| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 5911181 | Infection, Genetics and Evolution | 2012 | 9 Pages |
Abstract
⺠Sample heterogeneity is an issue inherent in many gene expression studies. ⺠Computational deconvolution is an attractive alternative to laboratory techniques. ⺠Unsupervised deconvolution methods cannot easily extract relevant signatures. ⺠We propose a semi-supervised approach that uses markers to improve estimation. ⺠Application to a real dataset showed its potential to provide more meaningful results.
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Authors
Renaud Gaujoux, Cathal Seoighe,