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67
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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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|
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265 return(expandedID)
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|
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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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|
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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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|
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276 numbGroups = sum(posGroups1)
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277 consDef = NA
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|
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278
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|
|
279 if(numbGermlines==0){
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|
280 posGermlines = 2
|
|
|
281 numbGermlines = 1
|
|
|
282 }
|
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|
283
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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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|
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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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|
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333
|
|
|
334
|
|
|
335 seqIn = replaceLeadingTrailingDashesHelper(x[1])
|
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|
336 seqGL = replaceLeadingTrailingDashesHelper(x[2])
|
|
|
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 #print(paste("x = '", x, "'", sep=""))
|
|
|
365 x = s2c(x)
|
|
|
366 if(x[1]=="-"){
|
|
|
367 x[1:grepResultsLen[1]] = "N"
|
|
|
368 }
|
|
|
369 if(x[length(x)]=="-"){
|
|
|
370 x[(length(x)-grepResultsLen[length(grepResultsLen)]+1):length(x)] = "N"
|
|
|
371 }
|
|
|
372 return(x)
|
|
|
373 }
|
|
|
374
|
|
|
375
|
|
|
376
|
|
|
377
|
|
|
378 # Check sequences for indels
|
|
|
379 checkForInDels <- function(matInputP){
|
|
|
380 insPos <- checkInsertion(matInputP)
|
|
|
381 delPos <- checkDeletions(matInputP)
|
|
|
382 return(list("Insertions"=insPos, "Deletions"=delPos))
|
|
|
383 }
|
|
|
384
|
|
|
385 # Check sequences for insertions
|
|
|
386 checkInsertion <- function(matInputP){
|
|
|
387 insertionCheck = apply( matInputP,1, function(x){
|
|
|
388 inputGaps <- as.vector( gregexpr("-",x[1])[[1]] )
|
|
|
389 glGaps <- as.vector( gregexpr("-",x[2])[[1]] )
|
|
|
390 return( is.finite( match(FALSE, glGaps%in%inputGaps ) ) )
|
|
|
391 })
|
|
|
392 return(as.vector(insertionCheck))
|
|
|
393 }
|
|
|
394 # Fix inserstions
|
|
|
395 fixInsertions <- function(matInputP){
|
|
|
396 insPos <- checkInsertion(matInputP)
|
|
|
397 sapply((1:nrow(matInputP))[insPos],function(rowIndex){
|
|
|
398 x <- matInputP[rowIndex,]
|
|
|
399 inputGaps <- gregexpr("-",x[1])[[1]]
|
|
|
400 glGaps <- gregexpr("-",x[2])[[1]]
|
|
|
401 posInsertions <- glGaps[!(glGaps%in%inputGaps)]
|
|
|
402 inputInsertionToN <- s2c(x[2])
|
|
|
403 inputInsertionToN[posInsertions]!="-"
|
|
|
404 inputInsertionToN[posInsertions] <- "N"
|
|
|
405 inputInsertionToN <- c2s(inputInsertionToN)
|
|
|
406 matInput[rowIndex,2] <<- inputInsertionToN
|
|
|
407 })
|
|
|
408 return(insPos)
|
|
|
409 }
|
|
|
410
|
|
|
411 # Check sequences for deletions
|
|
|
412 checkDeletions <-function(matInputP){
|
|
|
413 deletionCheck = apply( matInputP,1, function(x){
|
|
|
414 inputGaps <- as.vector( gregexpr("-",x[1])[[1]] )
|
|
|
415 glGaps <- as.vector( gregexpr("-",x[2])[[1]] )
|
|
|
416 return( is.finite( match(FALSE, inputGaps%in%glGaps ) ) )
|
|
|
417 })
|
|
|
418 return(as.vector(deletionCheck))
|
|
|
419 }
|
|
|
420 # Fix sequences with deletions
|
|
|
421 fixDeletions <- function(matInputP){
|
|
|
422 delPos <- checkDeletions(matInputP)
|
|
|
423 sapply((1:nrow(matInputP))[delPos],function(rowIndex){
|
|
|
424 x <- matInputP[rowIndex,]
|
|
|
425 inputGaps <- gregexpr("-",x[1])[[1]]
|
|
|
426 glGaps <- gregexpr("-",x[2])[[1]]
|
|
|
427 posDeletions <- inputGaps[!(inputGaps%in%glGaps)]
|
|
|
428 inputDeletionToN <- s2c(x[1])
|
|
|
429 inputDeletionToN[posDeletions] <- "N"
|
|
|
430 inputDeletionToN <- c2s(inputDeletionToN)
|
|
|
431 matInput[rowIndex,1] <<- inputDeletionToN
|
|
|
432 })
|
|
|
433 return(delPos)
|
|
|
434 }
|
|
|
435
|
|
|
436
|
|
|
437 # Trim DNA sequence to the last codon
|
|
|
438 trimToLastCodon <- function(seqToTrim){
|
|
|
439 seqLen = nchar(seqToTrim)
|
|
|
440 trimmedSeq = s2c(seqToTrim)
|
|
|
441 poi = seqLen
|
|
|
442 tailLen = 0
|
|
|
443
|
|
|
444 while(trimmedSeq[poi]=="-" || trimmedSeq[poi]=="."){
|
|
|
445 tailLen = tailLen + 1
|
|
|
446 poi = poi - 1
|
|
|
447 }
|
|
|
448
|
|
|
449 trimmedSeq = c2s(trimmedSeq[1:(seqLen-tailLen)])
|
|
|
450 seqLen = nchar(trimmedSeq)
|
|
|
451 # Trim sequence to last codon
|
|
|
452 if( getCodonPos(seqLen)[3] > seqLen )
|
|
|
453 trimmedSeq = substr(seqToTrim,1, ( (getCodonPos(seqLen)[1])-1 ) )
|
|
|
454
|
|
|
455 return(trimmedSeq)
|
|
|
456 }
|
|
|
457
|
|
|
458 # Given a nuclotide position, returns the pos of the 3 nucs that made the codon
|
|
|
459 # e.g. nuc 86 is part of nucs 85,86,87
|
|
|
460 getCodonPos <- function(nucPos){
|
|
|
461 codonNum = (ceiling(nucPos/3))*3
|
|
|
462 return( (codonNum-2):codonNum)
|
|
|
463 }
|
|
|
464
|
|
|
465 # Given a nuclotide position, returns the codon number
|
|
|
466 # e.g. nuc 86 = codon 29
|
|
|
467 getCodonNumb <- function(nucPos){
|
|
|
468 return( ceiling(nucPos/3) )
|
|
|
469 }
|
|
|
470
|
|
|
471 # Given a codon, returns all the nuc positions that make the codon
|
|
|
472 getCodonNucs <- function(codonNumb){
|
|
|
473 getCodonPos(codonNumb*3)
|
|
|
474 }
|
|
|
475
|
|
|
476 computeCodonTable <- function(testID=1){
|
|
|
477
|
|
|
478 if(testID<=4){
|
|
|
479 # Pre-compute every codons
|
|
|
480 intCounter = 1
|
|
|
481 for(pOne in NUCLEOTIDES){
|
|
|
482 for(pTwo in NUCLEOTIDES){
|
|
|
483 for(pThree in NUCLEOTIDES){
|
|
|
484 codon = paste(pOne,pTwo,pThree,sep="")
|
|
|
485 colnames(CODON_TABLE)[intCounter] = codon
|
|
|
486 intCounter = intCounter + 1
