About: Methods for simultaneously identifying coherent local clusters with smooth global patterns in gene expression profiles

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About: Methods for simultaneously identifying coherent local clusters with smooth global patterns in gene expression profiles Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Methods for simultaneously identifying coherent local clusters with smooth global patterns in gene expression profiles
Creator	Chen, Chun-Houh Lee, Yun-Shien Tien, Yin-Jing Wu, Han-Ming
Source	Medline; PMC
abstract	BACKGROUND: The hierarchical clustering tree (HCT) with a dendrogram [1] and the singular value decomposition (SVD) with a dimension-reduced representative map [2] are popular methods for two-way sorting the gene-by-array matrix map employed in gene expression profiling. While HCT dendrograms tend to optimize local coherent clustering patterns, SVD leading eigenvectors usually identify better global grouping and transitional structures. RESULTS: This study proposes a flipping mechanism for a conventional agglomerative HCT using a rank-two ellipse (R2E, an improved SVD algorithm for sorting purpose) seriation by Chen [3] as an external reference. While HCTs always produce permutations with good local behaviour, the rank-two ellipse seriation gives the best global grouping patterns and smooth transitional trends. The resulting algorithm automatically integrates the desirable properties of each method so that users have access to a clustering and visualization environment for gene expression profiles that preserves coherent local clusters and identifies global grouping trends. CONCLUSION: We demonstrate, through four examples, that the proposed method not only possesses better numerical and statistical properties, it also provides more meaningful biomedical insights than other sorting algorithms. We suggest that sorted proximity matrices for genes and arrays, in addition to the gene-by-array expression matrix, can greatly aid in the search for comprehensive understanding of gene expression structures. Software for the proposed methods can be obtained at .
has issue date	2008-03-20(xsd:dateTime)
bibo:doi	10.1186/1471-2105-9-155
bibo:pmid	18366693
has license	cc-by
sha1sum (hex)	b281ffd13e1ca8492cc60ae608b6b24a7be46a1f
schema:url	https://doi.org/10.1186/1471-2105-9-155
resource representing a document's title	Methods for simultaneously identifying coherent local clusters with smooth global patterns in gene expression profiles
has PubMed Central identifier	PMC2322988
has PubMed identifier	18366693
schema:publication	BMC Bioinformatics
resource representing a document's body	covid:b281ffd13e1ca8492cc60ae608b6b24a7be46a1f#body_text
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