|
rdf:type |
|
|
lifeskim:mentions |
umls-concept:C0019409,
umls-concept:C0026336,
umls-concept:C0040300,
umls-concept:C0085862,
umls-concept:C0242485,
umls-concept:C0242568,
umls-concept:C0449475,
umls-concept:C0684321,
umls-concept:C1299583,
umls-concept:C1449575,
umls-concept:C1549571,
umls-concept:C1608386,
umls-concept:C1709707,
umls-concept:C2699488
|
|
pubmed:issue |
5
|
|
pubmed:dateCreated |
2006-10-4
|
|
pubmed:abstractText |
Microarray analysis requires standardized specimens and evaluation procedures to achieve acceptable results. A major limitation of this method is caused by heterogeneity in the cellular composition of tissue specimens, which frequently confounds data analysis. We introduce a linear model to deconfound gene expression data from tissue heterogeneity for genes exclusively expressed by a single cell type.
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pubmed:language |
eng
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pubmed:journal |
|
|
pubmed:citationSubset |
IM
|
|
pubmed:status |
MEDLINE
|
|
pubmed:issn |
0026-1270
|
|
pubmed:author |
|
|
pubmed:issnType |
Print
|
|
pubmed:volume |
45
|
|
pubmed:owner |
NLM
|
|
pubmed:authorsComplete |
Y
|
|
pubmed:pagination |
557-63
|
|
pubmed:dateRevised |
2008-11-21
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|
pubmed:meshHeading |
|
|
pubmed:year |
2006
|
|
pubmed:articleTitle |
Deconfounding microarray analysis - independent measurements of cell type proportions used in a regression model to resolve tissue heterogeneity bias.
|
|
pubmed:affiliation |
Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany.
|
|
pubmed:publicationType |
Journal Article,
Research Support, Non-U.S. Gov't
|
