VisHiC--hierarchical functional enrichment analysis of microarray data

VisHiC—hierarchical functional enrichment analysis of microarray data

Measuring gene expression levels with microarrays is one of the key technologies of modern genomics. Clustering of microarray data is an important application, as genes with similar expression profiles may be regulated by common pathways and involved in related functions. Gene Ontology (GO) analysis and visualization allows researchers to study the biological context of discovered clusters and ...

Extracellular exosomes and preeclampsia: a microarray-based study and functional enrichment analysis

Background:  Preeclampsia (PE) is a heterogeneous pregnancy disease which the exact pathophysiology of it is unknown. Recently exosomes have been indicated as a causative factor in the pathogenesis of PE. The aim of the study was to investigate in microarray library data to extract the differentially expressed genes (DEGs) in PE and to perform a functional enrichment analysis to predict the rol...

Bayesian integrated functional analysis of microarray data

MOTIVATION The statistical analysis of microarray data usually proceeds in a sequential manner, with the output of the previous step always serving as the input of the next one. However, the methods currently used in such analyses do not properly account for the fact that the intermediate results may not always be correct, then leading to cumulating error in the inferences drawn based on such s...

cDNA Microarray Data Normalization

Semiparametric classification in hierarchical functional data analysis

Motivated by the problem of classifying hormone trajectories, this article proposes a flexible semiparametric Bayesian methodology for hierarchical functional data. The approach is based on a hierarchical spline model, with the number and location of knots and the distribution of the random spline coefficients treated as unknown. Assuming a discrete distribution for the spline coefficients, we ...