Scanning microarrays at multiple intensities enhances discovery of differentially expressed genes
Motivation: Scanning parameters are often overlooked when optimizing microarray experiments. A scanning approach that extends the dynamic data range by acquiring multiple scans of different intensities has been developed. Results: Data from each of three scan intensities (low, medium, high) were ana...
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| Veröffentlicht in: | Bioinformatics 2006-08, Vol.22 (15), p.1863-1870 |
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| creator | Skibbe, David S. Wang, Xiujuan Zhao, Xuefeng Borsuk, Lisa A. Nettleton, Dan Schnable, Patrick S. |
| description | Motivation: Scanning parameters are often overlooked when optimizing microarray experiments. A scanning approach that extends the dynamic data range by acquiring multiple scans of different intensities has been developed. Results: Data from each of three scan intensities (low, medium, high) were analyzed separately using multiple scan and linear regression approaches to identify and compare the sets of genes that exhibit statistically significant differential expression. In the multiple scan approach only one-third of the differentially expressed genes were shared among the three intensities, and each scan intensity identified unique sets of differentially expressed genes. The set of differentially expressed genes from any one scan amounted to |
| doi_str_mv | 10.1093/bioinformatics/btl270 |
| format | Article |
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A scanning approach that extends the dynamic data range by acquiring multiple scans of different intensities has been developed. Results: Data from each of three scan intensities (low, medium, high) were analyzed separately using multiple scan and linear regression approaches to identify and compare the sets of genes that exhibit statistically significant differential expression. In the multiple scan approach only one-third of the differentially expressed genes were shared among the three intensities, and each scan intensity identified unique sets of differentially expressed genes. The set of differentially expressed genes from any one scan amounted to <70% of the total number of genes identified in at least one scan. The average signal intensity of genes that exhibited statistically significant changes in expression was highest for the low-intensity scan and lowest for the high-intensity scan, suggesting that low-intensity scans may be best for detecting expression differences in high-signal genes, while high-intensity scans may be best for detecting expression differences in low-signal genes. Comparison of the differentially expressed genes identified in the multiple scan and linear regression approaches revealed that the multiple scan approach effectively identifies a subset of statistically significant genes that linear regression approach is unable to identify. Quantitative RT–PCR (qRT–PCR) tests demonstrated that statistically significant differences identified at all three scan intensities can be verified. Availability: The data presented can be viewed at under GEO accession no. GSE3017. 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A scanning approach that extends the dynamic data range by acquiring multiple scans of different intensities has been developed. Results: Data from each of three scan intensities (low, medium, high) were analyzed separately using multiple scan and linear regression approaches to identify and compare the sets of genes that exhibit statistically significant differential expression. In the multiple scan approach only one-third of the differentially expressed genes were shared among the three intensities, and each scan intensity identified unique sets of differentially expressed genes. The set of differentially expressed genes from any one scan amounted to <70% of the total number of genes identified in at least one scan. The average signal intensity of genes that exhibited statistically significant changes in expression was highest for the low-intensity scan and lowest for the high-intensity scan, suggesting that low-intensity scans may be best for detecting expression differences in high-signal genes, while high-intensity scans may be best for detecting expression differences in low-signal genes. Comparison of the differentially expressed genes identified in the multiple scan and linear regression approaches revealed that the multiple scan approach effectively identifies a subset of statistically significant genes that linear regression approach is unable to identify. Quantitative RT–PCR (qRT–PCR) tests demonstrated that statistically significant differences identified at all three scan intensities can be verified. Availability: The data presented can be viewed at under GEO accession no. GSE3017. 