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1 #########################################################################################
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2 # License Agreement
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3 #
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4 # THIS WORK IS PROVIDED UNDER THE TERMS OF THIS CREATIVE COMMONS PUBLIC LICENSE
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5 # ("CCPL" OR "LICENSE"). THE WORK IS PROTECTED BY COPYRIGHT AND/OR OTHER
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6 # APPLICABLE LAW. ANY USE OF THE WORK OTHER THAN AS AUTHORIZED UNDER THIS LICENSE
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7 # OR COPYRIGHT LAW IS PROHIBITED.
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8 #
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9 # BY EXERCISING ANY RIGHTS TO THE WORK PROVIDED HERE, YOU ACCEPT AND AGREE TO BE
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10 # BOUND BY THE TERMS OF THIS LICENSE. TO THE EXTENT THIS LICENSE MAY BE CONSIDERED
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11 # TO BE A CONTRACT, THE LICENSOR GRANTS YOU THE RIGHTS CONTAINED HERE IN
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12 # CONSIDERATION OF YOUR ACCEPTANCE OF SUCH TERMS AND CONDITIONS.
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13 #
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14 # BASELIne: Bayesian Estimation of Antigen-Driven Selection in Immunoglobulin Sequences
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15 # Coded by: Mohamed Uduman & Gur Yaari
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16 # Copyright 2012 Kleinstein Lab
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17 # Version: 1.3 (01/23/2014)
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18 #########################################################################################
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19
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20 # Global variables
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21
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22 FILTER_BY_MUTATIONS = 1000
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23
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24 # Nucleotides
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25 NUCLEOTIDES = c("A","C","G","T")
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26
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27 # Amino Acids
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28 AMINO_ACIDS <- c("F", "F", "L", "L", "S", "S", "S", "S", "Y", "Y", "*", "*", "C", "C", "*", "W", "L", "L", "L", "L", "P", "P", "P", "P", "H", "H", "Q", "Q", "R", "R", "R", "R", "I", "I", "I", "M", "T", "T", "T", "T", "N", "N", "K", "K", "S", "S", "R", "R", "V", "V", "V", "V", "A", "A", "A", "A", "D", "D", "E", "E", "G", "G", "G", "G")
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29 names(AMINO_ACIDS) <- c("TTT", "TTC", "TTA", "TTG", "TCT", "TCC", "TCA", "TCG", "TAT", "TAC", "TAA", "TAG", "TGT", "TGC", "TGA", "TGG", "CTT", "CTC", "CTA", "CTG", "CCT", "CCC", "CCA", "CCG", "CAT", "CAC", "CAA", "CAG", "CGT", "CGC", "CGA", "CGG", "ATT", "ATC", "ATA", "ATG", "ACT", "ACC", "ACA", "ACG", "AAT", "AAC", "AAA", "AAG", "AGT", "AGC", "AGA", "AGG", "GTT", "GTC", "GTA", "GTG", "GCT", "GCC", "GCA", "GCG", "GAT", "GAC", "GAA", "GAG", "GGT", "GGC", "GGA", "GGG")
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30 names(AMINO_ACIDS) <- names(AMINO_ACIDS)
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31
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32 #Amino Acid Traits
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33 #"*" "A" "C" "D" "E" "F" "G" "H" "I" "K" "L" "M" "N" "P" "Q" "R" "S" "T" "V" "W" "Y"
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34 #B = "Hydrophobic/Burried" N = "Intermediate/Neutral" S="Hydrophilic/Surface")
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35 TRAITS_AMINO_ACIDS_CHOTHIA98 <- c("*","N","B","S","S","B","N","N","B","S","B","B","S","N","S","S","N","N","B","B","N")
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36 names(TRAITS_AMINO_ACIDS_CHOTHIA98) <- sort(unique(AMINO_ACIDS))
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37 TRAITS_AMINO_ACIDS <- array(NA,21)
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38
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39 # Codon Table
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40 CODON_TABLE <- as.data.frame(matrix(NA,ncol=64,nrow=12))
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41
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42 # Substitution Model: Smith DS et al. 1996
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43 substitution_Literature_Mouse <- matrix(c(0, 0.156222928, 0.601501588, 0.242275484, 0.172506739, 0, 0.241239892, 0.586253369, 0.54636291, 0.255795364, 0, 0.197841727, 0.290240811, 0.467680608, 0.24207858, 0),nrow=4,byrow=T,dimnames=list(NUCLEOTIDES,NUCLEOTIDES))
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44 substitution_Flu_Human <- matrix(c(0,0.2795596,0.5026927,0.2177477,0.1693210,0,0.3264723,0.5042067,0.4983549,0.3328321,0,0.1688130,0.2021079,0.4696077,0.3282844,0),4,4,byrow=T,dimnames=list(NUCLEOTIDES,NUCLEOTIDES))
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45 substitution_Flu25_Human <- matrix(c(0,0.2580641,0.5163685,0.2255674,0.1541125,0,0.3210224,0.5248651,0.5239281,0.3101292,0,0.1659427,0.1997207,0.4579444,0.3423350,0),4,4,byrow=T,dimnames=list(NUCLEOTIDES,NUCLEOTIDES))
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46 load("FiveS_Substitution.RData")
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47
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48 # Mutability Models: Shapiro GS et al. 2002
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49 triMutability_Literature_Human <- matrix(c(0.24, 1.2, 0.96, 0.43, 2.14, 2, 1.11, 1.9, 0.85, 1.83, 2.36, 1.31, 0.82, 0.52, 0.89, 1.33, 1.4, 0.82, 1.83, 0.73, 1.83, 1.62, 1.53, 0.57, 0.92, 0.42, 0.42, 1.47, 3.44, 2.58, 1.18, 0.47, 0.39, 1.12, 1.8, 0.68, 0.47, 2.19, 2.35, 2.19, 1.05, 1.84, 1.26, 0.28, 0.98, 2.37, 0.66, 1.58, 0.67, 0.92, 1.76, 0.83, 0.97, 0.56, 0.75, 0.62, 2.26, 0.62, 0.74, 1.11, 1.16, 0.61, 0.88, 0.67, 0.37, 0.07, 1.08, 0.46, 0.31, 0.94, 0.62, 0.57, 0.29, NA, 1.44, 0.46, 0.69, 0.57, 0.24, 0.37, 1.1, 0.99, 1.39, 0.6, 2.26, 1.24, 1.36, 0.52, 0.33, 0.26, 1.25, 0.37, 0.58, 1.03, 1.2, 0.34, 0.49, 0.33, 2.62, 0.16, 0.4, 0.16, 0.35, 0.75, 1.85, 0.94, 1.61, 0.85, 2.09, 1.39, 0.3, 0.52, 1.33, 0.29, 0.51, 0.26, 0.51, 3.83, 2.01, 0.71, 0.58, 0.62, 1.07, 0.28, 1.2, 0.74, 0.25, 0.59, 1.09, 0.91, 1.36, 0.45, 2.89, 1.27, 3.7, 0.69, 0.28, 0.41, 1.17, 0.56, 0.93, 3.41, 1, 1, NA, 5.9, 0.74, 2.51, 2.24, 2.24, 1.95, 3.32, 2.34, 1.3, 2.3, 1, 0.66, 0.73, 0.93, 0.41, 0.65, 0.89, 0.65, 0.32, NA, 0.43, 0.85, 0.43, 0.31, 0.31, 0.23, 0.29, 0.57, 0.71, 0.48, 0.44, 0.76, 0.51, 1.7, 0.85, 0.74, 2.23, 2.08, 1.16, 0.51, 0.51, 1, 0.5, NA, NA, 0.71, 2.14), nrow=64,byrow=T)
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50 triMutability_Literature_Mouse <- matrix(c(1.31, 1.35, 1.42, 1.18, 2.02, 2.02, 1.02, 1.61, 1.99, 1.42, 2.01, 1.03, 2.02, 0.97, 0.53, 0.71, 1.19, 0.83, 0.96, 0.96, 0, 1.7, 2.22, 0.59, 1.24, 1.07, 0.51, 1.68, 3.36, 3.36, 1.14, 0.29, 0.33, 0.9, 1.11, 0.63, 1.08, 2.07, 2.27, 1.74, 0.22, 1.19, 2.37, 1.15, 1.15, 1.56, 0.81, 0.34, 0.87, 0.79, 2.13, 0.49, 0.85, 0.97, 0.36, 0.82, 0.66, 0.63, 1.15, 0.94, 0.85, 0.25, 0.93, 1.19, 0.4, 0.2, 0.44, 0.44, 0.88, 1.06, 0.77, 0.39, 0, 0, 0, 0, 0, 0, 0.43, 0.43, 0.86, 0.59, 0.59, 0, 1.18, 0.86, 2.9, 1.66, 0.4, 0.2, 1.54, 0.43, 0.69, 1.71, 0.68, 0.55, 0.91, 0.7, 1.71, 0.09, 0.27, 0.63, 0.2, 0.45, 1.01, 1.63, 0.96, 1.48, 2.18, 1.2, 1.31, 0.66, 2.13, 0.49, 0, 0, 0, 2.97, 2.8, 0.79, 0.4, 0.5, 0.4, 0.11, 1.68, 0.42, 0.13, 0.44, 0.93, 0.71, 1.11, 1.19, 2.71, 1.08, 3.43, 0.4, 0.67, 0.47, 1.02, 0.14, 1.56, 1.98, 0.53, 0.33, 0.63, 2.06, 1.77, 1.46, 3.74, 2.93, 2.1, 2.18, 0.78, 0.73, 2.93, 0.63, 0.57, 0.17, 0.85, 0.52, 0.31, 0.31, 0, 0, 0.51, 0.29, 0.83, 0.54, 0.28, 0.47, 0.9, 0.99, 1.24, 2.47, 0.73, 0.23, 1.13, 0.24, 2.12, 0.24, 0.33, 0.83, 1.41, 0.62, 0.28, 0.35, 0.77, 0.17, 0.72, 0.58, 0.45, 0.41), nrow=64,byrow=T)
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51 triMutability_Names <- c("AAA", "AAC", "AAG", "AAT", "ACA", "ACC", "ACG", "ACT", "AGA", "AGC", "AGG", "AGT", "ATA", "ATC", "ATG", "ATT", "CAA", "CAC", "CAG", "CAT", "CCA", "CCC", "CCG", "CCT", "CGA", "CGC", "CGG", "CGT", "CTA", "CTC", "CTG", "CTT", "GAA", "GAC", "GAG", "GAT", "GCA", "GCC", "GCG", "GCT", "GGA", "GGC", "GGG", "GGT", "GTA", "GTC", "GTG", "GTT", "TAA", "TAC", "TAG", "TAT", "TCA", "TCC", "TCG", "TCT", "TGA", "TGC", "TGG", "TGT", "TTA", "TTC", "TTG", "TTT")
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52 load("FiveS_Mutability.RData")
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53
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54 # Functions
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55
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56 # Translate codon to amino acid
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57 translateCodonToAminoAcid<-function(Codon){
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58 return(AMINO_ACIDS[Codon])
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59 }
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60
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61 # Translate amino acid to trait change
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62 translateAminoAcidToTraitChange<-function(AminoAcid){
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63 return(TRAITS_AMINO_ACIDS[AminoAcid])
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64 }
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65
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66 # Initialize Amino Acid Trait Changes
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67 initializeTraitChange <- function(traitChangeModel=1,species=1,traitChangeFileName=NULL){
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68 if(!is.null(traitChangeFileName)){
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69 tryCatch(
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70 traitChange <- read.delim(traitChangeFileName,sep="\t",header=T)
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71 , error = function(ex){
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72 cat("Error|Error reading trait changes. Please check file name/path and format.\n")
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73 q()
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74 }
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75 )
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76 }else{
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77 traitChange <- TRAITS_AMINO_ACIDS_CHOTHIA98
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78 }
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79 TRAITS_AMINO_ACIDS <<- traitChange
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80 }
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81
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82 # Read in formatted nucleotide substitution matrix
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83 initializeSubstitutionMatrix <- function(substitutionModel,species,subsMatFileName=NULL){
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84 if(!is.null(subsMatFileName)){
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85 tryCatch(
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86 subsMat <- read.delim(subsMatFileName,sep="\t",header=T)
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87 , error = function(ex){
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88 cat("Error|Error reading substitution matrix. Please check file name/path and format.\n")
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89 q()
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90 }
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91 )
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92 if(sum(apply(subsMat,1,sum)==1)!=4) subsMat = t(apply(subsMat,1,function(x)x/sum(x)))
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93 }else{
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94 if(substitutionModel==1)subsMat <- substitution_Literature_Mouse
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95 if(substitutionModel==2)subsMat <- substitution_Flu_Human
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96 if(substitutionModel==3)subsMat <- substitution_Flu25_Human
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97
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98 }
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99
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100 if(substitutionModel==0){
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101 subsMat <- matrix(1,4,4)
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102 subsMat[,] = 1/3
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103 subsMat[1,1] = 0
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104 subsMat[2,2] = 0
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105 subsMat[3,3] = 0
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106 subsMat[4,4] = 0
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107 }
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108
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109
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110 NUCLEOTIDESN = c(NUCLEOTIDES,"N", "-")
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111 if(substitutionModel==5){
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112 subsMat <- FiveS_Substitution
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113 return(subsMat)
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114 }else{
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115 subsMat <- rbind(subsMat,rep(NA,4),rep(NA,4))
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116 return( matrix(data.matrix(subsMat),6,4,dimnames=list(NUCLEOTIDESN,NUCLEOTIDES) ) )
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117 }
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118 }
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119
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120
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121 # Read in formatted Mutability file
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122 initializeMutabilityMatrix <- function(mutabilityModel=1, species=1,mutabilityMatFileName=NULL){
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123 if(!is.null(mutabilityMatFileName)){
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124 tryCatch(
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125 mutabilityMat <- read.delim(mutabilityMatFileName,sep="\t",header=T)
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126 , error = function(ex){
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127 cat("Error|Error reading mutability matrix. Please check file name/path and format.\n")
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128 q()
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129 }
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130 )
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131 }else{
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132 mutabilityMat <- triMutability_Literature_Human
