Add to ORKGAbstract: An algorithm to reduce multi-sample array CGH data from thousands of clones to tens or hundreds of clone regions is introduced. This reduction of the data is performed such that little information is lost, which is possible due to the high dependencies between neighboring clones. The algorithm is explained using a small example. The potential beneficial effects of the algorithm for downstream analysis are illustrated by re-analysis of previously published colorectal cancer data. Using multiple testing corrections suitable for these data, we provide statistical evidence for genomic differences on several clone regions between MSI+ and CIN+ tumors. The algorithm, named CGHregions, is available as an easy-to-use script in R
Motivation: Array comparative genomic hybridization (aCGH) provides a genome- wide technique to scre...
BACKGROUND: Array CGH (Comparative Genomic Hybridisation) is a molecular cytogenetic technique for t...
We propose a new approach for clustering DNA features using array CGH data from multiple tumor sampl...
An algorithm to reduce multi-sample array CGH data from thousands of clones to tens or hundreds of c...
The aim of the study is comparative analysis of algorithms for identifying copy number variation in ...
Motivation: Genomic DNA regions are frequently lost or gained during tumor progression. Array Compar...
Background: Microarray-CGH experiments are used to detect and map chromosomal imbalances, by hybridi...
Motivation: Genomic instability is one of the fundamental factors in tumorigenesis and tumor progres...
Motivation: Genome analysis has become one of the most important tools for understanding the complex...
Summary: CGHcall achieves high calling accuracy for array CGH data by effective use of breakpoint in...
International audienceWe propose a new approach for clustering DNA features using array CGH data fro...
International audienceAlthough Next Generation Sequencing technologies are becoming the new referenc...
In cancer research, prediction of time to death or relapse is important for a meaningful tumor class...
In cancer research, prediction of time to death or relapse is important for a meaningful tumor clas...
Summary: CGHcall achieves high calling accuracy for array CGH data by effective use of breakpoint in...
Motivation: Array comparative genomic hybridization (aCGH) provides a genome- wide technique to scre...
BACKGROUND: Array CGH (Comparative Genomic Hybridisation) is a molecular cytogenetic technique for t...
We propose a new approach for clustering DNA features using array CGH data from multiple tumor sampl...
An algorithm to reduce multi-sample array CGH data from thousands of clones to tens or hundreds of c...
The aim of the study is comparative analysis of algorithms for identifying copy number variation in ...
Motivation: Genomic DNA regions are frequently lost or gained during tumor progression. Array Compar...
Background: Microarray-CGH experiments are used to detect and map chromosomal imbalances, by hybridi...
Motivation: Genomic instability is one of the fundamental factors in tumorigenesis and tumor progres...
Motivation: Genome analysis has become one of the most important tools for understanding the complex...
Summary: CGHcall achieves high calling accuracy for array CGH data by effective use of breakpoint in...
International audienceWe propose a new approach for clustering DNA features using array CGH data fro...
International audienceAlthough Next Generation Sequencing technologies are becoming the new referenc...
In cancer research, prediction of time to death or relapse is important for a meaningful tumor class...
In cancer research, prediction of time to death or relapse is important for a meaningful tumor clas...
Summary: CGHcall achieves high calling accuracy for array CGH data by effective use of breakpoint in...
Motivation: Array comparative genomic hybridization (aCGH) provides a genome- wide technique to scre...
BACKGROUND: Array CGH (Comparative Genomic Hybridisation) is a molecular cytogenetic technique for t...
We propose a new approach for clustering DNA features using array CGH data from multiple tumor sampl...