Cluster analysis and display of genome-wide expression patterns
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.
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Hierarchical clustering based on average linkage of Pearson Correlation of miRNA profiles revealed non-random partition of samples into two major branches, one containing all five individual neuron types and the other containing the two tissue types.
“Hierarchical clustering based on the average linkage of Pearson Correlation (Eisen et al., 1998) of miRNA profiles revealed non-random partition of the samples into two major branches, one containing all five individual neuron types and the other containing the two tissue types (Figure 3).”
From Cell-Type-Based Analysis of MicroRNA Profiles in the Mouse Brain (2012) · cites this paper as Eisen et al., 1998
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Loss of genomic methylation causes p53-dependent apoptosis and epigenetic deregulation
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Biological networks: the tinkerer as an engineer
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A module map showing conditional activity of expression modules in cancer
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Multidimensional Drug Profiling By Automated Microscopy
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T-cell exhaustion, co-stimulation and clinical outcome in autoimmunity and infection
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Reverse engineering gene networks using singular value decomposition and robust regression
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Self-citationWeight loss regulates inflammation‐related genes in white adipose tissue of obese subjects
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Cooperative Binding of Transcription Factors Orchestrates Reprogramming
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Defining functional areas in individual human brains using resting functional connectivity MRI
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Chromatin modifications as determinants of muscle stem cell quiescence and chronological aging
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- Authors
- Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D.
- Affiliations
- Department of Genetics and Department of Biochemistry and Howard Hughes Medical Institute, Stanford University School of Medicine, 300 Pasteur Avenue, Stanford, CA 94305
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 1998-12-08
- Volume
- 95
- Issue
- 25
- Pages
- 14863-14868
- ISSN
- 0027-8424
- 1091-6490
- Cited by
- 12,617
- References
- 0
- Language
- en
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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