[Objective] The 15urpose was to seek for the different phenotypes between wild type and Arabidopsis Mutants in response to CO2. [Method] The epidermis bioassays and seed germination test were carried out to analyze th...[Objective] The 15urpose was to seek for the different phenotypes between wild type and Arabidopsis Mutants in response to CO2. [Method] The epidermis bioassays and seed germination test were carried out to analyze the physiological characteristics of two Arabidopsis mu- tants and their wild type. [Result] There existed distinct differences in stomata apertures, water loss and leaf temperature compared with wild type except for stomata density. In addition, seed germination test on the medium indicated that cdfl was insensitive to ABA, mannitol and NaCI, but cdsl performed contrary to cdil. [ Conclusion] There are some different physiological characteristics between wild type and mutants.展开更多
Objective:To establish a systematic framework for selecting the best clustering algorithm and provide an evaluation method for clustering analyses of gene expression data. Methods: Based on data structure (internal in...Objective:To establish a systematic framework for selecting the best clustering algorithm and provide an evaluation method for clustering analyses of gene expression data. Methods: Based on data structure (internal information) and function classification (external information), the evaluation of gene expression data analyses were carried out by using 2 approaches. Firstly, to assess the predictive power of clusteringalgorithms, Entropy was introduced to measure the consistency between the clustering results from different algorithms and the known and validated functional classifications. Secondly, a modified method of figure of merit (adjust-FOM) was used as internal assessment method. In this method, one clustering algorithm was used to analyze all data but one experimental condition, the remaining condition was used to assess the predictive power of the resulting clusters. This method was applied on 3 gene expression data sets (2 from the Lyer's Serum Data Sets, and 1 from the Ferea's Saccharomyces Cerevisiae Data Set). Results: A method based on entropy and figure of merit (FOM) was proposed to explore the results of the 3 data sets obtained by 6 different algorithms, SOM and Fuzzy clustering methods were confirmed to possess the highest ability to cluster. Conclusion: A method based on entropy is firstly brought forward to evaluate clustering analyses.Different results are attained in evaluating same data set due to different function classification. According to the curves of adjust_FOM and Entropy_FOM, SOM and Fuzzy clustering methods show the highest ability to cluster on the 3 data sets.展开更多
文摘[Objective] The 15urpose was to seek for the different phenotypes between wild type and Arabidopsis Mutants in response to CO2. [Method] The epidermis bioassays and seed germination test were carried out to analyze the physiological characteristics of two Arabidopsis mu- tants and their wild type. [Result] There existed distinct differences in stomata apertures, water loss and leaf temperature compared with wild type except for stomata density. In addition, seed germination test on the medium indicated that cdfl was insensitive to ABA, mannitol and NaCI, but cdsl performed contrary to cdil. [ Conclusion] There are some different physiological characteristics between wild type and mutants.
文摘Objective:To establish a systematic framework for selecting the best clustering algorithm and provide an evaluation method for clustering analyses of gene expression data. Methods: Based on data structure (internal information) and function classification (external information), the evaluation of gene expression data analyses were carried out by using 2 approaches. Firstly, to assess the predictive power of clusteringalgorithms, Entropy was introduced to measure the consistency between the clustering results from different algorithms and the known and validated functional classifications. Secondly, a modified method of figure of merit (adjust-FOM) was used as internal assessment method. In this method, one clustering algorithm was used to analyze all data but one experimental condition, the remaining condition was used to assess the predictive power of the resulting clusters. This method was applied on 3 gene expression data sets (2 from the Lyer's Serum Data Sets, and 1 from the Ferea's Saccharomyces Cerevisiae Data Set). Results: A method based on entropy and figure of merit (FOM) was proposed to explore the results of the 3 data sets obtained by 6 different algorithms, SOM and Fuzzy clustering methods were confirmed to possess the highest ability to cluster. Conclusion: A method based on entropy is firstly brought forward to evaluate clustering analyses.Different results are attained in evaluating same data set due to different function classification. According to the curves of adjust_FOM and Entropy_FOM, SOM and Fuzzy clustering methods show the highest ability to cluster on the 3 data sets.
