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A response to information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments
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 inferen...
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2009
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2813245/ https://www.ncbi.nlm.nih.gov/pubmed/20028515 http://dx.doi.org/10.1186/1471-2105-10-438 |
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author | Peddada, Shyamal D Umbach, David M Harris, Shawn F |
author_facet | Peddada, Shyamal D Umbach, David M Harris, Shawn F |
author_sort | Peddada, Shyamal D |
collection | PubMed |
description | 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 (σ(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. |
format | Text |
id | pubmed-2813245 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-28132452010-01-29 A response to information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments Peddada, Shyamal D Umbach, David M Harris, Shawn F BMC Bioinformatics Correspondence 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 (σ(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. BioMed Central 2009-12-22 /pmc/articles/PMC2813245/ /pubmed/20028515 http://dx.doi.org/10.1186/1471-2105-10-438 Text en Copyright ©2009 Peddada et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Correspondence Peddada, Shyamal D Umbach, David M Harris, Shawn F A response to information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments |
title | A response to information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments |
title_full | A response to information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments |
title_fullStr | A response to information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments |
title_full_unstemmed | A response to information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments |
title_short | A response to information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments |
title_sort | response to information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments |
topic | Correspondence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2813245/ https://www.ncbi.nlm.nih.gov/pubmed/20028515 http://dx.doi.org/10.1186/1471-2105-10-438 |
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