|
|
|
487 CODON_TABLE[,codon] = mutationTypeOptimized(cbind(permutateAllCodon(codon),rep(codon,12)))
|
|
|
488 }
|
|
|
489 }
|
|
|
490 }
|
|
|
491 chars = c("N","A","C","G","T", "-")
|
|
|
492 for(a in chars){
|
|
|
493 for(b in chars){
|
|
|
494 for(c in chars){
|
|
|
495 if(a=="N" | b=="N" | c=="N"){
|
|
|
496 #cat(paste(a,b,c),sep="","\n")
|
|
|
497 CODON_TABLE[,paste(a,b,c,sep="")] = rep(NA,12)
|
|
|
498 }
|
|
|
499 }
|
|
|
500 }
|
|
|
501 }
|
|
|
502
|
|
|
503 chars = c("-","A","C","G","T")
|
|
|
504 for(a in chars){
|
|
|
505 for(b in chars){
|
|
|
506 for(c in chars){
|
|
|
507 if(a=="-" | b=="-" | c=="-"){
|
|
|
508 #cat(paste(a,b,c),sep="","\n")
|
|
|
509 CODON_TABLE[,paste(a,b,c,sep="")] = rep(NA,12)
|
|
|
510 }
|
|
|
511 }
|
|
|
512 }
|
|
|
513 }
|
|
|
514 CODON_TABLE <<- as.matrix(CODON_TABLE)
|
|
|
515 }
|
|
|
516 }
|
|
|
517
|
|
|
518 collapseClone <- function(vecInputSeqs,glSeq,readEnd,nonTerminalOnly=0){
|
|
|
519 #print(length(vecInputSeqs))
|
|
|
520 vecInputSeqs = unique(vecInputSeqs)
|
|
|
521 if(length(vecInputSeqs)==1){
|
|
|
522 return( list( c(vecInputSeqs,glSeq), F) )
|
|
|
523 }else{
|
|
|
524 charInputSeqs <- sapply(vecInputSeqs, function(x){
|
|
|
525 s2c(x)[1:readEnd]
|
|
|
526 })
|
|
|
527 charGLSeq <- s2c(glSeq)
|
|
|
528 matClone <- sapply(1:readEnd, function(i){
|
|
|
529 posNucs = unique(charInputSeqs[i,])
|
|
|
530 posGL = charGLSeq[i]
|
|
|
531 error = FALSE
|
|
|
532 if(posGL=="-" & sum(!(posNucs%in%c("-","N")))==0 ){
|
|
|
533 return(c("-",error))
|
|
|
534 }
|
|
|
535 if(length(posNucs)==1)
|
|
|
536 return(c(posNucs[1],error))
|
|
|
537 else{
|
|
|
538 if("N"%in%posNucs){
|
|
|
539 error=TRUE
|
|
|
540 }
|
|
|
541 if(sum(!posNucs[posNucs!="N"]%in%posGL)==0){
|
|
|
542 return( c(posGL,error) )
|
|
|
543 }else{
|
|
|
544 #return( c(sample(posNucs[posNucs!="N"],1),error) )
|
|
|
545 if(nonTerminalOnly==0){
|
|
|
546 return( c(sample(charInputSeqs[i,charInputSeqs[i,]!="N" & charInputSeqs[i,]!=posGL],1),error) )
|
|
|
547 }else{
|
|
|
548 posNucs = charInputSeqs[i,charInputSeqs[i,]!="N" & charInputSeqs[i,]!=posGL]
|
|
|
549 posNucsTable = table(posNucs)
|
|
|
550 if(sum(posNucsTable>1)==0){
|
|
|
551 return( c(posGL,error) )
|
|
|
552 }else{
|
|
|
553 return( c(sample( posNucs[posNucs%in%names(posNucsTable)[posNucsTable>1]],1),error) )
|
|
|
554 }
|
|
|
555 }
|
|
|
556
|
|
|
557 }
|
|
|
558 }
|
|
|
559 })
|
|
|
560
|
|
|
561
|
|
|
562 #print(length(vecInputSeqs))
|
|
|
563 return(list(c(c2s(matClone[1,]),glSeq),"TRUE"%in%matClone[2,]))
|
|
|
564 }
|
|
|
565 }
|
|
|
566
|
|
|
567 # Compute the expected for each sequence-germline pair
|
|
|
568 getExpectedIndividual <- function(matInput){
|
|
|
569 if( any(grep("multicore",search())) ){
|
|
|
570 facGL <- factor(matInput[,2])
|
|
|
571 facLevels = levels(facGL)
|
|
|
572 LisGLs_MutabilityU = mclapply(1:length(facLevels), function(x){
|
|
|
573 computeMutabilities(facLevels[x])
|
|
|
574 })
|
|
|
575 facIndex = match(facGL,facLevels)
|
|
|
576
|
|
|
577 LisGLs_Mutability = mclapply(1:nrow(matInput), function(x){
|
|
|
578 cInput = rep(NA,nchar(matInput[x,1]))
|
|
|
579 cInput[s2c(matInput[x,1])!="N"] = 1
|
|
|
580 LisGLs_MutabilityU[[facIndex[x]]] * cInput
|
|
|
581 })
|
|
|
582
|
|
|
583 LisGLs_Targeting = mclapply(1:dim(matInput)[1], function(x){
|
|
|
584 computeTargeting(matInput[x,2],LisGLs_Mutability[[x]])
|
|
|
585 })
|
|
|
586
|
|
|
587 LisGLs_MutationTypes = mclapply(1:length(matInput[,2]),function(x){
|
|
|
588 #print(x)
|
|
|
589 computeMutationTypes(matInput[x,2])
|
|
|
590 })
|
|
|
591
|
|
|
592 LisGLs_Exp = mclapply(1:dim(matInput)[1], function(x){
|
|
|
593 computeExpected(LisGLs_Targeting[[x]],LisGLs_MutationTypes[[x]])
|
|
|
594 })
|
|
|
595
|
|
|
596 ul_LisGLs_Exp = unlist(LisGLs_Exp)
|
|
|
597 return(matrix(ul_LisGLs_Exp,ncol=4,nrow=(length(ul_LisGLs_Exp)/4),byrow=T))
|
|
|
598 }else{
|
|
|
599 facGL <- factor(matInput[,2])
|
|
|
600 facLevels = levels(facGL)
|
|
|
601 LisGLs_MutabilityU = lapply(1:length(facLevels), function(x){
|
|
|
602 computeMutabilities(facLevels[x])
|
|
|
603 })
|
|
|
604 facIndex = match(facGL,facLevels)
|
|
|
605
|
|
|
606 LisGLs_Mutability = lapply(1:nrow(matInput), function(x){
|
|
|
607 cInput = rep(NA,nchar(matInput[x,1]))
|
|
|
608 cInput[s2c(matInput[x,1])!="N"] = 1
|
|
|
609 LisGLs_MutabilityU[[facIndex[x]]] * cInput
|
|
|
610 })
|
|
|
611
|
|
|
612 LisGLs_Targeting = lapply(1:dim(matInput)[1], function(x){
|
|
|
613 computeTargeting(matInput[x,2],LisGLs_Mutability[[x]])
|
|
|
614 })
|
|
|
615
|
|
|
616 LisGLs_MutationTypes = lapply(1:length(matInput[,2]),function(x){
|
|
|
617 #print(x)
|
|
|
618 computeMutationTypes(matInput[x,2])
|
|
|
619 })
|
|
|
620
|
|
|
621 LisGLs_Exp = lapply(1:dim(matInput)[1], function(x){
|
|
|
622 computeExpected(LisGLs_Targeting[[x]],LisGLs_MutationTypes[[x]])
|
|
|
623 })
|
|
|
624
|
|
|
625 ul_LisGLs_Exp = unlist(LisGLs_Exp)
|
|
|
626 return(matrix(ul_LisGLs_Exp,ncol=4,nrow=(length(ul_LisGLs_Exp)/4),byrow=T))
|
|
|
627
|
|
|
628 }
|
|
|
629 }
|
|
|
630
|
|
|
631 # Compute mutabilities of sequence based on the tri-nucleotide model
|
|
|
632 computeMutabilities <- function(paramSeq){
|
|
|
633 seqLen = nchar(paramSeq)
|
|
|
634 seqMutabilites = rep(NA,seqLen)
|
|
|
635
|
|
|
636 gaplessSeq = gsub("-", "", paramSeq)
|
|
|
637 gaplessSeqLen = nchar(gaplessSeq)
|
|
|
638 gaplessSeqMutabilites = rep(NA,gaplessSeqLen)
|
|
|
639
|
|
|
640 if(mutabilityModel!=5){
|
|
|
641 pos<- 3:(gaplessSeqLen)
|
|
|
642 subSeq = substr(rep(gaplessSeq,gaplessSeqLen-2),(pos-2),(pos+2))
|
|
|
643 gaplessSeqMutabilites[pos] =
|
|
|
644 tapply( c(
|
|
|
645 getMutability( substr(subSeq,1,3), 3) ,
|
|
|
646 getMutability( substr(subSeq,2,4), 2),
|
|
|
647 getMutability( substr(subSeq,3,5), 1)
|
|
|
648 ),rep(1:(gaplessSeqLen-2),3),mean,na.rm=TRUE
|
|
|
649 )
|
|
|
650 #Pos 1
|
|
|
651 subSeq = substr(gaplessSeq,1,3)
|
|
|
652 gaplessSeqMutabilites[1] = getMutability(subSeq , 1)
|
|
|
653 #Pos 2
|
|
|
654 subSeq = substr(gaplessSeq,1,4)
|
|
|
655 gaplessSeqMutabilites[2] = mean( c(
|
|
|
656 getMutability( substr(subSeq,1,3), 2) ,
|
|
|
657 getMutability( substr(subSeq,2,4), 1)
|
|
|
658 ),na.rm=T
|
|
|
659 )
|
|
|
660 seqMutabilites[which(s2c(paramSeq)!="-")]<- gaplessSeqMutabilites
|
|
|
661 return(seqMutabilites)
|
|
|
662 }else{
