Filtering, FDR and power
Background In high-dimensional data analysis such as differential gene expression analysis, people often use filtering methods like fold-change or variance filters in an attempt to reduce the multiple testing penalty and improve power. However, filtering may introduce a bias on the multiple testing...
Saved in:
| Published in | BMC bioinformatics Vol. 11; no. 1; p. 450 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
07.09.2010
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1471-2105 1471-2105 |
| DOI | 10.1186/1471-2105-11-450 |
Cover
| Abstract | Background
In high-dimensional data analysis such as differential gene expression analysis, people often use filtering methods like fold-change or variance filters in an attempt to reduce the multiple testing penalty and improve power. However, filtering may introduce a bias on the multiple testing correction. The precise amount of bias depends on many quantities, such as fraction of probes filtered out, filter statistic and test statistic used.
Results
We show that a biased multiple testing correction results if non-differentially expressed probes are not filtered out with equal probability from the entire range of p-values. We illustrate our results using both a simulation study and an experimental dataset, where the FDR is shown to be biased mostly by filters that are associated with the hypothesis being tested, such as the fold change. Filters that induce little bias on the FDR yield less additional power of detecting differentially expressed genes. Finally, we propose a statistical test that can be used in practice to determine whether any chosen filter introduces bias on the FDR estimate used, given a general experimental setup.
Conclusions
Filtering out of probes must be used with care as it may bias the multiple testing correction. Researchers can use our test for FDR bias to guide their choice of filter and amount of filtering in practice. |
|---|---|
| AbstractList | Background In high-dimensional data analysis such as differential gene expression analysis, people often use filtering methods like fold-change or variance filters in an attempt to reduce the multiple testing penalty and improve power. However, filtering may introduce a bias on the multiple testing correction. The precise amount of bias depends on many quantities, such as fraction of probes filtered out, filter statistic and test statistic used. Results We show that a biased multiple testing correction results if non-differentially expressed probes are not filtered out with equal probability from the entire range of p-values. We illustrate our results using both a simulation study and an experimental dataset, where the FDR is shown to be biased mostly by filters that are associated with the hypothesis being tested, such as the fold change. Filters that induce little bias on the FDR yield less additional power of detecting differentially expressed genes. Finally, we propose a statistical test that can be used in practice to determine whether any chosen filter introduces bias on the FDR estimate used, given a general experimental setup. Conclusions Filtering out of probes must be used with care as it may bias the multiple testing correction. Researchers can use our test for FDR bias to guide their choice of filter and amount of filtering in practice. Background In high-dimensional data analysis such as differential gene expression analysis, people often use filtering methods like fold-change or variance filters in an attempt to reduce the multiple testing penalty and improve power. However, filtering may introduce a bias on the multiple testing correction. The precise amount of bias depends on many quantities, such as fraction of probes filtered out, filter statistic and test statistic used. Results We show that a biased multiple testing correction results if non-differentially expressed probes are not filtered out with equal probability from the entire range of p-values. We illustrate our results using both a simulation study and an experimental dataset, where the FDR is shown to be biased mostly by filters that are associated with the hypothesis being tested, such as the fold change. Filters that induce little bias on the FDR yield less additional power of detecting differentially expressed genes. Finally, we propose a statistical test that can be used in practice to determine whether any chosen filter introduces bias on the FDR estimate used, given a general experimental setup. Conclusions Filtering out of probes must be used with care as it may bias the multiple testing correction. Researchers can use our test for FDR bias to guide their choice of filter and amount of filtering in practice. Abstract Background: In high-dimensional data analysis such as differential gene expression analysis, people often use filtering methods like fold-change or variance filters in an attempt to reduce the multiple testing penalty and improve power. However, filtering may introduce a bias