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GESearch: An Interactive GUI Tool for Identifying Gene Expression Signature.

Ye N, Yin H, Liu J, Dai X, Yin T - Biomed Res Int (2015)

Bottom Line: For this purpose, we developed GESearch, an interactive graphical user interface (GUI) toolkit, which is written in MATLAB and supports a variety of gene expression data files.Subsequently, the utility of this analytical toolkit is demonstrated by analyzing two sets of real-life microarray datasets from cell-cycle experiments.Overall, we have developed an interactive GUI toolkit that allows for choosing multiple algorithms for analyzing the gene expression signatures.

View Article: PubMed Central - PubMed

Affiliation: The Southern Modern Forestry Collaborative Innovation Center, Nanjing Forestry University, Nanjing 210037, China ; College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China.

ABSTRACT
The huge amount of gene expression data generated by microarray and next-generation sequencing technologies present challenges to exploit their biological meanings. When searching for the coexpression genes, the data mining process is largely affected by selection of algorithms. Thus, it is highly desirable to provide multiple options of algorithms in the user-friendly analytical toolkit to explore the gene expression signatures. For this purpose, we developed GESearch, an interactive graphical user interface (GUI) toolkit, which is written in MATLAB and supports a variety of gene expression data files. This analytical toolkit provides four models, including the mean, the regression, the delegate, and the ensemble models, to identify the coexpression genes, and enables the users to filter data and to select gene expression patterns by browsing the display window or by importing knowledge-based genes. Subsequently, the utility of this analytical toolkit is demonstrated by analyzing two sets of real-life microarray datasets from cell-cycle experiments. Overall, we have developed an interactive GUI toolkit that allows for choosing multiple algorithms for analyzing the gene expression signatures.

No MeSH data available.


Related in: MedlinePlus

Results of using priori knowledge of cell-cycle marks in human Hela cell data. (a) Heat map displays the searching results. (b) Plotting the mean values of each group, in which black lines indicate the trends of peaks.
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fig4: Results of using priori knowledge of cell-cycle marks in human Hela cell data. (a) Heat map displays the searching results. (b) Plotting the mean values of each group, in which black lines indicate the trends of peaks.

Mentions: In this example, we selected the human Hela cell-cycle dataset to test the feasibilities of this package [27]. Previous studies have shown that there were specified marker genes representing phases of cell cycle, so we chose a subset of the dataset containing 118 time points and used 20 cell-cycle marker genes representing G1/S, S, G2, G2/M, and M/G1 as priori knowledge to search for the coexpressed periodic genes (http://genome-www.stanford.edu/Human-CellCycle/Hela/data/). This dataset also contained 42920 transcript IDs in which the periodic patterns were not easily seen. In this case, the use of prior known genes was essential for the identification of coexpression genes with defined expression signatures. By using the mean model and default filtering parameter (p value threshold < 0.67), the searching process identified five groups of genes which had very similar periodic expression patterns (Figure 4(a)). The final results were visualized by a heat map chart (Figure 4(a)). To further evaluate the accuracy of the output results, the mean values of each group were plotted together and the expected progression of cell-cycle phases was evident (Figure 4(b)). As a result, this example addressed a complex dataset by using priori knowledge, and the resulting groups of genes were ready for further functional analysis.


GESearch: An Interactive GUI Tool for Identifying Gene Expression Signature.

Ye N, Yin H, Liu J, Dai X, Yin T - Biomed Res Int (2015)

Results of using priori knowledge of cell-cycle marks in human Hela cell data. (a) Heat map displays the searching results. (b) Plotting the mean values of each group, in which black lines indicate the trends of peaks.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4496643&req=5

fig4: Results of using priori knowledge of cell-cycle marks in human Hela cell data. (a) Heat map displays the searching results. (b) Plotting the mean values of each group, in which black lines indicate the trends of peaks.
Mentions: In this example, we selected the human Hela cell-cycle dataset to test the feasibilities of this package [27]. Previous studies have shown that there were specified marker genes representing phases of cell cycle, so we chose a subset of the dataset containing 118 time points and used 20 cell-cycle marker genes representing G1/S, S, G2, G2/M, and M/G1 as priori knowledge to search for the coexpressed periodic genes (http://genome-www.stanford.edu/Human-CellCycle/Hela/data/). This dataset also contained 42920 transcript IDs in which the periodic patterns were not easily seen. In this case, the use of prior known genes was essential for the identification of coexpression genes with defined expression signatures. By using the mean model and default filtering parameter (p value threshold < 0.67), the searching process identified five groups of genes which had very similar periodic expression patterns (Figure 4(a)). The final results were visualized by a heat map chart (Figure 4(a)). To further evaluate the accuracy of the output results, the mean values of each group were plotted together and the expected progression of cell-cycle phases was evident (Figure 4(b)). As a result, this example addressed a complex dataset by using priori knowledge, and the resulting groups of genes were ready for further functional analysis.

Bottom Line: For this purpose, we developed GESearch, an interactive graphical user interface (GUI) toolkit, which is written in MATLAB and supports a variety of gene expression data files.Subsequently, the utility of this analytical toolkit is demonstrated by analyzing two sets of real-life microarray datasets from cell-cycle experiments.Overall, we have developed an interactive GUI toolkit that allows for choosing multiple algorithms for analyzing the gene expression signatures.

View Article: PubMed Central - PubMed

Affiliation: The Southern Modern Forestry Collaborative Innovation Center, Nanjing Forestry University, Nanjing 210037, China ; College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China.

ABSTRACT
The huge amount of gene expression data generated by microarray and next-generation sequencing technologies present challenges to exploit their biological meanings. When searching for the coexpression genes, the data mining process is largely affected by selection of algorithms. Thus, it is highly desirable to provide multiple options of algorithms in the user-friendly analytical toolkit to explore the gene expression signatures. For this purpose, we developed GESearch, an interactive graphical user interface (GUI) toolkit, which is written in MATLAB and supports a variety of gene expression data files. This analytical toolkit provides four models, including the mean, the regression, the delegate, and the ensemble models, to identify the coexpression genes, and enables the users to filter data and to select gene expression patterns by browsing the display window or by importing knowledge-based genes. Subsequently, the utility of this analytical toolkit is demonstrated by analyzing two sets of real-life microarray datasets from cell-cycle experiments. Overall, we have developed an interactive GUI toolkit that allows for choosing multiple algorithms for analyzing the gene expression signatures.

No MeSH data available.


Related in: MedlinePlus