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133 if(species==2) mutabilityMat <- triMutability_Literature_Mouse
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134 }
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135
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136 if(mutabilityModel==0){ mutabilityMat <- matrix(1,64,3)}
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137
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138 if(mutabilityModel==5){
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139 mutabilityMat <- FiveS_Mutability
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140 return(mutabilityMat)
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141 }else{
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142 return( matrix( data.matrix(mutabilityMat), 64, 3, dimnames=list(triMutability_Names,1:3)) )
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143 }
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144 }
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145
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146 # Read FASTA file formats
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147 # Modified from read.fasta from the seqinR package
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148 baseline.read.fasta <-
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149 function (file = system.file("sequences/sample.fasta", package = "seqinr"),
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150 seqtype = c("DNA", "AA"), as.string = FALSE, forceDNAtolower = TRUE,
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151 set.attributes = TRUE, legacy.mode = TRUE, seqonly = FALSE,
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152 strip.desc = FALSE, sizeof.longlong = .Machine$sizeof.longlong,
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153 endian = .Platform$endian, apply.mask = TRUE)
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154 {
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155 seqtype <- match.arg(seqtype)
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156
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157 lines <- readLines(file)
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158
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159 if (legacy.mode) {
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160 comments <- grep("^;", lines)
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161 if (length(comments) > 0)
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162 lines <- lines[-comments]
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163 }
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164
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165
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166 ind_groups<-which(substr(lines, 1L, 3L) == ">>>")
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167 lines_mod<-lines
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168
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169 if(!length(ind_groups)){
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170 lines_mod<-c(">>>All sequences combined",lines)
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171 }
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172
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173 ind_groups<-which(substr(lines_mod, 1L, 3L) == ">>>")
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174
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175 lines <- array("BLA",dim=(length(ind_groups)+length(lines_mod)))
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176 id<-sapply(1:length(ind_groups),function(i)ind_groups[i]+i-1)+1
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177 lines[id] <- "THIS IS A FAKE SEQUENCE"
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178 lines[-id] <- lines_mod
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179 rm(lines_mod)
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180
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181 ind <- which(substr(lines, 1L, 1L) == ">")
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182 nseq <- length(ind)
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183 if (nseq == 0) {
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184 stop("no line starting with a > character found")
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185 }
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186 start <- ind + 1
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187 end <- ind - 1
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188
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189 while( any(which(ind%in%end)) ){
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190 ind=ind[-which(ind%in%end)]
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191 nseq <- length(ind)
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192 if (nseq == 0) {
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193 stop("no line starting with a > character found")
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194 }
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195 start <- ind + 1
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196 end <- ind - 1
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197 }
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198
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199 end <- c(end[-1], length(lines))
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200 sequences <- lapply(seq_len(nseq), function(i) paste(lines[start[i]:end[i]], collapse = ""))
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201 if (seqonly)
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202 return(sequences)
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203 nomseq <- lapply(seq_len(nseq), function(i) {
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204
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205 #firstword <- strsplit(lines[ind[i]], " ")[[1]][1]
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206 substr(lines[ind[i]], 2, nchar(lines[ind[i]]))
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207
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208 })
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209 if (seqtype == "DNA") {
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210 if (forceDNAtolower) {
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211 sequences <- as.list(tolower(chartr(".","-",sequences)))
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212 }else{
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213 sequences <- as.list(toupper(chartr(".","-",sequences)))
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214 }
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215 }
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216 if (as.string == FALSE)
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217 sequences <- lapply(sequences, s2c)
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218 if (set.attributes) {
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219 for (i in seq_len(nseq)) {
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220 Annot <- lines[ind[i]]
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221 if (strip.desc)
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222 Annot <- substr(Annot, 2L, nchar(Annot))
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223 attributes(sequences[[i]]) <- list(name = nomseq[[i]],
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224 Annot = Annot, class = switch(seqtype, AA = "SeqFastaAA",
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225 DNA = "SeqFastadna"))
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226 }
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227 }
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228 names(sequences) <- nomseq
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229 return(sequences)
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230 }
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231
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232
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233 # Replaces non FASTA characters in input files with N
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234 replaceNonFASTAChars <-function(inSeq="ACGTN-AApA"){
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235 gsub('[^ACGTNacgt[:punct:]-[:punct:].]','N',inSeq,perl=TRUE)
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236 }
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237
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238 # Find the germlines in the FASTA list
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239 germlinesInFile <- function(seqIDs){
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240 firstChar = sapply(seqIDs,function(x){substr(x,1,1)})
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241 secondChar = sapply(seqIDs,function(x){substr(x,2,2)})
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242 return(firstChar==">" & secondChar!=">")
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243 }
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244
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245 # Find the groups in the FASTA list
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246 groupsInFile <- function(seqIDs){
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247 sapply(seqIDs,function(x){substr(x,1,2)})==">>"
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248 }
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249
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250 # In the process of finding germlines/groups, expand from the start to end of the group
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251 expandTillNext <- function(vecPosToID){
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252 IDs = names(vecPosToID)
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253 posOfInterests = which(vecPosToID)
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254
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255 expandedID = rep(NA,length(IDs))
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256 expandedIDNames = gsub(">","",IDs[posOfInterests])
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257 startIndexes = c(1,posOfInterests[-1])
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258 stopIndexes = c(posOfInterests[-1]-1,length(IDs))
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259 expandedID = unlist(sapply(1:length(startIndexes),function(i){
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260 rep(i,stopIndexes[i]-startIndexes[i]+1)
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261 }))
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262 names(expandedID) = unlist(sapply(1:length(startIndexes),function(i){
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263 rep(expandedIDNames[i],stopIndexes[i]-startIndexes[i]+1)
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264 }))
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265 return(expandedID)
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266 }
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267
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268 # Process FASTA (list) to return a matrix[input, germline)
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|
|
269 processInputAdvanced <- function(inputFASTA){
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270
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|
271 seqIDs = names(inputFASTA)
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|
|
272 numbSeqs = length(seqIDs)
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|
273 posGermlines1 = germlinesInFile(seqIDs)
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274 numbGermlines = sum(posGermlines1)
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275 posGroups1 = groupsInFile(seqIDs)
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276 numbGroups = sum(posGroups1)
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277 consDef = NA
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278
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|
|
279 if(numbGermlines==0){
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280 posGermlines = 2
|
|
|
281 numbGermlines = 1
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|
|
282 }
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283
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|
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284 glPositionsSum = cumsum(posGermlines1)
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285 glPositions = table(glPositionsSum)
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286 #Find the position of the conservation row
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287 consDefPos = as.numeric(names(glPositions[names(glPositions)!=0 & glPositions==1]))+1
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|
|
288 if( length(consDefPos)> 0 ){
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289 consDefID = match(consDefPos, glPositionsSum)
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290 #The coservation rows need to be pulled out and stores seperately
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291 consDef = inputFASTA[consDefID]
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292 inputFASTA = inputFASTA[-consDefID]
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293
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294 seqIDs = names(inputFASTA)
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295 numbSeqs = length(seqIDs)
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296 posGermlines1 = germlinesInFile(seqIDs)
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297 numbGermlines = sum(posGermlines1)
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298 posGroups1 = groupsInFile(seqIDs)
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299 numbGroups = sum(posGroups1)
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300 if(numbGermlines==0){
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301 posGermlines = 2
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302 numbGermlines = 1
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303 }
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304 }
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305
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306 posGroups <- expandTillNext(posGroups1)
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307 posGermlines <- expandTillNext(posGermlines1)
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308 posGermlines[posGroups1] = 0
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309 names(posGermlines)[posGroups1] = names(posGroups)[posGroups1]
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310 posInput = rep(TRUE,numbSeqs)
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311 posInput[posGroups1 | posGermlines1] = FALSE
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312
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313 matInput = matrix(NA, nrow=sum(posInput), ncol=2)
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314 rownames(matInput) = seqIDs[posInput]
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315 colnames(matInput) = c("Input","Germline")
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316
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317 vecInputFASTA = unlist(inputFASTA)