on the multiple testing correction. The precise amount of bias depends on many quantities, such as fraction of probes filtered out, filter statistic and test statistic used. Results: We show that a biased multiple testing correction results if non-differentially expressed probes are not filtered out with equal probability from the entire range of p-values. We illustrate our results using both a simulation study and an experimental dataset, where the FDR is shown to be biased mostly by filters that are associated with the hypothesis being tested, such as the fold change. Filters that induce little bias on the FDR yield less additional power of detecting differentially expressed genes. Finally, we propose a statistical test that can be used in practice to determine whether any chosen filter introduces bias on the FDR estimate used, given a general experimental setup. Conclusions: Filtering out of probes must be used with care as it may bias the multiple testing correction. Researchers can use our test for FDR bias to guide their choice of filter and amount of filtering in practice. In high-dimensional data analysis such as differential gene expression analysis, people often use filtering methods like fold-change or variance filters in an attempt to reduce the multiple testing penalty and improve power. However, filtering may introduce a bias on the multiple testing correction. The precise amount of bias depends on many quantities, such as fraction of probes filtered out, filter statistic and test statistic used. We show that a biased multiple testing correction results if non-differentially expressed probes are not filtered out with equal probability from the entire range of p-values. We illustrate our results using both a simulation study and an experimental dataset, where the FDR is shown to be biased mostly by filters that are associated with the hypothesis being tested, such as the fold change. Filters that induce little bias on the FDR yield less additional power of detecting differentially expressed genes. Finally, we propose a statistical test that can be used in practice to determine whether any chosen filter introduces bias on the FDR estimate used, given a general experimental setup. Filtering out of probes must be used with care as it may bias the multiple testing correction. Researchers can use our test for FDR bias to guide their choice of filter and amount of filtering in practice. In high-dimensional data analysis such as differential gene expression analysis, people often use filtering methods like fold-change or variance filters in an attempt to reduce the multiple testing penalty and improve power. However, filtering may introduce a bias on the multiple testing correction. The precise amount of bias depends on many quantities, such as fraction of probes filtered out, filter statistic and test statistic used.BACKGROUNDIn high-dimensional data analysis such as differential gene expression analysis, people often use filtering methods like fold-change or variance filters in an attempt to reduce the multiple testing penalty and improve power. However, filtering may introduce a bias on the multiple testing correction. The precise amount of bias depends on many quantities, such as fraction of probes filtered out, filter statistic and test statistic used.We show that a biased multiple testing correction results if non-differentially expressed probes are not filtered out with equal probability from the entire range of p-values. We illustrate our results using both a simulation study and an experimental dataset, where the FDR is shown to be biased mostly by filters that are associated with the hypothesis being tested, such as the fold change. Filters that induce little bias on the FDR yield less additional power of detecting differentially expressed genes. Finally, we propose a statistical test that can be used in practice to determine whether any chosen filter introduces bias on the FDR estimate used, given a general experimental setup.RESULTSWe show that a biased multiple testing correction results if non-differentially expressed probes are not filtered out with equal probability from the entire range of p-values. We illustrate our results using both a simulation study and an experimental dataset, where the FDR is shown to be biased mostly by filters that are associated with the hypothesis being tested, such as the fold change. Filters that induce little bias on the FDR yield less additional power of detecting differentially expressed genes. Finally, we propose a statistical test that can be used in practice to determine whether any chosen filter introduces bias on the FDR estimate used, given a general experimental setup.Filtering out of probes must be used with care as it may bias the multiple testing correction. Researchers can use our test for FDR bias to guide their choice of filter and amount of filtering in practice.CONCLUSIONSFiltering out of probes must be used with care as it may bias the multiple testing