Journal ArticleDOI
Microarray data normalization and transformation
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TLDR
This review focuses on the much more mundane but indispensable tasks of 'normalizing' data from individual hybridizations to make meaningful comparisons of expression levels, and of 'transforming' them to select genes for further analysis and data mining.Abstract:
Underlying every microarray experiment is an experimental question that one would like to address. Finding a useful and satisfactory answer relies on careful experimental design and the use of a variety of data-mining tools to explore the relationships between genes or reveal patterns of expression. While other sections of this issue deal with these lofty issues, this review focuses on the much more mundane but indispensable tasks of 'normalizing' data from individual hybridizations to make meaningful comparisons of expression levels, and of 'transforming' them to select genes for further analysis and data mining.read more
Citations
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In-Silico Study of Immune System Associated Genes in Case of Type-2 Diabetes With Insulin Action and Resistance, and/or Obesity
TL;DR: In this paper, a simplified computational approach was applied to understand differential gene expression and patterns and the enriched pathways for obesity and type-2 diabetes, and also analyzed genes by using network-level understanding.
Journal ArticleDOI
RNA-Seq for Plant Pathogenic Bacteria
TL;DR: The technical and statistical challenges in the practical application of RNA-Seq for studying bacterial transcriptomes are discussed and some of the currently available solutions are described.
Journal ArticleDOI
Statistical Analysis of Protein Microarray Data: A Case Study in Type 1Diabetes Research
TL;DR: An overview of protein microarrays is provided and Reproducibility-Optimized Test Statistic (ROTS) shows better detection over the widely used LIMMA and new protein biomarkers that were not previously reported in original investigation are identified.
Journal ArticleDOI
Developmental aberrations of liver gene expression in bovine fetuses derived from somatic cell nuclear transplantation.
Chandana B Herath,Hiroko Ishiwata,Satoshi Shiojima,Satoshi Shiojima,Tadashi Kadowaki,Tadashi Kadowaki,Susumu Katsuma,Susumu Katsuma,Koichi Ushizawa,Kei Imai,Toru Takahashi,Akira Hirasawa,Akira Hirasawa,Seiya Takahashi,Yoshiaki Izaike,Gozoh Tsujimoto,Gozoh Tsujimoto,Kazuyoshi Hashizume +17 more
TL;DR: It is demonstrated that widespread dysregulation of liver genes occurs in the developing liver of NT bovine fetuses, suggesting that inappropriate genomic reprogramming after NT is a key factor associated with abnormal gene expressions in the livers of NT fetuses.
References
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Journal ArticleDOI
Cluster analysis and display of genome-wide expression patterns
TL;DR: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression, finding in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function.
Book
Data Reduction and Error Analysis for the Physical Sciences
TL;DR: In this paper, Monte Carlo techniques are used to fit dependent and independent variables least squares fit to a polynomial least-squares fit to an arbitrary function fitting composite peaks direct application of the maximum likelihood.
Journal ArticleDOI
Data Reduction and Error Analysis for the Physical Sciences.
TL;DR: Numerical methods matrices graphs and tables histograms and graphs computer routines in Pascal and Monte Carlo techniques dependent and independent variables least-squares fit to a polynomial least-square fit to an arbitrary function fitting composite peaks direct application of the maximum likelihood.
Journal ArticleDOI
Robust Locally Weighted Regression and Smoothing Scatterplots
TL;DR: Robust locally weighted regression as discussed by the authors is a method for smoothing a scatterplot, in which the fitted value at z k is the value of a polynomial fit to the data using weighted least squares, where the weight for (x i, y i ) is large if x i is close to x k and small if it is not.
Book
Regression Analysis by Example
Samprit Chatterjee,B. Price +1 more
TL;DR: Simple linear regression Multiple linear regression Regression Diagnostics: Detection of Model Violations Qualitative Variables as Predictors Transformation of Variables Weighted Least Squares The Problem of Correlated Errors Analysis of Collinear Data Biased Estimation of Regression Coefficients Variable Selection Procedures Logistic Regression Appendix References as discussed by the authors