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318 matInput[,1] = vecInputFASTA[posInput]
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319 matInput[,2] = vecInputFASTA[ which( names(inputFASTA)%in%paste(">",names(posGermlines)[posInput],sep="") )[ posGermlines[posInput]] ]
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320
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321 germlines = posGermlines[posInput]
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322 groups = posGroups[posInput]
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323
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324 return( list("matInput"=matInput, "germlines"=germlines, "groups"=groups, "conservationDefinition"=consDef ))
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|
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325 }
|
|
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326
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327
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|
328 # Replace leading and trailing dashes in the sequence
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329 replaceLeadingTrailingDashes <- function(x,readEnd){
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330 iiGap = unlist(gregexpr("-",x[1]))
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331 ggGap = unlist(gregexpr("-",x[2]))
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332 #posToChange = intersect(iiGap,ggGap)
|
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333
|
|
|
334
|
|
|
335 seqIn = replaceLeadingTrailingDashesHelper(x[1])
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|
|
336 seqGL = replaceLeadingTrailingDashesHelper(x[2])
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|
337 seqTemplate = rep('N',readEnd)
|
|
|
338 seqIn <- c(seqIn,seqTemplate[(length(seqIn)+1):readEnd])
|
|
|
339 seqGL <- c(seqGL,seqTemplate[(length(seqGL)+1):readEnd])
|
|
|
340 # if(posToChange!=-1){
|
|
|
341 # seqIn[posToChange] = "-"
|
|
|
342 # seqGL[posToChange] = "-"
|
|
|
343 # }
|
|
|
344
|
|
|
345 seqIn = c2s(seqIn[1:readEnd])
|
|
|
346 seqGL = c2s(seqGL[1:readEnd])
|
|
|
347
|
|
|
348 lenGL = nchar(seqGL)
|
|
|
349 if(lenGL<readEnd){
|
|
|
350 seqGL = paste(seqGL,c2s(rep("N",readEnd-lenGL)),sep="")
|
|
|
351 }
|
|
|
352
|
|
|
353 lenInput = nchar(seqIn)
|
|
|
354 if(lenInput<readEnd){
|
|
|
355 seqIn = paste(seqIn,c2s(rep("N",readEnd-lenInput)),sep="")
|
|
|
356 }
|
|
|
357 return( c(seqIn,seqGL) )
|
|
|
358 }
|
|
|
359
|
|
|
360 replaceLeadingTrailingDashesHelper <- function(x){
|
|
|
361 grepResults = gregexpr("-*",x)
|
|
|
362 grepResultsPos = unlist(grepResults)
|
|
|
363 grepResultsLen = attr(grepResults[[1]],"match.length")
|
|
|
364 x = s2c(x)
|
|
|
365 if(x[1]=="-"){
|
|
|
366 x[1:grepResultsLen[1]] = "N"
|
|
|
367 }
|
|
|
368 if(x[length(x)]=="-"){
|
|
|
369 x[(length(x)-grepResultsLen[length(grepResultsLen)]+1):length(x)] = "N"
|
|
|
370 }
|
|
|
371 return(x)
|
|
|
372 }
|
|
|
373
|
|
|
374
|
|
|
375
|
|
|
376
|
|
|
377 # Check sequences for indels
|
|
|
378 checkForInDels <- function(matInputP){
|
|
|
379 insPos <- checkInsertion(matInputP)
|
|
|
380 delPos <- checkDeletions(matInputP)
|
|
|
381 return(list("Insertions"=insPos, "Deletions"=delPos))
|
|
|
382 }
|
|
|
383
|
|
|
384 # Check sequences for insertions
|
|
|
385 checkInsertion <- function(matInputP){
|
|
|
386 insertionCheck = apply( matInputP,1, function(x){
|
|
|
387 inputGaps <- as.vector( gregexpr("-",x[1])[[1]] )
|
|
|
388 glGaps <- as.vector( gregexpr("-",x[2])[[1]] )
|
|
|
389 return( is.finite( match(FALSE, glGaps%in%inputGaps ) ) )
|
|
|
390 })
|
|
|
391 return(as.vector(insertionCheck))
|
|
|
392 }
|
|
|
393 # Fix inserstions
|
|
|
394 fixInsertions <- function(matInputP){
|
|
|
395 insPos <- checkInsertion(matInputP)
|
|
|
396 sapply((1:nrow(matInputP))[insPos],function(rowIndex){
|
|
|
397 x <- matInputP[rowIndex,]
|
|
|
398 inputGaps <- gregexpr("-",x[1])[[1]]
|
|
|
399 glGaps <- gregexpr("-",x[2])[[1]]
|
|
|
400 posInsertions <- glGaps[!(glGaps%in%inputGaps)]
|
|
|
401 inputInsertionToN <- s2c(x[2])
|
|
|
402 inputInsertionToN[posInsertions]!="-"
|
|
|
403 inputInsertionToN[posInsertions] <- "N"
|
|
|
404 inputInsertionToN <- c2s(inputInsertionToN)
|
|
|
405 matInput[rowIndex,2] <<- inputInsertionToN
|
|
|
406 })
|
|
|
407 return(insPos)
|
|
|
408 }
|
|
|
409
|
|
|
410 # Check sequences for deletions
|
|
|
411 checkDeletions <-function(matInputP){
|
|
|
412 deletionCheck = apply( matInputP,1, function(x){
|
|
|
413 inputGaps <- as.vector( gregexpr("-",x[1])[[1]] )
|
|
|
414 glGaps <- as.vector( gregexpr("-",x[2])[[1]] )
|
|
|
415 return( is.finite( match(FALSE, inputGaps%in%glGaps ) ) )
|
|
|
416 })
|
|
|
417 return(as.vector(deletionCheck))
|
|
|
418 }
|
|
|
419 # Fix sequences with deletions
|
|
|
420 fixDeletions <- function(matInputP){
|
|
|
421 delPos <- checkDeletions(matInputP)
|
|
|
422 sapply((1:nrow(matInputP))[delPos],function(rowIndex){
|
|
|
423 x <- matInputP[rowIndex,]
|
|
|
424 inputGaps <- gregexpr("-",x[1])[[1]]
|
|
|
425 glGaps <- gregexpr("-",x[2])[[1]]
|
|
|
426 posDeletions <- inputGaps[!(inputGaps%in%glGaps)]
|
|
|
427 inputDeletionToN <- s2c(x[1])
|
|
|
428 inputDeletionToN[posDeletions] <- "N"
|
|
|
429 inputDeletionToN <- c2s(inputDeletionToN)
|
|
|
430 matInput[rowIndex,1] <<- inputDeletionToN
|
|
|
431 })
|
|
|
432 return(delPos)
|
|
|
433 }
|
|
|
434
|
|
|
435
|
|
|
436 # Trim DNA sequence to the last codon
|
|
|
437 trimToLastCodon <- function(seqToTrim){
|
|
|
438 seqLen = nchar(seqToTrim)
|
|
|
439 trimmedSeq = s2c(seqToTrim)
|
|
|
440 poi = seqLen
|
|
|
441 tailLen = 0
|
|
|
442
|
|
|
443 while(trimmedSeq[poi]=="-" || trimmedSeq[poi]=="."){
|
|
|
444 tailLen = tailLen + 1
|
|
|
445 poi = poi - 1
|
|
|
446 }
|
|
|
447
|
|
|
448 trimmedSeq = c2s(trimmedSeq[1:(seqLen-tailLen)])
|
|
|
449 seqLen = nchar(trimmedSeq)
|
|
|
450 # Trim sequence to last codon
|
|
|
451 if( getCodonPos(seqLen)[3] > seqLen )
|
|
|
452 trimmedSeq = substr(seqToTrim,1, ( (getCodonPos(seqLen)[1])-1 ) )
|
|
|
453
|
|
|
454 return(trimmedSeq)
|
|
|
455 }
|
|
|
456
|
|
|
457 # Given a nuclotide position, returns the pos of the 3 nucs that made the codon
|
|
|
458 # e.g. nuc 86 is part of nucs 85,86,87
|
|
|
459 getCodonPos <- function(nucPos){
|
|
|
460 codonNum = (ceiling(nucPos/3))*3
|
|
|
461 return( (codonNum-2):codonNum)
|
|
|
462 }
|
|
|
463
|
|
|
464 # Given a nuclotide position, returns the codon number
|
|
|
465 # e.g. nuc 86 = codon 29
|
|
|
466 getCodonNumb <- function(nucPos){
|
|
|
467 return( ceiling(nucPos/3) )
|
|
|
468 }
|
|
|
469
|
|
|
470 # Given a codon, returns all the nuc positions that make the codon
|
|
|
471 getCodonNucs <- function(codonNumb){
|
|
|
472 getCodonPos(codonNumb*3)
|
|
|
473 }
|
|
|
474
|
|
|
475 computeCodonTable <- function(testID=1){
|
|
|
476
|
|
|
477 if(testID<=4){
|
|
|
478 # Pre-compute every codons
|
|
|
479 intCounter = 1
|
|
|
480 for(pOne in NUCLEOTIDES){
|
|
|
481 for(pTwo in NUCLEOTIDES){
|
|
|
482 for(pThree in NUCLEOTIDES){
|
|
|
483 codon = paste(pOne,pTwo,pThree,sep="")
|
|
|
484 colnames(CODON_TABLE)[intCounter] = codon
|
|
|
485 intCounter = intCounter + 1
|
|
|
486 CODON_TABLE[,codon] = mutationTypeOptimized(cbind(permutateAllCodon(codon),rep(codon,12)))
|
|
|
487 }
|
|
|
488 }
|
|
|
489 }
|
|
|
490 chars = c("N","A","C","G","T", "-")
|
|
|
491 for(a in chars){
|
|
|
492 for(b in chars){
|
|
|
493 for(c in chars){
|
|
|
494 if(a=="N" | b=="N" | c=="N"){
|
|
|
495 #cat(paste(a,b,c),sep="","\n")
|
|
|
496 CODON_TABLE[,paste(a,b,c,sep="")] = rep(NA,12)
|
|
|
497 }
|
|
|
498 }
|
|
|
499 }
|
|
|
500 }
|
|
|
501
|
|
|
502 chars = c("-","A","C","G","T")
|
|
|
503 for(a in chars){
|
|
|
504 for(b in chars){
|
|
|
505 for(c in chars){
|
|
|
506 if(a=="-" | b=="-" | c=="-"){
|
|
|
507 #cat(paste(a,b,c),sep="","\n")
|
|
|
508 CODON_TABLE[,paste(a,b,c,sep="")] = rep(NA,12)
|
|
|
509 }
|
|
|
510 }
|
|
|
511 }
|
|
|
512 }
|
|
|
513 CODON_TABLE <<- as.matrix(CODON_TABLE)
|
|
|
514 }
|
|
|
515 }
|
|
|
516
|
|
|
517 collapseClone <- function(vecInputSeqs,glSeq,readEnd,nonTerminalOnly=0){
|
|
|
518 #print(length(vecInputSeqs))
|
|
|
519 vecInputSeqs = unique(vecInputSeqs)
|
|
|
520 if(length(vecInputSeqs)==1){
|
|
|
521 return( list( c(vecInputSeqs,glSeq), F) )
|
|
|
522 }else{
|
|
|
523 charInputSeqs <- sapply(vecInputSeqs, function(x){
|
|
|
524 s2c(x)[1:readEnd]
|
|
|
525 })
|
|
|
526 charGLSeq <- s2c(glSeq)
|
|
|
527 matClone <- sapply(1:readEnd, function(i){
|
|
|
528 posNucs = unique(charInputSeqs[i,])
|
|
|
529 posGL = charGLSeq[i]
|
|
|
530 error = FALSE
|
|
|
531 if(posGL=="-" & sum(!(posNucs%in%c("-","N")))==0 ){
|
|
|
532 return(c("-",error))
|
|
|
533 }
|
|
|
534 if(length(posNucs)==1)
|
|
|
535 return(c(posNucs[1],error))
|
|
|
536 else{
|
|
|
537 if("N"%in%posNucs){
|
|
|
538 error=TRUE
|
|
|
539 }
|
|
|
540 if(sum(!posNucs[posNucs!="N"]%in%posGL)==0){
|
|
|
541 return( c(posGL,error) )
|
|
|
542 }else{
|
|
|
543 #return( c(sample(posNucs[posNucs!="N"],1),error) )
|
|
|
544 if(nonTerminalOnly==0){
|
|
|
545 return( c(sample(charInputSeqs[i,charInputSeqs[i,]!="N" & charInputSeqs[i,]!=posGL],1),error) )
|
|
|
546 }else{
|
|
|
547 posNucs = charInputSeqs[i,charInputSeqs[i,]!="N" & charInputSeqs[i,]!=posGL]
|
|
|
548 posNucsTable = table(posNucs)
|
|
|
549 if(sum(posNucsTable>1)==0){
|
|
|
550 return( c(posGL,error) )
|
|
|
551 }else{
|
|
|
552 return( c(sample( posNucs[posNucs%in%names(posNucsTable)[posNucsTable>1]],1),error) )
|
|
|
553 }
|
|
|
554 }
|
|
|
555
|
|
|
556 }
|
|
|
557 }
|
|
|
558 })
|
|
|
559
|
|
|
560
|
|
|
561 #print(length(vecInputSeqs))
|
|
|
562 return(list(c(c2s(matClone[1,]),glSeq),"TRUE"%in%matClone[2,]))
|
|
|
563 }
|
|
|
564 }
|
|
|
565
|
|
|
566 # Compute the expected for each sequence-germline pair
|
|
|
567 getExpectedIndividual <- function(matInput){
|
|
|
568 if( any(grep("multicore",search())) ){
|
|
|
569 facGL <- factor(matInput[,2])
|
|
|
570 facLevels = levels(facGL)
|
|
|
571 LisGLs_MutabilityU = mclapply(1:length(facLevels), function(x){
|
|
|
572 computeMutabilities(facLevels[x])
|
|
|
573 })
|
|
|
574 facIndex = match(facGL,facLevels)
|
|
|
575
|
|
|
576 LisGLs_Mutability = mclapply(1:nrow(matInput), function(x){
|
|
|
577 cInput = rep(NA,nchar(matInput[x,1]))
|
|
|
578 cInput[s2c(matInput[x,1])!="N"] = 1
|
|
|
579 LisGLs_MutabilityU[[facIndex[x]]] * cInput
|
|
|
580 })
|
|
|
581
|
|
|
582 LisGLs_Targeting = mclapply(1:dim(matInput)[1], function(x){
|
|
|
583 computeTargeting(matInput[x,2],LisGLs_Mutability[[x]])
|
|
|
584 })
|
|
|
585
|
|
|
586 LisGLs_MutationTypes = mclapply(1:length(matInput[,2]),function(x){
|
|
|
587 #print(x)
|
|
|
588 computeMutationTypes(matInput[x,2])
|
|
|
589 })
|
|
|
590
|
|
|
591 LisGLs_Exp = mclapply(1:dim(matInput)[1], function(x){
|
|
|
592 computeExpected(LisGLs_Targeting[[x]],LisGLs_MutationTypes[[x]])
|
|
|
593 })
|
|
|
594
|
|
|
595 ul_LisGLs_Exp = unlist(LisGLs_Exp)
|
|
|
596 return(matrix(ul_LisGLs_Exp,ncol=4,nrow=(length(ul_LisGLs_Exp)/4),byrow=T))
|
|
|
597 }else{
|
|
|
598 facGL <- factor(matInput[,2])
|
|
|
599 facLevels = levels(facGL)
|
|
|
600 LisGLs_MutabilityU = lapply(1:length(facLevels), function(x){
|
|
|
601 computeMutabilities(facLevels[x])
|
|
|
602 })
|
|
|
603 facIndex = match(facGL,facLevels)
|
|
|
604
|
|
|
605 LisGLs_Mutability = lapply(1:nrow(matInput), function(x){
|
|
|
606 cInput = rep(NA,nchar(matInput[x,1]))
|
|
|
607 cInput[s2c(matInput[x,1])!="N"] = 1
|
|
|
608 LisGLs_MutabilityU[[facIndex[x]]] * cInput
|
|
|
609 })
|
|
|
610
|
|
|
611 LisGLs_Targeting = lapply(1:dim(matInput)[1], function(x){
|
|
|
612 computeTargeting(matInput[x,2],LisGLs_Mutability[[x]])
|
|
|
613 })
|
|
|
614
|
|
|
615 LisGLs_MutationTypes = lapply(1:length(matInput[,2]),function(x){
|
|
|
616 #print(x)
|
|
|
617 computeMutationTypes(matInput[x,2])
|
|
|
618 })
|
|
|
619
|
|
|
620 LisGLs_Exp = lapply(1:dim(matInput)[1], function(x){
|
|
|
621 computeExpected(LisGLs_Targeting[[x]],LisGLs_MutationTypes[[x]])
|
|
|
622 })
|
|
|
623
|
|
|
624 ul_LisGLs_Exp = unlist(LisGLs_Exp)
|
|
|
625 return(matrix(ul_LisGLs_Exp,ncol=4,nrow=(length(ul_LisGLs_Exp)/4),byrow=T))
|
|
|
626
|
|
|
627 }
|
|
|
628 }
|
|
|
629
|
|
|
630 # Compute mutabilities of sequence based on the tri-nucleotide model
|
|
|
631 computeMutabilities <- function(paramSeq){
|
|
|
632 seqLen = nchar(paramSeq)
|
|
|
633 seqMutabilites = rep(NA,seqLen)
|
|
|
634
|
|
|
635 gaplessSeq = gsub("-", "", paramSeq)
|
|
|
636 gaplessSeqLen = nchar(gaplessSeq)
|
|
|
637 gaplessSeqMutabilites = rep(NA,gaplessSeqLen)
|
|
|
638
|
|
|
639 if(mutabilityModel!=5){
|
|
|
640 pos<- 3:(gaplessSeqLen)
|
|
|
641 subSeq = substr(rep(gaplessSeq,gaplessSeqLen-2),(pos-2),(pos+2))
|
|
|
642 gaplessSeqMutabilites[pos] =
|
|
|
643 tapply( c(
|
|
|
644 getMutability( substr(subSeq,1,3), 3) ,
|
|
|
645 getMutability( substr(subSeq,2,4), 2),
|
|
|
646 getMutability( substr(subSeq,3,5), 1)
|
|
|
647 ),rep(1:(gaplessSeqLen-2),3),mean,na.rm=TRUE
|
|
|
648 )
|
|
|
649 #Pos 1
|
|
|
650 subSeq = substr(gaplessSeq,1,3)
|
|
|
651 gaplessSeqMutabilites[1] = getMutability(subSeq , 1)
|
|
|
652 #Pos 2
|
|
|
653 subSeq = substr(gaplessSeq,1,4)
|
|
|
654 gaplessSeqMutabilites[2] = mean( c(
|
|
|
655 getMutability( substr(subSeq,1,3), 2) ,
|
|
|
656 getMutability( substr(subSeq,2,4), 1)
|
|
|
657 ),na.rm=T
|
|
|
658 )
|
|
|
659 seqMutabilites[which(s2c(paramSeq)!="-")]<- gaplessSeqMutabilites
|
|
|
660 return(seqMutabilites)
|
|
|
661 }else{
|
|
|
662
|
|
|
663 pos<- 3:(gaplessSeqLen)
|
|
|
664 subSeq = substr(rep(gaplessSeq,gaplessSeqLen-2),(pos-2),(pos+2))
|
|
|
665 gaplessSeqMutabilites[pos] = sapply(subSeq,function(x){ getMutability5(x) }, simplify=T)
|
|
|
666 seqMutabilites[which(s2c(paramSeq)!="-")]<- gaplessSeqMutabilites
|
|
|
667 return(seqMutabilites)
|
|
|
668 }
|
|
|
669
|
|
|
670 }
|
|
|
671
|
|
|
672 # Returns the mutability of a triplet at a given position
|
|
|
673 getMutability <- function(codon, pos=1:3){
|
|
|
674 triplets <- rownames(mutability)
|
|
|
675 mutability[ match(codon,triplets) ,pos]
|
|
|
676 }
|
|
|
677
|
|
|
678 getMutability5 <- function(fivemer){
|
|
|
679 return(mutability[fivemer])
|
|
|
680 }
|
|
|
681
|
|
|
682 # Returns the substitution probabilty
|
|
|
683 getTransistionProb <- function(nuc){
|
|
|
684 substitution[nuc,]
|
|
|
685 }
|
|
|
686
|
|
|
687 getTransistionProb5 <- function(fivemer){
|
|
|
688 if(any(which(fivemer==colnames(substitution)))){
|
|
|
689 return(substitution[,fivemer])
|
|
|
690 }else{
|
|
|
691 return(array(NA,4))
|
|
|
692 }
|
|
|
693 }
|
|
|
694
|
|
|
695 # Given a nuc, returns the other 3 nucs it can mutate to
|
|
|
696 canMutateTo <- function(nuc){
|
|
|
697 NUCLEOTIDES[- which(NUCLEOTIDES==nuc)]
|
|
|
698 }
|
|
|
699
|
|
|
700 # Given a nucleotide, returns the probabilty of other nucleotide it can mutate to
|
|
|
701 canMutateToProb <- function(nuc){
|
|
|
702 substitution[nuc,canMutateTo(nuc)]
|
|
|
703 }
|
|
|
704
|
|
|
705 # Compute targeting, based on precomputed mutatbility & substitution
|
|
|
706 computeTargeting <- function(param_strSeq,param_vecMutabilities){
|
|
|
707
|
|
|
708 if(substitutionModel!=5){
|
|
|
709 vecSeq = s2c(param_strSeq)
|
|
|
710 matTargeting = sapply( 1:length(vecSeq), function(x) { param_vecMutabilities[x] * getTransistionProb(vecSeq[x]) } )
|
|
|
711 #matTargeting = apply( rbind(vecSeq,param_vecMutabilities),2, function(x) { as.vector(as.numeric(x[2]) * getTransistionProb(x[1])) } )
|
|
|
712 dimnames( matTargeting ) = list(NUCLEOTIDES,1:(length(vecSeq)))
|
|
|
713 return (matTargeting)
|
|
|
714 }else{
|
|
|
715
|
|
|
716 seqLen = nchar(param_strSeq)
|
|
|
