Microarray enriched gene rank

SEGS: Search for enriched gene sets in microarray data

Gene Ontology (GO) terms are often used to interpret the results of microarray experiments. The most common approach is to perform Fisher's exact tests to find gene sets annotated by GO terms which are over-represented among the genes declared to be differentially expressed in the analysis of microarray data. Another way is to apply Gene Set Enrichment Analysis (GSEA) that uses predefined gene ...

Generalized rank tests for replicated microarray data.

Gene expression data from microarray experiments have been studied using several statistical models. Significance Analysis of Microarrays (SAM), for example, has proved to be useful in analyzing microarray data. In the spirit of the SAM procedures, we develop permutation based rank-tests for generalized Wilcoxon ranksum test for two-group comparisons of replicated microarray data. Also, for mic...

An immune-enriched oligo-microarray analysis of gene expression in Manila clam

Identifying Subspace Gene Clusters from Microarray Data Using Low-Rank Representation

Identifying subspace gene clusters from the gene expression data is useful for discovering novel functional gene interactions. In this paper, we propose to use low-rank representation (LRR) to identify the subspace gene clusters from microarray data. LRR seeks the lowest-rank representation among all the candidates that can represent the genes as linear combinations of the bases in the dataset....

Enriched Heatmaps for Visualizing Uncertainty in Microarray Data

Since their invention in the 1990’s, DNA microarrays have advanced into a well-established, high-throughput experimentation technique for determining gene regulation. The data resulting from microarray experiments are image scans of physical microarrays. During a multi-step preparation process, a signal intensity for each gene of each sample is extracted, corrected, and aggregated into a numeri...