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Read depth ratio is defined as read depth at each site divided by median of read depth of that sequencing library. SomatiCA corrects GC bias for read depth ratio at each site based on a linear model described in Diskin et al.(2008).
● Data Source:
BioConductor
● Keywords: methods
● Alias: GCbiasRemoval
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The function downloads the .fa.gz of a given chromosome from UCSC genome browser and calculate the GC content for given window size.
● Data Source:
BioConductor
● Keywords: methods
● Alias: GCcount
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Take segments with subclonality characterization as input and merge same events.
● Data Source:
BioConductor
● Keywords: methods
● Alias: MergeSegment
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SomaticCNA is a software suite that is capable of identifying, characterizing, and quantifying somatic CNAs from cancer genome sequencing. First, it uses read depths and lesser allele frequencies (LAF) from mapped short sequence reads to segment the genome and identify candidate CNAs. Second, SomaticCNA estimates the admixture rate from the relative copy-number profile of tumor-normal pair by a Bayesian finite mixture model. Third, SomaticCNA quantifies absolute somatic copy-number and subclonality for each genomic segment to guide its characterization.
● Data Source:
BioConductor
● Keywords: package
● Alias: SomatiCA, SomatiCA-package
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Convert a data frame or read a file into SomatiCA input format. Remove missing values from input, check validity of data type and convert it into SomatiCA input format, which is a GRanges object.
● Data Source:
BioConductor
● Keywords: methods, ~~ other possible keyword(s) ~~
● Alias: SomatiCAFormat, SomatiCAFormat,GRanges-method, SomatiCAFormat,character-method, SomatiCAFormat,data.frame-method, SomatiCAFormat-methods
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Finds the location of the SomatiCA User's Guide and opens it.
● Data Source:
BioConductor
● Keywords: documentation
● Alias: SomatiCAUsersGuide
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First, it uses read depths and lesser allele frequencies (LAF) from mapped short sequence reads to segment the genome and identify candidate CNAs. Second, SomaticCNA estimates the admixture rate from the relative copy-number profile of tumor-normal pair by a Bayesian finite mixture model. Third, SomaticCNA quantifies somatic copy-number and subclonality for each genomic segment to guide its characterization.
● Data Source:
BioConductor
● Keywords: methods
● Alias: SomatiCApipe
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The estimation of the admixture rate is accomplished by fitting the input tumor somatic copy number (somatic ratio*2) of all segments with a Bayesian finite mixture model, with components centered at the discrete levels. Each segment was assigned with a discrete level based on corresponding posterior probability. Segments with ambiguous assignments will be classified as candidate subclonal events and excluded from admixture rate inference. The admixture rate will be estimated by an optimal solution contributed by explanation of tumor copy number with all remaining segments as integer level.
● Data Source:
BioConductor
● Keywords: methods
● Alias: admixtureRate
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Collapse measurements with a certain bin size.
● Data Source:
BioConductor
● Keywords: methods
● Alias: collapse
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Somatic copy number corrected by admixture rate.
● Data Source:
BioConductor
● Keywords: methods
● Alias: copynumberCorrected
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providing 
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