717 seqsubstitution = matrix(NA,ncol=seqLen,nrow=4)
|
|
|
718 paramSeq <- param_strSeq
|
|
|
719 gaplessSeq = gsub("-", "", paramSeq)
|
|
|
720 gaplessSeqLen = nchar(gaplessSeq)
|
|
|
721 gaplessSeqSubstitution = matrix(NA,ncol=gaplessSeqLen,nrow=4)
|
|
|
722
|
|
|
723 pos<- 3:(gaplessSeqLen)
|
|
|
724 subSeq = substr(rep(gaplessSeq,gaplessSeqLen-2),(pos-2),(pos+2))
|
|
|
725 gaplessSeqSubstitution[,pos] = sapply(subSeq,function(x){ getTransistionProb5(x) }, simplify=T)
|
|
|
726 seqsubstitution[,which(s2c(paramSeq)!="-")]<- gaplessSeqSubstitution
|
|
|
727 #matTargeting <- param_vecMutabilities %*% seqsubstitution
|
|
|
728 matTargeting <- sweep(seqsubstitution,2,param_vecMutabilities,`*`)
|
|
|
729 dimnames( matTargeting ) = list(NUCLEOTIDES,1:(seqLen))
|
|
|
730 return (matTargeting)
|
|
|
731 }
|
|
|
732 }
|
|
|
733
|
|
|
734 # Compute the mutations types
|
|
|
735 computeMutationTypes <- function(param_strSeq){
|
|
|
736 #cat(param_strSeq,"\n")
|
|
|
737 #vecSeq = trimToLastCodon(param_strSeq)
|
|
|
738 lenSeq = nchar(param_strSeq)
|
|
|
739 vecCodons = sapply({1:(lenSeq/3)}*3-2,function(x){substr(param_strSeq,x,x+2)})
|
|
|
740 matMutationTypes = matrix( unlist(CODON_TABLE[,vecCodons]) ,ncol=lenSeq,nrow=4, byrow=F)
|
|
|
741 dimnames( matMutationTypes ) = list(NUCLEOTIDES,1:(ncol(matMutationTypes)))
|
|
|
742 return(matMutationTypes)
|
|
|
743 }
|
|
|
744 computeMutationTypesFast <- function(param_strSeq){
|
|
|
745 matMutationTypes = matrix( CODON_TABLE[,param_strSeq] ,ncol=3,nrow=4, byrow=F)
|
|
|
746 #dimnames( matMutationTypes ) = list(NUCLEOTIDES,1:(length(vecSeq)))
|
|
|
747 return(matMutationTypes)
|
|
|
748 }
|
|
|
749 mutationTypeOptimized <- function( matOfCodons ){
|
|
|
750 apply( matOfCodons,1,function(x){ mutationType(x[2],x[1]) } )
|
|
|
751 }
|
|
|
752
|
|
|
753 # Returns a vector of codons 1 mutation away from the given codon
|
|
|
754 permutateAllCodon <- function(codon){
|
|
|
755 cCodon = s2c(codon)
|
|
|
756 matCodons = t(array(cCodon,dim=c(3,12)))
|
|
|
757 matCodons[1:4,1] = NUCLEOTIDES
|
|
|
758 matCodons[5:8,2] = NUCLEOTIDES
|
|
|
759 matCodons[9:12,3] = NUCLEOTIDES
|
|
|
760 apply(matCodons,1,c2s)
|
|
|
761 }
|
|
|
762
|
|
|
763 # Given two codons, tells you if the mutation is R or S (based on your definition)
|
|
|
764 mutationType <- function(codonFrom,codonTo){
|
|
|
765 if(testID==4){
|
|
|
766 if( is.na(codonFrom) | is.na(codonTo) | is.na(translateCodonToAminoAcid(codonFrom)) | is.na(translateCodonToAminoAcid(codonTo)) ){
|
|
|
767 return(NA)
|
|
|
768 }else{
|
|
|
769 mutationType = "S"
|
|
|
770 if( translateAminoAcidToTraitChange(translateCodonToAminoAcid(codonFrom)) != translateAminoAcidToTraitChange(translateCodonToAminoAcid(codonTo)) ){
|
|
|
771 mutationType = "R"
|
|
|
772 }
|
|
|
773 if(translateCodonToAminoAcid(codonTo)=="*" | translateCodonToAminoAcid(codonFrom)=="*"){
|
|
|
774 mutationType = "Stop"
|
|
|
775 }
|
|
|
776 return(mutationType)
|
|
|
777 }
|
|
|
778 }else if(testID==5){
|
|
|
779 if( is.na(codonFrom) | is.na(codonTo) | is.na(translateCodonToAminoAcid(codonFrom)) | is.na(translateCodonToAminoAcid(codonTo)) ){
|
|
|
780 return(NA)
|
|
|
781 }else{
|
|
|
782 if(codonFrom==codonTo){
|
|
|
783 mutationType = "S"
|
|
|
784 }else{
|
|
|
785 codonFrom = s2c(codonFrom)
|
|
|
786 codonTo = s2c(codonTo)
|
|
|
787 mutationType = "Stop"
|
|
|
788 nucOfI = codonFrom[which(codonTo!=codonFrom)]
|
|
|
789 if(nucOfI=="C"){
|
|
|
790 mutationType = "R"
|
|
|
791 }else if(nucOfI=="G"){
|
|
|
792 mutationType = "S"
|
|
|
793 }
|
|
|
794 }
|
|
|
795 return(mutationType)
|
|
|
796 }
|
|
|
797 }else{
|
|
|
798 if( is.na(codonFrom) | is.na(codonTo) | is.na(translateCodonToAminoAcid(codonFrom)) | is.na(translateCodonToAminoAcid(codonTo)) ){
|
|
|
799 return(NA)
|
|
|
800 }else{
|
|
|
801 mutationType = "S"
|
|
|
802 if( translateCodonToAminoAcid(codonFrom) != translateCodonToAminoAcid(codonTo) ){
|
|
|
803 mutationType = "R"
|
|
|
804 }
|
|
|
805 if(translateCodonToAminoAcid(codonTo)=="*" | translateCodonToAminoAcid(codonFrom)=="*"){
|
|
|
806 mutationType = "Stop"
|
|
|
807 }
|
|
|
808 return(mutationType)
|
|
|
809 }
|
|
|
810 }
|
|
|
811 }
|
|
|
812
|
|
|
813
|
|
|
814 #given a mat of targeting & it's corresponding mutationtypes returns
|
|
|
815 #a vector of Exp_RCDR,Exp_SCDR,Exp_RFWR,Exp_RFWR
|
|
|
816 computeExpected <- function(paramTargeting,paramMutationTypes){
|
|
|
817 # Replacements
|
|
|
818 RPos = which(paramMutationTypes=="R")
|
|
|
819 #FWR
|
|
|
820 Exp_R_FWR = sum(paramTargeting[ RPos[which(FWR_Nuc_Mat[RPos]==T)] ],na.rm=T)
|
|
|
821 #CDR
|
|
|
822 Exp_R_CDR = sum(paramTargeting[ RPos[which(CDR_Nuc_Mat[RPos]==T)] ],na.rm=T)
|
|
|
823 # Silents
|
|
|
824 SPos = which(paramMutationTypes=="S")
|
|
|
825 #FWR
|
|
|
826 Exp_S_FWR = sum(paramTargeting[ SPos[which(FWR_Nuc_Mat[SPos]==T)] ],na.rm=T)
|
|
|
827 #CDR
|
|
|
828 Exp_S_CDR = sum(paramTargeting[ SPos[which(CDR_Nuc_Mat[SPos]==T)] ],na.rm=T)
|
|
|
829
|
|
|
830 return(c(Exp_R_CDR,Exp_S_CDR,Exp_R_FWR,Exp_S_FWR))
|
|
|
831 }
|
|
|
832
|
|
|
833 # Count the mutations in a sequence
|
|
|
834 # each mutation is treated independently
|
|
|
835 analyzeMutations2NucUri_website <- function( rev_in_matrix ){
|
|
|
836 paramGL = rev_in_matrix[2,]
|
|
|
837 paramSeq = rev_in_matrix[1,]
|
|
|
838
|
|
|
839 #Fill seq with GL seq if gapped
|
|
|
840 #if( any(paramSeq=="-") ){
|
|
|
841 # gapPos_Seq = which(paramSeq=="-")
|
|
|
842 # gapPos_Seq_ToReplace = gapPos_Seq[paramGL[gapPos_Seq] != "-"]
|
|
|
843 # paramSeq[gapPos_Seq_ToReplace] = paramGL[gapPos_Seq_ToReplace]
|
|
|
844 #}
|
|
|
845
|
|
|
846
|
|
|
847 #if( any(paramSeq=="N") ){
|
|
|
848 # gapPos_Seq = which(paramSeq=="N")
|
|
|
849 # gapPos_Seq_ToReplace = gapPos_Seq[paramGL[gapPos_Seq] != "N"]
|
|
|
850 # paramSeq[gapPos_Seq_ToReplace] = paramGL[gapPos_Seq_ToReplace]
|
|
|
851 #}
|
|
|
852
|
|
|
853 analyzeMutations2NucUri( matrix(c( paramGL, paramSeq ),2,length(paramGL),byrow=T) )
|
|
|
854
|
|
|
855 }
|
|
|
856
|
|
|
857 #1 = GL
|
|
|
858 #2 = Seq
|
|
|
859 analyzeMutations2NucUri <- function( in_matrix=matrix(c(c("A","A","A","C","C","C"),c("A","G","G","C","C","A")),2,6,byrow=T) ){
|
|
|
860 paramGL = in_matrix[2,]
|
|
|
861 paramSeq = in_matrix[1,]
|
|
|
862 paramSeqUri = paramGL
|
|
|
863 #mutations = apply(rbind(paramGL,paramSeq), 2, function(x){!x[1]==x[2]})
|
|
|
864 mutations_val = paramGL != paramSeq
|
|
|
865 if(any(mutations_val)){
|
|
|
866 mutationPos = {1:length(mutations_val)}[mutations_val]
|
|
|
867 mutationPos = mutationPos[sapply(mutationPos, function(x){!any(paramSeq[getCodonPos(x)]=="N")})]
|
|
|
868 length_mutations =length(mutationPos)
|
|
|
869 mutationInfo = rep(NA,length_mutations)
|
|
|
870 if(any(mutationPos)){
|
|
|
871
|
|
|
872 pos<- mutationPos
|
|
|
873 pos_array<-array(sapply(pos,getCodonPos))
|
|
|
874 codonGL = paramGL[pos_array]
|
|
|
875
|
|
|
876 codonSeq = sapply(pos,function(x){
|
|
|
877 seqP = paramGL[getCodonPos(x)]
|
|
|
878 muCodonPos = {x-1}%%3+1
|
|
|
879 seqP[muCodonPos] = paramSeq[x]
|
|
|
880 return(seqP)
|
|
|
881 })
|
|
|
882 GLcodons = apply(matrix(codonGL,length_mutations,3,byrow=TRUE),1,c2s)
|
|
|
883 Seqcodons = apply(codonSeq,2,c2s)
|
|
|
884 mutationInfo = apply(rbind(GLcodons , Seqcodons),2,function(x){mutationType(c2s(x[1]),c2s(x[2]))})
|
|
|
885 names(mutationInfo) = mutationPos
|
|
|
886 }
|
|
|
887 if(any(!is.na(mutationInfo))){
|
|
|
888 return(mutationInfo[!is.na(mutationInfo)])
|
|
|
889 }else{
|
|
|
890 return(NA)
|
|
|
891 }
|
|
|
892
|
|
|
893
|
|
|
894 }else{
|
|
|
895 return (NA)
|
|
|
896 }
|
|
|
897 }
|
|
|
898
|
|
|
899 processNucMutations2 <- function(mu){
|
|
|
900 if(!is.na(mu)){
|
|
|
901 #R
|
|
|
902 if(any(mu=="R")){
|
|
|
903 Rs = mu[mu=="R"]
|
|
|
904 nucNumbs = as.numeric(names(Rs))
|
|
|
905 R_CDR = sum(as.integer(CDR_Nuc[nucNumbs]),na.rm=T)
|
|
|
906 R_FWR = sum(as.integer(FWR_Nuc[nucNumbs]),na.rm=T)
|
|
|
907 }else{
|
|
|
908 R_CDR = 0
|
|
|
909 R_FWR = 0
|
|
|
910 }
|
|
|
911
|
|
|
912 #S
|
|
|
913 if(any(mu=="S")){
|
|
|
914 Ss = mu[mu=="S"]
|
|
|
915 nucNumbs = as.numeric(names(Ss))
|
|
|
916 S_CDR = sum(as.integer(CDR_Nuc[nucNumbs]),na.rm=T)
|
|
|
917 S_FWR = sum(as.integer(FWR_Nuc[nucNumbs]),na.rm=T)
|
|
|
918 }else{
|
|
|
919 S_CDR = 0
|
|
|
920 S_FWR = 0
|
|
|
921 }
|
|
|
922
|
|
|
923
|
|
|
924 retVec = c(R_CDR,S_CDR,R_FWR,S_FWR)
|
|
|
925 retVec[is.na(retVec)]=0
|
|
|
926 return(retVec)
|
|
|
927 }else{
|
|
|
928 return(rep(0,4))
|
|
|
929 }
|
|
|
930 }
|
|
|
931
|
|
|
932
|
|
|
933 ## Z-score Test
|
|
|
934 computeZScore <- function(mat, test="Focused"){
|
|
|
935 matRes <- matrix(NA,ncol=2,nrow=(nrow(mat)))
|
|
|
936 if(test=="Focused"){
|
|
|
937 #Z_Focused_CDR
|
|
|
938 #P_Denom = sum( mat[1,c(5,6,8)], na.rm=T )
|
|
|
939 P = apply(mat[,c(5,6,8)],1,function(x){(x[1]/sum(x))})
|
|
|
940 R_mean = apply(cbind(mat[,c(1,2,4)],P),1,function(x){x[4]*(sum(x[1:3]))})
|
|
|
941 R_sd=sqrt(R_mean*(1-P))
|
|
|
942 matRes[,1] = (mat[,1]-R_mean)/R_sd
|
|
|
943
|
|
|
944 #Z_Focused_FWR
|
|
|
945 #P_Denom = sum( mat[1,c(7,6,8)], na.rm=T )
|
|
|
946 P = apply(mat[,c(7,6,8)],1,function(x){(x[1]/sum(x))})
|
|
|
947 R_mean = apply(cbind(mat[,c(3,2,4)],P),1,function(x){x[4]*(sum(x[1:3]))})
|
|
|
948 R_sd=sqrt(R_mean*(1-P))
|
|
|
949 matRes[,2] = (mat[,3]-R_mean)/R_sd
|
|
|
950 }
|
|
|
951
|
|
|
952 if(test=="Local"){
|
|
|
953 #Z_Focused_CDR
|
|
|
954 #P_Denom = sum( mat[1,c(5,6,8)], na.rm=T )
|
|
|
955 P = apply(mat[,c(5,6)],1,function(x){(x[1]/sum(x))})
|
|
|
956 R_mean = apply(cbind(mat[,c(1,2)],P),1,function(x){x[3]*(sum(x[1:2]))})
|
|
|
957 R_sd=sqrt(R_mean*(1-P))
|
|
|
958 matRes[,1] = (mat[,1]-R_mean)/R_sd
|
|
|
959
|
|
|
960 #Z_Focused_FWR
|
|
|
961 #P_Denom = sum( mat[1,c(7,6,8)], na.rm=T )
|
|
|
962 P = apply(mat[,c(7,8)],1,function(x){(x[1]/sum(x))})
|
|
|
963 R_mean = apply(cbind(mat[,c(3,4)],P),1,function(x){x[3]*(sum(x[1:2]))})
|
|
|
964 R_sd=sqrt(R_mean*(1-P))
|
|
|
965 matRes[,2] = (mat[,3]-R_mean)/R_sd
|
|
|
966 }
|
|
|
967
|
|
|
968 if(test=="Imbalanced"){
|
|
|
969 #Z_Focused_CDR
|
|
|
970 #P_Denom = sum( mat[1,c(5,6,8)], na.rm=T )
|
|
|
971 P = apply(mat[,5:8],1,function(x){((x[1]+x[2])/sum(x))})
|
|
|
972 R_mean = apply(cbind(mat[,1:4],P),1,function(x){x[5]*(sum(x[1:4]))})
|
|
|
973 R_sd=sqrt(R_mean*(1-P))
|
|
|
974 matRes[,1] = (mat[,1]-R_mean)/R_sd
|
|
|
975
|
|
|
976 #Z_Focused_FWR
|
|
|
977 #P_Denom = sum( mat[1,c(7,6,8)], na.rm=T )
|
|
|
978 P = apply(mat[,5:8],1,function(x){((x[3]+x[4])/sum(x))})
|
|
|
979 R_mean = apply(cbind(mat[,1:4],P),1,function(x){x[5]*(sum(x[1:4]))})
|
|
|
980 R_sd=sqrt(R_mean*(1-P))
|
|
|
981 matRes[,2] = (mat[,3]-R_mean)/R_sd
|
|
|
982 }
|
|
|
983
|
|
|
984 matRes[is.nan(matRes)] = NA
|
|
|
985 return(matRes)
|
|
|
986 }
|
|
|
987
|
|
|
988 # Return a p-value for a z-score
|
|
|
989 z2p <- function(z){
|
|
|
990 p=NA
|
|
|
991 if( !is.nan(z) && !is.na(z)){
|
|
|
992 if(z>0){
|
|
|
993 p = (1 - pnorm(z,0,1))
|
|
|
994 } else if(z<0){
|
|
|
995 p = (-1 * pnorm(z,0,1))
|
|
|
996 } else{
|
|
|
997 p = 0.5
|
|
|
998 }
|
|
|
999 }else{
|
|
|
1000 p = NA
|
|
|
1001 }
|
|
|
1002 return(p)
|
|
|
1003 }
|
|
|
1004
|
|
|
1005
|
|
|
1006 ## Bayesian Test
|
|
|
1007
|
|
|
1008 # Fitted parameter for the bayesian framework
|
|
|
1009 BAYESIAN_FITTED<-c(0.407277142798302, 0.554007336744485, 0.63777155771234, 0.693989162719009, 0.735450014674917, 0.767972534429806, 0.794557287143399, 0.816906816601605, 0.83606796225341, 0.852729446430296, 0.867370424541641, 0.880339760590323, 0.891900995024999, 0.902259181289864, 0.911577919359,0.919990301665853, 0.927606458124537, 0.934518806350661, 0.940805863754375, 0.946534836475715, 0.951763691199255, 0.95654428191308, 0.960920179487397, 0.964930893680829, 0.968611312149038, 0.971992459313836, 0.975102110004818, 0.977964943023096, 0.980603428208439, 0.983037660179428, 0.985285800977406, 0.987364285326685, 0.989288037855441, 0.991070478823525, 0.992723699729969, 0.994259575477392, 0.995687688867975, 0.997017365051493, 0.998257085153047, 0.999414558305388, 1.00049681357804, 1.00151036237481, 1.00246080204981, 1.00335370751909, 1.0041939329768, 1.0049859393417, 1.00573382091263, 1.00644127217376, 1.00711179729107, 1.00774845526417, 1.00835412715854, 1.00893143010366, 1.00948275846309, 1.01001030293661, 1.01051606798079, 1.01100188771288, 1.01146944044216, 1.01192026195449, 1.01235575766094, 1.01277721370986)
|
|
|
1010 CONST_i <- sort(c(((2^(seq(-39,0,length.out=201)))/2)[1:200],(c(0:11,13:99)+0.5)/100,1-(2^(seq(-39,0,length.out=201)))/2))
|
|
|
1011
|
|
|
1012 # Given x, M & p, returns a pdf
|
|
|
1013 calculate_bayes <- function ( x=3, N=10, p=0.33,
|
|
|
1014 i=CONST_i,
|
|
|
1015 max_sigma=20,length_sigma=4001
|
|
|
1016 ){
|
|
|
1017 if(!0%in%N){
|
|
|
1018 G <- max(length(x),length(N),length(p))
|
|
|
1019 x=array(x,dim=G)
|
|
|
1020 N=array(N,dim=G)
|
|
|
1021 p=array(p,dim=G)
|
|
|
1022 sigma_s<-seq(-max_sigma,max_sigma,length.out=length_sigma)
|
|
|
1023 sigma_1<-log({i/{1-i}}/{p/{1-p}})
|
|
|
1024 index<-min(N,60)
|
|
|
1025 y<-dbeta(i,x+BAYESIAN_FITTED[index],N+BAYESIAN_FITTED[index]-x)*(1-p)*p*exp(sigma_1)/({1-p}^2+2*p*{1-p}*exp(sigma_1)+{p^2}*exp(2*sigma_1))
|
|
|
1026 if(!sum(is.na(y))){
|
|
|
1027 tmp<-approx(sigma_1,y,sigma_s)$y
|
|
|
1028 tmp/sum(tmp)/{2*max_sigma/{length_sigma-1}}
|
|
|
1029 }else{
|
|
|
1030 return(NA)
|
|
|
1031 }
|
|
|
1032 }else{
|
|
|
1033 return(NA)
|
|
|
1034 }
|
|
|
1035 }
|
|
|
1036 # Given a mat of observed & expected, return a list of CDR & FWR pdf for selection
|
|
|
1037 computeBayesianScore <- function(mat, test="Focused", max_sigma=20,length_sigma=4001){
|
|
|
1038 flagOneSeq = F
|
|
|
1039 if(nrow(mat)==1){
|
|
|
1040 mat=rbind(mat,mat)
|
|
|
1041 flagOneSeq = T
|
|
|
1042 }
|
|
|
1043 if(test=="Focused"){
|
|
|
1044 #CDR
|
|
|
1045 P = c(apply(mat[,c(5,6,8)],1,function(x){(x[1]/sum(x))}),0.5)
|
|
|
1046 N = c(apply(mat[,c(1,2,4)],1,function(x){(sum(x))}),0)
|
|
|
1047 X = c(mat[,1],0)
|
|
|
1048 bayesCDR = apply(cbind(X,N,P),1,function(x){calculate_bayes(x=x[1],N=x[2],p=x[3],max_sigma=max_sigma,length_sigma=length_sigma)})
|
|
|
1049 bayesCDR = bayesCDR[-length(bayesCDR)]
|
|
|
1050
|
|
|
1051 #FWR
|
|
|
1052 P = c(apply(mat[,c(7,6,8)],1,function(x){(x[1]/sum(x))}),0.5)
|
|
|
1053 N = c(apply(mat[,c(3,2,4)],1,function(x){(sum(x))}),0)
|
|
|
1054 X = c(mat[,3],0)
|
|
|
1055 bayesFWR = apply(cbind(X,N,P),1,function(x){calculate_bayes(x=x[1],N=x[2],p=x[3],max_sigma=max_sigma,length_sigma=length_sigma)})
|
|
|
1056 bayesFWR = bayesFWR[-length(bayesFWR)]
|
|
|
1057 }
|
|
|
1058
|
|
|
1059 if(test=="Local"){
|
|
|
1060 #CDR
|
|
|
1061 P = c(apply(mat[,c(5,6)],1,function(x){(x[1]/sum(x))}),0.5)
|
|
|
1062 N = c(apply(mat[,c(1,2)],1,function(x){(sum(x))}),0)
|
|
|
1063 X = c(mat[,1],0)
|
|
|
1064 bayesCDR = apply(cbind(X,N,P),1,function(x){calculate_bayes(x=x[1],N=x[2],p=x[3],max_sigma=max_sigma,length_sigma=length_sigma)})
|
|
|
1065 bayesCDR = bayesCDR[-length(bayesCDR)]
|
|
|
1066
|
|
|
1067 #FWR
|
|
|
1068 P = c(apply(mat[,c(7,8)],1,function(x){(x[1]/sum(x))}),0.5)
|
|
|
1069 N = c(apply(mat[,c(3,4)],1,function(x){(sum(x))}),0)
|
|
|
1070 X = c(mat[,3],0)
|
|
|
1071 bayesFWR = apply(cbind(X,N,P),1,function(x){calculate_bayes(x=x[1],N=x[2],p=x[3],max_sigma=max_sigma,length_sigma=length_sigma)})
|
|
|
1072 bayesFWR = bayesFWR[-length(bayesFWR)]
|
|
|
1073 }
|
|
|
1074
|
|
|
1075 if(test=="Imbalanced"){
|
|
|
1076 #CDR
|
|
|
1077 P = c(apply(mat[,c(5:8)],1,function(x){((x[1]+x[2])/sum(x))}),0.5)
|
|
|
1078 N = c(apply(mat[,c(1:4)],1,function(x){(sum(x))}),0)
|
|
|
1079 X = c(apply(mat[,c(1:2)],1,function(x){(sum(x))}),0)
|
|
|
1080 bayesCDR = apply(cbind(X,N,P),1,function(x){calculate_bayes(x=x[1],N=x[2],p=x[3],max_sigma=max_sigma,length_sigma=length_sigma)})
