Integrating gene expression and GO classification for PCA by preclustering
<p>Abstract</p> <p>Background</p> <p>Gene expression data can be analyzed by summarizing groups of individual gene expression profiles based on GO annotation information. The mean expression profile per group can then be used to identify interesting GO categories in rel...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2010-03-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/11/158 |
| _version_ | 1811329293791264768 |
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| author | Bauerschmidt Susanne de Vlieg Jacob van Schaik Rene C Piek Ester De Haan Jorn R Buydens Lutgarde MC Wehrens Ron |
| author_facet | Bauerschmidt Susanne de Vlieg Jacob van Schaik Rene C Piek Ester De Haan Jorn R Buydens Lutgarde MC Wehrens Ron |
| author_sort | Bauerschmidt Susanne |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Gene expression data can be analyzed by summarizing groups of individual gene expression profiles based on GO annotation information. The mean expression profile per group can then be used to identify interesting GO categories in relation to the experimental settings. However, the expression profiles present in GO classes are often heterogeneous, i.e., there are several different expression profiles within one class. As a result, important experimental findings can be obscured because the summarizing profile does not seem to be of interest. We propose to tackle this problem by finding homogeneous subclasses within GO categories: preclustering.</p> <p>Results</p> <p>Two microarray datasets are analyzed. First, a selection of genes from a well-known <it>Saccharomyces cerevisiae </it>dataset is used. The GO class "cell wall organization and biogenesis" is shown as a specific example. After preclustering, this term can be associated with different phases in the cell cycle, where it could not be associated with a specific phase previously. Second, a dataset of differentiation of human Mesenchymal Stem Cells (MSC) into osteoblasts is used. For this dataset results are shown in which the GO term "skeletal development" is a specific example of a heterogeneous GO class for which better associations can be made after preclustering. The Intra Cluster Correlation (ICC), a measure of cluster tightness, is applied to identify relevant clusters.</p> <p>Conclusions</p> <p>We show that this method leads to an improved interpretability of results in Principal Component Analysis.</p> |
| first_indexed | 2024-04-13T15:41:16Z |
| format | Article |
| id | doaj.art-d3046b1b424a4a86b7c722e438a82557 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-13T15:41:16Z |
| publishDate | 2010-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-d3046b1b424a4a86b7c722e438a825572022-12-22T02:41:09ZengBMCBMC Bioinformatics1471-21052010-03-0111115810.1186/1471-2105-11-158Integrating gene expression and GO classification for PCA by preclusteringBauerschmidt Susannede Vlieg Jacobvan Schaik Rene CPiek EsterDe Haan Jorn RBuydens Lutgarde MCWehrens Ron<p>Abstract</p> <p>Background</p> <p>Gene expression data can be analyzed by summarizing groups of individual gene expression profiles based on GO annotation information. The mean expression profile per group can then be used to identify interesting GO categories in relation to the experimental settings. However, the expression profiles present in GO classes are often heterogeneous, i.e., there are several different expression profiles within one class. As a result, important experimental findings can be obscured because the summarizing profile does not seem to be of interest. We propose to tackle this problem by finding homogeneous subclasses within GO categories: preclustering.</p> <p>Results</p> <p>Two microarray datasets are analyzed. First, a selection of genes from a well-known <it>Saccharomyces cerevisiae </it>dataset is used. The GO class "cell wall organization and biogenesis" is shown as a specific example. After preclustering, this term can be associated with different phases in the cell cycle, where it could not be associated with a specific phase previously. Second, a dataset of differentiation of human Mesenchymal Stem Cells (MSC) into osteoblasts is used. For this dataset results are shown in which the GO term "skeletal development" is a specific example of a heterogeneous GO class for which better associations can be made after preclustering. The Intra Cluster Correlation (ICC), a measure of cluster tightness, is applied to identify relevant clusters.</p> <p>Conclusions</p> <p>We show that this method leads to an improved interpretability of results in Principal Component Analysis.</p>http://www.biomedcentral.com/1471-2105/11/158 |
| spellingShingle | Bauerschmidt Susanne de Vlieg Jacob van Schaik Rene C Piek Ester De Haan Jorn R Buydens Lutgarde MC Wehrens Ron Integrating gene expression and GO classification for PCA by preclustering BMC Bioinformatics |
| title | Integrating gene expression and GO classification for PCA by preclustering |
| title_full | Integrating gene expression and GO classification for PCA by preclustering |
| title_fullStr | Integrating gene expression and GO classification for PCA by preclustering |
| title_full_unstemmed | Integrating gene expression and GO classification for PCA by preclustering |
| title_short | Integrating gene expression and GO classification for PCA by preclustering |
| title_sort | integrating gene expression and go classification for pca by preclustering |
| url | http://www.biomedcentral.com/1471-2105/11/158 |
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