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Title | Analysis of variance components in gene expression data |
Creator | Hsueh, Huey-miin 薛慧敏 |
Contributor | 統計系 |
Date | 2004 |
Date Issued | 7-Apr-2015 17:02:13 (UTC+8) |
Summary | Motivation: A microarray experiment is a multi-step process, and each step is a potential source of variation. There are two major sources of variation: biological variation and tech- nical variation. This study presents a variance-components approach to investigating animal-to-animal, between-array, within-array and day-to-day variations for two data sets. The first data set involved estimation of technical variances for pooled control and pooled treated RNA samples. The vari- ance components included between-array, and two nested within-array variances: between-section (the upper- and lower- sections of the array are replicates) and within-section (two adjacent spots of the same gene are printed within each section). The second experiment was conducted on four differ- ent weeks. Each week there were reference and test samples with a dye-flip replicate in two hybridization days. The vari- ance components included week-to-week, animal-to-animal and between-array and within-array variances. Results: We applied the linear mixed-effects model to quantify different sources of variation. In the first data set, we found that the between-array variance is greater than the between- section variance, which, in turn, is greater than the within- section variance. In the second data set, for the refer- ence samples, the week-to-week variance is larger than the between-array variance, which, in turn, is slightly larger than the within-array variance. For the test samples, the week-to- week variance has the largest variation. The animal-to-animal variance is slightly larger than the between-array and within- array variances. However, in a gene-by-gene analysis, the animal-to-animal variance is smaller than the between-array variance in four out of five housekeeping genes. In sum- mary, the largest variation observed is the week-to-week effect. ∗To whom correspondence should be addressed. |
Relation | Bioinformatics/computer Applications in The Biosciences - BIOINFORMATICS , vol. 20, no. 9, pp. 1436-1446 |
Type | article |
DOI | http://dx.doi.org/10.1093/bioinformatics/bth118 |
dc.contributor | 統計系 | |
dc.creator (作者) | Hsueh, Huey-miin | |
dc.creator (作者) | 薛慧敏 | zh_TW |
dc.date (日期) | 2004 | |
dc.date.accessioned | 7-Apr-2015 17:02:13 (UTC+8) | - |
dc.date.available | 7-Apr-2015 17:02:13 (UTC+8) | - |
dc.date.issued (上傳時間) | 7-Apr-2015 17:02:13 (UTC+8) | - |
dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/74372 | - |
dc.description.abstract (摘要) | Motivation: A microarray experiment is a multi-step process, and each step is a potential source of variation. There are two major sources of variation: biological variation and tech- nical variation. This study presents a variance-components approach to investigating animal-to-animal, between-array, within-array and day-to-day variations for two data sets. The first data set involved estimation of technical variances for pooled control and pooled treated RNA samples. The vari- ance components included between-array, and two nested within-array variances: between-section (the upper- and lower- sections of the array are replicates) and within-section (two adjacent spots of the same gene are printed within each section). The second experiment was conducted on four differ- ent weeks. Each week there were reference and test samples with a dye-flip replicate in two hybridization days. The vari- ance components included week-to-week, animal-to-animal and between-array and within-array variances. Results: We applied the linear mixed-effects model to quantify different sources of variation. In the first data set, we found that the between-array variance is greater than the between- section variance, which, in turn, is greater than the within- section variance. In the second data set, for the refer- ence samples, the week-to-week variance is larger than the between-array variance, which, in turn, is slightly larger than the within-array variance. For the test samples, the week-to- week variance has the largest variation. The animal-to-animal variance is slightly larger than the between-array and within- array variances. However, in a gene-by-gene analysis, the animal-to-animal variance is smaller than the between-array variance in four out of five housekeeping genes. In sum- mary, the largest variation observed is the week-to-week effect. ∗To whom correspondence should be addressed. | |
dc.format.extent | 226203 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.relation (關聯) | Bioinformatics/computer Applications in The Biosciences - BIOINFORMATICS , vol. 20, no. 9, pp. 1436-1446 | |
dc.title (題名) | Analysis of variance components in gene expression data | |
dc.type (資料類型) | article | en |
dc.identifier.doi (DOI) | 10.1093/bioinformatics/bth118 | en_US |
dc.doi.uri (DOI) | http://dx.doi.org/10.1093/bioinformatics/bth118 | en_US |