|
|
|
1081 bayesCDR = bayesCDR[-length(bayesCDR)]
|
|
|
1082
|
|
|
1083 #FWR
|
|
|
1084 P = c(apply(mat[,c(5:8)],1,function(x){((x[3]+x[4])/sum(x))}),0.5)
|
|
|
1085 N = c(apply(mat[,c(1:4)],1,function(x){(sum(x))}),0)
|
|
|
1086 X = c(apply(mat[,c(3:4)],1,function(x){(sum(x))}),0)
|
|
|
1087 bayesFWR = apply(cbind(X,N,P),1,function(x){calculate_bayes(x=x[1],N=x[2],p=x[3],max_sigma=max_sigma,length_sigma=length_sigma)})
|
|
|
1088 bayesFWR = bayesFWR[-length(bayesFWR)]
|
|
|
1089 }
|
|
|
1090
|
|
|
1091 if(test=="ImbalancedSilent"){
|
|
|
1092 #CDR
|
|
|
1093 P = c(apply(mat[,c(6,8)],1,function(x){((x[1])/sum(x))}),0.5)
|
|
|
1094 N = c(apply(mat[,c(2,4)],1,function(x){(sum(x))}),0)
|
|
|
1095 X = c(apply(mat[,c(2,4)],1,function(x){(x[1])}),0)
|
|
|
1096 bayesCDR = apply(cbind(X,N,P),1,function(x){calculate_bayes(x=x[1],N=x[2],p=x[3],max_sigma=max_sigma,length_sigma=length_sigma)})
|
|
|
1097 bayesCDR = bayesCDR[-length(bayesCDR)]
|
|
|
1098
|
|
|
1099 #FWR
|
|
|
1100 P = c(apply(mat[,c(6,8)],1,function(x){((x[2])/sum(x))}),0.5)
|
|
|
1101 N = c(apply(mat[,c(2,4)],1,function(x){(sum(x))}),0)
|
|
|
1102 X = c(apply(mat[,c(2,4)],1,function(x){(x[2])}),0)
|
|
|
1103 bayesFWR = apply(cbind(X,N,P),1,function(x){calculate_bayes(x=x[1],N=x[2],p=x[3],max_sigma=max_sigma,length_sigma=length_sigma)})
|
|
|
1104 bayesFWR = bayesFWR[-length(bayesFWR)]
|
|
|
1105 }
|
|
|
1106
|
|
|
1107 if(flagOneSeq==T){
|
|
|
1108 bayesCDR = bayesCDR[1]
|
|
|
1109 bayesFWR = bayesFWR[1]
|
|
|
1110 }
|
|
|
1111 return( list("CDR"=bayesCDR, "FWR"=bayesFWR) )
|
|
|
1112 }
|
|
|
1113
|
|
|
1114 ##Covolution
|
|
|
1115 break2chunks<-function(G=1000){
|
|
|
1116 base<-2^round(log(sqrt(G),2),0)
|
|
|
1117 return(c(rep(base,floor(G/base)-1),base+G-(floor(G/base)*base)))
|
|
|
1118 }
|
|
|
1119
|
|
|
1120 PowersOfTwo <- function(G=100){
|
|
|
1121 exponents <- array()
|
|
|
1122 i = 0
|
|
|
1123 while(G > 0){
|
|
|
1124 i=i+1
|
|
|
1125 exponents[i] <- floor( log2(G) )
|
|
|
1126 G <- G-2^exponents[i]
|
|
|
1127 }
|
|
|
1128 return(exponents)
|
|
|
1129 }
|
|
|
1130
|
|
|
1131 convolutionPowersOfTwo <- function( cons, length_sigma=4001 ){
|
|
|
1132 G = ncol(cons)
|
|
|
1133 if(G>1){
|
|
|
1134 for(gen in log(G,2):1){
|
|
|
1135 ll<-seq(from=2,to=2^gen,by=2)
|
|
|
1136 sapply(ll,function(l){cons[,l/2]<<-weighted_conv(cons[,l],cons[,l-1],length_sigma=length_sigma)})
|
|
|
1137 }
|
|
|
1138 }
|
|
|
1139 return( cons[,1] )
|
|
|
1140 }
|
|
|
1141
|
|
|
1142 convolutionPowersOfTwoByTwos <- function( cons, length_sigma=4001,G=1 ){
|
|
|
1143 if(length(ncol(cons))) G<-ncol(cons)
|
|
|
1144 groups <- PowersOfTwo(G)
|
|
|
1145 matG <- matrix(NA, ncol=length(groups), nrow=length(cons)/G )
|
|
|
1146 startIndex = 1
|
|
|
1147 for( i in 1:length(groups) ){
|
|
|
1148 stopIndex <- 2^groups[i] + startIndex - 1
|
|
|
1149 if(stopIndex!=startIndex){
|
|
|
1150 matG[,i] <- convolutionPowersOfTwo( cons[,startIndex:stopIndex], length_sigma=length_sigma )
|
|
|
1151 startIndex = stopIndex + 1
|
|
|
1152 }
|
|
|
1153 else {
|
|
|
1154 if(G>1) matG[,i] <- cons[,startIndex:stopIndex]
|
|
|
1155 else matG[,i] <- cons
|
|
|
1156 #startIndex = stopIndex + 1
|
|
|
1157 }
|
|
|
1158 }
|
|
|
1159 return( list( matG, groups ) )
|
|
|
1160 }
|
|
|
1161
|
|
|
1162 weighted_conv<-function(x,y,w=1,m=100,length_sigma=4001){
|
|
|
1163 lx<-length(x)
|
|
|
1164 ly<-length(y)
|
|
|
1165 if({lx<m}| {{lx*w}<m}| {{ly}<m}| {{ly*w}<m}){
|
|
|
1166 if(w<1){
|
|
|
1167 y1<-approx(1:ly,y,seq(1,ly,length.out=m))$y
|
|
|
1168 x1<-approx(1:lx,x,seq(1,lx,length.out=m/w))$y
|
|
|
1169 lx<-length(x1)
|
|
|
1170 ly<-length(y1)
|
|
|
1171 }
|
|
|
1172 else {
|
|
|
1173 y1<-approx(1:ly,y,seq(1,ly,length.out=m*w))$y
|
|
|
1174 x1<-approx(1:lx,x,seq(1,lx,length.out=m))$y
|
|
|
1175 lx<-length(x1)
|
|
|
1176 ly<-length(y1)
|
|
|
1177 }
|
|
|
1178 }
|
|
|
1179 else{
|
|
|
1180 x1<-x
|
|
|
1181 y1<-approx(1:ly,y,seq(1,ly,length.out=floor(lx*w)))$y
|
|
|
1182 ly<-length(y1)
|
|
|
1183 }
|
|
|
1184 tmp<-approx(x=1:(lx+ly-1),y=convolve(x1,rev(y1),type="open"),xout=seq(1,lx+ly-1,length.out=length_sigma))$y
|
|
|
1185 tmp[tmp<=0] = 0
|
|
|
1186 return(tmp/sum(tmp))
|
|
|
1187 }
|
|
|
1188
|
|
|
1189 calculate_bayesGHelper <- function( listMatG,length_sigma=4001 ){
|
|
|
1190 matG <- listMatG[[1]]
|
|
|
1191 groups <- listMatG[[2]]
|
|
|
1192 i = 1
|
|
|
1193 resConv <- matG[,i]
|
|
|
1194 denom <- 2^groups[i]
|
|
|
1195 if(length(groups)>1){
|
|
|
1196 while( i<length(groups) ){
|
|
|
1197 i = i + 1
|
|
|
1198 resConv <- weighted_conv(resConv, matG[,i], w= {{2^groups[i]}/denom} ,length_sigma=length_sigma)
|
|
|
1199 #cat({{2^groups[i]}/denom},"\n")
|
|
|
1200 denom <- denom + 2^groups[i]
|
|
|
1201 }
|
|
|
1202 }
|
|
|
1203 return(resConv)
|
|
|
1204 }
|
|
|
1205
|
|
|
1206 # Given a list of PDFs, returns a convoluted PDF
|
|
|
1207 groupPosteriors <- function( listPosteriors, max_sigma=20, length_sigma=4001 ,Threshold=2 ){
|
|
|
1208 listPosteriors = listPosteriors[ !is.na(listPosteriors) ]
|
|
|
1209 Length_Postrior<-length(listPosteriors)
|
|
|
1210 if(Length_Postrior>1 & Length_Postrior<=Threshold){
|
|
|
1211 cons = matrix(unlist(listPosteriors),length(listPosteriors[[1]]),length(listPosteriors))
|
|
|
1212 listMatG <- convolutionPowersOfTwoByTwos(cons,length_sigma=length_sigma)
|
|
|
1213 y<-calculate_bayesGHelper(listMatG,length_sigma=length_sigma)
|
|
|
1214 return( y/sum(y)/(2*max_sigma/(length_sigma-1)) )
|
|
|
1215 }else if(Length_Postrior==1) return(listPosteriors[[1]])
|
|
|
1216 else if(Length_Postrior==0) return(NA)
|
|
|
1217 else {
|
|
|
1218 cons = matrix(unlist(listPosteriors),length(listPosteriors[[1]]),length(listPosteriors))
|
|
|
1219 y = fastConv(cons,max_sigma=max_sigma, length_sigma=length_sigma )
|
|
|
1220 return( y/sum(y)/(2*max_sigma/(length_sigma-1)) )
|
|
|
1221 }
|
|
|
1222 }
|
|
|
1223
|
|
|
1224 fastConv<-function(cons, max_sigma=20, length_sigma=4001){
|
|
|
1225 chunks<-break2chunks(G=ncol(cons))
|
|
|
1226 if(ncol(cons)==3) chunks<-2:1
|
|
|
1227 index_chunks_end <- cumsum(chunks)
|
|
|
1228 index_chunks_start <- c(1,index_chunks_end[-length(index_chunks_end)]+1)
|
|
|
1229 index_chunks <- cbind(index_chunks_start,index_chunks_end)
|
|
|
1230
|
|
|
1231 case <- sum(chunks!=chunks[1])
|
|
|
1232 if(case==1) End <- max(1,((length(index_chunks)/2)-1))
|
|
|
1233 else End <- max(1,((length(index_chunks)/2)))
|
|
|
1234
|
|
|
1235 firsts <- sapply(1:End,function(i){
|
|
|
1236 indexes<-index_chunks[i,1]:index_chunks[i,2]
|
|
|
1237 convolutionPowersOfTwoByTwos(cons[ ,indexes])[[1]]
|
|
|
1238 })
|
|
|
1239 if(case==0){
|
|
|
1240 result<-calculate_bayesGHelper( convolutionPowersOfTwoByTwos(firsts) )
|
|
|
1241 }else if(case==1){
|
|
|
1242 last<-list(calculate_bayesGHelper(
|
|
|
1243 convolutionPowersOfTwoByTwos( cons[ ,index_chunks[length(index_chunks)/2,1]:index_chunks[length(index_chunks)/2,2]] )
|
|
|
1244 ),0)
|
|
|
1245 result_first<-calculate_bayesGHelper(convolutionPowersOfTwoByTwos(firsts))
|
|
|
1246 result<-calculate_bayesGHelper(
|
|
|
1247 list(
|
|
|
1248 cbind(
|
|
|
1249 result_first,last[[1]]),
|
|
|
1250 c(log(index_chunks_end[length(index_chunks)/2-1],2),log(index_chunks[length(index_chunks)/2,2]-index_chunks[length(index_chunks)/2,1]+1,2))
|
|
|
1251 )
|
|
|
1252 )
|
|
|
1253 }
|
|
|
1254 return(as.vector(result))
|
|
|
1255 }
|
|
|
1256
|
|
|
1257 # Computes the 95% CI for a pdf
|
|
|
1258 calcBayesCI <- function(Pdf,low=0.025,up=0.975,max_sigma=20, length_sigma=4001){
|
|
|
1259 if(length(Pdf)!=length_sigma) return(NA)
|
|
|
1260 sigma_s=seq(-max_sigma,max_sigma,length.out=length_sigma)
|
|
|
1261 cdf = cumsum(Pdf)
|
|
|
1262 cdf = cdf/cdf[length(cdf)]
|
|
|
1263 return( c(sigma_s[findInterval(low,cdf)-1] , sigma_s[findInterval(up,cdf)]) )
|
|
|
1264 }
|
|
|
1265
|
|
|
1266 # Computes a mean for a pdf
|
|
|
1267 calcBayesMean <- function(Pdf,max_sigma=20,length_sigma=4001){
|
|
|
1268 if(length(Pdf)!=length_sigma) return(NA)
|
|
|
1269 sigma_s=seq(-max_sigma,max_sigma,length.out=length_sigma)
|
|
|
1270 norm = {length_sigma-1}/2/max_sigma
|
|
|
1271 return( (Pdf%*%sigma_s/norm) )
|
|
|
1272 }
|
|
|
1273
|
|
|
1274 # Returns the mean, and the 95% CI for a pdf
|
|
|
1275 calcBayesOutputInfo <- function(Pdf,low=0.025,up=0.975,max_sigma=20, length_sigma=4001){
|
|
|
1276 if(is.na(Pdf))
|
|
|
1277 return(rep(NA,3))
|
|
|
1278 bCI = calcBayesCI(Pdf=Pdf,low=low,up=up,max_sigma=max_sigma,length_sigma=length_sigma)
|
|
|
1279 bMean = calcBayesMean(Pdf=Pdf,max_sigma=max_sigma,length_sigma=length_sigma)
|
|
|
1280 return(c(bMean, bCI))
|
|
|
1281 }
|
|
|
1282
|
|
|
1283 # Computes the p-value of a pdf
|
|
|
1284 computeSigmaP <- function(Pdf, length_sigma=4001, max_sigma=20){
|
|
|
1285 if(length(Pdf)>1){
|
|
|
1286 norm = {length_sigma-1}/2/max_sigma
|
|
|
1287 pVal = {sum(Pdf[1:{{length_sigma-1}/2}]) + Pdf[{{length_sigma+1}/2}]/2}/norm
|
|
|
1288 if(pVal>0.5){
|
|
|
1289 pVal = pVal-1
|
|
|
1290 }
|
|
|
1291 return(pVal)
|
|
|
1292 }else{
|
|
|
1293 return(NA)
|
|
|
1294 }
|
|
|
1295 }
|
|
|
1296
|
|
|
1297 # Compute p-value of two distributions
|
|
|
1298 compareTwoDistsFaster <-function(sigma_S=seq(-20,20,length.out=4001), N=10000, dens1=runif(4001,0,1), dens2=runif(4001,0,1)){
|
|
|
1299 #print(c(length(dens1),length(dens2)))
|
|
|
1300 if(length(dens1)>1 & length(dens2)>1 ){
|
|
|
1301 dens1<-dens1/sum(dens1)
|
|
|
1302 dens2<-dens2/sum(dens2)
|
|
|
1303 cum2 <- cumsum(dens2)-dens2/2
|
|
|
1304 tmp<- sum(sapply(1:length(dens1),function(i)return(dens1[i]*cum2[i])))
|
|
|
1305 #print(tmp)
|
|
|
1306 if(tmp>0.5)tmp<-tmp-1
|
|
|
1307 return( tmp )
|
|
|
1308 }
|
|
|
1309 else {
|
|
|
1310 return(NA)
|
|
|
1311 }
|
|
|
1312 #return (sum(sapply(1:N,function(i)(sample(sigma_S,1,prob=dens1)>sample(sigma_S,1,prob=dens2))))/N)
|
|
|
1313 }
|
|
|
1314
|
|
|
1315 # get number of seqeunces contributing to the sigma (i.e. seqeunces with mutations)
|
|
|
1316 numberOfSeqsWithMutations <- function(matMutations,test=1){
|
|
|
1317 if(test==4)test=2
|
|
|
1318 cdrSeqs <- 0
|
|
|
1319 fwrSeqs <- 0
|
|
|
1320 if(test==1){#focused
|
|
|
1321 cdrMutations <- apply(matMutations, 1, function(x){ sum(x[c(1,2,4)]) })
|
|
|
1322 fwrMutations <- apply(matMutations, 1, function(x){ sum(x[c(3,4,2)]) })
|
|
|
1323 if( any(which(cdrMutations>0)) ) cdrSeqs <- sum(cdrMutations>0)
|
|
|
1324 if( any(which(fwrMutations>0)) ) fwrSeqs <- sum(fwrMutations>0)
|
|
|
1325 }
|
|
|
1326 if(test==2){#local
|
|
|
1327 cdrMutations <- apply(matMutations, 1, function(x){ sum(x[c(1,2)]) })
|
|
|
1328 fwrMutations <- apply(matMutations, 1, function(x){ sum(x[c(3,4)]) })
|
|
|
1329 if( any(which(cdrMutations>0)) ) cdrSeqs <- sum(cdrMutations>0)
|
|
|
1330 if( any(which(fwrMutations>0)) ) fwrSeqs <- sum(fwrMutations>0)
|
|
|
1331 }
|
|
|
1332 return(c("CDR"=cdrSeqs, "FWR"=fwrSeqs))
|
|
|
1333 }
|
|
|
1334
|
|
|
1335
|
|
|
1336
|
|
|
1337 shadeColor <- function(sigmaVal=NA,pVal=NA){
|
|
|
1338 if(is.na(sigmaVal) & is.na(pVal)) return(NA)
|
|
|
1339 if(is.na(sigmaVal) & !is.na(pVal)) sigmaVal=sign(pVal)
|
|
|
1340 if(is.na(pVal) || pVal==1 || pVal==0){
|
|
|
1341 returnColor = "#FFFFFF";
|
|
|
1342 }else{
|
|
|
1343 colVal=abs(pVal);
|
|
|
1344
|
|
|
1345 if(sigmaVal<0){
|
|
|
1346 if(colVal>0.1)
|
|
|
1347 returnColor = "#CCFFCC";
|
|
|
1348 if(colVal<=0.1)
|
|
|
1349 returnColor = "#99FF99";
|
|
|
1350 if(colVal<=0.050)
|
|
|
1351 returnColor = "#66FF66";
|
|
|
1352 if(colVal<=0.010)
|
|
|
1353 returnColor = "#33FF33";
|
|
|
1354 if(colVal<=0.005)
|
|
|
1355 returnColor = "#00FF00";
|
|
|
1356
|
|
|
1357 }else{
|
|
|
1358 if(colVal>0.1)
|
|
|
1359 returnColor = "#FFCCCC";
|
|
|
1360 if(colVal<=0.1)
|
|
|
1361 returnColor = "#FF9999";
|
|
|
1362 if(colVal<=0.05)
|
|
|
1363 returnColor = "#FF6666";
|
|
|
1364 if(colVal<=0.01)
|
|
|
1365 returnColor = "#FF3333";
|
|
|
1366 if(colVal<0.005)
|
|
|
1367 returnColor = "#FF0000";
|
|
|
1368 }
|
|
|
1369 }
|
|
|
1370
|
|
|
1371 return(returnColor)
|
|
|
1372 }
|
|
|
1373
|
|
|
1374
|
|
|
1375
|
|
|
1376 plotHelp <- function(xfrac=0.05,yfrac=0.05,log=FALSE){
|
|
|
1377 if(!log){
|
|
|
1378 x = par()$usr[1]-(par()$usr[2]-par()$usr[1])*xfrac
|
|
|
1379 y = par()$usr[4]+(par()$usr[4]-par()$usr[3])*yfrac
|
|
|
1380 }else {
|
|
|
1381 if(log==2){
|
|
|
1382 x = par()$usr[1]-(par()$usr[2]-par()$usr[1])*xfrac
|
|
|
1383 y = 10^((par()$usr[4])+((par()$usr[4])-(par()$usr[3]))*yfrac)
|
|
|
1384 }
|
|
|
1385 if(log==1){
|
|
|
1386 x = 10^((par()$usr[1])-((par()$usr[2])-(par()$usr[1]))*xfrac)
|
|
|
1387 y = par()$usr[4]+(par()$usr[4]-par()$usr[3])*yfrac
|
|
|
1388 }
|
|
|
1389 if(log==3){
|
|
|
1390 x = 10^((par()$usr[1])-((par()$usr[2])-(par()$usr[1]))*xfrac)
|
|
|
1391 y = 10^((par()$usr[4])+((par()$usr[4])-(par()$usr[3]))*yfrac)
|
|
|
1392 }
|
|
|
1393 }
|
|
|
1394 return(c("x"=x,"y"=y))
|
|
|
1395 }
|
|
|
1396
|
|
|
1397 # SHMulation
|
|
|
1398
|
|
|
1399 # Based on targeting, introduce a single mutation & then update the targeting
|
|
|
1400 oneMutation <- function(){
|
|
|
1401 # Pick a postion + mutation
|
|
|
1402 posMutation = sample(1:(seqGermlineLen*4),1,replace=F,prob=as.vector(seqTargeting))
|
|
|
1403 posNucNumb = ceiling(posMutation/4) # Nucleotide number
|
|
|
1404 posNucKind = 4 - ( (posNucNumb*4) - posMutation ) # Nuc the position mutates to
|
|
|
1405
|
|
|
1406 #mutate the simulation sequence
|
|
|
1407 seqSimVec <- s2c(seqSim)
|
|
|
1408 seqSimVec[posNucNumb] <- NUCLEOTIDES[posNucKind]
|
|
|
1409 seqSim <<- c2s(seqSimVec)
|
|
|
1410
|
|
|
1411 #update Mutability, Targeting & MutationsTypes
|
|
|
1412 updateMutabilityNTargeting(posNucNumb)
|
|
|
1413
|
|
|
1414 #return(c(posNucNumb,NUCLEOTIDES[posNucKind]))
|
|
|
1415 return(posNucNumb)
|
|
|
1416 }
|
|
|
1417
|
|
|
1418 updateMutabilityNTargeting <- function(position){
|
|
|
1419 min_i<-max((position-2),1)
|
|
|
1420 max_i<-min((position+2),nchar(seqSim))
|
|
|
1421 min_ii<-min(min_i,3)
|
|
|
1422
|
|
|
1423 #mutability - update locally
|
|
|
1424 seqMutability[(min_i):(max_i)] <<- computeMutabilities(substr(seqSim,position-4,position+4))[(min_ii):(max_i-min_i+min_ii)]
|
|
|
1425
|
|
|
1426
|
|
|
1427 #targeting - compute locally
|
|
|
1428 seqTargeting[,min_i:max_i] <<- computeTargeting(substr(seqSim,min_i,max_i),seqMutability[min_i:max_i])
|
|
|
1429 seqTargeting[is.na(seqTargeting)] <<- 0
|
|
|
1430 #mutCodonPos = getCodonPos(position)
|
|
|
1431 mutCodonPos = seq(getCodonPos(min_i)[1],getCodonPos(max_i)[3])
|
|
|
1432 #cat(mutCodonPos,"\n")
|
|
|
1433 mutTypeCodon = getCodonPos(position)
|
|
|
1434 seqMutationTypes[,mutTypeCodon] <<- computeMutationTypesFast( substr(seqSim,mutTypeCodon[1],mutTypeCodon[3]) )
|
|
|
1435 # Stop = 0
|
|
|
1436 if(any(seqMutationTypes[,mutCodonPos]=="Stop",na.rm=T )){
|
|
|
1437 seqTargeting[,mutCodonPos][seqMutationTypes[,mutCodonPos]=="Stop"] <<- 0
|
|
|
1438 }
|
|
|
1439
|
|
|
1440
|
|
|
1441 #Selection
|
|
|
1442 selectedPos = (min_i*4-4)+(which(seqMutationTypes[,min_i:max_i]=="R"))
|
|
|
1443 # CDR
|
|
|
1444 selectedCDR = selectedPos[which(matCDR[selectedPos]==T)]
|
|
|
1445 seqTargeting[selectedCDR] <<- seqTargeting[selectedCDR] * exp(selCDR)
|
|
|
1446 seqTargeting[selectedCDR] <<- seqTargeting[selectedCDR]/baseLineCDR_K
|
|
|
1447
|
|
|
1448 # FWR
|
|
|
1449 selectedFWR = selectedPos[which(matFWR[selectedPos]==T)]
|
|
|
1450 seqTargeting[selectedFWR] <<- seqTargeting[selectedFWR] * exp(selFWR)
|
|
|
1451 seqTargeting[selectedFWR] <<- seqTargeting[selectedFWR]/baseLineFWR_K
|
|
|
1452
|
|
|
1453 }
|
|
|
1454
|
|
|
1455
|
|
|
1456
|
|
|
1457 # Validate the mutation: if the mutation has not been sampled before validate it, else discard it.
