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62 lines
2.1 KiB
R
62 lines
2.1 KiB
R
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CT_GA_count <- function(SampleID,Ref_Base,Alt_Base){
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# #<---------------------------->
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# # You must include this section when:
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# # Distributing, Using and/or Modifying this code.
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# # Please read and abide by the terms of the included LICENSE.
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# # Copyright 2020, Deepankar Chakroborty, All rights reserved.
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# #
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# # Author : Deepankar Chakroborty (https://github.com/dchakro)
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# # Report issues: https://github.com/dchakro/shared_Rscripts/issues
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# # License: https://github.com/dchakro/shared_Rscripts/blob/master/LICENSE
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# #<---------------------------->
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# # PURPOSE:
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# # This function takes in three vectors:
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# # SampleID = Sample IDs
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# # Ref_Base = Reference Base
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# # Alt_Base = altered base that created the mutation.
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# # And calculates the number of C > T and G > A changes are there (per sample)
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# # The function returns a data frame listing the number of mutations (per sample):
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# # SampleID = Sample ID
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# # Total = Total number of mutations
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# # CT = C > T changes
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# # GA = G > A changes
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# # Others = all other types of transitions and transversions combined.
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MutMatrix <- data.frame(SampleID,
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Ref_Base,
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Alt_Base,
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stringsAsFactors = F)
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return.df <- data.frame(SampleID=NA,
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Total=0,
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CT=0,
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GA=0,
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Others=0)
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for(SampleID in levels(MutMatrix$SampleID)){
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set <- MutMatrix[ MutMatrix$SampleID == SampleID, ]
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# if(dim(set)[1]==0){
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# return.df <- rbind.data.frame(return.df,c(SampleID,0,0,0,0))
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# next
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# }
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Total <- dim(set)[1]
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CT <- dim(subset(set, set$Ref_Base == "C" & set$Alt_Base == "T"))[1]
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GA <- dim(subset(set, set$Ref_Base == "G" & set$Alt_Base == "A"))[1]
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Others=Total-CT-GA
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return.df <- rbind.data.frame(return.df,list(SampleID,Total,CT,GA,Others),stringsAsFactors = F)
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Total <- 0;CT <- 0;GA <- 0;Others <- 0 # re-initialize
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rm(set)
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}
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return(return.df[-1,]) # Removes the first empty row
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}
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