|
|
|
663
|
|
|
664 pos<- 3:(gaplessSeqLen)
|
|
|
665 subSeq = substr(rep(gaplessSeq,gaplessSeqLen-2),(pos-2),(pos+2))
|
|
|
666 gaplessSeqMutabilites[pos] = sapply(subSeq,function(x){ getMutability5(x) }, simplify=T)
|
|
|
667 seqMutabilites[which(s2c(paramSeq)!="-")]<- gaplessSeqMutabilites
|
|
|
668 return(seqMutabilites)
|
|
|
669 }
|
|
|
670
|
|
|
671 }
|
|
|
672
|
|
|
673 # Returns the mutability of a triplet at a given position
|
|
|
674 getMutability <- function(codon, pos=1:3){
|
|
|
675 triplets <- rownames(mutability)
|
|
|
676 mutability[ match(codon,triplets) ,pos]
|
|
|
677 }
|
|
|
678
|
|
|
679 getMutability5 <- function(fivemer){
|
|
|
680 return(mutability[fivemer])
|
|
|
681 }
|
|
|
682
|
|
|
683 # Returns the substitution probabilty
|
|
|
684 getTransistionProb <- function(nuc){
|
|
|
685 substitution[nuc,]
|
|
|
686 }
|
|
|
687
|
|
|
688 getTransistionProb5 <- function(fivemer){
|
|
|
689 if(any(which(fivemer==colnames(substitution)))){
|
|
|
690 return(substitution[,fivemer])
|
|
|
691 }else{
|
|
|
692 return(array(NA,4))
|
|
|
693 }
|
|
|
694 }
|
|
|
695
|
|
|
696 # Given a nuc, returns the other 3 nucs it can mutate to
|
|
|
697 canMutateTo <- function(nuc){
|
|
|
698 NUCLEOTIDES[- which(NUCLEOTIDES==nuc)]
|
|
|
699 }
|
|
|
700
|
|
|
701 # Given a nucleotide, returns the probabilty of other nucleotide it can mutate to
|
|
|
702 canMutateToProb <- function(nuc){
|
|
|
703 substitution[nuc,canMutateTo(nuc)]
|
|
|
704 }
|
|
|
705
|
|
|
706 # Compute targeting, based on precomputed mutatbility & substitution
|
|
|
707 computeTargeting <- function(param_strSeq,param_vecMutabilities){
|
|
|
708
|
|
|
709 if(substitutionModel!=5){
|
|
|
710 vecSeq = s2c(param_strSeq)
|
|
|
711 matTargeting = sapply( 1:length(vecSeq), function(x) { param_vecMutabilities[x] * getTransistionProb(vecSeq[x]) } )
|
|
|
712 #matTargeting = apply( rbind(vecSeq,param_vecMutabilities),2, function(x) { as.vector(as.numeric(x[2]) * getTransistionProb(x[1])) } )
|
|
|
713 dimnames( matTargeting ) = list(NUCLEOTIDES,1:(length(vecSeq)))
|
|
|
714 return (matTargeting)
|
|
|
715 }else{
|
|
|
716
|
|
|
717 seqLen = nchar(param_strSeq)
|
|
|
718 seqsubstitution = matrix(NA,ncol=seqLen,nrow=4)
|
|
|
719 paramSeq <- param_strSeq
|
|
|
720 gaplessSeq = gsub("-", "", paramSeq)
|
|
|
721 gaplessSeqLen = nchar(gaplessSeq)
|
|
|
722 gaplessSeqSubstitution = matrix(NA,ncol=gaplessSeqLen,nrow=4)
|
|
|
723
|
|
|
724 pos<- 3:(gaplessSeqLen)
|
|
|
725 subSeq = substr(rep(gaplessSeq,gaplessSeqLen-2),(pos-2),(pos+2))
|
|
|
726 gaplessSeqSubstitution[,pos] = sapply(subSeq,function(x){ getTransistionProb5(x) }, simplify=T)
|
|
|
727 seqsubstitution[,which(s2c(paramSeq)!="-")]<- gaplessSeqSubstitution
|
|
|
728 #matTargeting <- param_vecMutabilities %*% seqsubstitution
|
|
|
729 matTargeting <- sweep(seqsubstitution,2,param_vecMutabilities,`*`)
|
|
|
730 dimnames( matTargeting ) = list(NUCLEOTIDES,1:(seqLen))
|
|
|
731 return (matTargeting)
|
|
|
732 }
|
|
|
733 }
|
|
|
734
|
|
|
735 # Compute the mutations types
|
|
|
736 computeMutationTypes <- function(param_strSeq){
|
|
|
737 #cat(param_strSeq,"\n")
|
|
|
738 #vecSeq = trimToLastCodon(param_strSeq)
|
|
|
739 lenSeq = nchar(param_strSeq)
|
|
|
740 vecCodons = sapply({1:(lenSeq/3)}*3-2,function(x){substr(param_strSeq,x,x+2)})
|
|
|
741 matMutationTypes = matrix( unlist(CODON_TABLE[,vecCodons]) ,ncol=lenSeq,nrow=4, byrow=F)
|
|
|
742 dimnames( matMutationTypes ) = list(NUCLEOTIDES,1:(ncol(matMutationTypes)))
|
|
|
743 return(matMutationTypes)
|
|
|
744 }
|
|
|
745 computeMutationTypesFast <- function(param_strSeq){
|
|
|
746 matMutationTypes = matrix( CODON_TABLE[,param_strSeq] ,ncol=3,nrow=4, byrow=F)
|
|
|
747 #dimnames( matMutationTypes ) = list(NUCLEOTIDES,1:(length(vecSeq)))
|
|
|
748 return(matMutationTypes)
|
|
|
749 }
|
|
|
750 mutationTypeOptimized <- function( matOfCodons ){
|
|
|
751 apply( matOfCodons,1,function(x){ mutationType(x[2],x[1]) } )
|
|
|
752 }
|
|
|
753
|
|
|
754 # Returns a vector of codons 1 mutation away from the given codon
|
|
|
755 permutateAllCodon <- function(codon){
|
|
|
756 cCodon = s2c(codon)
|
|
|
757 matCodons = t(array(cCodon,dim=c(3,12)))
|
|
|
758 matCodons[1:4,1] = NUCLEOTIDES
|
|
|
759 matCodons[5:8,2] = NUCLEOTIDES
|
|
|
760 matCodons[9:12,3] = NUCLEOTIDES
|
|
|
761 apply(matCodons,1,c2s)
|
|
|
762 }
|
|
|
763
|
|
|
764 # Given two codons, tells you if the mutation is R or S (based on your definition)
|
|
|
765 mutationType <- function(codonFrom,codonTo){
|
|
|
766 if(testID==4){
|
|
|
767 if( is.na(codonFrom) | is.na(codonTo) | is.na(translateCodonToAminoAcid(codonFrom)) | is.na(translateCodonToAminoAcid(codonTo)) ){
|
|
|
768 return(NA)
|
|
|
769 }else{
|
|
|
770 mutationType = "S"
|
|
|
771 if( translateAminoAcidToTraitChange(translateCodonToAminoAcid(codonFrom)) != translateAminoAcidToTraitChange(translateCodonToAminoAcid(codonTo)) ){
|
|
|
772 mutationType = "R"
|
|
|
773 }
|
|
|
774 if(translateCodonToAminoAcid(codonTo)=="*" | translateCodonToAminoAcid(codonFrom)=="*"){
|
|
|
775 mutationType = "Stop"
|
|
|
776 }
|
|
|
777 return(mutationType)
|
|
|
778 }
|
|
|
779 }else if(testID==5){
|
|
|
780 if( is.na(codonFrom) | is.na(codonTo) | is.na(translateCodonToAminoAcid(codonFrom)) | is.na(translateCodonToAminoAcid(codonTo)) ){
|
|
|
781 return(NA)
|
|
|
782 }else{
|
|
|
783 if(codonFrom==codonTo){
|
|
|
784 mutationType = "S"
|
|
|
785 }else{
|
|
|
786 codonFrom = s2c(codonFrom)
|
|
|
787 codonTo = s2c(codonTo)
|
|
|
788 mutationType = "Stop"
|
|
|
789 nucOfI = codonFrom[which(codonTo!=codonFrom)]
|
|
|
790 if(nucOfI=="C"){
|
|
|
791 mutationType = "R"
|
|
|
792 }else if(nucOfI=="G"){
|
|
|
793 mutationType = "S"
|
|
|
794 }
|
|
|
795 }
|
|
|
796 return(mutationType)
|
|
|
797 }
|
|
|
798 }else{
|
|
|
799 if( is.na(codonFrom) | is.na(codonTo) | is.na(translateCodonToAminoAcid(codonFrom)) | is.na(translateCodonToAminoAcid(codonTo)) ){
|
|
|
800 return(NA)
|
|
|
801 }else{
|
|
|
802 mutationType = "S"
|
|
|
803 if( translateCodonToAminoAcid(codonFrom) != translateCodonToAminoAcid(codonTo) ){
|
|
|
804 mutationType = "R"
|
|
|
805 }
|
|
|
806 if(translateCodonToAminoAcid(codonTo)=="*" | translateCodonToAminoAcid(codonFrom)=="*"){
|
|
|
807 mutationType = "Stop"
|
|
|
808 }
|
|
|
809 return(mutationType)
|
|
|
810 }
|
|
|