|
|
|
1458 validateMutation <- function(){
|
|
|
1459 if( !(mutatedPos%in%mutatedPositions) ){ # if it's a new mutation
|
|
|
1460 uniqueMutationsIntroduced <<- uniqueMutationsIntroduced + 1
|
|
|
1461 mutatedPositions[uniqueMutationsIntroduced] <<- mutatedPos
|
|
|
1462 }else{
|
|
|
1463 if(substr(seqSim,mutatedPos,mutatedPos)==substr(seqGermline,mutatedPos,mutatedPos)){ # back to germline mutation
|
|
|
1464 mutatedPositions <<- mutatedPositions[-which(mutatedPositions==mutatedPos)]
|
|
|
1465 uniqueMutationsIntroduced <<- uniqueMutationsIntroduced - 1
|
|
|
1466 }
|
|
|
1467 }
|
|
|
1468 }
|
|
|
1469
|
|
|
1470
|
|
|
1471
|
|
|
1472 # Places text (labels) at normalized coordinates
|
|
|
1473 myaxis <- function(xfrac=0.05,yfrac=0.05,log=FALSE,w="text",cex=1,adj=1,thecol="black"){
|
|
|
1474 par(xpd=TRUE)
|
|
|
1475 if(!log)
|
|
|
1476 text(par()$usr[1]-(par()$usr[2]-par()$usr[1])*xfrac,par()$usr[4]+(par()$usr[4]-par()$usr[3])*yfrac,w,cex=cex,adj=adj,col=thecol)
|
|
|
1477 else {
|
|
|
1478 if(log==2)
|
|
|
1479 text(
|
|
|
1480 par()$usr[1]-(par()$usr[2]-par()$usr[1])*xfrac,
|
|
|
1481 10^((par()$usr[4])+((par()$usr[4])-(par()$usr[3]))*yfrac),
|
|
|
1482 w,cex=cex,adj=adj,col=thecol)
|
|
|
1483 if(log==1)
|
|
|
1484 text(
|
|
|
1485 10^((par()$usr[1])-((par()$usr[2])-(par()$usr[1]))*xfrac),
|
|
|
1486 par()$usr[4]+(par()$usr[4]-par()$usr[3])*yfrac,
|
|
|
1487 w,cex=cex,adj=adj,col=thecol)
|
|
|
1488 if(log==3)
|
|
|
1489 text(
|
|
|
1490 10^((par()$usr[1])-((par()$usr[2])-(par()$usr[1]))*xfrac),
|
|
|
1491 10^((par()$usr[4])+((par()$usr[4])-(par()$usr[3]))*yfrac),
|
|
|
1492 w,cex=cex,adj=adj,col=thecol)
|
|
|
1493 }
|
|
|
1494 par(xpd=FALSE)
|
|
|
1495 }
|
|
|
1496
|
|
|
1497
|
|
|
1498
|
|
|
1499 # Count the mutations in a sequence
|
|
|
1500 analyzeMutations <- function( inputMatrixIndex, model = 0 , multipleMutation=0, seqWithStops=0){
|
|
|
1501
|
|
|
1502 paramGL = s2c(matInput[inputMatrixIndex,2])
|
|
|
1503 paramSeq = s2c(matInput[inputMatrixIndex,1])
|
|
|
1504
|
|
|
1505 #if( any(paramSeq=="N") ){
|
|
|
1506 # gapPos_Seq = which(paramSeq=="N")
|
|
|
1507 # gapPos_Seq_ToReplace = gapPos_Seq[paramGL[gapPos_Seq] != "N"]
|
|
|
1508 # paramSeq[gapPos_Seq_ToReplace] = paramGL[gapPos_Seq_ToReplace]
|
|
|
1509 #}
|
|
|
1510 mutations_val = paramGL != paramSeq
|
|
|
1511
|
|
|
1512 if(any(mutations_val)){
|
|
|
1513 mutationPos = which(mutations_val)#{1:length(mutations_val)}[mutations_val]
|
|
|
1514 length_mutations =length(mutationPos)
|
|
|
1515 mutationInfo = rep(NA,length_mutations)
|
|
|
1516
|
|
|
1517 pos<- mutationPos
|
|
|
1518 pos_array<-array(sapply(pos,getCodonPos))
|
|
|
1519 codonGL = paramGL[pos_array]
|
|
|
1520 codonSeqWhole = paramSeq[pos_array]
|
|
|
1521 codonSeq = sapply(pos,function(x){
|
|
|
1522 seqP = paramGL[getCodonPos(x)]
|
|
|
1523 muCodonPos = {x-1}%%3+1
|
|
|
1524 seqP[muCodonPos] = paramSeq[x]
|
|
|
1525 return(seqP)
|
|
|
1526 })
|
|
|
1527 GLcodons = apply(matrix(codonGL,length_mutations,3,byrow=TRUE),1,c2s)
|
|
|
1528 SeqcodonsWhole = apply(matrix(codonSeqWhole,length_mutations,3,byrow=TRUE),1,c2s)
|
|
|
1529 Seqcodons = apply(codonSeq,2,c2s)
|
|
|
1530
|
|
|
1531 mutationInfo = apply(rbind(GLcodons , Seqcodons),2,function(x){mutationType(c2s(x[1]),c2s(x[2]))})
|
|
|
1532 names(mutationInfo) = mutationPos
|
|
|
1533
|
|
|
1534 mutationInfoWhole = apply(rbind(GLcodons , SeqcodonsWhole),2,function(x){mutationType(c2s(x[1]),c2s(x[2]))})
|
|
|
1535 names(mutationInfoWhole) = mutationPos
|
|
|
1536
|
|
|
1537 mutationInfo <- mutationInfo[!is.na(mutationInfo)]
|
|
|
1538 mutationInfoWhole <- mutationInfoWhole[!is.na(mutationInfoWhole)]
|
|
|
1539
|
|
|
1540 if(any(!is.na(mutationInfo))){
|
|
|
1541
|
|
|
1542 #Filter based on Stop (at the codon level)
|
|
|
1543 if(seqWithStops==1){
|
|
|
1544 nucleotidesAtStopCodons = names(mutationInfoWhole[mutationInfoWhole!="Stop"])
|
|
|
1545 mutationInfo = mutationInfo[nucleotidesAtStopCodons]
|
|
|
1546 mutationInfoWhole = mutationInfo[nucleotidesAtStopCodons]
|
|
|
1547 }else{
|
|
|
1548 countStops = sum(mutationInfoWhole=="Stop")
|
|
|
1549 if(seqWithStops==2 & countStops==0) mutationInfo = NA
|
|
|
1550 if(seqWithStops==3 & countStops>0) mutationInfo = NA
|
|
|
1551 }
|
|
|
1552
|
|
|
1553 if(any(!is.na(mutationInfo))){
|
|
|
1554 #Filter mutations based on multipleMutation
|
|
|
1555 if(multipleMutation==1 & !is.na(mutationInfo)){
|
|
|
1556 mutationCodons = getCodonNumb(as.numeric(names(mutationInfoWhole)))
|
|
|
1557 tableMutationCodons <- table(mutationCodons)
|
|
|
1558 codonsWithMultipleMutations <- as.numeric(names(tableMutationCodons[tableMutationCodons>1]))
|
|
|
1559 if(any(codonsWithMultipleMutations)){
|
|
|
1560 #remove the nucleotide mutations in the codons with multiple mutations
|
|
|
1561 mutationInfo <- mutationInfo[!(mutationCodons %in% codonsWithMultipleMutations)]
|
|
|
1562 #replace those codons with Ns in the input sequence
|
|
|
1563 paramSeq[unlist(lapply(codonsWithMultipleMutations, getCodonNucs))] = "N"
|
|
|
1564 matInput[inputMatrixIndex,1] <<- c2s(paramSeq)
|
|
|
1565 }
|
|
|
1566 }
|
|
|
1567
|
|
|
1568 #Filter mutations based on the model
|
|
|
1569 if(any(mutationInfo)==T | is.na(any(mutationInfo))){
|
|
|
1570
|
|
|
1571 if(model==1 & !is.na(mutationInfo)){
|
|
|
1572 mutationInfo <- mutationInfo[mutationInfo=="S"]
|
|
|
1573 }
|
|
|
1574 if(any(mutationInfo)==T | is.na(any(mutationInfo))) return(mutationInfo)
|
|
|
1575 else return(NA)
|
|
|
1576 }else{
|
|
|
1577 return(NA)
|
|
|
1578 }
|
|
|
1579 }else{
|
|
|
1580 return(NA)
|
|
|
1581 }
|
|
|
1582
|
|
|
1583
|
|
|
1584 }else{
|
|
|
1585 return(NA)
|
|
|
1586 }
|
|
|
1587
|
|
|
1588
|
|
|
1589 }else{
|
|
|
1590 return (NA)
|
|
|
1591 }
|
|
|
1592 }
|
|
|
1593
|
|
|
1594 analyzeMutationsFixed <- function( inputArray, model = 0 , multipleMutation=0, seqWithStops=0){
|
|
|
1595
|
|
|
1596 paramGL = s2c(inputArray[2])
|
|
|
1597 paramSeq = s2c(inputArray[1])
|
|
|
1598 inputSeq <- inputArray[1]
|
|
|
1599 #if( any(paramSeq=="N") ){
|
|
|
1600 # gapPos_Seq = which(paramSeq=="N")
|
|
|
1601 # gapPos_Seq_ToReplace = gapPos_Seq[paramGL[gapPos_Seq] != "N"]
|
|
|
1602 # paramSeq[gapPos_Seq_ToReplace] = paramGL[gapPos_Seq_ToReplace]
|
|
|
1603 #}
|
|
|
1604 mutations_val = paramGL != paramSeq
|
|
|
1605
|
|
|
1606 if(any(mutations_val)){
|
|
|
1607 mutationPos = which(mutations_val)#{1:length(mutations_val)}[mutations_val]
|
|
|
1608 length_mutations =length(mutationPos)
|
|
|
1609 mutationInfo = rep(NA,length_mutations)
|
|
|
1610
|
|
|
1611 pos<- mutationPos
|
|
|
1612 pos_array<-array(sapply(pos,getCodonPos))
|
|
|
1613 codonGL = paramGL[pos_array]
|
|
|
1614 codonSeqWhole = paramSeq[pos_array]
|
|
|
1615 codonSeq = sapply(pos,function(x){
|
|
|
1616 seqP = paramGL[getCodonPos(x)]
|
|
|
1617 muCodonPos = {x-1}%%3+1
|
|
|
1618 seqP[muCodonPos] = paramSeq[x]
|
|
|
1619 return(seqP)
|
|
|
1620 })
|
|
|
1621 GLcodons = apply(matrix(codonGL,length_mutations,3,byrow=TRUE),1,c2s)
|
|
|
1622 SeqcodonsWhole = apply(matrix(codonSeqWhole,length_mutations,3,byrow=TRUE),1,c2s)
|
|
|
1623 Seqcodons = apply(codonSeq,2,c2s)
|
|
|
1624
|
|
|
1625 mutationInfo = apply(rbind(GLcodons , Seqcodons),2,function(x){mutationType(c2s(x[1]),c2s(x[2]))})
|
|
|
1626 names(mutationInfo) = mutationPos
|
|
|
1627
|
|
|
1628 mutationInfoWhole = apply(rbind(GLcodons , SeqcodonsWhole),2,function(x){mutationType(c2s(x[1]),c2s(x[2]))})
|
|
|
1629 names(mutationInfoWhole) = mutationPos
|
|
|
1630
|
|
|
1631 mutationInfo <- mutationInfo[!is.na(mutationInfo)]
|
|
|
1632 mutationInfoWhole <- mutationInfoWhole[!is.na(mutationInfoWhole)]
|
|
|
1633
|
|
|
1634 if(any(!is.na(mutationInfo))){
|
|
|
1635
|
|
|
1636 #Filter based on Stop (at the codon level)
|
|
|
1637 if(seqWithStops==1){
|
|
|
1638 nucleotidesAtStopCodons = names(mutationInfoWhole[mutationInfoWhole!="Stop"])
|
|
|
1639 mutationInfo = mutationInfo[nucleotidesAtStopCodons]
|
|
|
1640 mutationInfoWhole = mutationInfo[nucleotidesAtStopCodons]
|
|
|
1641 }else{
|
|
|
1642 countStops = sum(mutationInfoWhole=="Stop")
|
|
|
1643 if(seqWithStops==2 & countStops==0) mutationInfo = NA
|
|
|
1644 if(seqWithStops==3 & countStops>0) mutationInfo = NA
|
|
|
1645 }
|
|
|
1646
|
|
|
1647 if(any(!is.na(mutationInfo))){
|
|
|
1648 #Filter mutations based on multipleMutation
|
|
|
1649 if(multipleMutation==1 & !is.na(mutationInfo)){
|
|
|
1650 mutationCodons = getCodonNumb(as.numeric(names(mutationInfoWhole)))
|
|
|
1651 tableMutationCodons <- table(mutationCodons)
|
|
|
1652 codonsWithMultipleMutations <- as.numeric(names(tableMutationCodons[tableMutationCodons>1]))
|
|
|
1653 if(any(codonsWithMultipleMutations)){
|
|
|
1654 #remove the nucleotide mutations in the codons with multiple mutations
|
|
|
1655 mutationInfo <- mutationInfo[!(mutationCodons %in% codonsWithMultipleMutations)]
|
|
|
1656 #replace those codons with Ns in the input sequence
|
|
|
1657 paramSeq[unlist(lapply(codonsWithMultipleMutations, getCodonNucs))] = "N"
|
|
|
1658 #matInput[inputMatrixIndex,1] <<- c2s(paramSeq)
|
|
|
1659 inputSeq <- c2s(paramSeq)
|
|
|
1660 }
|
|
|
1661 }
|
|
|
1662
|
|
|
1663 #Filter mutations based on the model
|
|
|
1664 if(any(mutationInfo)==T | is.na(any(mutationInfo))){
|
|
|
1665
|
|
|
1666 if(model==1 & !is.na(mutationInfo)){
|
|
|
1667 mutationInfo <- mutationInfo[mutationInfo=="S"]
|
|
|
1668 }
|
|
|
1669 if(any(mutationInfo)==T | is.na(any(mutationInfo))) return(list(mutationInfo,inputSeq))
|
|
|
1670 else return(list(NA,inputSeq))
|
|
|
1671 }else{
|
|
|
1672 return(list(NA,inputSeq))
|
|
|
1673 }
|
|
|
1674 }else{
|
|
|
1675 return(list(NA,inputSeq))
|
|
|
1676 }
|
|
|
1677
|
|
|
1678
|
|
|
1679 }else{
|
|
|
1680 return(list(NA,inputSeq))
|
|
|
1681 }
|
|
|
1682
|
|
|
1683
|
|
|
1684 }else{
|
|
|
1685 return (list(NA,inputSeq))
|
|
|
1686 }
|
|
|
1687 }
|
|
|
1688
|
|
|
1689 # triMutability Background Count
|
|
|
1690 buildMutabilityModel <- function( inputMatrixIndex, model=0 , multipleMutation=0, seqWithStops=0, stopMutations=0){
|
|
|
1691
|
|
|
1692 #rowOrigMatInput = matInput[inputMatrixIndex,]
|
|
|
1693 seqGL = gsub("-", "", matInput[inputMatrixIndex,2])
|
|
|
1694 seqInput = gsub("-", "", matInput[inputMatrixIndex,1])
|
|
|
1695 #matInput[inputMatrixIndex,] <<- cbind(seqInput,seqGL)
|
|
|
1696 tempInput <- cbind(seqInput,seqGL)
|
|
|
1697 seqLength = nchar(seqGL)
|
|
|
1698 list_analyzeMutationsFixed<- analyzeMutationsFixed(tempInput, model, multipleMutation, seqWithStops)
|
|
|
1699 mutationCount <- list_analyzeMutationsFixed[[1]]
|
|
|
1700 seqInput <- list_analyzeMutationsFixed[[2]]
|
|
|
1701 BackgroundMatrix = mutabilityMatrix
|
|
|
1702 MutationMatrix = mutabilityMatrix
|
|
|
1703 MutationCountMatrix = mutabilityMatrix
|
|
|
1704 if(!is.na(mutationCount)){
|
|
|
1705 if((stopMutations==0 & model==0) | (stopMutations==1 & (sum(mutationCount=="Stop")<length(mutationCount))) | (model==1 & (sum(mutationCount=="S")>0)) ){
|
|
|
1706
|
|
|
1707 fivermerStartPos = 1:(seqLength-4)
|
|
|
1708 fivemerLength <- length(fivermerStartPos)
|
|
|
1709 fivemerGL <- substr(rep(seqGL,length(fivermerStartPos)),(fivermerStartPos),(fivermerStartPos+4))
|
|
|
1710 fivemerSeq <- substr(rep(seqInput,length(fivermerStartPos)),(fivermerStartPos),(fivermerStartPos+4))
|
|
|
1711
|
|
|
1712 #Background
|
|
|
1713 for(fivemerIndex in 1:fivemerLength){
|
|
|
1714 fivemer = fivemerGL[fivemerIndex]
|
|
|
1715 if(!any(grep("N",fivemer))){
|
|
|
1716 fivemerCodonPos = fivemerCodon(fivemerIndex)
|
|
|
1717 fivemerReadingFrameCodon = substr(fivemer,fivemerCodonPos[1],fivemerCodonPos[3])
|
|
|
1718 fivemerReadingFrameCodonInputSeq = substr(fivemerSeq[fivemerIndex],fivemerCodonPos[1],fivemerCodonPos[3])
|
|
|
1719
|
|
|
1720 # All mutations model
|
|
|
1721 #if(!any(grep("N",fivemerReadingFrameCodon))){
|
|
|
1722 if(model==0){
|
|
|
1723 if(stopMutations==0){
|
|
|
1724 if(!any(grep("N",fivemerReadingFrameCodonInputSeq)))
|
|
|
1725 BackgroundMatrix[fivemer] <- (BackgroundMatrix[fivemer] + 1)
|
|
|
1726 }else{
|
|
|
1727 if( !any(grep("N",fivemerReadingFrameCodonInputSeq)) & translateCodonToAminoAcid(fivemerReadingFrameCodon)!="*" ){
|
|
|
1728 positionWithinCodon = which(fivemerCodonPos==3)#positionsWithinCodon[(fivemerCodonPos[1]%%3)+1]
|
|
|
1729 BackgroundMatrix[fivemer] <- (BackgroundMatrix[fivemer] + probNonStopMutations[fivemerReadingFrameCodon,positionWithinCodon])
|
|
|
1730 }
|
|
|
1731 }
|
|
|
1732 }else{ # Only silent mutations
|
|
|
1733 if( !any(grep("N",fivemerReadingFrameCodonInputSeq)) & translateCodonToAminoAcid(fivemerReadingFrameCodon)!="*" & translateCodonToAminoAcid(fivemerReadingFrameCodonInputSeq)==translateCodonToAminoAcid(fivemerReadingFrameCodon)){