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Data processing in biology (general aspects)</subject><subject>Microscopy, Confocal - methods</subject><subject>Microscopy, Fluorescence - methods</subject><subject>Oligonucleotide Array Sequence Analysis - methods</subject><subject>Plant Proteins - analysis</subject><subject>Plant Proteins - genetics</subject><subject>Zea mays - genetics</subject><subject>Zea mays - metabolism</subject><issn>1367-4803</issn><issn>1460-2059</issn><issn>1367-4811</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkV1rFTEQhhdRbK3-BGUR9G5tvj8utWgrHhCpongTstlJTc1mj0lWev69kXOw6I1XmZBnXibzdN1jjF5gpOnpGJaQ_JJnW4Mrp2ONRKI73TFmAg0EcX231VTIgSlEj7oHpVwjxDFj7H53hIWkWGh-3NlLZ1MK6aqfg8uLzdnuSm9rP6-xhm2EPqQKqYQaoPSQvtnkWjGF4pafkHf94tvFe8iQarAx7nq42WYoBab-ChKUh909b2OBR4fzpPv05vXHs4th8_787dnLzeDahHXwgmiLQEuFMFdW48mORHiutZVsREhqwpGAySmASWHFsNYcKUWpdoxOlJ50z_e527z8WKFUM7chIUabYFmLEUpiJin6L4g1E0xL3cCn_4DXy5pT-0RjlKSaCNwgvofa9krJ4M02h9nmncHI_DZl_jZl9qZa35ND-DrOMN12HdQ04NkBsMXZ6HPbfCi3XNNKsCKNG_ZcKBVu_rzb_N20KMnNxZev5hXekMt3Hz6bc_oLWgexog</recordid><startdate>20060801</startdate><enddate>20060801</enddate><creator>Skibbe, David S.</creator><creator>Wang, Xiujuan</creator><creator>Zhao, Xuefeng</creator><creator>Borsuk, Lisa A.</creator><creator>Nettleton, Dan</creator><creator>Schnable, Patrick S.</creator><general>Oxford University Press</general><general>Oxford Publishing Limited (England)</general><scope>BSCLL</scope><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TM</scope><scope>7TO</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>H94</scope><scope>JG9</scope><scope>JQ2</scope><scope>K9.</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope></search><sort><creationdate>20060801</creationdate><title>Scanning microarrays at multiple intensities enhances discovery of differentially expressed genes</title><author>Skibbe, David S. ; Wang, Xiujuan ; Zhao, Xuefeng ; Borsuk, Lisa A. ; Nettleton, Dan ; Schnable, Patrick S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c480t-f629a0e9780158a91dab26f599a74b00792506edc8eed81841995088339c43d33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Biological and medical sciences</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Gene Expression Profiling - methods</topic><topic>General aspects</topic><topic>Image Enhancement - methods</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>In Situ Hybridization, Fluorescence - methods</topic><topic>Mathematics in biology. Statistical analysis. Models. Metrology. 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A scanning approach that extends the dynamic data range by acquiring multiple scans of different intensities has been developed. Results: Data from each of three scan intensities (low, medium, high) were analyzed separately using multiple scan and linear regression approaches to identify and compare the sets of genes that exhibit statistically significant differential expression. In the multiple scan approach only one-third of the differentially expressed genes were shared among the three intensities, and each scan intensity identified unique sets of differentially expressed genes. The set of differentially expressed genes from any one scan amounted to <70% of the total number of genes identified in at least one scan. The average signal intensity of genes that exhibited statistically significant changes in expression was highest for the low-intensity scan and lowest for the high-intensity scan, suggesting that low-intensity scans may be best for detecting expression differences in high-signal genes, while high-intensity scans may be best for detecting expression differences in low-signal genes. Comparison of the differentially expressed genes identified in the multiple scan and linear regression approaches revealed that the multiple scan approach effectively identifies a subset of statistically significant genes that linear regression approach is unable to identify. Quantitative RT–PCR (qRT–PCR) tests demonstrated that statistically significant differences identified at all three scan intensities can be verified. Availability: The data presented can be viewed at under GEO accession no. GSE3017. Contact: schnable@iastate.edu Supplementary information: Data from these experiments can be viewed at</abstract><cop>Oxford</cop><pub>Oxford University Press</pub><pmid>16731695</pmid><doi>10.1093/bioinformatics/btl270</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Oxford Journals Open Access Collection; Alma/SFX Local Collection |
| subjects | Biological and medical sciences Fundamental and applied biological sciences. Psychology Gene Expression Profiling - methods General aspects Image Enhancement - methods Image Interpretation, Computer-Assisted - methods In Situ Hybridization, Fluorescence - methods Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Microscopy, Confocal - methods Microscopy, Fluorescence - methods Oligonucleotide Array Sequence Analysis - methods Plant Proteins - analysis Plant Proteins - genetics Zea mays - genetics Zea mays - metabolism |
| title | Scanning microarrays at multiple intensities enhances discovery of differentially expressed genes |
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