811 }
|
|
|
812 }
|
|
|
813
|
|
|
814
|
|
|
815 #given a mat of targeting & it's corresponding mutationtypes returns
|
|
|
816 #a vector of Exp_RCDR,Exp_SCDR,Exp_RFWR,Exp_RFWR
|
|
|
817 computeExpected <- function(paramTargeting,paramMutationTypes){
|
|
|
818 # Replacements
|
|
|
819 RPos = which(paramMutationTypes=="R")
|
|
|
820 #FWR
|
|
|
821 Exp_R_FWR = sum(paramTargeting[ RPos[which(FWR_Nuc_Mat[RPos]==T)] ],na.rm=T)
|
|
|
822 #CDR
|
|
|
823 Exp_R_CDR = sum(paramTargeting[ RPos[which(CDR_Nuc_Mat[RPos]==T)] ],na.rm=T)
|
|
|
824 # Silents
|
|
|
825 SPos = which(paramMutationTypes=="S")
|
|
|
826 #FWR
|
|
|
827 Exp_S_FWR = sum(paramTargeting[ SPos[which(FWR_Nuc_Mat[SPos]==T)] ],na.rm=T)
|
|
|
828 #CDR
|
|
|
829 Exp_S_CDR = sum(paramTargeting[ SPos[which(CDR_Nuc_Mat[SPos]==T)] ],na.rm=T)
|
|
|
830
|
|
|
831 return(c(Exp_R_CDR,Exp_S_CDR,Exp_R_FWR,Exp_S_FWR))
|
|
|
832 }
|
|
|
833
|
|
|
834 # Count the mutations in a sequence
|
|
|
835 # each mutation is treated independently
|
|
|
836 analyzeMutations2NucUri_website <- function( rev_in_matrix ){
|
|
|
837 paramGL = rev_in_matrix[2,]
|
|
|
838 paramSeq = rev_in_matrix[1,]
|
|
|
839
|
|
|
840 #Fill seq with GL seq if gapped
|
|
|
841 #if( any(paramSeq=="-") ){
|
|
|
842 # gapPos_Seq = which(paramSeq=="-")
|
|
|
843 # gapPos_Seq_ToReplace = gapPos_Seq[paramGL[gapPos_Seq] != "-"]
|
|
|
844 # paramSeq[gapPos_Seq_ToReplace] = paramGL[gapPos_Seq_ToReplace]
|
|
|
845 #}
|
|
|
846
|
|
|
847
|
|
|
848 #if( any(paramSeq=="N") ){
|
|
|
849 # gapPos_Seq = which(paramSeq=="N")
|
|
|
850 # gapPos_Seq_ToReplace = gapPos_Seq[paramGL[gapPos_Seq] != "N"]
|
|
|
851 # paramSeq[gapPos_Seq_ToReplace] = paramGL[gapPos_Seq_ToReplace]
|
|
|
852 #}
|
|
|
853
|
|
|
854 analyzeMutations2NucUri( matrix(c( paramGL, paramSeq ),2,length(paramGL),byrow=T) )
|
|
|
855
|
|
|
856 }
|
|
|
857
|
|
|
858 #1 = GL
|
|
|
859 #2 = Seq
|
|
|
860 analyzeMutations2NucUri <- function( in_matrix=matrix(c(c("A","A","A","C","C","C"),c("A","G","G","C","C","A")),2,6,byrow=T) ){
|
|
|
861 paramGL = in_matrix[2,]
|
|
|
862 paramSeq = in_matrix[1,]
|
|
|
863 paramSeqUri = paramGL
|
|
|
864 #mutations = apply(rbind(paramGL,paramSeq), 2, function(x){!x[1]==x[2]})
|
|
|
865 mutations_val = paramGL != paramSeq
|
|
|
866 if(any(mutations_val)){
|
|
|
867 mutationPos = {1:length(mutations_val)}[mutations_val]
|
|
|
868 mutationPos = mutationPos[sapply(mutationPos, function(x){!any(paramSeq[getCodonPos(x)]=="N")})]
|
|
|
869 length_mutations =length(mutationPos)
|
|
|
870 mutationInfo = rep(NA,length_mutations)
|
|
|
871 if(any(mutationPos)){
|
|
|
872
|
|
|
873 pos<- mutationPos
|
|
|
874 pos_array<-array(sapply(pos,getCodonPos))
|
|
|
875 codonGL = paramGL[pos_array]
|
|
|
876
|
|
|
877 codonSeq = sapply(pos,function(x){
|
|
|
878 seqP = paramGL[getCodonPos(x)]
|
|
|
879 muCodonPos = {x-1}%%3+1
|
|
|
880 seqP[muCodonPos] = paramSeq[x]
|
|
|
881 return(seqP)
|
|
|
882 })
|
|
|
883 GLcodons = apply(matrix(codonGL,length_mutations,3,byrow=TRUE),1,c2s)
|
|
|
884 Seqcodons = apply(codonSeq,2,c2s)
|
|
|
885 mutationInfo = apply(rbind(GLcodons , Seqcodons),2,function(x){mutationType(c2s(x[1]),c2s(x[2]))})
|
|
|
886 names(mutationInfo) = mutationPos
|
|
|
887 }
|
|
|
888 if(any(!is.na(mutationInfo))){
|
|
|
889 return(mutationInfo[!is.na(mutationInfo)])
|
|
|
890 }else{
|
|
|
891 return(NA)
|
|
|
892 }
|
|
|
893
|
|
|
894
|
|
|
895 }else{
|
|
|
896 return (NA)
|
|
|
897 }
|
|
|
898 }
|
|
|
899
|
|
|
900 processNucMutations2 <- function(mu){
|
|
|
901 if(!is.na(mu)){
|
|
|
902 #R
|
|
|
903 if(any(mu=="R")){
|
|
|
904 Rs = mu[mu=="R"]
|
|
|
905 nucNumbs = as.numeric(names(Rs))
|
|
|
906 R_CDR = sum(as.integer(CDR_Nuc[nucNumbs]),na.rm=T)
|
|
|
907 R_FWR = sum(as.integer(FWR_Nuc[nucNumbs]),na.rm=T)
|
|
|
908 }else{
|
|
|
909 R_CDR = 0
|
|
|
910 R_FWR = 0
|
|
|
911 }
|
|
|
912
|
|
|
913 #S
|
|
|
914 if(any(mu=="S")){
|
|
|
915 Ss = mu[mu=="S"]
|
|
|
916 nucNumbs = as.numeric(names(Ss))
|
|
|
917 S_CDR = sum(as.integer(CDR_Nuc[nucNumbs]),na.rm=T)
|
|
|
918 S_FWR = sum(as.integer(FWR_Nuc[nucNumbs]),na.rm=T)
|
|
|
919 }else{
|
|
|
920 S_CDR = 0
|
|
|
921 S_FWR = 0
|
|
|
922 }
|
|
|
923
|
|
|
924
|
|
|
925 retVec = c(R_CDR,S_CDR,R_FWR,S_FWR)
|
|
|
926 retVec[is.na(retVec)]=0
|
|
|
927 return(retVec)
|
|
|
928 }else{
|
|
|
929 return(rep(0,4))
|
|
|
930 }
|
|
|
931 }
|
|
|
932
|
|
|
933
|
|
|
934 ## Z-score Test
|
|
|
935 computeZScore <- function(mat, test="Focused"){
|
|
|
936 matRes <- matrix(NA,ncol=2,nrow=(nrow(mat)))
|
|
|
937 if(test=="Focused"){
|
|
|
938 #Z_Focused_CDR
|
|
|
939 #P_Denom = sum( mat[1,c(5,6,8)], na.rm=T )
|
|
|
940 P = apply(mat[,c(5,6,8)],1,function(x){(x[1]/sum(x))})
|
|
|
941 R_mean = apply(cbind(mat[,c(1,2,4)],P),1,function(x){x[4]*(sum(x[1:3]))})
|
|
|
942 R_sd=sqrt(R_mean*(1-P))
|
|
|
943 matRes[,1] = (mat[,1]-R_mean)/R_sd
|
|
|
944
|
|
|
945 #Z_Focused_FWR
|
|
|
946 #P_Denom = sum( mat[1,c(7,6,8)], na.rm=T )
|
|
|
947 P = apply(mat[,c(7,6,8)],1,function(x){(x[1]/sum(x))})
|
|
|
948 R_mean = apply(cbind(mat[,c(3,2,4)],P),1,function(x){x[4]*(sum(x[1:3]))})
|
|
|
949 R_sd=sqrt(R_mean*(1-P))
|
|
|
950 matRes[,2] = (mat[,3]-R_mean)/R_sd
|
|
|
951 }
|
|
|
952
|
|
|
953 if(test=="Local"){
|
|
|
954 #Z_Focused_CDR
|
|
|
955 #P_Denom = sum( mat[1,c(5,6,8)], na.rm=T )
|
|
|
956 P = apply(mat[,c(5,6)],1,function(x){(x[1]/sum(x))})
|
|
|
957 R_mean = apply(cbind(mat[,c(1,2)],P),1,function(x){x[3]*(sum(x[1:2]))})
|
|
|
958 R_sd=sqrt(R_mean*(1-P))
|
|
|
959 matRes[,1] = (mat[,1]-R_mean)/R_sd
|
|
|
960
|
|
|
961 #Z_Focused_FWR
|
|
|
962 #P_Denom = sum( mat[1,c(7,6,8)], na.rm=T )
|
|
|
963 P = apply(mat[,c(7,8)],1,function(x){(x[1]/sum(x))})
|
|
|
964 R_mean = apply(cbind(mat[,c(3,4)],P),1,function(x){x[3]*(sum(x[1:2]))})
|
|
|
965 R_sd=sqrt(R_mean*(1-P))
|
|
|
966 matRes[,2] = (mat[,3]-R_mean)/R_sd
|
|
|
967 }
|
|
|
968
|
|
|
969 if(test=="Imbalanced"){
|
|
|
970 #Z_Focused_CDR
|
|
|
971 #P_Denom = sum( mat[1,c(5,6,8)], na.rm=T )
|
|
|
972 P = apply(mat[,5:8],1,function(x){((x[1]+x[2])/sum(x))})
|
|
|
973 R_mean = apply(cbind(mat[,1:4],P),1,function(x){x[5]*(sum(x[1:4]))})
|
|
|
974 R_sd=sqrt(R_mean*(1-P))
|
|
|
975 matRes[,1] = (mat[,1]-R_mean)/R_sd
|
|
|
976
|
|
|
977 #Z_Focused_FWR
|
|
|
978 #P_Denom = sum( mat[1,c(7,6,8)], na.rm=T )
|
|
|
979 P = apply(mat[,5:8],1,function(x){((x[3]+x[4])/sum(x))})