|
|
|
1734 positionWithinCodon = which(fivemerCodonPos==3)
|
|
|
1735 BackgroundMatrix[fivemer] <- (BackgroundMatrix[fivemer] + probSMutations[fivemerReadingFrameCodon,positionWithinCodon])
|
|
|
1736 }
|
|
|
1737 }
|
|
|
1738 #}
|
|
|
1739 }
|
|
|
1740 }
|
|
|
1741
|
|
|
1742 #Mutations
|
|
|
1743 if(stopMutations==1) mutationCount = mutationCount[mutationCount!="Stop"]
|
|
|
1744 if(model==1) mutationCount = mutationCount[mutationCount=="S"]
|
|
|
1745 mutationPositions = as.numeric(names(mutationCount))
|
|
|
1746 mutationCount = mutationCount[mutationPositions>2 & mutationPositions<(seqLength-1)]
|
|
|
1747 mutationPositions = mutationPositions[mutationPositions>2 & mutationPositions<(seqLength-1)]
|
|
|
1748 countMutations = 0
|
|
|
1749 for(mutationPosition in mutationPositions){
|
|
|
1750 fivemerIndex = mutationPosition-2
|
|
|
1751 fivemer = fivemerSeq[fivemerIndex]
|
|
|
1752 GLfivemer = fivemerGL[fivemerIndex]
|
|
|
1753 fivemerCodonPos = fivemerCodon(fivemerIndex)
|
|
|
1754 fivemerReadingFrameCodon = substr(fivemer,fivemerCodonPos[1],fivemerCodonPos[3])
|
|
|
1755 fivemerReadingFrameCodonGL = substr(GLfivemer,fivemerCodonPos[1],fivemerCodonPos[3])
|
|
|
1756 if(!any(grep("N",fivemer)) & !any(grep("N",GLfivemer))){
|
|
|
1757 if(model==0){
|
|
|
1758 countMutations = countMutations + 1
|
|
|
1759 MutationMatrix[GLfivemer] <- (MutationMatrix[GLfivemer] + 1)
|
|
|
1760 MutationCountMatrix[GLfivemer] <- (MutationCountMatrix[GLfivemer] + 1)
|
|
|
1761 }else{
|
|
|
1762 if( translateCodonToAminoAcid(fivemerReadingFrameCodonGL)!="*" ){
|
|
|
1763 countMutations = countMutations + 1
|
|
|
1764 positionWithinCodon = which(fivemerCodonPos==3)
|
|
|
1765 glNuc = substr(fivemerReadingFrameCodonGL,positionWithinCodon,positionWithinCodon)
|
|
|
1766 inputNuc = substr(fivemerReadingFrameCodon,positionWithinCodon,positionWithinCodon)
|
|
|
1767 MutationMatrix[GLfivemer] <- (MutationMatrix[GLfivemer] + substitution[glNuc,inputNuc])
|
|
|
1768 MutationCountMatrix[GLfivemer] <- (MutationCountMatrix[GLfivemer] + 1)
|
|
|
1769 }
|
|
|
1770 }
|
|
|
1771 }
|
|
|
1772 }
|
|
|
1773
|
|
|
1774 seqMutability = MutationMatrix/BackgroundMatrix
|
|
|
1775 seqMutability = seqMutability/sum(seqMutability,na.rm=TRUE)
|
|
|
1776 #cat(inputMatrixIndex,"\t",countMutations,"\n")
|
|
|
1777 return(list("seqMutability" = seqMutability,"numbMutations" = countMutations,"seqMutabilityCount" = MutationCountMatrix, "BackgroundMatrix"=BackgroundMatrix))
|
|
|
1778
|
|
|
1779 }
|
|
|
1780 }
|
|
|
1781
|
|
|
1782 }
|
|
|
1783
|
|
|
1784 #Returns the codon position containing the middle nucleotide
|
|
|
1785 fivemerCodon <- function(fivemerIndex){
|
|
|
1786 codonPos = list(2:4,1:3,3:5)
|
|
|
1787 fivemerType = fivemerIndex%%3
|
|
|
1788 return(codonPos[[fivemerType+1]])
|
|
|
1789 }
|
|
|
1790
|
|
|
1791 #returns probability values for one mutation in codons resulting in R, S or Stop
|
|
|
1792 probMutations <- function(typeOfMutation){
|
|
|
1793 matMutationProb <- matrix(0,ncol=3,nrow=125,dimnames=list(words(alphabet = c(NUCLEOTIDES,"N"), length=3),c(1:3)))
|
|
|
1794 for(codon in rownames(matMutationProb)){
|
|
|
1795 if( !any(grep("N",codon)) ){
|
|
|
1796 for(muPos in 1:3){
|
|
|
1797 matCodon = matrix(rep(s2c(codon),3),nrow=3,ncol=3,byrow=T)
|
|
|
1798 glNuc = matCodon[1,muPos]
|
|
|
1799 matCodon[,muPos] = canMutateTo(glNuc)
|
|
|
1800 substitutionRate = substitution[glNuc,matCodon[,muPos]]
|
|
|
1801 typeOfMutations = apply(rbind(rep(codon,3),apply(matCodon,1,c2s)),2,function(x){mutationType(c2s(x[1]),c2s(x[2]))})
|
|
|
1802 matMutationProb[codon,muPos] <- sum(substitutionRate[typeOfMutations==typeOfMutation])
|
|
|
1803 }
|
|
|
1804 }
|
|
|
1805 }
|
|
|
1806
|
|
|
1807 return(matMutationProb)
|
|
|
1808 }
|
|
|
1809
|
|
|
1810
|
|
|
1811
|
|
|
1812
|
|
|
1813 #Mapping Trinucleotides to fivemers
|
|
|
1814 mapTriToFivemer <- function(triMutability=triMutability_Literature_Human){
|
|
|
1815 rownames(triMutability) <- triMutability_Names
|
|
|
1816 Fivemer<-rep(NA,1024)
|
|
|
1817 names(Fivemer)<-words(alphabet=NUCLEOTIDES,length=5)
|
|
|
1818 Fivemer<-sapply(names(Fivemer),function(Word)return(sum( c(triMutability[substring(Word,3,5),1],triMutability[substring(Word,2,4),2],triMutability[substring(Word,1,3),3]),na.rm=TRUE)))
|
|
|
1819 Fivemer<-Fivemer/sum(Fivemer)
|
|
|
1820 return(Fivemer)
|
|
|
1821 }
|
|
|
1822
|
|
|
1823 collapseFivemerToTri<-function(Fivemer,Weights=MutabilityWeights,position=1,NUC="A"){
|
|
|
1824 Indices<-substring(names(Fivemer),3,3)==NUC
|
|
|
1825 Factors<-substring(names(Fivemer[Indices]),(4-position),(6-position))
|
|
|
1826 tapply(which(Indices),Factors,function(i)weighted.mean(Fivemer[i],Weights[i],na.rm=TRUE))
|
|
|
1827 }
|
|
|
1828
|
|
|
1829
|
|
|
1830
|
|
|
1831 CountFivemerToTri<-function(Fivemer,Weights=MutabilityWeights,position=1,NUC="A"){
|
|
|
1832 Indices<-substring(names(Fivemer),3,3)==NUC
|
|
|
1833 Factors<-substring(names(Fivemer[Indices]),(4-position),(6-position))
|
|
|
1834 tapply(which(Indices),Factors,function(i)sum(Weights[i],na.rm=TRUE))
|
|
|
1835 }
|
|
|
1836
|
|
|
1837 #Uses the real counts of the mutated fivemers
|
|
|
1838 CountFivemerToTri2<-function(Fivemer,Counts=MutabilityCounts,position=1,NUC="A"){
|
|
|
1839 Indices<-substring(names(Fivemer),3,3)==NUC
|
|
|
1840 Factors<-substring(names(Fivemer[Indices]),(4-position),(6-position))
|
|
|
1841 tapply(which(Indices),Factors,function(i)sum(Counts[i],na.rm=TRUE))
|
|
|
1842 }
|
|
|
1843
|
|
|
1844 bootstrap<-function(x=c(33,12,21),M=10000,alpha=0.05){
|
|
|
1845 N<-sum(x)
|
|
|
1846 if(N){
|
|
|
1847 p<-x/N
|
|
|
1848 k<-length(x)-1
|
|
|
1849 tmp<-rmultinom(M, size = N, prob=p)
|
|
|
1850 tmp_p<-apply(tmp,2,function(y)y/N)
|
|
|
1851 (apply(tmp_p,1,function(y)quantile(y,c(alpha/2/k,1-alpha/2/k))))
|
|
|
1852 }
|
|
|
1853 else return(matrix(0,2,length(x)))
|
|
|
1854 }
|
|
|
1855
|
|
|
1856
|
|
|
1857
|
|
|
1858
|
|
|
1859 bootstrap2<-function(x=c(33,12,21),n=10,M=10000,alpha=0.05){
|
|
|
1860
|
|
|
1861 N<-sum(x)
|
|
|
1862 k<-length(x)
|
|
|
1863 y<-rep(1:k,x)
|
|
|
1864 tmp<-sapply(1:M,function(i)sample(y,n))
|
|
|
1865 if(n>1)tmp_p<-sapply(1:M,function(j)sapply(1:k,function(i)sum(tmp[,j]==i)))/n
|
|
|
1866 if(n==1)tmp_p<-sapply(1:M,function(j)sapply(1:k,function(i)sum(tmp[j]==i)))/n
|
|
|
1867 (apply(tmp_p,1,function(z)quantile(z,c(alpha/2/(k-1),1-alpha/2/(k-1)))))
|
|
|
1868 }
|
|
|
1869
|
|
|
1870
|
|
|
1871
|
|
|
1872 p_value<-function(x=c(33,12,21),M=100000,x_obs=c(2,5,3)){
|
|
|
1873 n=sum(x_obs)
|
|
|
1874 N<-sum(x)
|
|
|
1875 k<-length(x)
|
|
|
1876 y<-rep(1:k,x)
|
|
|
1877 tmp<-sapply(1:M,function(i)sample(y,n))
|
|
|
1878 if(n>1)tmp_p<-sapply(1:M,function(j)sapply(1:k,function(i)sum(tmp[,j]==i)))
|
|
|
1879 if(n==1)tmp_p<-sapply(1:M,function(j)sapply(1:k,function(i)sum(tmp[j]==i)))
|
|
|
1880 tmp<-rbind(sapply(1:3,function(i)sum(tmp_p[i,]>=x_obs[i])/M),
|
|
|
1881 sapply(1:3,function(i)sum(tmp_p[i,]<=x_obs[i])/M))
|
|
|
1882 sapply(1:3,function(i){if(tmp[1,i]>=tmp[2,i])return(-tmp[2,i])else return(tmp[1,i])})
|
|
|
1883 }
|
|
|
1884
|
|
|
1885 #"D:\\Sequences\\IMGT Germlines\\Human_SNPless_IGHJ.FASTA"
|
|
|
1886 # Remove SNPs from IMGT germline segment alleles
|
|
|
1887 generateUnambiguousRepertoire <- function(repertoireInFile,repertoireOutFile){
|
|
|
1888 repertoireIn <- read.fasta(repertoireInFile, seqtype="DNA",as.string=T,set.attributes=F,forceDNAtolower=F)
|
|
|
1889 alleleNames <- sapply(names(repertoireIn),function(x)strsplit(x,"|",fixed=TRUE)[[1]][2])
|
|
|
1890 SNPs <- tapply(repertoireIn,sapply(alleleNames,function(x)strsplit(x,"*",fixed=TRUE)[[1]][1]),function(x){
|
|
|
1891 Indices<-NULL
|
|
|
1892 for(i in 1:length(x)){
|
|
|
1893 firstSeq = s2c(x[[1]])
|
|
|
1894 iSeq = s2c(x[[i]])
|
|
|
1895 Indices<-c(Indices,which(firstSeq[1:320]!=iSeq[1:320] & firstSeq[1:320]!="." & iSeq[1:320]!="." ))
|
|
|
1896 }
|
|
|
1897 return(sort(unique(Indices)))
|
|
|
1898 })
|
|
|
1899 repertoireOut <- repertoireIn
|
|
|
1900 repertoireOut <- lapply(names(repertoireOut), function(repertoireName){
|
|
|
1901 alleleName <- strsplit(repertoireName,"|",fixed=TRUE)[[1]][2]
|
|
|
1902 geneSegmentName <- strsplit(alleleName,"*",fixed=TRUE)[[1]][1]
|
|
|
1903 alleleSeq <- s2c(repertoireOut[[repertoireName]])
|
|
|
1904 alleleSeq[as.numeric(unlist(SNPs[geneSegmentName]))] <- "N"
|
|
|
1905 alleleSeq <- c2s(alleleSeq)
|
|
|
1906 repertoireOut[[repertoireName]] <- alleleSeq
|
|
|
1907 })
|
|
|
1908 names(repertoireOut) <- names(repertoireIn)
|
|
|
1909 write.fasta(repertoireOut,names(repertoireOut),file.out=repertoireOutFile)
|
|
|
1910
|
|
|
1911 }
|
|
|
1912
|
|
|
1913
|
|
|
1914
|
|
|
1915
|
|
|
1916
|
|
|
1917
|
|
|
1918 ############
|
|
|
1919 groupBayes2 = function(indexes, param_resultMat){
|
|
|
1920
|
|
|
1921 BayesGDist_Focused_CDR = calculate_bayesG( x=param_resultMat[indexes,1], N=apply(param_resultMat[indexes,c(1,2,4)],1,sum,na.rm=T), p=apply(param_resultMat[indexes,5:8],1,function(x){x[1]/(x[1]+x[2]+x[4])}))
|
|
|
1922 BayesGDist_Focused_FWR = calculate_bayesG( x=param_resultMat[indexes,3], N=apply(param_resultMat[indexes,c(3,2,4)],1,sum,na.rm=T), p=apply(param_resultMat[indexes,5:8],1,function(x){x[3]/(x[3]+x[2]+x[4])}))
|
|
|
1923 #BayesGDist_Local_CDR = calculate_bayesG( x=param_resultMat[indexes,1], N=apply(param_resultMat[indexes,c(1,2)],1,sum,na.rm=T), p=apply(param_resultMat[indexes,5:8],1,function(x){x[1]/(x[1]+x[2])}))
|
|
|
1924 #BayesGDist_Local_FWR = calculate_bayesG( x=param_resultMat[indexes,3], N=apply(param_resultMat[indexes,c(3,4)],1,sum,na.rm=T), p=apply(param_resultMat[indexes,5:8],1,function(x){x[3]/(x[3]+x[4])}))
|
|
|
1925 #BayesGDist_Global_CDR = calculate_bayesG( x=param_resultMat[indexes,1], N=apply(param_resultMat[indexes,c(1,2,3,4)],1,sum,na.rm=T), p=apply(param_resultMat[indexes,5:8],1,function(x){x[1]/(x[1]+x[2]+x[3]+x[4])}))
|
|
|
1926 #BayesGDist_Global_FWR = calculate_bayesG( x=param_resultMat[indexes,3], N=apply(param_resultMat[indexes,c(1,2,3,4)],1,sum,na.rm=T), p=apply(param_resultMat[indexes,5:8],1,function(x){x[3]/(x[1]+x[2]+x[3]+x[4])}))
|
|
|
1927 return ( list("BayesGDist_Focused_CDR"=BayesGDist_Focused_CDR,
|
|
|
1928 "BayesGDist_Focused_FWR"=BayesGDist_Focused_FWR) )
|
|
|
1929 #"BayesGDist_Local_CDR"=BayesGDist_Local_CDR,
|
|
|
1930 #"BayesGDist_Local_FWR" = BayesGDist_Local_FWR))
|
|
|
1931 # "BayesGDist_Global_CDR" = BayesGDist_Global_CDR,
|
|
|
1932 # "BayesGDist_Global_FWR" = BayesGDist_Global_FWR) )
|
|
|
1933
|
|
|
1934
|
|
|
1935 }
|
|
|
1936
|
|
|
1937
|
|
|
1938 calculate_bayesG <- function( x=array(), N=array(), p=array(), max_sigma=20, length_sigma=4001){
|
|
|
1939 G <- max(length(x),length(N),length(p))
|
|
|
1940 x=array(x,dim=G)
|
|
|
1941 N=array(N,dim=G)
|
|
|
1942 p=array(p,dim=G)
|
|
|
1943
|
|
|
1944 indexOfZero = N>0 & p>0
|
|
|
1945 N = N[indexOfZero]
|
|
|
1946 x = x[indexOfZero]
|
|
|
1947 p = p[indexOfZero]
|
|
|
1948 G <- length(x)
|
|
|
1949
|
|
|
1950 if(G){
|
|
|
1951
|
|
|
1952 cons<-array( dim=c(length_sigma,G) )
|
|
|
1953 if(G==1) {
|
|
|
1954 return(calculate_bayes(x=x[G],N=N[G],p=p[G],max_sigma=max_sigma,length_sigma=length_sigma))
|
|
|
1955 }
|
|
|
1956 else {
|
|
|
1957 for(g in 1:G) cons[,g] <- calculate_bayes(x=x[g],N=N[g],p=p[g],max_sigma=max_sigma,length_sigma=length_sigma)
|
|
|
1958 listMatG <- convolutionPowersOfTwoByTwos(cons,length_sigma=length_sigma)
|
|
|
1959 y<-calculate_bayesGHelper(listMatG,length_sigma=length_sigma)
|
|
|
1960 return( y/sum(y)/(2*max_sigma/(length_sigma-1)) )
|
|
|
1961 }
|
|
|
1962 }else{
|
|
|
1963 return(NA)
|
|
|
1964 }
|
|
|
1965 }
|
|
|
1966
|
|
|
1967
|
|
|
1968 calculate_bayesGHelper <- function( listMatG,length_sigma=4001 ){
|
|
|
1969 matG <- listMatG[[1]]
|
|
|
1970 groups <- listMatG[[2]]
|
|
|
1971 i = 1
|
|
|
1972 resConv <- matG[,i]
|
|
|
1973 denom <- 2^groups[i]
|
|
|
1974 if(length(groups)>1){
|
|
|
1975 while( i<length(groups) ){
|
|
|
1976 i = i + 1
|
|
|
1977 resConv <- weighted_conv(resConv, matG[,i], w= {{2^groups[i]}/denom} ,length_sigma=length_sigma)
|
|
|
1978 #cat({{2^groups[i]}/denom},"\n")
|
|
|
1979 denom <- denom + 2^groups[i]
|
|
|
1980 }
|
|
|
1981 }
|
|
|
1982 return(resConv)
|
|
|
1983 }
|
|
|
1984
|
|
|
1985 weighted_conv<-function(x,y,w=1,m=100,length_sigma=4001){
|
|
|
1986 lx<-length(x)
|
|
|
1987 ly<-length(y)
|
|
|
1988 if({lx<m}| {{lx*w}<m}| {{ly}<m}| {{ly*w}<m}){
|
|
|
1989 if(w<1){
|
|
|
1990 y1<-approx(1:ly,y,seq(1,ly,length.out=m))$y
|
|
|
1991 x1<-approx(1:lx,x,seq(1,lx,length.out=m/w))$y
|
|
|
1992 lx<-length(x1)
|
|
|
1993 ly<-length(y1)
|
|
|
1994 }
|
|
|
1995 else {
|
|
|
1996 y1<-approx(1:ly,y,seq(1,ly,length.out=m*w))$y
