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Literature DB >> 11473019

Separation of samples into their constituents using gene expression data.

D Venet¹, F Pecasse, C Maenhaut, H Bersini.

Abstract

Gene expression measurements are a powerful tool in molecular biology, but when applied to heterogeneous samples containing more than one cellular type the results are difficult to interpret. We present here a new approach to this problem allowing to deduce the gene expression profile of the various cellular types contained in a set of samples directly from the measurements taken on the whole sample.

Mesh：

Substances：
RNA, Messenger

Year: 2001 PMID： 11473019 DOI： 10.1093/bioinformatics/17.suppl_1.s279

Source DB: PubMed Journal: Bioinformatics ISSN： 1367-4803 Impact factor: 6.937

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Cited

55 in total

1. Population-specific expression analysis (PSEA) reveals molecular changes in diseased brain.

Authors: Alexandre Kuhn; Doris Thu; Henry J Waldvogel; Richard L M Faull; Ruth Luthi-Carter
Journal: Nat Methods Date: 2011-10-09 Impact factor: 28.547

2. MMAD: microarray microdissection with analysis of differences is a computational tool for deconvoluting cell type-specific contributions from tissue samples.

Authors: David A Liebner; Kun Huang; Jeffrey D Parvin
Journal: Bioinformatics Date: 2013-10-01 Impact factor: 6.937

3. Deconvoluting complex tissues for expression quantitative trait locus-based analyses.

Authors: Ji-Heui Seo; Qiyuan Li; Aquila Fatima; Aron Eklund; Zoltan Szallasi; Kornelia Polyak; Andrea L Richardson; Matthew L Freedman
Journal: Philos Trans R Soc Lond B Biol Sci Date: 2013-05-06 Impact factor: 6.237

Review 4. An assessment of computational methods for estimating purity and clonality using genomic data derived from heterogeneous tumor tissue samples.

Authors: Vinod Kumar Yadav; Subhajyoti De
Journal: Brief Bioinform Date: 2014-02-20 Impact factor: 11.622

Separation of samples into their constituents using gene expression data.

1. Population-specific expression analysis (PSEA) reveals molecular changes in diseased brain.

2. MMAD: microarray microdissection with analysis of differences is a computational tool for deconvoluting cell type-specific contributions from tissue samples.

3. Deconvoluting complex tissues for expression quantitative trait locus-based analyses.

Review 4. An assessment of computational methods for estimating purity and clonality using genomic data derived from heterogeneous tumor tissue samples.

5. Statistical expression deconvolution from mixed tissue samples.

6. Complex Sources of Variation in Tissue Expression Data: Analysis of the GTEx Lung Transcriptome.

7. Epigenomic Deconvolution of Breast Tumors Reveals Metabolic Coupling between Constituent Cell Types.

8. Probabilistic analysis of gene expression measurements from heterogeneous tissues.

9. DeMix: deconvolution for mixed cancer transcriptomes using raw measured data.

10. A prototype tobacco-associated oral squamous cell carcinoma classifier using RNA from brush cytology.