Systematic interpretation of microarray data using experiment annotations
<p>Abstract</p> <p>Background</p> <p>Up to now, microarray data are mostly assessed in context with only one or few parameters characterizing the experimental conditions under study. More explicit experiment annotations, however, are highly useful for interpreting micro...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2006-12-01
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| Series: | BMC Genomics |
| Online Access: | http://www.biomedcentral.com/1471-2164/7/319 |
| _version_ | 1818787758026522624 |
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| author | Frohme Marcus Hauser Nicole C Beckmann Boris Bauer Andrea Witt Olaf Busold Christian H Fellenberg Kurt Winter Stefan Dippon Jürgen Hoheisel Jörg D |
| author_facet | Frohme Marcus Hauser Nicole C Beckmann Boris Bauer Andrea Witt Olaf Busold Christian H Fellenberg Kurt Winter Stefan Dippon Jürgen Hoheisel Jörg D |
| author_sort | Frohme Marcus |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Up to now, microarray data are mostly assessed in context with only one or few parameters characterizing the experimental conditions under study. More explicit experiment annotations, however, are highly useful for interpreting microarray data, when available in a statistically accessible format.</p> <p>Results</p> <p>We provide means to preprocess these additional data, and to extract relevant traits corresponding to the transcription patterns under study. We found correspondence analysis particularly well-suited for mapping such extracted traits. It visualizes associations both among and between the traits, the hereby annotated experiments, and the genes, revealing how they are all interrelated. Here, we apply our methods to the systematic interpretation of radioactive (single channel) and two-channel data, stemming from model organisms such as yeast and <it>drosophila </it>up to complex human cancer samples. Inclusion of technical parameters allows for identification of artifacts and flaws in experimental design.</p> <p>Conclusion</p> <p>Biological and clinical traits can act as landmarks in transcription space, systematically mapping the variance of large datasets from the predominant changes down toward intricate details.</p> |
| first_indexed | 2024-12-18T14:12:51Z |
| format | Article |
| id | doaj.art-0f722b7129c74b6295fe35b0c933e30b |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-12-18T14:12:51Z |
| publishDate | 2006-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-0f722b7129c74b6295fe35b0c933e30b2022-12-21T21:05:05ZengBMCBMC Genomics1471-21642006-12-017131910.1186/1471-2164-7-319Systematic interpretation of microarray data using experiment annotationsFrohme MarcusHauser Nicole CBeckmann BorisBauer AndreaWitt OlafBusold Christian HFellenberg KurtWinter StefanDippon JürgenHoheisel Jörg D<p>Abstract</p> <p>Background</p> <p>Up to now, microarray data are mostly assessed in context with only one or few parameters characterizing the experimental conditions under study. More explicit experiment annotations, however, are highly useful for interpreting microarray data, when available in a statistically accessible format.</p> <p>Results</p> <p>We provide means to preprocess these additional data, and to extract relevant traits corresponding to the transcription patterns under study. We found correspondence analysis particularly well-suited for mapping such extracted traits. It visualizes associations both among and between the traits, the hereby annotated experiments, and the genes, revealing how they are all interrelated. Here, we apply our methods to the systematic interpretation of radioactive (single channel) and two-channel data, stemming from model organisms such as yeast and <it>drosophila </it>up to complex human cancer samples. Inclusion of technical parameters allows for identification of artifacts and flaws in experimental design.</p> <p>Conclusion</p> <p>Biological and clinical traits can act as landmarks in transcription space, systematically mapping the variance of large datasets from the predominant changes down toward intricate details.</p>http://www.biomedcentral.com/1471-2164/7/319 |
| spellingShingle | Frohme Marcus Hauser Nicole C Beckmann Boris Bauer Andrea Witt Olaf Busold Christian H Fellenberg Kurt Winter Stefan Dippon Jürgen Hoheisel Jörg D Systematic interpretation of microarray data using experiment annotations BMC Genomics |
| title | Systematic interpretation of microarray data using experiment annotations |
| title_full | Systematic interpretation of microarray data using experiment annotations |
| title_fullStr | Systematic interpretation of microarray data using experiment annotations |
| title_full_unstemmed | Systematic interpretation of microarray data using experiment annotations |
| title_short | Systematic interpretation of microarray data using experiment annotations |
| title_sort | systematic interpretation of microarray data using experiment annotations |
| url | http://www.biomedcentral.com/1471-2164/7/319 |
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