correction. Researchers can use our test for FDR bias to guide their choice of filter and amount of filtering in practice. In high-dimensional data analysis such as differential gene expression analysis, people often use filtering methods like fold-change or variance filters in an attempt to reduce the multiple testing penalty and improve power. However, filtering may introduce a bias on the multiple testing correction. The precise amount of bias depends on many quantities, such as fraction of probes filtered out, filter statistic and test statistic used. We show that a biased multiple testing correction results if non-differentially expressed probes are not filtered out with equal probability from the entire range of p-values. We illustrate our results using both a simulation study and an experimental dataset, where the FDR is shown to be biased mostly by filters that are associated with the hypothesis being tested, such as the fold change. Filters that induce little bias on the FDR yield less additional power of detecting differentially expressed genes. Finally, we propose a statistical test that can be used in practice to determine whether any chosen filter introduces bias on the FDR estimate used, given a general experimental setup. Filtering out of probes must be used with care as it may bias the multiple testing correction. Researchers can use our test for FDR bias to guide their choice of filter and amount of filtering in practice. |
| ArticleNumber | 450 |
| Audience | Academic |
| Author | Boer, Judith M Menezes, Renée X van Iterson, Maarten |
| AuthorAffiliation | 1 Center for Human and Clinical Genetics, Leiden University Medical Center, Postzone S4-P, P.O. Box 9600 Leiden, 2300 RC, The Netherlands 2 Netherlands Bioinformatics Centre, P.O. Box 9101, Nijmegen, 6500 HB, The Netherlands 4 Laboratory of Pediatrics, Erasmus Medical Center/Sophia Children's Hospital, P.O. Box 2060, Rotterdam, 3000 CB, The Netherlands 3 Department of Epidemiology and Biostatistics, VU University Medical Center, PK 6Z 183, P.O. Box 7057, Amsterdam, 1007 MB, The Netherlands 5 Centre for Medical Systems Biology, P.O. Box 9600, Leiden, 2300 RC, The Netherlands |
| AuthorAffiliation_xml | – name: 1 Center for Human and Clinical Genetics, Leiden University Medical Center, Postzone S4-P, P.O. Box 9600 Leiden, 2300 RC, The Netherlands – name: 5 Centre for Medical Systems Biology, P.O. Box 9600, Leiden, 2300 RC, The Netherlands – name: 3 Department of Epidemiology and Biostatistics, VU University Medical Center, PK 6Z 183, P.O. Box 7057, Amsterdam, 1007 MB, The Netherlands – name: 2 Netherlands Bioinformatics Centre, P.O. Box 9101, Nijmegen, 6500 HB, The Netherlands – name: 4 Laboratory of Pediatrics, Erasmus Medical Center/Sophia Children's Hospital, P.O. Box 2060, Rotterdam, 3000 CB, The Netherlands |
| Author_xml | – sequence: 1 givenname: Maarten surname: van Iterson fullname: van Iterson, Maarten organization: Center for Human and Clinical Genetics, Leiden University Medical Center, Netherlands Bioinformatics Centre – sequence: 2 givenname: Judith M surname: Boer fullname: Boer, Judith M organization: Center for Human and Clinical Genetics, Leiden University Medical Center, Netherlands Bioinformatics Centre, Centre for Medical Systems Biology – sequence: 3 givenname: Renée X surname: Menezes fullname: Menezes, Renée X email: r.menezes@vumc.nl organization: Department of Epidemiology and Biostatistics, VU University Medical Center, Laboratory of Pediatrics, Erasmus Medical Center/Sophia Children's Hospital, Centre for Medical Systems Biology |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/20822518$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkktv1DAUhSNURB-w7wqNYIGQSLEdPzdIVWFgpEpIBdbWje0EjzLxYCeU_nscZhg6FS9l4fj6O0dX597j4qAPvSuKU4zOMJb8JaYClwQjVmJcUobuFUe70sGt_8PiOKUlQlhIxB4UhwRJQhiWR8Xp3HeDi75vX8zmr69m0NvZOly7-LC430CX3KPteVJ8mr_5ePGuvHz_dnFxflkazquhFJYbJCrG66pylFLChWXSACcNrp1CgmALQIXC0lXUNtwRChWigmJTIWuqk2Kx8bUBlnod_QrijQ7g9Y9CiK2GOHjTOQ21oQ0wwlTTUFZbpYiQ4CiIBtvsnb3wxmvs13BzDV23M8RIT4npKRI9RZKvOieWNa82mvVYr5w1rh8idHuN7L_0_rNuw1dNFFVS8mzwbGsQw5fRpUGvfDKu66B3YUxaMcoREujfpGCcc4HR1NSTO-QyjLHPc9AKYcmUkCJDTzdQCzkc3zch92cmS31OKsmxkpxk6uw3VP6sW3mT16nxub4neL4nyMzgvg0tjCnpxYerffbx7fB2qf3crwzwDWBiSCm6Rhs_wODDlKXv_jYWdEf4H5PcTj-tp3128Vdof9R8B5uM-ww |
