Dataset Information

Probabilistic analysis of gene expression measurements from heterogeneous tissues.

ABSTRACT:

Motivation

Tissue heterogeneity, arising from multiple cell types, is a major confounding factor in experiments that focus on studying cell types, e.g. their expression profiles, in isolation. Although sample heterogeneity can be addressed by manual microdissection, prior to conducting experiments, computational treatment on heterogeneous measurements have become a reliable alternative to perform this microdissection in silico. Favoring computation over manual purification has its advantages, such as time consumption, measuring responses of multiple cell types simultaneously, keeping samples intact of external perturbations and unaltered yield of molecular content.

Results

We formalize a probabilistic model, DSection, and show with simulations as well as with real microarray data that DSection attains increased modeling accuracy in terms of (i) estimating cell-type proportions of heterogeneous tissue samples, (ii) estimating replication variance and (iii) identifying differential expression across cell types under various experimental conditions. As our reference we use the corresponding linear regression model, which mirrors the performance of the majority of current non-probabilistic modeling approaches. AVAILABILITY AND SOFTWARE: All codes are written in Matlab, and are freely available upon request as well as at the project web page http://www.cs.tut.fi/∼erkkila2/. Furthermore, a web-application for DSection exists at http://informatics.systemsbiology.net/DSection.

Contact

timo.p.erkkila@tut.fi; harri.lahdesmaki@tut.fi

SUBMITTER: Erkkila T

PROVIDER: S-EPMC2951082 | biostudies-literature | 2010 Oct

REPOSITORIES: biostudies-literature

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Publications

Probabilistic analysis of gene expression measurements from heterogeneous tissues.

Erkkilä Timo T Lehmusvaara Saara S Ruusuvuori Pekka P Visakorpi Tapio T Shmulevich Ilya I Lähdesmäki Harri H

Bioinformatics (Oxford, England) 20100714 20

<h4>Motivation</h4>Tissue heterogeneity, arising from multiple cell types, is a major confounding factor in experiments that focus on studying cell types, e.g. their expression profiles, in isolation. Although sample heterogeneity can be addressed by manual microdissection, prior to conducting experiments, computational treatment on heterogeneous measurements have become a reliable alternative to perform this microdissection in silico. Favoring computation over manual purification has its advant ...[more]

PMID: 20631160

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Dataset Information

Probabilistic analysis of gene expression measurements from heterogeneous tissues.

Motivation

Results

Contact

Publications

Probabilistic analysis of gene expression measurements from heterogeneous tissues.

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