Alignment of gene expression profiles from test samples against a reference database: New method for context-specific interpretation of microarray data
<p>Abstract</p> <p>Background</p> <p>Gene expression microarray data have been organized and made available as public databases, but the utilization of such highly heterogeneous reference datasets in the interpretation of data from individual test samples is not as deve...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2011-03-01
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| Series: | BioData Mining |
| Online Access: | http://www.biodatamining.org/content/4/1/5 |
| _version_ | 1811297636444012544 |
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| author | Kilpinen Sami K Ojala Kalle A Kallioniemi Olli P |
| author_facet | Kilpinen Sami K Ojala Kalle A Kallioniemi Olli P |
| author_sort | Kilpinen Sami K |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Gene expression microarray data have been organized and made available as public databases, but the utilization of such highly heterogeneous reference datasets in the interpretation of data from individual test samples is not as developed as e.g. in the field of nucleotide sequence comparisons. We have created a rapid and powerful approach for the alignment of microarray gene expression profiles (AGEP) from test samples with those contained in a large annotated public reference database and demonstrate here how this can facilitate interpretation of microarray data from individual samples.</p> <p>Methods</p> <p>AGEP is based on the calculation of kernel density distributions for the levels of expression of each gene in each reference tissue type and provides a quantitation of the similarity between the test sample and the reference tissue types as well as the identity of the typical and atypical genes in each comparison. As a reference database, we used 1654 samples from 44 normal tissues (extracted from the Genesapiens database).</p> <p>Results</p> <p>Using leave-one-out validation, AGEP correctly defined the tissue of origin for 1521 (93.6%) of all the 1654 samples in the original database. Independent validation of 195 external normal tissue samples resulted in 87% accuracy for the exact tissue type and 97% accuracy with related tissue types. AGEP analysis of 10 Duchenne muscular dystrophy (DMD) samples provided quantitative description of the key pathogenetic events, such as the extent of inflammation, in individual samples and pinpointed tissue-specific genes whose expression changed (<it>SAMD4A</it>) in DMD. AGEP analysis of microarray data from adipocytic differentiation of mesenchymal stem cells and from normal myeloid cell types and leukemias provided quantitative characterization of the transcriptomic changes during normal and abnormal cell differentiation.</p> <p>Conclusions</p> <p>The AGEP method is a widely applicable method for the rapid comprehensive interpretation of microarray data, as proven here by the definition of tissue- and disease-specific changes in gene expression as well as during cellular differentiation. The capability to quantitatively compare data from individual samples against a large-scale annotated reference database represents a widely applicable paradigm for the analysis of all types of high-throughput data. AGEP enables systematic and quantitative comparison of gene expression data from test samples against a comprehensive collection of different cell/tissue types previously studied by the entire research community.</p> |
| first_indexed | 2024-04-13T06:08:07Z |
| format | Article |
| id | doaj.art-aa4d556dcf24497da30c797e90ec8cc5 |
| institution | Directory Open Access Journal |
| issn | 1756-0381 |
| language | English |
| last_indexed | 2024-04-13T06:08:07Z |
| publishDate | 2011-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BioData Mining |
| spelling | doaj.art-aa4d556dcf24497da30c797e90ec8cc52022-12-22T02:59:11ZengBMCBioData Mining1756-03812011-03-0141510.1186/1756-0381-4-5Alignment of gene expression profiles from test samples against a reference database: New method for context-specific interpretation of microarray dataKilpinen Sami KOjala Kalle AKallioniemi Olli P<p>Abstract</p> <p>Background</p> <p>Gene expression microarray data have been organized and made available as public databases, but the utilization of such highly heterogeneous reference datasets in the interpretation of data from individual test samples is not as developed as e.g. in the field of nucleotide sequence comparisons. We have created a rapid and powerful approach for the alignment of microarray gene expression profiles (AGEP) from test samples with those contained in a large annotated public reference database and demonstrate here how this can facilitate interpretation of microarray data from individual samples.</p> <p>Methods</p> <p>AGEP is based on the calculation of kernel density distributions for the levels of expression of each gene in each reference tissue type and provides a quantitation of the similarity between the test sample and the reference tissue types as well as the identity of the typical and atypical genes in each comparison. As a reference database, we used 1654 samples from 44 normal tissues (extracted from the Genesapiens database).</p> <p>Results</p> <p>Using leave-one-out validation, AGEP correctly defined the tissue of origin for 1521 (93.6%) of all the 1654 samples in the original database. Independent validation of 195 external normal tissue samples resulted in 87% accuracy for the exact tissue type and 97% accuracy with related tissue types. AGEP analysis of 10 Duchenne muscular dystrophy (DMD) samples provided quantitative description of the key pathogenetic events, such as the extent of inflammation, in individual samples and pinpointed tissue-specific genes whose expression changed (<it>SAMD4A</it>) in DMD. AGEP analysis of microarray data from adipocytic differentiation of mesenchymal stem cells and from normal myeloid cell types and leukemias provided quantitative characterization of the transcriptomic changes during normal and abnormal cell differentiation.</p> <p>Conclusions</p> <p>The AGEP method is a widely applicable method for the rapid comprehensive interpretation of microarray data, as proven here by the definition of tissue- and disease-specific changes in gene expression as well as during cellular differentiation. The capability to quantitatively compare data from individual samples against a large-scale annotated reference database represents a widely applicable paradigm for the analysis of all types of high-throughput data. AGEP enables systematic and quantitative comparison of gene expression data from test samples against a comprehensive collection of different cell/tissue types previously studied by the entire research community.</p>http://www.biodatamining.org/content/4/1/5 |
| spellingShingle | Kilpinen Sami K Ojala Kalle A Kallioniemi Olli P Alignment of gene expression profiles from test samples against a reference database: New method for context-specific interpretation of microarray data BioData Mining |
| title | Alignment of gene expression profiles from test samples against a reference database: New method for context-specific interpretation of microarray data |
| title_full | Alignment of gene expression profiles from test samples against a reference database: New method for context-specific interpretation of microarray data |
| title_fullStr | Alignment of gene expression profiles from test samples against a reference database: New method for context-specific interpretation of microarray data |
| title_full_unstemmed | Alignment of gene expression profiles from test samples against a reference database: New method for context-specific interpretation of microarray data |
| title_short | Alignment of gene expression profiles from test samples against a reference database: New method for context-specific interpretation of microarray data |
| title_sort | alignment of gene expression profiles from test samples against a reference database new method for context specific interpretation of microarray data |
| url | http://www.biodatamining.org/content/4/1/5 |
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