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Accounting for sources of bias and uncertainty in copy number-based statistical deconvolution of heterogeneous tumour samples

bioRxiv

Apr 30, 2014

Christopher Yau

Abstract

Deconvolving heterogeneous tumour samples to identify constituent cell populations with differing copy number profiles using whole genome sequencing data is a challenging problem. Copy number calling algorithms have differential detection rates for different sizes and classes of copy nu...read more

Mentioned in this Paper

Detection
Clone
Neoplasms
Whole Genome Sequencing
Size
Classification
Simulation
Population Group
2
10
Paper Details
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Accounting for sources of bias and uncertainty in copy number-based statistical deconvolution of heterogeneous tumour samples

bioRxiv

Apr 30, 2014

Christopher Yau

PMID: 990004655

DOI: 10.1101/004655

Abstract

Deconvolving heterogeneous tumour samples to identify constituent cell populations with differing copy number profiles using whole genome sequencing data is a challenging problem. Copy number calling algorithms have differential detection rates for different sizes and classes of copy nu...read more

Mentioned in this Paper

Detection
Clone
Neoplasms
Whole Genome Sequencing
Size
Classification
Simulation
Population Group
2
10
Paper Details
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  • Citations
  • finger pointing at paper

    References currently unavailable

    We're still populating references for this paper, please check back later.
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