ArrayCluster is one of the significant challenges in gene expression analysis to find unknown subtypes of several diseases at the molecular levels. This task can be addressed by grouping gene expression patterns of the collected samples on the basis of a large number of genes. Application of commonly used clustering methods to such a dataset however are likely to fail due to over-learning, because the number of samples to be grouped is much smaller than the data dimension which is equal to the number of genes involved in the dataset. To overcome such difficulty, we developed a novel model-based clustering method, referred to as the mixed factors analysis.
MAIA is a software package for automatic processing of the one- and two- (typically, Cy3-green/Cy5-red) color images produced in cDNA, CGH (comparative genome hybridization) or protein microarray technologies. It incorporates the following modules:
E-Predict compares observed hybridization patterns with theoretical energy profiles representing different species. We demonstrate the application of the algorithm to viral detection in a set of clinical samples and discuss its relevance to other metagenomic applications.
QTModel is user-friendly computer software which packaged with modules for microarray data analysis, diallele design analysis and mixed model analysis.The mixed model module is developed for analyzing data from experimental designs with random factors. It is now available for commonly used randomized block design, randomized complete block design, latin square design, factorial design, multi-factor factorial design, nested design, and cross nested design etc. For fixed factors, pair-wised comparisons are done for all possible pairs of fixed effects of one factor. For random factors, some mixed linear model approaches, such as MINQUE, MIVQUE, REML and EM, will be used to estimate the variances of these random factors, and also unbiased prediction methods, such as BLUP, LUP and AUP, are used to predict the random effects of the random factors.
chipchipnorm (ChIP-chip normalization) is a R package that can be incorporated into the normalization workflow for chip-chip data, chromatin immunoprecipitation (ChIP) with microarray technology (chip).
GECS (Gene Expression to Chemical Structure) is a collection of prediction methods linking genomic or transcriptomic contents of genes to chemical structures of biosynthetic substances. This N-Glycan Prediction Server is based on the repertoire of glycosyltransferases for N-glycan biosynthesis.