| CitedBy_id | crossref_primary_10_1093_bioinformatics_btx138 crossref_primary_10_1093_bioinformatics_btx214 crossref_primary_10_1371_journal_pone_0212032 crossref_primary_10_15252_embj_2022111485 crossref_primary_10_3389_fcell_2020_592518 crossref_primary_10_1016_j_fertnstert_2018_08_052 crossref_primary_10_1371_journal_pone_0132310 crossref_primary_10_1038_s41437_018_0160_4 crossref_primary_10_1080_15592294_2020_1767277 crossref_primary_10_1016_j_pld_2022_10_002 crossref_primary_10_1111_biom_13457 crossref_primary_10_1002_jcp_29829 crossref_primary_10_1016_j_envexpbot_2021_104514 crossref_primary_10_1016_j_fm_2023_104285 crossref_primary_10_1002_ana_22497 crossref_primary_10_1038_s41540_022_00222_z crossref_primary_10_1186_s40537_018_0128_5 crossref_primary_10_1016_j_jprot_2013_05_030 crossref_primary_10_1016_j_isci_2024_110640 crossref_primary_10_1016_j_jri_2021_103307 crossref_primary_10_1289_ehp_1307892 crossref_primary_10_1080_15592294_2020_1712876 crossref_primary_10_3390_ijms241713318 crossref_primary_10_1186_s13148_016_0212_7 crossref_primary_10_1038_s42003_024_06325_z crossref_primary_10_1186_1471_2164_14_376 crossref_primary_10_1186_s13059_023_02917_w crossref_primary_10_1109_TCBB_2018_2858825 crossref_primary_10_1111_pce_14224 crossref_primary_10_3390_biom13050872 crossref_primary_10_1007_s00253_022_11796_3 crossref_primary_10_1007_s00421_011_2048_3 crossref_primary_10_1186_s12859_023_05142_1 crossref_primary_10_1002_sim_6082 crossref_primary_10_3390_ijms21082873 crossref_primary_10_1002_advs_202308313 |
| Cites_doi | 10.1007/0-387-29362-0_23 10.1089/cmb.2005.12.482 10.1002/bimj.200710473 10.1186/1471-2105-7-49 10.1016/S0378-3758(99)00041-5 10.1214/009053607000000046 10.1016/S1470-2045(08)70339-5 10.1214/aos/1013699998 10.1093/bioinformatics/btp053 10.1111/1467-9868.00346 10.1111/j.1541-0420.2008.01052.x 10.1038/ni.1688 10.1186/1471-2105-10-11 10.1111/j.2517-6161.1995.tb02031.x 10.1093/biomet/93.3.491 10.1186/1471-2105-10-402 10.1111/j.1467-9868.2005.00515.x |
| ContentType | Journal Article |
| Copyright | van Iterson et al; licensee BioMed Central Ltd. 2010 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. COPYRIGHT 2010 BioMed Central Ltd. 2010 van Iterson et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright ©2010 van Iterson et al; licensee BioMed Central Ltd. 2010 van Iterson et al; licensee BioMed Central Ltd. |
| Copyright_xml | – notice: van Iterson et al; licensee BioMed Central Ltd. 2010 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. – notice: COPYRIGHT 2010 BioMed Central Ltd. – notice: 2010 van Iterson et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. – notice: Copyright ©2010 van Iterson et al; licensee BioMed Central Ltd. 2010 van Iterson et al; licensee BioMed Central Ltd. |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM ISR 3V. 7QO 7SC 7X7 7XB 88E 8AL 8AO 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ JQ2 K7- K9. L7M LK8 L~C L~D M0N M0S M1P M7P P5Z P62 P64 PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 5PM ADTOC UNPAY DOA |
| DOI | 10.1186/1471-2105-11-450 |
| DatabaseName | Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale In Context: Science ProQuest Central (Corporate) Biotechnology Research Abstracts Computer and Information Systems Abstracts Health & Medical Collection (Proquest) ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Computing Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Journals Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central Advanced Technologies & Computer Science Collection ProQuest Central Essentials Biological Science Collection ProQuest Central ProQuest Technology Collection Natural Science Collection ProQuest One Community College ProQuest Central Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database (Proquest) ProQuest Health & Medical Complete (Alumni) Advanced Technologies Database with Aerospace Biological Sciences Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Computing Database Health & Medical Collection (Alumni Edition) Medical Database Biological Science Database Advanced Technologies & Aerospace Collection ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) Unpaywall for CDI: Periodical Content Unpaywall DOAJ (Directory of Open Access Journals) eJournal Collection |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database Computer Science Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China ProQuest One Applied & Life Sciences ProQuest One Sustainability Health Research Premium Collection Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Advanced Technologies & Aerospace Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central ProQuest Health & Medical Research Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Advanced Technologies Database with Aerospace ProQuest Computing ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest Medical Library ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Publicly Available Content Database Engineering Research Database MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: C6C name: Springer Nature OA Free Journals url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 3 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 4 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 5 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository – sequence: 6 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1471-2105 |
| EndPage | 450 |
| ExternalDocumentID | oai_doaj_org_article_abc4fa5259ff45bd99278ae4a7f1d4df 10.1186/1471-2105-11-450 PMC2949886 2501705051 A238619862 20822518 10_1186_1471_2105_11_450 |
| Genre | Research Support, Non-U.S. Gov't Journal Article |
| GeographicLocations | Netherlands |
