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Function impute.lowess [aCGH v1.50.0]
keywords
models
title
Imputing log2 ratios
description
Imputing log2 ratios
Function mergeFunc [aCGH v1.50.0]
keywords
models
title
Funtion to merge states based on their state means
description
mergeFunc takes the output of hmm.run.func (or find.hmm.states) with a particular model selection criterion and iteratively merges the states with means closer than a supplied threshold. mergeHmmStates is a frontend for mergeFunc using aCGH object.
Function findAmplif.func [aCGH v1.50.0]
keywords
models
title
Function to determine high level amplifications
description
This function identifies high level amplifications by considering the height, the width of an amplicon relative to the urrounding clones. Only narrow peaks much higher than its neigbors are considered as high level amplifications.
Function findOutliers.func [aCGH v1.50.0]
keywords
models
title
Function to identify outlier clones
description
The function identified the clones that are outliers.
Function states.hmm.func [aCGH v1.50.0]
keywords
models
title
A function to fit unsupervised Hidden Markov model
description
This function is a workhorse of find.hmm.states. It operates on the individual chromosomes/samples and is not called directly by users.
Function find.genomic.events [aCGH v1.50.0]
keywords
models
title
Finds the genomic events associated with each of the array CGH samples
description
Finds the genomic events associated with each of the array CGH samples. Events include whole chromosomal gains and losses, aberrations, transitions, amplifications and their respective counts and sizes. The hmm states has to be computed before using this function.
Function impute.HMM [aCGH v1.50.0]
keywords
models
title
Imputing log2 ratios using HMM
description
Imputing log2 ratios using the output of the HMM segmenttation
Function computeSD.func [aCGH v1.50.0]
keywords
models
title
Function to estimate experimental variability of a sample
description
This functions estimate experimental variability of a given sample. This value can be used to rank samples in terms of the quality as well as to derive thresholds for declaring gained and lost clones.
Function findTrans.func [aCGH v1.50.0]
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models
title
Funtion identifying the transitions
description
This function identifies the start and end of the states (regions with the constant estimated copy number).
Function find.hmm.states [aCGH v1.50.0]
keywords
models
title
Determines states of the clones
description
This function runs unsupervised HMM algorithm and produces the essentual state information which is used for the subsequent structure determination.