A summarization approach for Affymetrix GeneChip data using a reference training set from a large, biologically diverse database
<p>Abstract</p> <p>Background</p> <p>Many of the most popular pre-processing methods for Affymetrix expression arrays, such as RMA, gcRMA, and PLIER, simultaneously analyze data across a set of predetermined arrays to improve precision of the final measures of expressio...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2006-10-01
|
| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/7/464 |
| _version_ | 1819082507546525696 |
|---|---|
| author | Tripputi Mark Lin Xue Irizarry Rafael A Katz Simon Porter Mark W |
| author_facet | Tripputi Mark Lin Xue Irizarry Rafael A Katz Simon Porter Mark W |
| author_sort | Tripputi Mark |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Many of the most popular pre-processing methods for Affymetrix expression arrays, such as RMA, gcRMA, and PLIER, simultaneously analyze data across a set of predetermined arrays to improve precision of the final measures of expression. One problem associated with these algorithms is that expression measurements for a particular sample are highly dependent on the set of samples used for normalization and results obtained by normalization with a different set may not be comparable. A related problem is that an organization producing and/or storing large amounts of data in a sequential fashion will need to either re-run the pre-processing algorithm every time an array is added or store them in batches that are pre-processed together. Furthermore, pre-processing of large numbers of arrays requires loading all the feature-level data into memory which is a difficult task even with modern computers. We utilize a scheme that produces all the information necessary for pre-processing using a very large training set that can be used for summarization of samples outside of the training set. All subsequent pre-processing tasks can be done on an individual array basis. We demonstrate the utility of this approach by defining a new version of the Robust Multi-chip Averaging (RMA) algorithm which we refer to as refRMA.</p> <p>Results</p> <p>We assess performance based on multiple sets of samples processed over HG U133A Affymetrix GeneChip<sup>® </sup>arrays. We show that the refRMA workflow, when used in conjunction with a large, biologically diverse training set, results in the same general characteristics as that of RMA in its classic form when comparing overall data structure, sample-to-sample correlation, and variation. Further, we demonstrate that the refRMA workflow and reference set can be robustly applied to naïve organ types and to benchmark data where its performance indicates respectable results.</p> <p>Conclusion</p> <p>Our results indicate that a biologically diverse reference database can be used to train a model for estimating probe set intensities of exclusive test sets, while retaining the overall characteristics of the base algorithm. Although the results we present are specific for RMA, similar versions of other multi-array normalization and summarization schemes can be developed.</p> |
| first_indexed | 2024-12-21T20:17:46Z |
| format | Article |
| id | doaj.art-c8d1f6bc39924270a495117b32c73599 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-21T20:17:46Z |
| publishDate | 2006-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-c8d1f6bc39924270a495117b32c735992022-12-21T18:51:33ZengBMCBMC Bioinformatics1471-21052006-10-017146410.1186/1471-2105-7-464A summarization approach for Affymetrix GeneChip data using a reference training set from a large, biologically diverse databaseTripputi MarkLin XueIrizarry Rafael AKatz SimonPorter Mark W<p>Abstract</p> <p>Background</p> <p>Many of the most popular pre-processing methods for Affymetrix expression arrays, such as RMA, gcRMA, and PLIER, simultaneously analyze data across a set of predetermined arrays to improve precision of the final measures of expression. One problem associated with these algorithms is that expression measurements for a particular sample are highly dependent on the set of samples used for normalization and results obtained by normalization with a different set may not be comparable. A related problem is that an organization producing and/or storing large amounts of data in a sequential fashion will need to either re-run the pre-processing algorithm every time an array is added or store them in batches that are pre-processed together. Furthermore, pre-processing of large numbers of arrays requires loading all the feature-level data into memory which is a difficult task even with modern computers. We utilize a scheme that produces all the information necessary for pre-processing using a very large training set that can be used for summarization of samples outside of the training set. All subsequent pre-processing tasks can be done on an individual array basis. We demonstrate the utility of this approach by defining a new version of the Robust Multi-chip Averaging (RMA) algorithm which we refer to as refRMA.</p> <p>Results</p> <p>We assess performance based on multiple sets of samples processed over HG U133A Affymetrix GeneChip<sup>® </sup>arrays. We show that the refRMA workflow, when used in conjunction with a large, biologically diverse training set, results in the same general characteristics as that of RMA in its classic form when comparing overall data structure, sample-to-sample correlation, and variation. Further, we demonstrate that the refRMA workflow and reference set can be robustly applied to naïve organ types and to benchmark data where its performance indicates respectable results.</p> <p>Conclusion</p> <p>Our results indicate that a biologically diverse reference database can be used to train a model for estimating probe set intensities of exclusive test sets, while retaining the overall characteristics of the base algorithm. Although the results we present are specific for RMA, similar versions of other multi-array normalization and summarization schemes can be developed.</p>http://www.biomedcentral.com/1471-2105/7/464 |
| spellingShingle | Tripputi Mark Lin Xue Irizarry Rafael A Katz Simon Porter Mark W A summarization approach for Affymetrix GeneChip data using a reference training set from a large, biologically diverse database BMC Bioinformatics |
| title | A summarization approach for Affymetrix GeneChip data using a reference training set from a large, biologically diverse database |
| title_full | A summarization approach for Affymetrix GeneChip data using a reference training set from a large, biologically diverse database |
| title_fullStr | A summarization approach for Affymetrix GeneChip data using a reference training set from a large, biologically diverse database |
| title_full_unstemmed | A summarization approach for Affymetrix GeneChip data using a reference training set from a large, biologically diverse database |
| title_short | A summarization approach for Affymetrix GeneChip data using a reference training set from a large, biologically diverse database |
| title_sort | summarization approach for affymetrix genechip data using a reference training set from a large biologically diverse database |
| url | http://www.biomedcentral.com/1471-2105/7/464 |
| work_keys_str_mv | AT tripputimark asummarizationapproachforaffymetrixgenechipdatausingareferencetrainingsetfromalargebiologicallydiversedatabase AT linxue asummarizationapproachforaffymetrixgenechipdatausingareferencetrainingsetfromalargebiologicallydiversedatabase AT irizarryrafaela asummarizationapproachforaffymetrixgenechipdatausingareferencetrainingsetfromalargebiologicallydiversedatabase AT katzsimon asummarizationapproachforaffymetrixgenechipdatausingareferencetrainingsetfromalargebiologicallydiversedatabase AT portermarkw asummarizationapproachforaffymetrixgenechipdatausingareferencetrainingsetfromalargebiologicallydiversedatabase AT tripputimark summarizationapproachforaffymetrixgenechipdatausingareferencetrainingsetfromalargebiologicallydiversedatabase AT linxue summarizationapproachforaffymetrixgenechipdatausingareferencetrainingsetfromalargebiologicallydiversedatabase AT irizarryrafaela summarizationapproachforaffymetrixgenechipdatausingareferencetrainingsetfromalargebiologicallydiversedatabase AT katzsimon summarizationapproachforaffymetrixgenechipdatausingareferencetrainingsetfromalargebiologicallydiversedatabase AT portermarkw summarizationapproachforaffymetrixgenechipdatausingareferencetrainingsetfromalargebiologicallydiversedatabase |