| GeographicLocations_xml | – name: Netherlands |
| GroupedDBID | --- 0R~ 23N 2VQ 2WC 4.4 53G 5VS 6J9 7X7 88E 8AO 8FE 8FG 8FH 8FI 8FJ AAFWJ AAJSJ AAKPC AASML ABDBF ABUWG ACGFO ACGFS ACIHN ACIWK ACPRK ACUHS ADBBV ADMLS ADRAZ ADUKV AEAQA AENEX AEUYN AFKRA AFPKN AFRAH AHBYD AHMBA AHSBF AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS ARAPS AZQEC BAPOH BAWUL BBNVY BCNDV BENPR BFQNJ BGLVJ BHPHI BMC BPHCQ BVXVI C1A C6C CCPQU CS3 DIK DU5 DWQXO E3Z EAD EAP EAS EBD EBLON EBS EJD EMB EMK EMOBN ESX F5P FYUFA GNUQQ GROUPED_DOAJ GX1 H13 HCIFZ HMCUK HYE IAO ICD IHR INH INR IPNFZ ISR ITC K6V K7- KQ8 LK8 M1P M48 M7P MK~ ML0 M~E O5R O5S OK1 OVT P2P P62 PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PUEGO RBZ RIG RNS ROL RPM RSV SBL SOJ SV3 TR2 TUS UKHRP W2D WOQ WOW XH6 XSB AAYXX CITATION -A0 3V. ACRMQ ADINQ ALIPV C24 CGR CUY CVF ECM EIF M0N NPM 7QO 7SC 7XB 8AL 8FD 8FK FR3 JQ2 K9. L7M L~C L~D P64 PKEHL PQEST PQUKI PRINS Q9U 7X8 5PM 123 ADTOC AFFHD UNPAY |
| ID | FETCH-LOGICAL-c663t-7d6c07356b33e444267d58ca62f1be90721daa47918e34df6e24a304741c30dc3 |
| IEDL.DBID | M48 |
| ISSN | 1471-2105 |
| IngestDate | Fri Oct 03 12:44:22 EDT 2025 Wed Oct 29 11:23:23 EDT 2025 Tue Sep 30 16:49:04 EDT 2025 Mon Oct 06 18:07:50 EDT 2025 Thu Sep 04 19:20:26 EDT 2025 Mon Oct 06 18:30:25 EDT 2025 Mon Oct 20 22:18:23 EDT 2025 Mon Oct 20 16:10:31 EDT 2025 Thu Oct 16 15:05:44 EDT 2025 Wed Feb 19 02:03:42 EST 2025 Wed Oct 01 06:55:10 EDT 2025 Thu Apr 24 22:55:45 EDT 2025 Sat Sep 06 07:27:15 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | False Discovery Rate Multiple Testing Correction Filter Statistic Variance Filter Signal Filter |
| Language | English |
| License | This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. cc-by |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c663t-7d6c07356b33e444267d58ca62f1be90721daa47918e34df6e24a304741c30dc3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 ObjectType-Article-2 ObjectType-Feature-1 |
| OpenAccessLink | http://journals.scholarsportal.info/openUrl.xqy?doi=10.1186/1471-2105-11-450 |
| PMID | 20822518 |
| PQID | 901859787 |
| PQPubID | 44065 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_abc4fa5259ff45bd99278ae4a7f1d4df unpaywall_primary_10_1186_1471_2105_11_450 pubmedcentral_primary_oai_pubmedcentral_nih_gov_2949886 proquest_miscellaneous_954600706 proquest_miscellaneous_756667100 proquest_journals_901859787 gale_infotracmisc_A238619862 gale_infotracacademiconefile_A238619862 gale_incontextgauss_ISR_A238619862 pubmed_primary_20822518 crossref_citationtrail_10_1186_1471_2105_11_450 crossref_primary_10_1186_1471_2105_11_450 springer_journals_10_1186_1471_2105_11_450 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2010-09-07 |
| PublicationDateYYYYMMDD | 2010-09-07 |
| PublicationDate_xml | – month: 09 year: 2010 text: 2010-09-07 day: 07 |
| PublicationDecade | 2010 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | BMC bioinformatics |
| PublicationTitleAbbrev | BMC Bioinformatics |
| PublicationTitleAlternate | BMC Bioinformatics |
| PublicationYear | 2010 |
| Publisher | BioMed Central BioMed Central Ltd Springer Nature B.V BMC |
| Publisher_xml | – name: BioMed Central – name: BioMed Central Ltd – name: Springer Nature B.V – name: BMC |
| References | S Pounds (3907_CR18) 2005; 12 Y Benjamini (3907_CR5) 2006; 93 W van Wieringen (3907_CR10) 2009; 65 A Hackstadt (3907_CR15) 2009; 10 G Smyth (3907_CR12) 2005 D McCarthy (3907_CR2) 2009; 25 S Dudoit (3907_CR17) 2008; 50 J Storey (3907_CR9) 2002; 64 J McClintick (3907_CR14) 2006; 7 Y Benjamini (3907_CR1) 1995; 57 D Yekutieli (3907_CR16) 1999; 82 H Finner (3907_CR4) 2007; 35 J Ferreira (3907_CR7) 2006 M Langaas (3907_CR8) 2005; 67 S Zhang (3907_CR3) 2009; 10 M Den Boer (3907_CR11) 2009; 10 R Development Core Team (3907_CR19) 2007 Y Benjamini (3907_CR6) 2001; 29 T Querec (3907_CR13) 2009; 10 15882143 - J Comput Biol. 2005 May;12(4):482-95 19995439 - BMC Bioinformatics. 2009;10:402 18479479 - Biometrics. 2009 Mar;65(1):19-29 18932138 - Biom J. 2008 Oct;50(5):716-44 19176553 - Bioinformatics. 2009 Mar 15;25(6):765-71 19133141 - BMC Bioinformatics. 2009;10:11 19138562 - Lancet Oncol. 2009 Feb;10(2):125-34 19029902 - Nat Immunol. 2009 Jan;10(1):116-25 16448562 - BMC Bioinformatics. 2006;7:49 |