|
|
|
980 R_mean = apply(cbind(mat[,1:4],P),1,function(x){x[5]*(sum(x[1:4]))})
|
|
|
981 R_sd=sqrt(R_mean*(1-P))
|
|
|
982 matRes[,2] = (mat[,3]-R_mean)/R_sd
|
|
|
983 }
|
|
|
984
|
|
|
985 matRes[is.nan(matRes)] = NA
|
|
|
986 return(matRes)
|
|
|
987 }
|
|
|
988
|
|
|
989 # Return a p-value for a z-score
|
|
|
990 z2p <- function(z){
|
|
|
991 p=NA
|
|
|
992 if( !is.nan(z) && !is.na(z)){
|
|
|
993 if(z>0){
|
|
|
994 p = (1 - pnorm(z,0,1))
|
|
|
995 } else if(z<0){
|
|
|
996 p = (-1 * pnorm(z,0,1))
|
|
|
997 } else{
|
|
|
998 p = 0.5
|
|
|
999 }
|
|
|
1000 }else{
|
|
|
1001 p = NA
|
|
|
1002 }
|
|
|
1003 return(p)
|
|
|
1004 }
|
|
|
1005
|
|
|
1006
|
|
|
1007 ## Bayesian Test
|
|
|
1008
|
|
|
1009 # Fitted parameter for the bayesian framework
|
|
|
1010 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)
|
|
|
1011 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))
|
|
|
1012
|
|
|
1013 # Given x, M & p, returns a pdf
|
|
|
1014 calculate_bayes <- function ( x=3, N=10, p=0.33,
|
|
|
1015 i=CONST_i,
|
|
|
1016 max_sigma=20,length_sigma=4001
|
|
|
1017 ){
|
|
|
1018 if(!0%in%N){
|
|
|
1019 G <- max(length(x),length(N),length(p))
|
|
|
1020 x=array(x,dim=G)
|
|
|
1021 N=array(N,dim=G)
|
|
|
1022 p=array(p,dim=G)
|
|
|
1023 sigma_s<-seq(-max_sigma,max_sigma,length.out=length_sigma)
|
|
|
1024 sigma_1<-log({i/{1-i}}/{p/{1-p}})
|
|
|
1025 index<-min(N,60)
|
|
|
1026 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))
|
|
|
1027 if(!sum(is.na(y))){
|
|
|
1028 tmp<-approx(sigma_1,y,sigma_s)$y
|
|
|
1029 tmp/sum(tmp)/{2*max_sigma/{length_sigma-1}}
|
|
|
1030 }else{
|
|
|
1031 return(NA)
|
|
|
1032 }
|
|
|
1033 }else{
|
|
|
1034 return(NA)
|
|
|
1035 }
|
|
|
1036 }
|
|
|
1037 # Given a mat of observed & expected, return a list of CDR & FWR pdf for selection
|
|
|
1038 computeBayesianScore <- function(mat, test="Focused", max_sigma=20,length_sigma=4001){
|
|
|
1039 flagOneSeq = F
|
|
|
1040 if(nrow(mat)==1){
|
|
|
1041 mat=rbind(mat,mat)
|
|
|
1042 flagOneSeq = T
|
|
|
1043 }
|
|
|
1044 if(test=="Focused"){
|
|
|
1045 #CDR
|
|
|
1046 P = c(apply(mat[,c(5,6,8)],1,function(x){(x[1]/sum(x))}),0.5)
|
|
|
1047 N = c(apply(mat[,c(1,2,4)],1,function(x){(sum(x))}),0)
|
|
|
1048 X = c(mat[,1],0)
|
|
|
1049 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)})
|
|
|
1050 bayesCDR = bayesCDR[-length(bayesCDR)]
|
|
|
1051
|
|
|
1052 #FWR
|
|
|
1053 P = c(apply(mat[,c(7,6,8)],1,function(x){(x[1]/sum(x))}),0.5)
|
|
|
1054 N = c(apply(mat[,c(3,2,4)],1,function(x){(sum(x))}),0)
|
|
|
1055 X = c(mat[,3],0)
|
|
|
1056 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)})
|
|
|
1057 bayesFWR = bayesFWR[-length(bayesFWR)]
|
|
|
1058 }
|
|
|
1059
|
|
|
1060 if(test=="Local"){
|
|
|
1061 #CDR
|
|
|
1062 P = c(apply(mat[,c(5,6)],1,function(x){(x[1]/sum(x))}),0.5)
|
|
|
1063 N = c(apply(mat[,c(1,2)],1,function(x){(sum(x))}),0)
|
|
|
1064 X = c(mat[,1],0)
|
|
|
1065 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)})
|
|
|
1066 bayesCDR = bayesCDR[-length(bayesCDR)]
|
|
|
1067
|
|
|
1068 #FWR
|
|
|
1069 P = c(apply(mat[,c(7,8)],1,function(x){(x[1]/sum(x))}),0.5)
|
|
|
1070 N = c(apply(mat[,c(3,4)],1,function(x){(sum(x))}),0)
|
|
|
1071 X = c(mat[,3],0)
|
|
|
1072 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)})
|
|
|
1073 bayesFWR = bayesFWR[-length(bayesFWR)]
|
|
|
1074 }
|
|
|
1075
|
|
|
1076 if(test=="Imbalanced"){
|
|
|
1077 #CDR
|
|
|
1078 P = c(apply(mat[,c(5:8)],1,function(x){((x[1]+x[2])/sum(x))}),0.5)
|
|
|
1079 N = c(apply(mat[,c(1:4)],1,function(x){(sum(x))}),0)
|
|
|
1080 X = c(apply(mat[,c(1:2)],1,function(x){(sum(x))}),0)
|
|
|
1081 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)})
|
|
|
1082 bayesCDR = bayesCDR[-length(bayesCDR)]
|
|
|
1083
|
|
|
1084 #FWR
|
|
|
1085 P = c(apply(mat[,c(5:8)],1,function(x){((x[3]+x[4])/sum(x))}),0.5)
|
|
|
1086 N = c(apply(mat[,c(1:4)],1,function(x){(sum(x))}),0)
|
|
|
1087 X = c(apply(mat[,c(3:4)],1,function(x){(sum(x))}),0)
|
|
|
1088 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)})
|
|
|
1089 bayesFWR = bayesFWR[-length(bayesFWR)]
|
|
|
1090 }
|
|
|
1091
|
|
|
1092 if(test=="ImbalancedSilent"){
|
|
|
1093 #CDR
|
|
|
1094 P = c(apply(mat[,c(6,8)],1,function(x){((x[1])/sum(x))}),0.5)
|
|
|
1095 N = c(apply(mat[,c(2,4)],1,function(x){(sum(x))}),0)
|
|
|
1096 X = c(apply(mat[,c(2,4)],1,function(x){(x[1])}),0)
|
|
|
1097 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)})
|
|
|
1098 bayesCDR = bayesCDR[-length(bayesCDR)]
|
|
|
1099
|
|
|
1100 #FWR
|
|
|
1101 P = c(apply(mat[,c(6,8)],1,function(x){((x[2])/sum(x))}),0.5)
|
|
|
1102 N = c(apply(mat[,c(2,4)],1,function(x){(sum(x))}),0)
|
|
|
1103 X = c(apply(mat[,c(2,4)],1,function(x){(x[2])}),0)
|
|
|
1104 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)})
|
|
|
1105 bayesFWR = bayesFWR[-length(bayesFWR)]
|
|
|
1106 }
|
|
|
1107
|
|
|
1108 if(flagOneSeq==T){
|
|
|
1109 bayesCDR = bayesCDR[1]
|
|
|
1110 bayesFWR = bayesFWR[1]
|
|
|
1111 }
|
|
|
1112 return( list("CDR"=bayesCDR, "FWR"=bayesFWR) )
|
|
|
1113 }
|
|
|
1114
|
|
|
1115 ##Covolution
|
|
|
1116 break2chunks<-function(G=1000){
|
|
|
1117 base<-2^round(log(sqrt(G),2),0)
|
|
|
1118 return(c(rep(base,floor(G/base)-1),base+G-(floor(G/base)*base)))
|
|
|
1119 }
|
|
|
1120
|
|
|
1121 PowersOfTwo <- function(G=100){
|
|
|
1122 exponents <- array()
|
|
|
1123 i = 0
|
|
|
1124 while(G > 0){
|
|
|
1125 i=i+1
|
|
|
1126 exponents[i] <- floor( log2(G) )
|
|
|
1127 G <- G-2^exponents[i]
|
|
|
1128 }
|
|
|
1129 return(exponents)
|
|
|
1130 }
|
|
|
1131
|
|
|
1132 convolutionPowersOfTwo <- function( cons, length_sigma=4001 ){
|
|
|
1133 G = ncol(cons)
|
|
|
1134 if(G>1){
|
|
|
1135 for(gen in log(G,2):1){
|
|
|
1136 ll<-seq(from=2,to=2^gen,by=2)
|
|
|
1137 sapply(ll,function(l){cons[,l/2]<<-weighted_conv(cons[,l],cons[,l-1],length_sigma=length_sigma)})
|
|
|
1138 }
|
|
|
1139 }
|
|
|
1140 return( cons[,1] )
|
|
|
1141 }
|
|
|
1142
|
|
|
1143 convolutionPowersOfTwoByTwos <- function( cons, length_sigma=4001,G=1 ){
|
|
|
1144 if(length(ncol(cons))) G<-ncol(cons)
|
|
|
1145 groups <- PowersOfTwo(G)
|
|
|
1146 matG <- matrix(NA, ncol=length(groups), nrow=length(cons)/G )
|
|
|
1147 startIndex = 1
|
|
|
1148 for( i in 1:length(groups) ){
|
|
|
1149 stopIndex <- 2^groups[i] + startIndex - 1
|
|
|
1150 if(stopIndex!=startIndex){
