Analysis of variance components in gene expression data

Chen, James J.; Delongchamp, Robert R.; Tsai, Chen‐An; Hsueh, Huey-Miin; Sistare, Frank D.; Thompson, Karol L.; Desai, Varsha G.; Fuscoe, James C.

doi:10.1093/bioinformatics/bth118

Cited by 75 publications

(51 citation statements)

References 13 publications

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“…The present analysis used two different samples and two different microarray chips to obtain the individual data points. Criteria to assess chip number has been previously described (Chen et al 2004). Previous studies have demonstrated two chips for each data point, assuming an R 2 O0.95 and confident P!0.05, provide a critical and statistically relevant analysis (McLean et al 2002, Kezele et al 2005, Small et al 2005.…”

Section: Discussionmentioning

confidence: 99%

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Regulation of the gonadal transcriptome during sex determination and testis morphogenesis: comparative candidate genes

Clement

Anway²,

Uzumcu³

et al. 2007

Reproduction

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Gene expression profiles during sex determination and gonadal differentiation were investigated to identify new potential regulatory factors. Embryonic day 13 (E13), E14, and E16 rat testes and ovaries were used for microarray analysis, as well as E13 testis organ cultures that undergo testis morphogenesis and develop seminiferous cords in vitro. A list of 109 genes resulted from a selective analysis for genes present in male gonadal development and with a 1.5-fold change in expression between E13 and E16. Characterization of these 109 genes potentially important for testis development revealed that cytoskeletal-associated proteins, extracellular matrix factors, and signaling factors were highly represented. Throughout the developmental period (E13-E16), sexenriched transcripts were more prevalent in the male with 34 of the 109 genes having testis-enriched expression during sex determination. In ovaries, the total number of transcripts with a 1.5-fold change in expression between E13 and E16 was similar to the testis, but none of those genes were both ovary enriched and regulated during the developmental period. Genes conserved in sex determination were identified by comparing changing transcripts in the rat analysis herein, to transcripts altered in previously published mouse studies of gonadal sex determination. A comparison of changing mouse and rat transcripts identified 43 genes with species conservation in sex determination and testis development. Profiles of gene expression during E13-E16 rat testis and ovary development are presented and candidate genes for involvement in sex determination and testis differentiation are identified. Analysis of cellular pathways did not reveal any specific pathways involving multiple candidate genes. However, the genes and gene network identified influence numerous cellular processes with cellular differentiation, proliferation, focal contact, RNA localization, and development being predominant. Reproduction (2007) 134 455-472

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Section: Discussionmentioning

confidence: 99%

“…The reproducibility between replicate chips was determined and an R 2 O0.95 was judged sufficient to allow two chips to be used per data point, with a P!0.05 confidence. The criteria to consider chip number has been previously described (Chen et al 2004).…”

Section: Bioinformatics and Statistical Analysismentioning

confidence: 99%

Regulation of the gonadal transcriptome during sex determination and testis morphogenesis: comparative candidate genes

Clement

Anway²,

Uzumcu³

et al. 2007

Reproduction

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“…In the context of gene expression data, that are usually characterized by relatively high level of noise [36], stability can be considered an important property: how much the characteristics and composition of the discovered clusters hold when perturbation such as added noise, resampling or random projections are introduced? Can we design stability measures to assess the reliability of the discovered clusters?…”

Section: Introductionmentioning

confidence: 99%

Randomized maps for assessing the reliability of patients clusters in DNA microarray data analyses

Bertoni

Valentini

2006

Artificial Intelligence in Medicine

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Objective: Clustering algorithms may be applied to the analysis of DNA microarray data to identify novel subgroups that may lead to new taxonomies of diseases defined at bio-molecular level. A major problem related to the identification of biologically meaningful clusters is the assessment of their reliability, since clustering algorithms may find clusters even if no structure is present. Methodology:Recently, methods based on random "perturbations" of the data, such as bootstrapping, noise injections techniques and random subspace methods have been applied to the problem of cluster validity estimation. In this framework, we propose stability measures that exploits the high dimensionality of DNA microarray data and the redundancy of information stored in microarray chips. To this end we randomly project the original gene expression data into lower dimensional subspaces, approximately preserving the distance between the examples according to the Johnson-Lindenstrauss (JL) theory. The stability of the clusters discovered in the original high dimensional space is estimated by comparing them with the clusters discovered in randomly projected lower dimensional subspaces. The proposed cluster-stability measures may be applied to validate and to quantitatively assess the reliability of the clusters obtained by a large class of clustering algorithms. Results and conclusion:We tested the effectiveness of our approach with high dimensional synthetic data, whose distribution is a priori known, showing that the stability measures based on randomized maps correctly predict the number of clusters and the reliability of each individual cluster. Then we showed how to apply the proposed measures to the analysis of DNA microarray data, whose underlying distribution is unknown. We evaluated the validity of clusters discovered by hierarchical clustering algorithms in diffuse large B-cell lymphoma (DLBCL) and malignant melanoma patients, showing that the proposed reliability measures can support bio-medical researchers in the identification of stable clusters of patients and in the discovery of new subtypes of diseases characterized at bio-molecular level.

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“…The effects of various factors (e.g. dye and slide) on the quality of DNA-microarray data have been studied quite extensively albeit for experiments performed with eukaryotic systems [14][15][16][17][18][19][20]. In contrast, no data quality determination has yet been performed on DNAmicroarray data from experiments with bacterial cultures.…”

Section: Introductionmentioning

confidence: 99%