|
|
|
1997 x1<-approx(1:lx,x,seq(1,lx,length.out=m))$y
|
|
|
1998 lx<-length(x1)
|
|
|
1999 ly<-length(y1)
|
|
|
2000 }
|
|
|
2001 }
|
|
|
2002 else{
|
|
|
2003 x1<-x
|
|
|
2004 y1<-approx(1:ly,y,seq(1,ly,length.out=floor(lx*w)))$y
|
|
|
2005 ly<-length(y1)
|
|
|
2006 }
|
|
|
2007 tmp<-approx(x=1:(lx+ly-1),y=convolve(x1,rev(y1),type="open"),xout=seq(1,lx+ly-1,length.out=length_sigma))$y
|
|
|
2008 tmp[tmp<=0] = 0
|
|
|
2009 return(tmp/sum(tmp))
|
|
|
2010 }
|
|
|
2011
|
|
|
2012 ########################
|
|
|
2013
|
|
|
2014
|
|
|
2015
|
|
|
2016
|
|
|
2017 mutabilityMatrixONE<-rep(0,4)
|
|
|
2018 names(mutabilityMatrixONE)<-NUCLEOTIDES
|
|
|
2019
|
|
|
2020 # triMutability Background Count
|
|
|
2021 buildMutabilityModelONE <- function( inputMatrixIndex, model=0 , multipleMutation=0, seqWithStops=0, stopMutations=0){
|
|
|
2022
|
|
|
2023 #rowOrigMatInput = matInput[inputMatrixIndex,]
|
|
|
2024 seqGL = gsub("-", "", matInput[inputMatrixIndex,2])
|
|
|
2025 seqInput = gsub("-", "", matInput[inputMatrixIndex,1])
|
|
|
2026 matInput[inputMatrixIndex,] <<- c(seqInput,seqGL)
|
|
|
2027 seqLength = nchar(seqGL)
|
|
|
2028 mutationCount <- analyzeMutations(inputMatrixIndex, model, multipleMutation, seqWithStops)
|
|
|
2029 BackgroundMatrix = mutabilityMatrixONE
|
|
|
2030 MutationMatrix = mutabilityMatrixONE
|
|
|
2031 MutationCountMatrix = mutabilityMatrixONE
|
|
|
2032 if(!is.na(mutationCount)){
|
|
|
2033 if((stopMutations==0 & model==0) | (stopMutations==1 & (sum(mutationCount=="Stop")<length(mutationCount))) | (model==1 & (sum(mutationCount=="S")>0)) ){
|
|
|
2034
|
|
|
2035 # ONEmerStartPos = 1:(seqLength)
|
|
|
2036 # ONEmerLength <- length(ONEmerStartPos)
|
|
|
2037 ONEmerGL <- s2c(seqGL)
|
|
|
2038 ONEmerSeq <- s2c(seqInput)
|
|
|
2039
|
|
|
2040 #Background
|
|
|
2041 for(ONEmerIndex in 1:seqLength){
|
|
|
2042 ONEmer = ONEmerGL[ONEmerIndex]
|
|
|
2043 if(ONEmer!="N"){
|
|
|
2044 ONEmerCodonPos = getCodonPos(ONEmerIndex)
|
|
|
2045 ONEmerReadingFrameCodon = c2s(ONEmerGL[ONEmerCodonPos])
|
|
|
2046 ONEmerReadingFrameCodonInputSeq = c2s(ONEmerSeq[ONEmerCodonPos] )
|
|
|
2047
|
|
|
2048 # All mutations model
|
|
|
2049 #if(!any(grep("N",ONEmerReadingFrameCodon))){
|
|
|
2050 if(model==0){
|
|
|
2051 if(stopMutations==0){
|
|
|
2052 if(!any(grep("N",ONEmerReadingFrameCodonInputSeq)))
|
|
|
2053 BackgroundMatrix[ONEmer] <- (BackgroundMatrix[ONEmer] + 1)
|
|
|
2054 }else{
|
|
|
2055 if( !any(grep("N",ONEmerReadingFrameCodonInputSeq)) & translateCodonToAminoAcid(ONEmerReadingFrameCodonInputSeq)!="*"){
|
|
|
2056 positionWithinCodon = which(ONEmerCodonPos==ONEmerIndex)#positionsWithinCodon[(ONEmerCodonPos[1]%%3)+1]
|
|
|
2057 BackgroundMatrix[ONEmer] <- (BackgroundMatrix[ONEmer] + probNonStopMutations[ONEmerReadingFrameCodon,positionWithinCodon])
|
|
|
2058 }
|
|
|
2059 }
|
|
|
2060 }else{ # Only silent mutations
|
|
|
2061 if( !any(grep("N",ONEmerReadingFrameCodonInputSeq)) & translateCodonToAminoAcid(ONEmerReadingFrameCodonInputSeq)!="*" & translateCodonToAminoAcid(ONEmerReadingFrameCodonInputSeq)==translateCodonToAminoAcid(ONEmerReadingFrameCodon) ){
|
|
|
2062 positionWithinCodon = which(ONEmerCodonPos==ONEmerIndex)
|
|
|
2063 BackgroundMatrix[ONEmer] <- (BackgroundMatrix[ONEmer] + probSMutations[ONEmerReadingFrameCodon,positionWithinCodon])
|
|
|
2064 }
|
|
|
2065 }
|
|
|
2066 }
|
|
|
2067 }
|
|
|
2068 }
|
|
|
2069
|
|
|
2070 #Mutations
|
|
|
2071 if(stopMutations==1) mutationCount = mutationCount[mutationCount!="Stop"]
|
|
|
2072 if(model==1) mutationCount = mutationCount[mutationCount=="S"]
|
|
|
2073 mutationPositions = as.numeric(names(mutationCount))
|
|
|
2074 mutationCount = mutationCount[mutationPositions>2 & mutationPositions<(seqLength-1)]
|
|
|
2075 mutationPositions = mutationPositions[mutationPositions>2 & mutationPositions<(seqLength-1)]
|
|
|
2076 countMutations = 0
|
|
|
2077 for(mutationPosition in mutationPositions){
|
|
|
2078 ONEmerIndex = mutationPosition
|
|
|
2079 ONEmer = ONEmerSeq[ONEmerIndex]
|
|
|
2080 GLONEmer = ONEmerGL[ONEmerIndex]
|
|
|
2081 ONEmerCodonPos = getCodonPos(ONEmerIndex)
|
|
|
2082 ONEmerReadingFrameCodon = c2s(ONEmerSeq[ONEmerCodonPos])
|
|
|
2083 ONEmerReadingFrameCodonGL =c2s(ONEmerGL[ONEmerCodonPos])
|
|
|
2084 if(!any(grep("N",ONEmer)) & !any(grep("N",GLONEmer))){
|
|
|
2085 if(model==0){
|
|
|
2086 countMutations = countMutations + 1
|
|
|
2087 MutationMatrix[GLONEmer] <- (MutationMatrix[GLONEmer] + 1)
|
|
|
2088 MutationCountMatrix[GLONEmer] <- (MutationCountMatrix[GLONEmer] + 1)
|
|
|
2089 }else{
|
|
|
2090 if( translateCodonToAminoAcid(ONEmerReadingFrameCodonGL)!="*" ){
|
|
|
2091 countMutations = countMutations + 1
|
|
|
2092 positionWithinCodon = which(ONEmerCodonPos==ONEmerIndex)
|
|
|
2093 glNuc = substr(ONEmerReadingFrameCodonGL,positionWithinCodon,positionWithinCodon)
|
|
|
2094 inputNuc = substr(ONEmerReadingFrameCodon,positionWithinCodon,positionWithinCodon)
|
|
|
2095 MutationMatrix[GLONEmer] <- (MutationMatrix[GLONEmer] + substitution[glNuc,inputNuc])
|
|
|
2096 MutationCountMatrix[GLONEmer] <- (MutationCountMatrix[GLONEmer] + 1)
|
|
|
2097 }
|
|
|
2098 }
|
|
|
2099 }
|
|
|
2100 }
|
|
|
2101
|
|
|
2102 seqMutability = MutationMatrix/BackgroundMatrix
|
|
|
2103 seqMutability = seqMutability/sum(seqMutability,na.rm=TRUE)
|
|
|
2104 #cat(inputMatrixIndex,"\t",countMutations,"\n")
|
|
|
2105 return(list("seqMutability" = seqMutability,"numbMutations" = countMutations,"seqMutabilityCount" = MutationCountMatrix, "BackgroundMatrix"=BackgroundMatrix))
|
|
|
2106 # tmp<-list("seqMutability" = seqMutability,"numbMutations" = countMutations,"seqMutabilityCount" = MutationCountMatrix)
|
|
|
2107 }
|
|
|
2108 }
|
|
|
2109
|
|
|
2110 ################
|
|
|
2111 # $Id: trim.R 989 2006-10-29 15:28:26Z ggorjan $
|
|
|
2112
|
|
|
2113 trim <- function(s, recode.factor=TRUE, ...)
|
|
|
2114 UseMethod("trim", s)
|
|
|
2115
|
|
|
2116 trim.default <- function(s, recode.factor=TRUE, ...)
|
|
|
2117 s
|
|
|
2118
|
|
|
2119 trim.character <- function(s, recode.factor=TRUE, ...)
|
|
|
2120 {
|
|
|
2121 s <- sub(pattern="^ +", replacement="", x=s)
|
|
|
2122 s <- sub(pattern=" +$", replacement="", x=s)
|
|
|
2123 s
|
|
|
2124 }
|
|
|
2125
|
|
|
2126 trim.factor <- function(s, recode.factor=TRUE, ...)
|
|
|
2127 {
|
|
|
2128 levels(s) <- trim(levels(s))
|
|
|
2129 if(recode.factor) {
|
|
|
2130 dots <- list(x=s, ...)
|
|
|
2131 if(is.null(dots$sort)) dots$sort <- sort
|
|
|
2132 s <- do.call(what=reorder.factor, args=dots)
|
|
|
2133 }
|
|
|
2134 s
|
|
|
2135 }
|
|
|
2136
|
|
|
2137 trim.list <- function(s, recode.factor=TRUE, ...)
|
|
|
2138 lapply(s, trim, recode.factor=recode.factor, ...)
|
|
|
2139
|
|
|
2140 trim.data.frame <- function(s, recode.factor=TRUE, ...)
|
|
|
2141 {
|
|
|
2142 s[] <- trim.list(s, recode.factor=recode.factor, ...)
|
|
|
2143 s
|
|
|
2144 }
|
|
|
2145 #######################################
|
|
|
2146 # Compute the expected for each sequence-germline pair by codon
|
|
|
2147 getExpectedIndividualByCodon <- function(matInput){
|
|
|
2148 if( any(grep("multicore",search())) ){
|
|
|
2149 facGL <- factor(matInput[,2])
|
|
|
2150 facLevels = levels(facGL)
|
|
|
2151 LisGLs_MutabilityU = mclapply(1:length(facLevels), function(x){
|
|
|
2152 computeMutabilities(facLevels[x])
|
|
|
2153 })
|
|
|
2154 facIndex = match(facGL,facLevels)
|
|
|
2155
|
|
|
2156 LisGLs_Mutability = mclapply(1:nrow(matInput), function(x){
|
|
|
2157 cInput = rep(NA,nchar(matInput[x,1]))
|
|
|
2158 cInput[s2c(matInput[x,1])!="N"] = 1
|
|
|
2159 LisGLs_MutabilityU[[facIndex[x]]] * cInput
|
|
|
2160 })
|
|
|
2161
|
|
|
2162 LisGLs_Targeting = mclapply(1:dim(matInput)[1], function(x){
|
|
|
2163 computeTargeting(matInput[x,2],LisGLs_Mutability[[x]])
|
|
|
2164 })
|
|
|
2165
|
|
|
2166 LisGLs_MutationTypes = mclapply(1:length(matInput[,2]),function(x){
|
|
|
2167 #print(x)
|
|
|
2168 computeMutationTypes(matInput[x,2])
|
|
|
2169 })
|
|
|
2170
|
|
|
2171 LisGLs_R_Exp = mclapply(1:nrow(matInput), function(x){
|
|
|
2172 Exp_R <- rollapply(as.zoo(1:readEnd),width=3,by=3,
|
|
|
2173 function(codonNucs){
|
|
|
2174 RPos = which(LisGLs_MutationTypes[[x]][,codonNucs]=="R")
|
|
|
2175 sum( LisGLs_Targeting[[x]][,codonNucs][RPos], na.rm=T )
|
|
|
2176 }
|
|
|
2177 )
|
|
|
2178 })
|
|
|
2179
|
|
|
2180 LisGLs_S_Exp = mclapply(1:nrow(matInput), function(x){
|
|
|
2181 Exp_S <- rollapply(as.zoo(1:readEnd),width=3,by=3,
|
|
|
2182 function(codonNucs){
|
|
|
2183 SPos = which(LisGLs_MutationTypes[[x]][,codonNucs]=="S")
|
|
|
2184 sum( LisGLs_Targeting[[x]][,codonNucs][SPos], na.rm=T )
|
|
|
2185 }
|
|
|
2186 )
|
|
|
2187 })
|
|
|
2188
|
|
|
2189 Exp_R = matrix(unlist(LisGLs_R_Exp),nrow=nrow(matInput),ncol=readEnd/3,T)
|
|
|
2190 Exp_S = matrix(unlist(LisGLs_S_Exp),nrow=nrow(matInput),ncol=readEnd/3,T)
|
|
|
2191 return( list( "Expected_R"=Exp_R, "Expected_S"=Exp_S) )
|
|
|
2192 }else{
|
|
|
2193 facGL <- factor(matInput[,2])
|
|
|
2194 facLevels = levels(facGL)
|
|
|
2195 LisGLs_MutabilityU = lapply(1:length(facLevels), function(x){
|
|
|
2196 computeMutabilities(facLevels[x])
|
|
|
2197 })
|
|
|
2198 facIndex = match(facGL,facLevels)
|
|
|
2199
|
|
|
2200 LisGLs_Mutability = lapply(1:nrow(matInput), function(x){
|
|
|
2201 cInput = rep(NA,nchar(matInput[x,1]))
|
|
|
2202 cInput[s2c(matInput[x,1])!="N"] = 1
|
|
|
2203 LisGLs_MutabilityU[[facIndex[x]]] * cInput
|
|
|
2204 })
|
|
|
2205
|
|
|
2206 LisGLs_Targeting = lapply(1:dim(matInput)[1], function(x){
|
|
|
2207 computeTargeting(matInput[x,2],LisGLs_Mutability[[x]])
|
|
|
2208 })
|
|
|
2209
|
|
|
2210 LisGLs_MutationTypes = lapply(1:length(matInput[,2]),function(x){
|
|
|
2211 #print(x)
|
|
|
2212 computeMutationTypes(matInput[x,2])
|
|
|
2213 })
|
|
|
2214
|
|
|
2215 LisGLs_R_Exp = lapply(1:nrow(matInput), function(x){
|
|
|
2216 Exp_R <- rollapply(as.zoo(1:readEnd),width=3,by=3,
|
|
|
2217 function(codonNucs){
|
|
|
2218 RPos = which(LisGLs_MutationTypes[[x]][,codonNucs]=="R")
|
|
|
2219 sum( LisGLs_Targeting[[x]][,codonNucs][RPos], na.rm=T )
|
|
|
2220 }
|
|
|
2221 )
|
|
|
2222 })
|
|
|
2223
|
|
|
2224 LisGLs_S_Exp = lapply(1:nrow(matInput), function(x){
|
|
|
2225 Exp_S <- rollapply(as.zoo(1:readEnd),width=3,by=3,
|
|
|
2226 function(codonNucs){
|
|
|
2227 SPos = which(LisGLs_MutationTypes[[x]][,codonNucs]=="S")
|
|
|
2228 sum( LisGLs_Targeting[[x]][,codonNucs][SPos], na.rm=T )
|
|
|
2229 }
|
|
|
2230 )
|
|
|
2231 })
|
|
|
2232
|
|
|
2233 Exp_R = matrix(unlist(LisGLs_R_Exp),nrow=nrow(matInput),ncol=readEnd/3,T)
|
|
|
2234 Exp_S = matrix(unlist(LisGLs_S_Exp),nrow=nrow(matInput),ncol=readEnd/3,T)
|
|
|
2235 return( list( "Expected_R"=Exp_R, "Expected_S"=Exp_S) )
|
|
|
2236 }
|
|
|
2237 }
|
|
|
2238
|
|
|
2239 # getObservedMutationsByCodon <- function(listMutations){
|
|
|
2240 # numbSeqs <- length(listMutations)
|
|
|
2241 # obsMu_R <- matrix(0,nrow=numbSeqs,ncol=readEnd/3,dimnames=list(c(1:numbSeqs),c(1:(readEnd/3))))
|
|
|
2242 # obsMu_S <- obsMu_R
|
|
|
2243 # temp <- mclapply(1:length(listMutations), function(i){
|
|
|
2244 # arrMutations = listMutations[[i]]
|
|
|
2245 # RPos = as.numeric(names(arrMutations)[arrMutations=="R"])
|
|
|
2246 # RPos <- sapply(RPos,getCodonNumb)
|
|
|
2247 # if(any(RPos)){
|
|
|
2248 # tabR <- table(RPos)
|
|
|
2249 # obsMu_R[i,as.numeric(names(tabR))] <<- tabR
|
|
|
2250 # }
|
|
|
2251 #
|
|
|
2252 # SPos = as.numeric(names(arrMutations)[arrMutations=="S"])
|
|
|
2253 # SPos <- sapply(SPos,getCodonNumb)
|
|
|
2254 # if(any(SPos)){
|
|
|
2255 # tabS <- table(SPos)
|
|
|
2256 # obsMu_S[i,names(tabS)] <<- tabS
|
|
|
2257 # }
|
|
|
2258 # }
|
|
|
2259 # )
|
|
|
2260 # return( list( "Observed_R"=obsMu_R, "Observed_S"=obsMu_S) )
|
|
|
2261 # }
|
|
|
2262
|
|
|
2263 getObservedMutationsByCodon <- function(listMutations){
|
|
|
2264 numbSeqs <- length(listMutations)
|
|
|
2265 obsMu_R <- matrix(0,nrow=numbSeqs,ncol=readEnd/3,dimnames=list(c(1:numbSeqs),c(1:(readEnd/3))))
|
|
|
2266 obsMu_S <- obsMu_R
|
|
|
2267 temp <- lapply(1:length(listMutations), function(i){
|
|
|
2268 arrMutations = listMutations[[i]]
|
|
|
2269 RPos = as.numeric(names(arrMutations)[arrMutations=="R"])
|
|
|
2270 RPos <- sapply(RPos,getCodonNumb)
|
|
|
2271 if(any(RPos)){
|
|
|
2272 tabR <- table(RPos)
|
|
|
2273 obsMu_R[i,as.numeric(names(tabR))] <<- tabR
|
|
|
2274 }
|
|
|
2275
|
|
|
2276 SPos = as.numeric(names(arrMutations)[arrMutations=="S"])
|
|
|
2277 SPos <- sapply(SPos,getCodonNumb)
|
|
|
2278 if(any(SPos)){
|
|
|
2279 tabS <- table(SPos)
|
|
|
2280 obsMu_S[i,names(tabS)] <<- tabS
|
|
|
2281 }
|
|
|
2282 }
|
|
|
2283 )
|
|
|
2284 return( list( "Observed_R"=obsMu_R, "Observed_S"=obsMu_S) )
|
|
|
2285 }
|
|
|
2286
|
