Rseg-an R package to optimize segmentation of SNP array data

Philippe Lamy; Carsten Wiuf; Torben F. Ørntoft; Claus L. Andersen

doi:10.1093/bioinformatics/btq668

Rseg-an R package to optimize segmentation of SNP array data

Philippe Lamy^*, Carsten Wiuf, Torben F. Ørntoft, Claus L. Andersen

^*Corresponding author for this work

4 Citations (Scopus)

Abstract

Summary: The use of high-density SNP arrays for investigating copy number alterations in clinical tumor samples, with intra tumor heterogeneity and varying degrees of normal cell contamination, imposes several problems for commonly used segmentation algorithms. This calls for flexibility when setting thresholds for calling gains and losses. In addition, sample normalization can induce artifacts in the copy-number ratios for the non-changed genomic elements in the tumor samples. Results: We present an open source R package, Rseg, which allows the user to define sample-specific thresholds to call gains and losses. It also allows the user to correct for normalization artifacts.

Original language	English
Article number	btq668
Journal	Bioinformatics
Volume	27
Issue number	3
Pages (from-to)	419-420
Number of pages	2
ISSN	1367-4803
DOIs	https://doi.org/10.1093/bioinformatics/btq668
Publication status	Published - 1 Feb 2011
Externally published	Yes

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title = "Rseg-an R package to optimize segmentation of SNP array data",

abstract = "Summary: The use of high-density SNP arrays for investigating copy number alterations in clinical tumor samples, with intra tumor heterogeneity and varying degrees of normal cell contamination, imposes several problems for commonly used segmentation algorithms. This calls for flexibility when setting thresholds for calling gains and losses. In addition, sample normalization can induce artifacts in the copy-number ratios for the non-changed genomic elements in the tumor samples. Results: We present an open source R package, Rseg, which allows the user to define sample-specific thresholds to call gains and losses. It also allows the user to correct for normalization artifacts.",

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AU - Andersen, Claus L.

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N2 - Summary: The use of high-density SNP arrays for investigating copy number alterations in clinical tumor samples, with intra tumor heterogeneity and varying degrees of normal cell contamination, imposes several problems for commonly used segmentation algorithms. This calls for flexibility when setting thresholds for calling gains and losses. In addition, sample normalization can induce artifacts in the copy-number ratios for the non-changed genomic elements in the tumor samples. Results: We present an open source R package, Rseg, which allows the user to define sample-specific thresholds to call gains and losses. It also allows the user to correct for normalization artifacts.

AB - Summary: The use of high-density SNP arrays for investigating copy number alterations in clinical tumor samples, with intra tumor heterogeneity and varying degrees of normal cell contamination, imposes several problems for commonly used segmentation algorithms. This calls for flexibility when setting thresholds for calling gains and losses. In addition, sample normalization can induce artifacts in the copy-number ratios for the non-changed genomic elements in the tumor samples. Results: We present an open source R package, Rseg, which allows the user to define sample-specific thresholds to call gains and losses. It also allows the user to correct for normalization artifacts.

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Rseg-an R package to optimize segmentation of SNP array data

Abstract

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