This document outlines the steps for processing and analyzing microarray data using R. The pipeline includes data preprocessing, quality control, and differential expression analysis. GEO Dataset This ...

The field of DNA microarray data analysis is getting increasingly complicated as scientists develop new array technologies and analysis algorithms, yet accurate introductory information on the ...

The impact of microarray technology on biology will depend on computational methods of data analysis. A supervised computer-learning method using support vector machines predicts gene function ...

pyLimma Differential expression analysis for microarray and RNA-seq data using linear models and empirical Bayes. Python implementation of the basics of R's limma package [1] including new features as ...

Many microarray experiments have factorial designs, but there are few statistical methods developed explicitly to handle the factorial analysis in these experiments. We propose a bootstrap-based ...

Using the latest tools in R and R RStudio® for calculations and data visualization, students learn valuable skills they can take with them into a variety of future careers in the public sector, the ...

As microarray data available to scientists continues to increase in size and complexity, it has become overwhelmingly important to find multiple ways to bring inference though analysis of DNA/mRNA ...

Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray ...

The "large p, small n" paradigm arises in microarray studies, image analysis, high throughput molecular screening, astronomy, and in many other high dimensional applications. False discovery rate (FDR ...