| References_xml | – start-page: 397 volume-title: Bioinformatics and Computational Biology Solutions using R and Bioconductor year: 2005 ident: 3907_CR12 doi: 10.1007/0-387-29362-0_23 – volume: 12 start-page: 482 issue: 4 year: 2005 ident: 3907_CR18 publication-title: Journal of Computational Biology: A Journal of Computational Molecular Cell Biology doi: 10.1089/cmb.2005.12.482 – volume: 50 start-page: 716 year: 2008 ident: 3907_CR17 publication-title: Biometrical Journal doi: 10.1002/bimj.200710473 – volume: 7 start-page: 49 year: 2006 ident: 3907_CR14 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-7-49 – volume-title: International Journal of Biostatistics year: 2006 ident: 3907_CR7 – volume: 82 start-page: 171 year: 1999 ident: 3907_CR16 publication-title: Journal of Statistical Planning and Inference doi: 10.1016/S0378-3758(99)00041-5 – volume: 35 start-page: 1432 issue: 4 year: 2007 ident: 3907_CR4 publication-title: The Annals of Statistics doi: 10.1214/009053607000000046 – volume: 10 start-page: 125 issue: 2 year: 2009 ident: 3907_CR11 publication-title: The Lancet Oncology doi: 10.1016/S1470-2045(08)70339-5 – volume: 29 start-page: 1165 issue: 4 year: 2001 ident: 3907_CR6 publication-title: Annals of Statistics doi: 10.1214/aos/1013699998 – volume: 25 start-page: 765 issue: 6 year: 2009 ident: 3907_CR2 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btp053 – volume: 64 start-page: 479 year: 2002 ident: 3907_CR9 publication-title: Journal of the Royal Statistical Society Series B doi: 10.1111/1467-9868.00346 – volume: 65 start-page: 19 year: 2009 ident: 3907_CR10 publication-title: Biometrics doi: 10.1111/j.1541-0420.2008.01052.x – volume-title: R: A Language and Environment for Statistical Computing year: 2007 ident: 3907_CR19 – volume: 10 start-page: 116 year: 2009 ident: 3907_CR13 publication-title: Nature Immunology doi: 10.1038/ni.1688 – volume: 10 start-page: 11 year: 2009 ident: 3907_CR15 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-10-11 – volume: 57 start-page: 289 year: 1995 ident: 3907_CR1 publication-title: Journal of the Royal Statistical Society Series B doi: 10.1111/j.2517-6161.1995.tb02031.x – volume: 93 start-page: 491 issue: 3 year: 2006 ident: 3907_CR5 publication-title: Biometrics doi: 10.1093/biomet/93.3.491 – volume: 10 start-page: 402 year: 2009 ident: 3907_CR3 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-10-402 – volume: 67 start-page: 555 issue: 4 year: 2005 ident: 3907_CR8 publication-title: Journal of the Royal Statistical Society Series B doi: 10.1111/j.1467-9868.2005.00515.x – reference: 15882143 - J Comput Biol. 2005 May;12(4):482-95 – reference: 16448562 - BMC Bioinformatics. 2006;7:49 – reference: 19995439 - BMC Bioinformatics. 2009;10:402 – reference: 18479479 - Biometrics. 2009 Mar;65(1):19-29 – reference: 19133141 - BMC Bioinformatics. 2009;10:11 – reference: 19029902 - Nat Immunol. 2009 Jan;10(1):116-25 – reference: 18932138 - Biom J. 2008 Oct;50(5):716-44 – reference: 19176553 - Bioinformatics. 2009 Mar 15;25(6):765-71 – reference: 19138562 - Lancet Oncol. 2009 Feb;10(2):125-34 |
| SSID | ssj0017805 |
| Score | 2.1886883 |
| Snippet | Background
In high-dimensional data analysis such as differential gene expression analysis, people often use filtering methods like fold-change or variance... In high-dimensional data analysis such as differential gene expression analysis, people often use filtering methods like fold-change or variance filters in an... Background In high-dimensional data analysis such as differential gene expression analysis, people often use filtering methods like fold-change or variance... Abstract Background: In high-dimensional data analysis such as differential gene expression analysis, people often use filtering methods like fold-change or... Abstract Background In high-dimensional data analysis such as differential gene expression analysis, people often use filtering methods like fold-change or... |
| SourceID | doaj unpaywall pubmedcentral proquest gale pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 450 |
| SubjectTerms | Algorithms Bias Bioinformatics Biomedical and Life Sciences Computational biology Computational Biology - methods Computational Biology/Bioinformatics Computer Appl. in Life Sciences Filters Gene expression Gene Expression Profiling - methods Humans Independent sample Leukemia - genetics Life Sciences Microarrays Networks analysis Nucleic acid probes Probes R&D Research & development Research Article Studies |
| SummonAdditionalLinks | – databaseName: DOAJ (Directory of Open Access Journals) eJournal Collection dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1La9wwEBYlENoeSvqMk7SYUigNNWvJsh7H9LGkhfaQNpCbkGQpDSzeJbtLyL_PjF9dt6S59GhpbOxPI2uGmfmGkDc5VQ55wTPmFDooSHlrC5dxGmVRRsu0wuLkb9_F8Sn_elaebbT6wpywlh64BW5inefRlmClx8hLV2nNpLKBWxlpxauIf99c6d6Z6uIHyNTf1BVJmoFTU_YBSiUmwxhWlHGst984kBre_r__zhvH05-pk0P89CG5v64X9vrKzmYbR9R0hzzqbMv0qP2mx-ReqJ-Q7bbb5PVTsju9wNA4POZ9Ov10ktq6ShfYJO0ZOZ1-_vnxOOsaI2QeDIRVJivhYWuWwhVF4BwOWVmVylvBInVBI-VZZS2XmqpQAEYiMG4xvsapL_LKF8_JVj2vwy5JHXV5yJnXwlnOq9wxalXuI1gGnHnmEzLp0TG-Yw3H5hUz03gPShjE0yCecGkAz4S8G-5YtIwZ_5D9gIAPcsh13QyABphOA8xdGpCQ17hcBtksakyXObfr5dJ8-XFijsAgAQ8RvLaEvO2E4hze39uu-gBQQAKskeTBSBK2mx9N7_daYbrtvjRgVCnwzJRMSDrM4o2YwVaH-XppJNjNAqmUbhfRJcduAblIyItWywZgGBLzl1QlRI70b4TceKa--NWwhTPNtVLwzMNeU3-_9-3rcjjo8p2LuPc_FnGfPGjzMrCk7oBsrS7X4SWYeyv3qtnZN0diSHs priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwhV3da9RAEB_qFdE-iN-NrRJEEIvhks0m2TyItNqjCi1yWujbsh_ZWjiSa-8O6X_vTL68KK2PyU5CbnZmZ-Zm5jcAb8JIaMIFD5gWFKAQ5K2KdcAjl8WJUywX1Jx8fJIenfKvZ8nZBhx3vTBUVtmdifVBbStD_5GP0W4JdH5F9nF-GdDQKEqudhM0VDtZwX6oEcbuwCYjYKwRbB4cnnyb9mkFAvDvcpUiHUd4MgdIl1BzGafW-zXbVEP4_3tQr1mqv6so-1TqFtxblXN1_UvNZmvWavIQHrRupr_fyMUj2CjKx3C3GTx5_QS2JxeUJcfXvPcnn6e-Kq0_p3lpT-F0cvjj01HQzkgIDPoKyyCzqUEtTVIdxwXnaG8zmwijUuYiXeSEfmaV4lkeiSLm1qUF44pSbTwycWhN_AxGZVUW2-DrSIdFyEyeasW5DTWLlAiNQyeBM8OMB-OOO9K0AOI0x2Im60BCpJL4KYmfeCmRnx6865-YN-AZt9AeEMN7OoK9rm9UV-ey1SKptOFOJRiyOccTbfOcZUIVXGUusvjrPHhN2yUJ2KKkyplztVos5JfvU7mPvgkGixjAefC2JXIVfr9RbSMCcoGwsAaUuwNK1DwzWN7ppEK2mr-QvZx64Per9CAVs5VFtVrIDF3olFCVbibJE06DA8LUg-eNlPWMYYTRj0LuQTaQvwHnhivlxc8aOJzlPBcC37nXSeqf7755X_Z6Wf7vJr64lSM7cL-pvaC2uV0YLa9WxUt06Zb6VauovwG7wUKv priority: 102 providerName: ProQuest – databaseName: Springer Nature OA Free Journals dbid: C6C link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3db9MwED-hIQQ8oPG5sIEihISYiBY7tuM8boNqIMHDYNLeLNuxx6QqrWgrtP-euyQNDTAQj63PVnofubue73cAL3OmHeGCZ9xpSlAI8tYWLhMsloWMlleampM_flInZ-LDuTzv_--gXpjN-j3T6oDhyzPDtERS_5eg5PwmuijVlmXV8VAvIGT-dRHyD7tGTqfF5v_9Dbzhgn69HjnUSO_C7VUzt1ff7XS64YYm23Cvjx_Tw07g9-FGaB7ArW6i5NVD2JlcUvkbj3mTTt6eprap0zkNQnsEZ5N3X45Psn74QeYxCFhmZa08mp9UriiCEOhIy1pqbxWPzIWKYM1qa0VZMR0KUUcVuLBUQxPMF3nti8ew1cyasAOpYy4POfeVclaIOnecWZ37iN5fcM99Agdr7hjfI4PTgIqpaTMErQzx0xA_8aNBfibwetgx71Ax_kJ7RAwf6AjPuv0CxWx68zDWeRGtxFwsRiFdXVW81DYIW0ZW469L4AWJyxBiRUNXYi7sarEw7z-fmkMMOjALxMwsgVc9UZzh83vbdxggFwjkakS5N6JEk_Kj5d21VpjepBcGAyeN2ZcuE0iHVdpIt9SaMFstTImxsSK4pOtJKiloIkCuEnjSadnAGE7g-5LpBMqR_o04N15pLr-2iOC8EpXWeOb-WlN_Pvf1ctkfdPmfQnz6Pyfvwp3ujgW1x-3B1vLbKjzD0G3pnrdW-wM1LjPl priority: 102 providerName: Springer Nature – databaseName: Unpaywall dbid: UNPAY link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3rb9MwELemTgj4wHssbKAIISEm0iaO4zgfy6MaSEzToNL4ZNmOPSpKUvoQGn89d82DZjAQEh8bnyuffXe-k-9-R8iTMBIaccEDqgUGKAh5q2IdsMilceIUzQQWJ7874odj9vY0Od0ix00tjP5i9KSsQUMRqLi_WYY-raocsIuCnQ9muauUXvBBBEY2gPAlwToxhkH8Nk_AO--R7fHR8fDjusioJmleK38zrXM7rUH8fzXVG3fVxTzK9jH1Orm6Kmbq_JuaTjfuq9FN8rXhtEpT-dxfLXXffL8AAvk_t-IWuVE7t_6wksbbZMsWd8iVqt3l-V2yO5rg2zws_bk_enXiqyL3Z9il7R4Zj15_eHkY1J0ZAgMeyjJIc27ANiRcx7FlDG75NE-EUZy6SNsMMddypViaRcLGLHfcUqbwgY9FJg5zE--QXlEWdpf4OtKhDanJuFaM5aGmkRKhceCaMGqo8cigORFpathy7J4xlevwRXCJ_ErkF35K4Ncjz9oZswqy4w-0L_CQWzoE215_KOdnstZdqbRhTiUQKDrHEp1nGU2FskylLsqBO488RhGRCKdRYL7OmVotFvLN-xM5BI8IQlQIGz3ytCZyJazfqLr8AXYBEbg6lPsdStB30xneayRR1vZmIcGrExAaitQjfjuKEzGFrrDlaiFTcNw5YjldTpIlDNsVhNwj9yvJbjeGYmeAJBIeSTsy39m57kgx-bSGK6cZy4SA_zxotOPnui8_l4NWf_56iA_-hXiPXKsSQLB2b5_0lvOVfQh-5VI_qk3FDwroa68 priority: 102 providerName: Unpaywall |
| Title | Filtering, FDR and power |
| URI | https://link.springer.com/article/10.1186/1471-2105-11-450 https://www.ncbi.nlm.nih.gov/pubmed/20822518 https://www.proquest.com/docview/901859787 https://www.proquest.com/docview/756667100 https://www.proquest.com/docview/954600706 https://pubmed.ncbi.nlm.nih.gov/PMC2949886 https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/1471-2105-11-450 https://doaj.org/article/abc4fa5259ff45bd99278ae4a7f1d4df |
| UnpaywallVersion | publishedVersion |
| Volume | 11 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVADU databaseName: BioMed Central_OA刊 customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: RBZ dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: KQ8 dateStart: 20000101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: KQ8 dateStart: 20000701 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: DOA dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVEBS databaseName: Academic Search Ultimate (EBSCOhost) customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: ABDBF dateStart: 20000101 isFulltext: true titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn providerName: EBSCOhost – providerCode: PRVEBS databaseName: Inspec with Full Text customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: ADMLS dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.ebsco.com/products/research-databases/inspec-full-text providerName: EBSCOhost – providerCode: PRVBFR databaseName: Free Medical Journals customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: DIK dateStart: 20000101 isFulltext: true titleUrlDefault: http://www.freemedicaljournals.com providerName: Flying Publisher – providerCode: PRVFQY databaseName: GFMER Free Medical Journals customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: GX1 dateStart: 0 isFulltext: true titleUrlDefault: http://www.gfmer.ch/Medical_journals/Free_medical.php providerName: Geneva Foundation for Medical Education and Research – providerCode: PRVHPJ databaseName: ROAD customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: M~E dateStart: 20000101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVAQN databaseName: PubMed Central customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: RPM dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.ncbi.nlm.nih.gov/pmc/ providerName: National Library of Medicine – providerCode: PRVPQU databaseName: ProQuest Central [NZ] customDbUrl: http://www.proquest.com/pqcentral?accountid=15518 eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Health & Medical customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: 7X7 dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Technology