|
|
|
1151 matG[,i] <- convolutionPowersOfTwo( cons[,startIndex:stopIndex], length_sigma=length_sigma )
|
|
|
1152 startIndex = stopIndex + 1
|
|
|
1153 }
|
|
|
1154 else {
|
|
|
1155 if(G>1) matG[,i] <- cons[,startIndex:stopIndex]
|
|
|
1156 else matG[,i] <- cons
|
|
|
1157 #startIndex = stopIndex + 1
|
|
|
1158 }
|
|
|
1159 }
|
|
|
1160 return( list( matG, groups ) )
|
|
|
1161 }
|
|
|
1162
|
|
|
1163 weighted_conv<-function(x,y,w=1,m=100,length_sigma=4001){
|
|
|
1164 lx<-length(x)
|
|
|
1165 ly<-length(y)
|
|
|
1166 if({lx<m}| {{lx*w}<m}| {{ly}<m}| {{ly*w}<m}){
|
|
|
1167 if(w<1){
|
|
|
1168 y1<-approx(1:ly,y,seq(1,ly,length.out=m))$y
|
|
|
1169 x1<-approx(1:lx,x,seq(1,lx,length.out=m/w))$y
|
|
|
1170 lx<-length(x1)
|
|
|
1171 ly<-length(y1)
|
|
|
1172 }
|
|
|
1173 else {
|
|
|
1174 y1<-approx(1:ly,y,seq(1,ly,length.out=m*w))$y
|
|
|
1175 x1<-approx(1:lx,x,seq(1,lx,length.out=m))$y
|
|
|
1176 lx<-length(x1)
|
|
|
1177 ly<-length(y1)
|
|
|
1178 }
|
|
|
1179 }
|
|
|
1180 else{
|
|
|
1181 x1<-x
|
|
|
1182 y1<-approx(1:ly,y,seq(1,ly,length.out=floor(lx*w)))$y
|
|
|
1183 ly<-length(y1)
|
|
|
1184 }
|
|
|
1185 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
|
|
|
1186 tmp[tmp<=0] = 0
|
|
|
1187 return(tmp/sum(tmp))
|
|
|
1188 }
|
|
|
1189
|
|
|
1190 calculate_bayesGHelper <- function( listMatG,length_sigma=4001 ){
|
|
|
1191 matG <- listMatG[[1]]
|
|
|
1192 groups <- listMatG[[2]]
|
|
|
1193 i = 1
|
|
|
1194 resConv <- matG[,i]
|
|
|
1195 denom <- 2^groups[i]
|
|
|
1196 if(length(groups)>1){
|
|
|
1197 while( i<length(groups) ){
|
|
|
1198 i = i + 1
|
|
|
1199 resConv <- weighted_conv(resConv, matG[,i], w= {{2^groups[i]}/denom} ,length_sigma=length_sigma)
|
|
|
1200 #cat({{2^groups[i]}/denom},"\n")
|
|
|
1201 denom <- denom + 2^groups[i]
|
|
|
1202 }
|
|
|
1203 }
|
|
|
1204 return(resConv)
|
|
|
1205 }
|
|
|
1206
|
|
|
1207 # Given a list of PDFs, returns a convoluted PDF
|
|
|
1208 groupPosteriors <- function( listPosteriors, max_sigma=20, length_sigma=4001 ,Threshold=2 ){
|
|
|
1209 listPosteriors = listPosteriors[ !is.na(listPosteriors) ]
|
|
|
1210 Length_Postrior<-length(listPosteriors)
|
|
|
1211 if(Length_Postrior>1 & Length_Postrior<=Threshold){
|
|
|
1212 cons = matrix(unlist(listPosteriors),length(listPosteriors[[1]]),length(listPosteriors))
|
|
|
1213 listMatG <- convolutionPowersOfTwoByTwos(cons,length_sigma=length_sigma)
|
|
|
1214 y<-calculate_bayesGHelper(listMatG,length_sigma=length_sigma)
|
|
|
1215 return( y/sum(y)/(2*max_sigma/(length_sigma-1)) )
|
|
|
1216 }else if(Length_Postrior==1) return(listPosteriors[[1]])
|
|
|
1217 else if(Length_Postrior==0) return(NA)
|
|
|
1218 else {
|
|
|
1219 cons = matrix(unlist(listPosteriors),length(listPosteriors[[1]]),length(listPosteriors))
|
|
|
1220 y = fastConv(cons,max_sigma=max_sigma, length_sigma=length_sigma )
|
|
|
1221 return( y/sum(y)/(2*max_sigma/(length_sigma-1)) )
|
|
|
1222 }
|
|
|
1223 }
|
|
|
1224
|
|
|
1225 fastConv<-function(cons, max_sigma=20, length_sigma=4001){
|
|
|
1226 chunks<-break2chunks(G=ncol(cons))
|
|
|
1227 if(ncol(cons)==3) chunks<-2:1
|
|
|
1228 index_chunks_end <- cumsum(chunks)
|
|
|
1229 index_chunks_start <- c(1,index_chunks_end[-length(index_chunks_end)]+1)
|
|
|
1230 index_chunks <- cbind(index_chunks_start,index_chunks_end)
|
|
|
1231
|
|
|
1232 case <- sum(chunks!=chunks[1])
|
|
|
1233 if(case==1) End <- max(1,((length(index_chunks)/2)-1))
|
|
|
1234 else End <- max(1,((length(index_chunks)/2)))
|
|
|
1235
|
|
|
1236 firsts <- sapply(1:End,function(i){
|
|
|
1237 indexes<-index_chunks[i,1]:index_chunks[i,2]
|
|
|
1238 convolutionPowersOfTwoByTwos(cons[ ,indexes])[[1]]
|
|
|
1239 })
|
|
|
1240 if(case==0){
|
|
|
1241 result<-calculate_bayesGHelper( convolutionPowersOfTwoByTwos(firsts) )
|
|
|
1242 }else if(case==1){
|
|
|
1243 last<-list(calculate_bayesGHelper(
|
|
|
1244 convolutionPowersOfTwoByTwos( cons[ ,index_chunks[length(index_chunks)/2,1]:index_chunks[length(index_chunks)/2,2]] )
|
|
|
1245 ),0)
|
|
|
1246 result_first<-calculate_bayesGHelper(convolutionPowersOfTwoByTwos(firsts))
|
|
|
1247 result<-calculate_bayesGHelper(
|
|
|
1248 list(
|
|
|
1249 cbind(
|
|
|
1250 result_first,last[[1]]),
|
|
|
1251 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))
|
|
|
1252 )
|
|
|
1253 )
|
|
|
1254 }
|
|
|
1255 return(as.vector(result))
|
|
|
1256 }
|
|
|
1257
|
|
|
1258 # Computes the 95% CI for a pdf
|
|
|
1259 calcBayesCI <- function(Pdf,low=0.025,up=0.975,max_sigma=20, length_sigma=4001){
|
|
|
1260 if(length(Pdf)!=length_sigma) return(NA)
|
|
|
1261 sigma_s=seq(-max_sigma,max_sigma,length.out=length_sigma)
|
|
|
1262 cdf = cumsum(Pdf)
|
|
|
1263 cdf = cdf/cdf[length(cdf)]
|
|
|
1264 return( c(sigma_s[findInterval(low,cdf)-1] , sigma_s[findInterval(up,cdf)]) )
|
|
|
1265 }
|
|
|
1266
|
|
|
1267 # Computes a mean for a pdf
|
|
|
1268 calcBayesMean <- function(Pdf,max_sigma=20,length_sigma=4001){
|
|
|
1269 if(length(Pdf)!=length_sigma) return(NA)
|
|
|
1270 sigma_s=seq(-max_sigma,max_sigma,length.out=length_sigma)
|
|
|
1271 norm = {length_sigma-1}/2/max_sigma
|
|
|
1272 return( (Pdf%*%sigma_s/norm) )
|
|
|
1273 }
|
|
|
1274
|
|
|
1275 # Returns the mean, and the 95% CI for a pdf
|
|
|
1276 calcBayesOutputInfo <- function(Pdf,low=0.025,up=0.975,max_sigma=20, length_sigma=4001){
|
|
|
1277 if(is.na(Pdf))
|
|
|
1278 return(rep(NA,3))
|
|
|
1279 bCI = calcBayesCI(Pdf=Pdf,low=low,up=up,max_sigma=max_sigma,length_sigma=length_sigma)
|
|
|
1280 bMean = calcBayesMean(Pdf=Pdf,max_sigma=max_sigma,length_sigma=length_sigma)
|
|
|
1281 return(c(bMean, bCI))
|
|
|
1282 }
|
|
|
1283
|
|
|
1284 # Computes the p-value of a pdf
|
|
|
1285 computeSigmaP <- function(Pdf, length_sigma=4001, max_sigma=20){
|
|
|
1286 if(length(Pdf)>1){
|
|
|
1287 norm = {length_sigma-1}/2/max_sigma
|
|
|
1288 pVal = {sum(Pdf[1:{{length_sigma-1}/2}]) + Pdf[{{length_sigma+1}/2}]/2}/norm
|
|
|
1289 if(pVal>0.5){
|
|
|
1290 pVal = pVal-1
|
|
|
1291 }
|
|
|
1292 return(pVal)
|
|
|
1293 }else{
|
|
|
1294 return(NA)
|
|
|
1295 }
|
|
|
1296 }
|
|
|
1297
|
|
|
1298 # Compute p-value of two distributions
|
|
|
1299 compareTwoDistsFaster <-function(sigma_S=seq(-20,20,length.out=4001), N=10000, dens1=runif(4001,0,1), dens2=runif(4001,0,1)){
|
|
|
1300 #print(c(length(dens1),length(dens2)))
|
|
|
