Quality assessment and data handling methods for Affymetrix Gene 1.0 ST arrays with variable RNA integrity

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dc.contributor.authorViljoen, Katieen_ZA
dc.contributor.authorBlackburn, Jonathanen_ZA
dc.date.accessioned2015-11-27T09:30:20Z
dc.date.available2015-11-27T09:30:20Z
dc.date.issued2013en_ZA
dc.description.abstractBBackground: RNA and microarray quality assessment form an integral part of gene expression analysis and, although methods such as the RNA integrity number (RIN) algorithm reliably asses RNA integrity, the relevance of RNA integrity in gene expression analysis as well as analysis methods to accommodate the possible effects of degradation requires further investigation. We investigated the relationship between RNA integrity and array quality on the commonly used Affymetrix Gene 1.0 ST array platform using reliable within-array and between-array quality assessment measures. The possibility of a transcript specific bias in the apparent effect of RNA degradation on the measured gene expression signal was evaluated after either excluding quality-flagged arrays or compensation for RNA degradation at different steps in the analysis. Results: Using probe-level and inter-array quality metrics to assess 34 Gene 1.0 ST array datasets derived from historical, paired tumour and normal primary colorectal cancer samples, 7 arrays (20.6%), with a mean sample RIN of 3.2 (SD = 0.42), were flagged during array quality assessment while 10 arrays from samples with RINs < 7 passed quality assessment, including one sample with a RIN < 3. We detected a transcript length bias in RNA degradation in only 5.8% of annotated transcript clusters (p-value 0.05, FC ≥ </td><td COLSPAN=1 VALIGN="top">2</td><td COLSPAN=1 VALIGN="top">), with longer and shorter than average transcripts under- and overrepresented in quality-flagged samples respectively. Applying compensatory measures for RNA degradation performed at least as well as excluding quality-flagged arrays, as judged by hierarchical clustering, gene expression analysis and Ingenuity Pathway Analysis; importantly, use of these compensatory measures had the significant benefit of enabling lower quality array data from irreplaceable clinical samples to be retained in downstream analyses. Conclusions: Here, we demonstrate an effective array-quality assessment strategy, which will allow the user to recognize lower quality arrays that can be included in the analysis once appropriate measures are applied to account for known or unknown sources of variation, such as array quality- and batch- effects, by implementing ComBat or Surrogate Variable Analysis. This approach of quality control and analysis will be especially useful for clinical samples with variable and low RNA qualities, with RIN scores ≥ 2.en_ZA
dc.identifier.apacitationViljoen, K., & Blackburn, J. (2013). Quality assessment and data handling methods for Affymetrix Gene 1.0 ST arrays with variable RNA integrity. <i>BMC Genomics</i>, http://hdl.handle.net/11427/15383en_ZA
dc.identifier.chicagocitationViljoen, Katie, and Jonathan Blackburn "Quality assessment and data handling methods for Affymetrix Gene 1.0 ST arrays with variable RNA integrity." <i>BMC Genomics</i> (2013) http://hdl.handle.net/11427/15383en_ZA
dc.identifier.citationViljoen, K. S., & Blackburn, J. M. (2013). Quality assessment and data handling methods for Affymetrix Gene 1.0 ST arrays with variable RNA integrity. BMC genomics, 14(1), 14.en_ZA
dc.identifier.ris TY - Journal Article AU - Viljoen, Katie AU - Blackburn, Jonathan AB - BBackground: RNA and microarray quality assessment form an integral part of gene expression analysis and, although methods such as the RNA integrity number (RIN) algorithm reliably asses RNA integrity, the relevance of RNA integrity in gene expression analysis as well as analysis methods to accommodate the possible effects of degradation requires further investigation. We investigated the relationship between RNA integrity and array quality on the commonly used Affymetrix Gene 1.0 ST array platform using reliable within-array and between-array quality assessment measures. The possibility of a transcript specific bias in the apparent effect of RNA degradation on the measured gene expression signal was evaluated after either excluding quality-flagged arrays or compensation for RNA degradation at different steps in the analysis. Results: Using probe-level and inter-array quality metrics to assess 34 Gene 1.0 ST array datasets derived from historical, paired tumour and normal primary colorectal cancer samples, 7 arrays (20.6%), with a mean sample RIN of 3.2 (SD = 0.42), were flagged during array quality assessment while 10 arrays from samples with RINs < 7 passed quality assessment, including one sample with a RIN < 3. We detected a transcript length bias in RNA degradation in only 5.8% of annotated transcript clusters (p-value 0.05, FC ≥ </td><td COLSPAN=1 VALIGN="top">2</td><td COLSPAN=1 VALIGN="top">), with longer and shorter than average transcripts under- and overrepresented in quality-flagged samples respectively. Applying compensatory measures for RNA degradation performed at least as well as excluding quality-flagged arrays, as judged by hierarchical clustering, gene expression analysis and Ingenuity Pathway Analysis; importantly, use of these compensatory measures had the significant benefit of enabling lower quality array data from irreplaceable clinical samples to be retained in downstream analyses. Conclusions: Here, we demonstrate an effective array-quality assessment strategy, which will allow the user to recognize lower quality arrays that can be included in the analysis once appropriate measures are applied to account for known or unknown sources of variation, such as array quality- and batch- effects, by implementing ComBat or Surrogate Variable Analysis. This approach of quality control and analysis will be especially useful for clinical samples with variable and low RNA qualities, with RIN scores ≥ 2. DA - 2013 DB - OpenUCT DO - 10.1186/1471-2164-14-14 DP - University of Cape Town J1 - BMC Genomics LK - https://open.uct.ac.za PB - University of Cape Town PY - 2013 T1 - Quality assessment and data handling methods for Affymetrix Gene 1.0 ST arrays with variable RNA integrity TI - Quality assessment and data handling methods for Affymetrix Gene 1.0 ST arrays with variable RNA integrity UR - http://hdl.handle.net/11427/15383 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/15383
dc.identifier.urihttp://dx.doi.org/10.1186/1471-2164-14-14
dc.identifier.vancouvercitationViljoen K, Blackburn J. Quality assessment and data handling methods for Affymetrix Gene 1.0 ST arrays with variable RNA integrity. BMC Genomics. 2013; http://hdl.handle.net/11427/15383.en_ZA
dc.language.isoengen_ZA
dc.publisherBioMed Central Ltden_ZA
dc.publisher.departmentInstitute of Infectious Disease and Molecular Medicineen_ZA
dc.publisher.facultyFaculty of Health Sciencesen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution Licenseen_ZA
dc.rights.holder2013 Viljoen and Blackburn; licensee BioMed Central Ltd.en_ZA
dc.rights.urihttp://creativecommons.org/licenses/by/2.0en_ZA
dc.sourceBMC Genomicsen_ZA
dc.source.urihttp://www.biomedcentral.com/bmcgenomics/en_ZA
dc.subject.otherGene expression profilingen_ZA
dc.subject.otherMicroarrayen_ZA
dc.subject.otherRNA qualityen_ZA
dc.subject.otherRNA integrity numberen_ZA
dc.subject.otherQuality controlen_ZA
dc.subject.otherComBaten_ZA
dc.subject.otherSurrogate variable analysisen_ZA
dc.subject.otherNon-biological experimental varianceen_ZA
dc.titleQuality assessment and data handling methods for Affymetrix Gene 1.0 ST arrays with variable RNA integrityen_ZA
dc.typeJournal Articleen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceArticleen_ZA
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