Collection customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: 8FG dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/technologycollection1 providerName: ProQuest – providerCode: PRVFZP databaseName: Scholars Portal Journals: Open Access customDbUrl: eissn: 1471-2105 dateEnd: 20250131 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: M48 dateStart: 20000701 isFulltext: true titleUrlDefault: http://journals.scholarsportal.info providerName: Scholars Portal – providerCode: PRVAVX databaseName: Springer Nature HAS Fully OA customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: AAJSJ dateStart: 20001201 isFulltext: true titleUrlDefault: https://www.springernature.com providerName: Springer Nature – providerCode: PRVAVX databaseName: Springer Nature OA Free Journals customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: C6C dateStart: 20000112 isFulltext: true titleUrlDefault: http://www.springeropen.com/ providerName: Springer Nature |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwhV3rb9NADLf2EAI-IN4LG1WEEIhpYXlccpcPCHVlZVRaNW1UKp9Od5dkTKrSrg9B_3vsNMkW2MaXRs05p8Rnx3Z8_hngresJTbjgjq8FBSgEeasC7TAv40GYKT8WVJx83I-OBqw3DIdrUFWXlAyc3RjaUT-pwXT08ffl8jMq_KdC4UW07-EL1sHQJaQaMRa67yaXDrWVovRr2WNjHTbRdMXU2-GYXaUZCNC_yl3eMFHDVhWQ_v--uK9Zrr93Vdap1Ydwf5FP1PKXGo2uWa_uY3hUup12eyUnT2AtzZ_CvVUjyuUz2OpeUNYcp9mzu19ObZUn9oT6pz2HQffwe-fIKXsmOAZ9h7nDk8ig1oaRDoKUMbS_PAmFUZGfeTqNCQ0tUYrx2BNpwJIsSn2mKPXGPBO4iQlewEY-ztMtsLWn3dT1TRxpxVjiat9TwjUZOg3MN76xYL_ijjQloDj1tRjJIrAQkSR-SuIn_pXITws-1FdMVmAad9AeEMNrOoLBLk6Mp-ey1CqptGGZCjGEyzIW6iSOfS5UyhTPvASfzoI3tFySgC5y2klzrhazmfx2dirb6Ktg8IgBnQXvS6JsjPdvVFmYgFwgbKwG5U6DEjXRNIa3K6mQlSBL9LcEBm2CW2DXo3QhbW7L0_FiJjm61BGhLN1OEoeMGgm4kQUvV1JWM8YnzP7QExbwhvw1ONccyS9-FkDifsxiIXDO3UpSr-779nXZrWX5v4v46k6ObMOD1V4MKqPbgY35dJG-Rhdvrluwzoccf0X3aws22-3eWQ-PB4f9k1M824k6reLjSatQZxwZ9E_aP_4AAqBOwg |
| linkProvider | Scholars Portal |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9NAEB6VIlQ4IN41LWAhEKLCine9ttcHhAolSunjUFopt-3u2i6VIic0iar8KP4jM34Rg1pOPSY7Xjmzs_PIzHwD8MZn0hAuuMeNpACFIG91YDzB8jgIc80TSc3JB4fR4ER8G4bDFfjV9MJQWWWjE0tFnY4t_UfeQ7sl0fmV8afJT4-GRlFytZmgUUnFXra4xIht-nF3B4_3Lef9r8dfBl49VMCzaFxnXpxGFsU6jEwQZEKggYrTUFod8ZyZLCG4sFRrESdMZoFI8yjjQlNuSjAb-KkNcN9bcFsEqErw-sTDNr5jNB6gyYTKqMdQ73sYUYXUuiaosX_J8pUDAv41A0t28O8azTZRew_W5sVELy71aLRkC_sP4H7txLrbldQ9hJWseAR3qrGWi8ew3j-nHDxu88Ht7xy5ukjdCU1jewInN8Ksp7BajItsHVzDjJ_53CaR0UKkvuFMS9_m6IIIbrl1oNdwR9kanpymZIxUGabISBE_FfETPyrkpwPv2ycmFTTHNbSfieEtHYFql1-ML85UfUeVNlbkOsSAMM9FaNIk4bHUmdBxzlL8dQ68puNSBJtRUF3OmZ5Pp2r3-5HaRs8HQ1EMDx14VxPlY3x_q-s2B-QCIW11KDc7lHivbWd5o5EKVeuVqWpvgQNuu0oPUqlckY3nUxWjgx4RZtPVJEkoaCyBHznwrJKyljGcJgCETDoQd-Svw7nuSnH-o4Ql54lIpMQ9txpJ_fPeV5_LVivL_z3E59dy5BWsDY4P9tX-7uHeBtytqjyoQW8TVmcX8-wFOo8z87K8si6c3rSO-A0jv3eU |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3rb9QwDI_QJh77gHivbECFkBDTqmvSNE0_jhvVxmNCg0n7FuXRjEmn3mm9E9p_j90XKzAQH-_iRD3Hru2z_TMhr2IqDeKCR8xIDFAQ8lYnJuLUZ0nqNcslNid_OhIHJ_z9aXra_eFW99XufUqy7WlAlKZqOVk436q4FBMKr9QIgpUUu8I4huzrHGwbTjCYiumQRUC8_j41-YddI1PUIPb__l6-Yph-LZocMqcb5PaqWujL73o2u2KcinvkbudVhnutGNwnN8rqAbnZzpm8fEg2i3NMisMxu2GxfxzqyoULHI_2iJwU775OD6JuJEJkwTVYRpkTFpQyFSZJSs7BvGYulVYL5qkpcwQ7c1rzLKeyTLjzomRcY2aNU5vEziaPyVo1r8pNEhpq4jJmNhdGc-5iw6iWsfXgE3BmmQ3IpOeOsh1eOI6tmKkmbpBCIT8V8hM-KuBnQN4MOxYtVsZfaN8iwwc6RLluvphfnKlOaZQ2lnudQoTmPU-Ny3OWSV1ynXnq4NcF5CVel0IciwoLZc70qq7V4ZdjtQeuCMSGEK8F5HVH5Ofw_FZ3fQfABYS-GlFujyhB0exoeauXCtUpeq3AnZIQk8ksIOGwihuxdq0q56taZeAxCwRRup4kTznOCYhFQJ60UjYwhiEkf0plQLKR_I04N16pzr81OOEs57mUcOZOL6k_n_v6e9kZZPmfl_j0f05-QW593i_Ux8OjD1vkTluEgf1z22RtebEqn4FvtzTPGwX-AUDkPxs |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3rb9MwELemTgj4wHssbKAIISEm0iaO4zgfy6MaSEzToNL4ZNmOPSpKUvoQGn89d82DZjAQEh8bnyuffXe-k-9-R8iTMBIaccEDqgUGKAh5q2IdsMilceIUzQQWJ7874odj9vY0Od0ix00tjP5i9KSsQUMRqLi_WYY-raocsIuCnQ9muauUXvBBBEY2gPAlwToxhkH8Nk_AO--R7fHR8fDjusioJmleK38zrXM7rUH8fzXVG3fVxTzK9jH1Orm6Kmbq_JuaTjfuq9FN8rXhtEpT-dxfLXXffL8AAvk_t-IWuVE7t_6wksbbZMsWd8iVqt3l-V2yO5rg2zws_bk_enXiqyL3Z9il7R4Zj15_eHkY1J0ZAgMeyjJIc27ANiRcx7FlDG75NE-EUZy6SNsMMddypViaRcLGLHfcUqbwgY9FJg5zE--QXlEWdpf4OtKhDanJuFaM5aGmkRKhceCaMGqo8cigORFpathy7J4xlevwRXCJ_ErkF35K4Ncjz9oZswqy4w-0L_CQWzoE215_KOdnstZdqbRhTiUQKDrHEp1nGU2FskylLsqBO488RhGRCKdRYL7OmVotFvLN-xM5BI8IQlQIGz3ytCZyJazfqLr8AXYBEbg6lPsdStB30xneayRR1vZmIcGrExAaitQjfjuKEzGFrrDlaiFTcNw5YjldTpIlDNsVhNwj9yvJbjeGYmeAJBIeSTsy39m57kgx-bSGK6cZy4SA_zxotOPnui8_l4NWf_56iA_-hXiPXKsSQLB2b5_0lvOVfQh-5VI_qk3FDwroa68 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Filtering%2C+FDR+and+power&rft.jtitle=BMC+bioinformatics&rft.au=van+Iterson%2C+Maarten&rft.au=Boer%2C+Judith+M&rft.au=Menezes%2C+Ren%C3%A9e+X&rft.date=2010-09-07&rft.pub=Springer+Nature+B.V&rft.eissn=1471-2105&rft.volume=11&rft.spage=450&rft_id=info:doi/10.1186%2F1471-2105-11-450&rft.externalDocID=2501705051 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1471-2105&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1471-2105&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1471-2105&client=summon |