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Analyzing Microarray Gene Expression Data
Geoffrey J. McLachlan,Kim Anh Do,Christophe Ambroise +2 more
- 04 Aug 2004
875
TL;DR: In this article, the authors proposed a supervised classification of Tissue Samples and linked the supervised classification with survival analysis, and showed that the classification of tissue samples is more accurate than that of microarray data.
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Abstract: Preface. 1. Microarrays in Gene Expression Studies. 2. Cleaning and Normalization. 3. Some Cluster Analysis Methods. 4. Clustering of Tissue Samples. 5. Screening and Clustering of Genes. 6. Discriminant Analysis. 7. Supervised Classification of Tissue Samples. 8. Linking Microarray Data with Survival Analysis. References. Author Index. Subject Index.
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Citations
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A new test statistic based on shrunken sample variance for identifying differentially expressed genes in small microarray experiments
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Endogenous Health Groups and Heterogeneous Dynamics of the Elderly
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Estimating the false discovery rate using mixed normal distribution for identifying differentially expressed genes in microarray data analysis.
TL;DR: A new method for estimating the FDR assuming a mixed normal distribution on the test statistic is proposed and the performance of the proposed method and SAM is examined using simulated data, which indicates that the accuracy of the estimated FDR, varied depending on the experimental conditions.
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Identifying Biologically Significant Pathways by Gene Set Enrichment Analysis Using Fisher's Criterion
Jae-Young Kim,Hyungmin Lee,Miyoung Shin +2 more
- 13 Dec 2008
TL;DR: The method of gene set enrichment analysis with Fisher's criterion for gene ranking, named FC-GSEA, is explored and its effects made in leukemia related pathway analyses are evaluated.
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