1301 if(length(dens1)>1 & length(dens2)>1 ){
|
|
|
1302 dens1<-dens1/sum(dens1)
|
|
|
1303 dens2<-dens2/sum(dens2)
|
|
|
1304 cum2 <- cumsum(dens2)-dens2/2
|
|
|
1305 tmp<- sum(sapply(1:length(dens1),function(i)return(dens1[i]*cum2[i])))
|
|
|
1306 #print(tmp)
|
|
|
1307 if(tmp>0.5)tmp<-tmp-1
|
|
|
1308 return( tmp )
|
|
|
1309 }
|
|
|
1310 else {
|
|
|
1311 return(NA)
|
|
|
1312 }
|
|
|
1313 #return (sum(sapply(1:N,function(i)(sample(sigma_S,1,prob=dens1)>sample(sigma_S,1,prob=dens2))))/N)
|
|
|
1314 }
|
|
|
1315
|
|
|
1316 # get number of seqeunces contributing to the sigma (i.e. seqeunces with mutations)
|
|
|
1317 numberOfSeqsWithMutations <- function(matMutations,test=1){
|
|
|
1318 if(test==4)test=2
|
|
|
1319 cdrSeqs <- 0
|
|
|
1320 fwrSeqs <- 0
|
|
|
1321 if(test==1){#focused
|
|
|
1322 cdrMutations <- apply(matMutations, 1, function(x){ sum(x[c(1,2,4)]) })
|
|
|
1323 fwrMutations <- apply(matMutations, 1, function(x){ sum(x[c(3,4,2)]) })
|
|
|
1324 if( any(which(cdrMutations>0)) ) cdrSeqs <- sum(cdrMutations>0)
|
|
|
1325 if( any(which(fwrMutations>0)) ) fwrSeqs <- sum(fwrMutations>0)
|
|
|
1326 }
|
|
|
1327 if(test==2){#local
|
|
|
1328 cdrMutations <- apply(matMutations, 1, function(x){ sum(x[c(1,2)]) })
|
|
|
1329 fwrMutations <- apply(matMutations, 1, function(x){ sum(x[c(3,4)]) })
|
|
|
1330 if( any(which(cdrMutations>0)) ) cdrSeqs <- sum(cdrMutations>0)
|
|
|
1331 if( any(which(fwrMutations>0)) ) fwrSeqs <- sum(fwrMutations>0)
|
|
|
1332 }
|
|
|
1333 return(c("CDR"=cdrSeqs, "FWR"=fwrSeqs))
|
|
|
1334 }
|
|
|
1335
|
|
|
1336
|
|
|
1337
|
|
|
1338 shadeColor <- function(sigmaVal=NA,pVal=NA){
|
|
|
1339 if(is.na(sigmaVal) & is.na(pVal)) return(NA)
|
|
|
1340 if(is.na(sigmaVal) & !is.na(pVal)) sigmaVal=sign(pVal)
|
|
|
1341 if(is.na(pVal) || pVal==1 || pVal==0){
|
|
|
1342 returnColor = "#FFFFFF";
|
|
|
1343 }else{
|
|
|
1344 colVal=abs(pVal);
|
|
|
1345
|
|
|
1346 if(sigmaVal<0){
|
|
|
1347 if(colVal>0.1)
|
|
|
1348 returnColor = "#CCFFCC";
|
|
|
1349 if(colVal<=0.1)
|
|
|
1350 returnColor = "#99FF99";
|
|
|
1351 if(colVal<=0.050)
|
|
|
1352 returnColor = "#66FF66";
|
|
|
1353 if(colVal<=0.010)
|
|
|
1354 returnColor = "#33FF33";
|
|
|
1355 if(colVal<=0.005)
|
|
|
1356 returnColor = "#00FF00";
|
|
|
1357
|
|
|
1358 }else{
|
|
|
1359 if(colVal>0.1)
|
|
|
1360 returnColor = "#FFCCCC";
|
|
|
1361 if(colVal<=0.1)
|
|
|
1362 returnColor = "#FF9999";
|
|
|
1363 if(colVal<=0.05)
|
|
|
1364 returnColor = "#FF6666";
|
|
|
1365 if(colVal<=0.01)
|
|
|
1366 returnColor = "#FF3333";
|
|
|
1367 if(colVal<0.005)
|
|
|
1368 returnColor = "#FF0000";
|
|
|
1369 }
|
|
|
1370 }
|
|
|
1371
|
|
|
1372 return(returnColor)
|
|
|
1373 }
|
|
|
1374
|
|
|
1375
|
|
|
1376
|
|
|
1377 plotHelp <- function(xfrac=0.05,yfrac=0.05,log=FALSE){
|
|
|
1378 if(!log){
|
|
|
1379 x = par()$usr[1]-(par()$usr[2]-par()$usr[1])*xfrac
|
|
|
1380 y = par()$usr[4]+(par()$usr[4]-par()$usr[3])*yfrac
|
|
|
1381 }else {
|
|
|
1382 if(log==2){
|
|
|
1383 x = par()$usr[1]-(par()$usr[2]-par()$usr[1])*xfrac
|
|
|
1384 y = 10^((par()$usr[4])+((par()$usr[4])-(par()$usr[3]))*yfrac)
|
|
|
1385 }
|
|
|
1386 if(log==1){
|
|
|
1387 x = 10^((par()$usr[1])-((par()$usr[2])-(par()$usr[1]))*xfrac)
|
|
|
1388 y = par()$usr[4]+(par()$usr[4]-par()$usr[3])*yfrac
|
|
|
1389 }
|
|
|
1390 if(log==3){
|
|
|
1391 x = 10^((par()$usr[1])-((par()$usr[2])-(par()$usr[1]))*xfrac)
|
|
|
1392 y = 10^((par()$usr[4])+((par()$usr[4])-(par()$usr[3]))*yfrac)
|
|
|
1393 }
|
|
|
1394 }
|
|
|
1395 return(c("x"=x,"y"=y))
|
|
|
1396 }
|
|
|
1397
|
|
|
1398 # SHMulation
|
|
|
1399
|
|
|
1400 # Based on targeting, introduce a single mutation & then update the targeting
|
|
|
1401 oneMutation <- function(){
|
|
|
1402 # Pick a postion + mutation
|
|
|
1403 posMutation = sample(1:(seqGermlineLen*4),1,replace=F,prob=as.vector(seqTargeting))
|
|
|
1404 posNucNumb = ceiling(posMutation/4) # Nucleotide number
|
|
|
1405 posNucKind = 4 - ( (posNucNumb*4) - posMutation ) # Nuc the position mutates to
|
|
|
1406
|
|
|
1407 #mutate the simulation sequence
|
|
|
1408 seqSimVec <- s2c(seqSim)
|
|
|
1409 seqSimVec[posNucNumb] <- NUCLEOTIDES[posNucKind]
|
|
|
1410 seqSim <<- c2s(seqSimVec)
|
|
|
1411
|
|
|
1412 #update Mutability, Targeting & MutationsTypes
|
|
|
1413 updateMutabilityNTargeting(posNucNumb)
|
|
|
1414
|
|
|
1415 #return(c(posNucNumb,NUCLEOTIDES[posNucKind]))
|
|
|
1416 return(posNucNumb)
|
|
|
1417 }
|
|
|
1418
|
|
|
1419 updateMutabilityNTargeting <- function(position){
|
|
|
1420 min_i<-max((position-2),1)
|
|
|
1421 max_i<-min((position+2),nchar(seqSim))
|
|
|
1422 min_ii<-min(min_i,3)
|
|
|
1423
|
|
|
1424 #mutability - update locally
|
|
|
1425 seqMutability[(min_i):(max_i)] <<- computeMutabilities(substr(seqSim,position-4,position+4))[(min_ii):(max_i-min_i+min_ii)]
|
|
|
1426
|
|
|
1427
|
|
|
1428 #targeting - compute locally
|
|
|
1429 seqTargeting[,min_i:max_i] <<- computeTargeting(substr(seqSim,min_i,max_i),seqMutability[min_i:max_i])
|
|
|
1430 seqTargeting[is.na(seqTargeting)] <<- 0
|
|
|
1431 #mutCodonPos = getCodonPos(position)
|
|
|
1432 mutCodonPos = seq(getCodonPos(min_i)[1],getCodonPos(max_i)[3])
|
|
|
1433 #cat(mutCodonPos,"\n")
|
|
|
1434 mutTypeCodon = getCodonPos(position)
|
|
|
1435 seqMutationTypes[,mutTypeCodon] <<- computeMutationTypesFast( substr(seqSim,mutTypeCodon[1],mutTypeCodon[3]) )
|
|
|
1436 # Stop = 0
|
|
|
1437 if(any(seqMutationTypes[,mutCodonPos]=="Stop",na.rm=T )){
|
|
|
1438 seqTargeting[,mutCodonPos][seqMutationTypes[,mutCodonPos]=="Stop"] <<- 0
|
|
|
1439 }
|
|
|
1440
|
|
|
1441
|
|
|
1442 #Selection
|
|
|
1443 selectedPos = (min_i*4-4)+(which(seqMutationTypes[,min_i:max_i]=="R"))
|
|
|
1444 # CDR
|
|
|
1445 selectedCDR = selectedPos[which(matCDR[selectedPos]==T)]
|
|
|
1446 seqTargeting[selectedCDR] <<- seqTargeting[selectedCDR] * exp(selCDR)
|
|
|
1447 seqTargeting[selectedCDR] <<- seqTargeting[selectedCDR]/baseLineCDR_K
|
|
|
1448
|
|
|
1449 # FWR
|
|
|
1450 selectedFWR = selectedPos[which(matFWR[selectedPos]==T)]
|
|
|
1451 seqTargeting[selectedFWR] <<- seqTargeting[selectedFWR] * exp(selFWR)
|
|
|
1452 seqTargeting[selectedFWR] <<- seqTargeting[selectedFWR]/baseLineFWR_K
|
|
|
1453
|
|
|
1454 }
|
|
|
1455
|
|
|
1456
|
|
|
1457
|
|
|
1458 # Validate the mutation: if the mutation has not been sampled before validate it, else discard it.
