Format

Send to

Choose Destination
Bioinformatics. 2013 Sep 1;29(17):2211-2. doi: 10.1093/bioinformatics/btt351. Epub 2013 Jul 3.

CellMix: a comprehensive toolbox for gene expression deconvolution.

Author information

1
Computational Biology Group, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town, South Africa. renaud@cbio.uct.ac.za

Abstract

Gene expression data are typically generated from heterogeneous biological samples that are composed of multiple cell or tissue types, in varying proportions, each contributing to global gene expression. This heterogeneity is a major confounder in standard analysis such as differential expression analysis, where differences in the relative proportions of the constituent cells may prevent or bias the detection of cell-specific differences. Computational deconvolution of global gene expression is an appealing alternative to costly physical sample separation techniques and enables a more detailed analysis of the underlying biological processes at the cell-type level. To facilitate and popularize the application of such methods, we developed CellMix, an R package that incorporates most state-of-the-art deconvolution methods, into an intuitive and extendible framework, providing a single entry point to explore, assess and disentangle gene expression data from heterogeneous samples.

AVAILABILITY AND IMPLEMENTATION:

The CellMix package builds on R/BioConductor and is available from http://web.cbio.uct.ac.za/∼renaud/CRAN/web/CellMix. It is currently being submitted to BioConductor. The package's vignettes notably contain additional information, examples and references.

PMID:
23825367
DOI:
10.1093/bioinformatics/btt351
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Silverchair Information Systems
Loading ...
Support Center