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BMC Bioinformatics. 2018 Nov 7;19(1):408. doi: 10.1186/s12859-018-2442-5.

Deblender: a semi-/unsupervised multi-operational computational method for complete deconvolution of expression data from heterogeneous samples.

Author information

1
Centre for Cancer Biomarkers CCBIO, Department of Informatics, University of Bergen, Bergen, Norway.
2
Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.
3
Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, Section for Pathology, University of Bergen, Bergen, Norway.
4
Department of Pathology, Haukeland University Hospital, Bergen, Norway.
5
Centre for Cancer Biomarkers CCBIO, Department of Informatics, University of Bergen, Bergen, Norway. Inge.Jonassen@uib.no.
6
Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway. Inge.Jonassen@uib.no.

Abstract

BACKGROUND:

Towards discovering robust cancer biomarkers, it is imperative to unravel the cellular heterogeneity of patient samples and comprehend the interactions between cancer cells and the various cell types in the tumor microenvironment. The first generation of 'partial' computational deconvolution methods required prior information either on the cell/tissue type proportions or the cell/tissue type-specific expression signatures and the number of involved cell/tissue types. The second generation of 'complete' approaches allowed estimating both of the cell/tissue type proportions and cell/tissue type-specific expression profiles directly from the mixed gene expression data, based on known (or automatically identified) cell/tissue type-specific marker genes.

RESULTS:

We present Deblender, a flexible complete deconvolution tool operating in semi-/unsupervised mode based on the user's access to known marker gene lists and information about cell/tissue composition. In case of no prior knowledge, global gene expression variability is used in clustering the mixed data to substitute marker sets with cluster sets. In addition, we integrate a model selection criterion to predict the number of constituent cell/tissue types. Moreover, we provide a tailored algorithmic scheme to estimate mixture proportions for realistic experimental cases where the number of involved cell/tissue types exceeds the number of mixed samples. We assess the performance of Deblender and a set of state-of-the-art existing tools on a comprehensive set of benchmark and patient cancer mixture expression datasets (including TCGA).

CONCLUSION:

Our results corroborate that Deblender can be a valuable tool to improve understanding of gene expression datasets with implications for prediction and clinical utilization. Deblender is implemented in MATLAB and is available from ( https://github.com/kondim1983/Deblender/ ).

KEYWORDS:

Cellular heterogeneity; Clustering; Deconvolution; Gene expression; Matrix factorization; Model selection; Particle swarm; Quadratic programming

PMID:
30404611
PMCID:
PMC6223087
DOI:
10.1186/s12859-018-2442-5
[Indexed for MEDLINE]
Free PMC Article

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