Multivariate analysis of microarray data: differential expression and differential connection

Background  

Typical analysis of microarray data ignores the correlation between gene expression values. In this paper we present a model for microarray data which specifically allows for correlation between genes. As a result we combine gene network ideas with linear models and differential expression.     

Array2BIO: from microarray expression data to functional annotation of co-regulated genes

Background  

There are several isolated tools for partial analysis of microarray expression data. To provide an integrative, easy-to-use and automated toolkit for the analysis of Affymetrix microarray expression data we have developed Array2BIO, an application that couples several analytical methods into a single web based utility.     

Probabilistic estimation of microarray data reliability and underlying gene expression

Background  

The availability of high throughput methods for measurement of mRNA concentrations makes the reliability of conclusions drawn from the data and global quality control of samples and hybridization important issues. We address these issues by an information theoretic approach, applied to discretized expression values in replicated gene expression data.     

Comparison of scores for bimodality of gene expression distributions and genome-wide evaluation of the prognostic relevance of high-scoring genes

Background  

A major goal of the analysis of high-dimensional RNA expression data from tumor tissue is to identify prognostic signatures for discriminating patient subgroups. For this purpose genome-wide identification of bimodally expressed genes from gene array data is relevant because distinguishability of high and low expression groups is easier compared to genes with unimodal expression distributions.     

Selection and evaluation of reference genes for improved interrogation of microbial transcriptomes: case study with the extremophile Acidithiobacillus ferrooxidans

Background  

Normalization is a prerequisite for accurate real time PCR (qPCR) expression analysis and for the validation of microarray profiling data in microbial systems. The choice and use of reference genes that are stably expressed across samples, experimental conditions and designs is a key consideration for the accurate interpretation of gene expression data.     

SBEAMS-Microarray: database software supporting genomic expression analyses for systems biology

Background  

The biological information in genomic expression data can be understood, and computationally extracted, in the context of systems of interacting molecules. The automation of this information extraction requires high throughput management and analysis of genomic expression data, and integration of these data with other data types.     

GEOGLE: context mining tool for the correlation between gene expression and the phenotypic distinction

Background  

In the post-genomic era, the development of high-throughput gene expression detection technology provides huge amounts of experimental data, which challenges the traditional pipelines for data processing and analyzing in scientific researches.     

Rank Difference Analysis of Microarrays (RDAM), a novel approach to statistical analysis of microarray expression profiling data

Background  

A key step in the analysis of microarray expression profiling data is the identification of genes that display statistically significant changes in expression signals between two biological conditions.     

Transcriptomic response to differentiation induction

Background  

Microarrays used for gene expression studies yield large amounts of data. The processing of such data typically leads to lists of differentially-regulated genes. A common terminal data analysis step is to map pathways of potentially interrelated genes.     

Kernel-based distance metric learning for microarray data classification

Background  

The most fundamental task using gene expression data in clinical oncology is to classify tissue samples according to their gene expression levels. Compared with traditional pattern classifications, gene expression-based data classification is typically characterized by high dimensionality and small sample size, which make the task quite challenging.     

DISCLOSE : DISsection of CLusters Obtained by SEries of transcriptome data using functional annotations and putative transcription factor binding sites

Background  

A typical step in the analysis of gene expression data is the determination of clusters of genes that exhibit similar expression patterns. Researchers are confronted with the seemingly arbitrary choice between numerous algorithms to perform cluster analysis.     

In search of functional association from time-series microarray data based on the change trend and level of gene expression

Background  

The increasing availability of time-series expression data opens up new possibilities to study functional linkages of genes. Present methods used to infer functional linkages between genes from expression data are mainly based on a point-to-point comparison. Change trends between consecutive time points in time-series data have been so far not well explored.     

STEM: a tool for the analysis of short time series gene expression data

Background  

Time series microarray experiments are widely used to study dynamical biological processes. Due to the cost of microarray experiments, and also in some cases the limited availability of biological material, about 80% of microarray time series experiments are short (3–8 time points). Previously short time series gene expression data has been mainly analyzed using more general gene expression analysis tools not designed for the unique challenges and opportunities inherent in short time series gene expression data.     

A method for analyzing censored survival phenotype with gene expression data

Background  

Survival time is an important clinical trait for many disease studies. Previous works have shown certain relationship between patients' gene expression profiles and survival time. However, due to the censoring effects of survival time and the high dimensionality of gene expression data, effective and unbiased selection of a gene expression signature to predict survival probabilities requires further study.     

Simultaneous clustering of gene expression data with clinical chemistry and pathological evaluations reveals phenotypic prototypes

Background  

Commonly employed clustering methods for analysis of gene expression data do not directly incorporate phenotypic data about the samples. Furthermore, clustering of samples with known phenotypes is typically performed in an informal fashion. The inability of clustering algorithms to incorporate biological data in the grouping process can limit proper interpretation of the data and its underlying biology.