Finding Unexpected Patterns in Microarray Data
We describe the performance of a protocol based on the sequential application of unsupervised and supervised methods to analyze microarray samples defined by a combination of factors. Correspondence analysis is used to visualize the emerging patterns of three set of novel or previously published dat...
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| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_00320889_v133_n4_p1717_Perelman |
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todo:paper_00320889_v133_n4_p1717_Perelman2023-10-03T14:44:54Z Finding Unexpected Patterns in Microarray Data Perelman, S. Mazzella, M.A. Muschietti, J. Zhu, T. Casal, J.J. Bacteria Fungi Genes Hospitals Physiology Viruses Microarray data Plants (botany) Arabidopsis protein Arabidopsis article automated pattern recognition biology DNA microarray gene gene deletion genetic transcription genetics light methodology photosynthesis physiology Arabidopsis Arabidopsis Proteins Computational Biology Gene Deletion Genes, Plant Light Oligonucleotide Array Sequence Analysis Pattern Recognition, Automated Photosynthetic Reaction Center Complex Proteins Transcription, Genetic Arabidopsis Bacteria (microorganisms) Fungi We describe the performance of a protocol based on the sequential application of unsupervised and supervised methods to analyze microarray samples defined by a combination of factors. Correspondence analysis is used to visualize the emerging patterns of three set of novel or previously published data: photoreceptor mutants of Arabidopsis grown under different light/dark conditions, Arabidopsis exposed to different types of biotic and abiotic stress, and human acute leukemia. We find, for instance, that light has a dramatic effect on plants despite the absence of the four major photoreceptors, that bacterial-, fungal-, and viral-induced responses converge at later stages of attack, and that sample preparation procedures used in different hospitals have large effects on transcriptome patterns. We use canonical discriminant analysis to identify the genes associated with these patters and hierarchical clustering to find groups of coregulated genes that are easily visualized in a second round of correspondence analysis and ordered tables. The unconventional combination of standard descriptive multivariate methods offers a previously unrecognized tool to uncover unexpected information. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_00320889_v133_n4_p1717_Perelman |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Bacteria Fungi Genes Hospitals Physiology Viruses Microarray data Plants (botany) Arabidopsis protein Arabidopsis article automated pattern recognition biology DNA microarray gene gene deletion genetic transcription genetics light methodology photosynthesis physiology Arabidopsis Arabidopsis Proteins Computational Biology Gene Deletion Genes, Plant Light Oligonucleotide Array Sequence Analysis Pattern Recognition, Automated Photosynthetic Reaction Center Complex Proteins Transcription, Genetic Arabidopsis Bacteria (microorganisms) Fungi |
| spellingShingle |
Bacteria Fungi Genes Hospitals Physiology Viruses Microarray data Plants (botany) Arabidopsis protein Arabidopsis article automated pattern recognition biology DNA microarray gene gene deletion genetic transcription genetics light methodology photosynthesis physiology Arabidopsis Arabidopsis Proteins Computational Biology Gene Deletion Genes, Plant Light Oligonucleotide Array Sequence Analysis Pattern Recognition, Automated Photosynthetic Reaction Center Complex Proteins Transcription, Genetic Arabidopsis Bacteria (microorganisms) Fungi Perelman, S. Mazzella, M.A. Muschietti, J. Zhu, T. Casal, J.J. Finding Unexpected Patterns in Microarray Data |
| topic_facet |
Bacteria Fungi Genes Hospitals Physiology Viruses Microarray data Plants (botany) Arabidopsis protein Arabidopsis article automated pattern recognition biology DNA microarray gene gene deletion genetic transcription genetics light methodology photosynthesis physiology Arabidopsis Arabidopsis Proteins Computational Biology Gene Deletion Genes, Plant Light Oligonucleotide Array Sequence Analysis Pattern Recognition, Automated Photosynthetic Reaction Center Complex Proteins Transcription, Genetic Arabidopsis Bacteria (microorganisms) Fungi |
| description |
We describe the performance of a protocol based on the sequential application of unsupervised and supervised methods to analyze microarray samples defined by a combination of factors. Correspondence analysis is used to visualize the emerging patterns of three set of novel or previously published data: photoreceptor mutants of Arabidopsis grown under different light/dark conditions, Arabidopsis exposed to different types of biotic and abiotic stress, and human acute leukemia. We find, for instance, that light has a dramatic effect on plants despite the absence of the four major photoreceptors, that bacterial-, fungal-, and viral-induced responses converge at later stages of attack, and that sample preparation procedures used in different hospitals have large effects on transcriptome patterns. We use canonical discriminant analysis to identify the genes associated with these patters and hierarchical clustering to find groups of coregulated genes that are easily visualized in a second round of correspondence analysis and ordered tables. The unconventional combination of standard descriptive multivariate methods offers a previously unrecognized tool to uncover unexpected information. |
| format |
JOUR |
| author |
Perelman, S. Mazzella, M.A. Muschietti, J. Zhu, T. Casal, J.J. |
| author_facet |
Perelman, S. Mazzella, M.A. Muschietti, J. Zhu, T. Casal, J.J. |
| author_sort |
Perelman, S. |
| title |
Finding Unexpected Patterns in Microarray Data |
| title_short |
Finding Unexpected Patterns in Microarray Data |
| title_full |
Finding Unexpected Patterns in Microarray Data |
| title_fullStr |
Finding Unexpected Patterns in Microarray Data |
| title_full_unstemmed |
Finding Unexpected Patterns in Microarray Data |
| title_sort |
finding unexpected patterns in microarray data |
| url |
http://hdl.handle.net/20.500.12110/paper_00320889_v133_n4_p1717_Perelman |
| work_keys_str_mv |
AT perelmans findingunexpectedpatternsinmicroarraydata AT mazzellama findingunexpectedpatternsinmicroarraydata AT muschiettij findingunexpectedpatternsinmicroarraydata AT zhut findingunexpectedpatternsinmicroarraydata AT casaljj findingunexpectedpatternsinmicroarraydata |
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1782029275539439616 |