Cluster analysis and display of genome-wide expression patterns¶
Why this mattered¶
TBD
Abstract¶
A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
Related¶
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- cite ← Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position — ATAC-seq uses Eisen-style clustering and heatmap display to visualize genome-wide chromatin accessibility patterns.
- cite ← Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications — The breast carcinoma study uses hierarchical clustering and heatmap display methods from genome-wide expression pattern analysis.
- cite ← Gene Ontology: tool for the unification of biology — Gene Ontology addresses the need exposed by genome-wide expression clustering for standardized functional annotations that make gene-expression patterns biologically comparable.
- cite ← Molecular portraits of human breast tumours — Molecular portraits uses hierarchical clustering and heat-map display methods from genome-wide expression pattern analysis.