had a false positive rate ranging approximately from 0.20 to 0.70 for the scenarios that they simulated. Conclusions: Our comparisons of run times indicate that the implementations of ORIOGEN's algorithm in Matlab and R by Liu et al. [1] 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. [1], perhaps due to a programming error.
- 出版日期2009-12-22