|
|
|
1459 validateMutation <- function(){
|
|
|
1460 if( !(mutatedPos%in%mutatedPositions) ){ # if it's a new mutation
|
|
|
1461 uniqueMutationsIntroduced <<- uniqueMutationsIntroduced + 1
|
|
|
1462 mutatedPositions[uniqueMutationsIntroduced] <<- mutatedPos
|
|
|
1463 }else{
|
|
|
1464 if(substr(seqSim,mutatedPos,mutatedPos)==substr(seqGermline,mutatedPos,mutatedPos)){ # back to germline mutation
|
|
|
1465 mutatedPositions <<- mutatedPositions[-which(mutatedPositions==mutatedPos)]
|
|
|
1466 uniqueMutationsIntroduced <<- uniqueMutationsIntroduced - 1
|
|
|
1467 }
|
|
|
1468 }
|
|
|
1469 }
|
|
|
1470
|
|
|
1471
|
|
|
1472
|
|
|
1473 # Places text (labels) at normalized coordinates
|
|
|
1474 myaxis <- function(xfrac=0.05,yfrac=0.05,log=FALSE,w="text",cex=1,adj=1,thecol="black"){
|
|
|
1475 par(xpd=TRUE)
|
|
|
1476 if(!log)
|
|
|
1477 text(par()$usr[1]-(par()$usr[2]-par()$usr[1])*xfrac,par()$usr[4]+(par()$usr[4]-par()$usr[3])*yfrac,w,cex=cex,adj=adj,col=thecol)
|
|
|
1478 else {
|
|
|
1479 if(log==2)
|
|
|
1480 text(
|
|
|
1481 par()$usr[1]-(par()$usr[2]-par()$usr[1])*xfrac,
|
|
|
1482 10^((par()$usr[4])+((par()$usr[4])-(par()$usr[3]))*yfrac),
|
|
|
1483 w,cex=cex,adj=adj,col=thecol)
|
|
|
1484 if(log==1)
|
|
|
1485 text(
|
|
|
1486 10^((par()$usr[1])-((par()$usr[2])-(par()$usr[1]))*xfrac),
|
|
|
1487 par()$usr[4]+(par()$usr[4]-par()$usr[3])*yfrac,
|
|
|
1488 w,cex=cex,adj=adj,col=thecol)
|
|
|
1489 if(log==3)
|
|
|
1490 text(
|
|
|
1491 10^((par()$usr[1])-((par()$usr[2])-(par()$usr[1]))*xfrac),
|
|
|
1492 10^((par()$usr[4])+((par()$usr[4])-(par()$usr[3]))*yfrac),
|
|
|
1493 w,cex=cex,adj=adj,col=thecol)
|
|
|
1494 }
|
|
|
1495 par(xpd=FALSE)
|
|
|
1496 }
|
|
|
1497
|
|
|
1498
|
|
|
1499
|
|
|
1500 # Count the mutations in a sequence
|
|
|
1501 analyzeMutations <- function( inputMatrixIndex, model = 0 , multipleMutation=0, seqWithStops=0){
|
|
|
1502
|
|
|
1503 paramGL = s2c(matInput[inputMatrixIndex,2])
|
|
|
1504 paramSeq = s2c(matInput[inputMatrixIndex,1])
|
|
|
1505
|
|
|
1506 #if( any(paramSeq=="N") ){
|
|
|
1507 # gapPos_Seq = which(paramSeq=="N")
|
|
|
1508 # gapPos_Seq_ToReplace = gapPos_Seq[paramGL[gapPos_Seq] != "N"]
|
|
|
1509 # paramSeq[gapPos_Seq_ToReplace] = paramGL[gapPos_Seq_ToReplace]
|
|
|
1510 #}
|
|
|
1511 mutations_val = paramGL != paramSeq
|
|
|
1512
|
|
|
1513 if(any(mutations_val)){
|
|
|
1514 mutationPos = which(mutations_val)#{1:length(mutations_val)}[mutations_val]
|
|
|
1515 length_mutations =length(mutationPos)
|
|
|
1516 mutationInfo = rep(NA,length_mutations)
|
|
|
1517
|
|
|
1518 pos<- mutationPos
|
|
|
1519 pos_array<-array(sapply(pos,getCodonPos))
|
|
|
1520 codonGL = paramGL[pos_array]
|
|
|
1521 codonSeqWhole = paramSeq[pos_array]
|
|
|
1522 codonSeq = sapply(pos,function(x){
|
|
|
1523 seqP = paramGL[getCodonPos(x)]
|
|
|
1524 muCodonPos = {x-1}%%3+1
|
|
|
1525 seqP[muCodonPos] = paramSeq[x]
|
|
|
1526 return(seqP)
|
|
|
1527 })
|
|
|
1528 GLcodons = apply(matrix(codonGL,length_mutations,3,byrow=TRUE),1,c2s)
|
|
|
1529 SeqcodonsWhole = apply(matrix(codonSeqWhole,length_mutations,3,byrow=TRUE),1,c2s)
|
|
|
1530 Seqcodons = apply(codonSeq,2,c2s)
|
|
|
1531
|
|
|
1532 mutationInfo = apply(rbind(GLcodons , Seqcodons),2,function(x){mutationType(c2s(x[1]),c2s(x[2]))})
|
|
|
1533 names(mutationInfo) = mutationPos
|
|
|
1534
|
|
|
1535 mutationInfoWhole = apply(rbind(GLcodons , SeqcodonsWhole),2,function(x){mutationType(c2s(x[1]),c2s(x[2]))})
|
|
|
1536 names(mutationInfoWhole) = mutationPos
|
|
|
1537
|
|
|
1538 mutationInfo <- mutationInfo[!is.na(mutationInfo)]
|
|
|
1539 mutationInfoWhole <- mutationInfoWhole[!is.na(mutationInfoWhole)]
|
|
|
1540
|
|
|
1541 if(any(!is.na(mutationInfo))){
|
|
|
1542
|
|
|
1543 #Filter based on Stop (at the codon level)
|
|
|
1544 if(seqWithStops==1){
|
|
|
1545 nucleotidesAtStopCodons = names(mutationInfoWhole[mutationInfoWhole!="Stop"])
|
|
|
1546 mutationInfo = mutationInfo[nucleotidesAtStopCodons]
|
|
|
1547 mutationInfoWhole = mutationInfo[nucleotidesAtStopCodons]
|
|
|
1548 }else{
|
|
|
1549 countStops = sum(mutationInfoWhole=="Stop")
|
|
|
1550 if(seqWithStops==2 & countStops==0) mutationInfo = NA
|
|
|
1551 if(seqWithStops==3 & countStops>0) mutationInfo = NA
|
|
|
1552 }
|
|
|
1553
|
|
|
1554 if(any(!is.na(mutationInfo))){
|
|
|
1555 #Filter mutations based on multipleMutation
|
|
|
1556 if(multipleMutation==1 & !is.na(mutationInfo)){
|
|
|
1557 mutationCodons = getCodonNumb(as.numeric(names(mutationInfoWhole)))
|
|
|
1558 tableMutationCodons <- table(mutationCodons)
|
|
|
1559 codonsWithMultipleMutations <- as.numeric(names(tableMutationCodons[tableMutationCodons>1]))
|
|
|
1560 if(any(codonsWithMultipleMutations)){
|
|
|
1561 #remove the nucleotide mutations in the codons with multiple mutations
|
|
|
1562 mutationInfo <- mutationInfo[!(mutationCodons %in% codonsWithMultipleMutations)]
|
|
|
1563 #replace those codons with Ns in the input sequence
|
|
|
1564 paramSeq[unlist(lapply(codonsWithMultipleMutations, getCodonNucs))] = "N"
|
|
|
1565 matInput[inputMatrixIndex,1] <<- c2s(paramSeq)
|
|
|
1566 }
|
|
|
1567 }
|
|
|
1568
|
|
|
1569 #Filter mutations based on the model
|
|
|
1570 if(any(mutationInfo)==T | is.na(any(mutationInfo))){
|
|
|
1571
|
|
|
1572 if(model==1 & !is.na(mutationInfo)){
|
|
|
1573 mutationInfo <- mutationInfo[mutationInfo=="S"]
|
|
|
1574 }
|
|
|
1575 if(any(mutationInfo)==T | is.na(any(mutationInfo))) return(mutationInfo)
|
|
|
1576 else return(NA)
|
|
|
1577 }else{
|
|
|
1578 return(NA)
|
|
|
1579 }
|
|
|
1580 }else{
|
|
|
1581 return(NA)
|
|
|
1582 }
|
|
|
1583
|
|
|
1584
|
|
|
1585 }else{
|
|
|
1586 return(NA)
|
|
|
1587 }
|
|
|
1588
|
|
|
1589
|
|
|
1590 }else{
|
|
|
1591 return (NA)
|
|
|
1592 }
|
|
|
1593 }
|
|
|
1594
|
|
|
1595 analyzeMutationsFixed <- function( inputArray, model = 0 , multipleMutation=0, seqWithStops=0){
|
|
|
1596
|
|
|
1597 paramGL = s2c(inputArray[2])
|
|
|
1598 paramSeq = s2c(inputArray[1])
|
|
|
1599 inputSeq <- inputArray[1]
|
|
|
1600 #if( any(paramSeq=="N") ){
|
|
|
1601 # gapPos_Seq = which(paramSeq=="N")
|
|
|
1602 # gapPos_Seq_ToReplace = gapPos_Seq[paramGL[gapPos_Seq] != "N"]
|
|
|
1603 # paramSeq[gapPos_Seq_ToReplace] = paramGL[gapPos_Seq_ToReplace]
|
|
|
1604 #}
|
|
|
1605 mutations_val = paramGL != paramSeq
|
|
|
1606
|
|
|
1607 if(any(mutations_val)){
|
|
|
1608 mutationPos = which(mutations_val)#{1:length(mutations_val)}[mutations_val]
|
|
|
1609 length_mutations =length(mutationPos)
|
|
|
1610 mutationInfo = rep(NA,length_mutations)
|
|
|
1611
|
|
|
1612 pos<- mutationPos
|
|
|
1613 pos_array<-array(sapply(pos,getCodonPos))
|
|
|
1614 codonGL = paramGL[pos_array]
|
|
|
1615 codonSeqWhole = paramSeq[pos_array]
|
|
|
1616 codonSeq = sapply(pos,function(x){
|
|
|
1617 seqP = paramGL[getCodonPos(x)]
|
|
|
1618 muCodonPos = {x-1}%%3+1
|
|
|
1619 seqP[muCodonPos] = paramSeq[x]
|
|
|
1620 return(seqP)
|
|
|
1621 })
|
|
|
1622 GLcodons = apply(matrix(codonGL,length_mutations,3,byrow=TRUE),1,c2s)
|
|
|
1623 SeqcodonsWhole = apply(matrix(codonSeqWhole,length_mutations,3,byrow=TRUE),1,c2s)
|
|
|
1624 Seqcodons = apply(codonSeq,2,c2s)
|
|
|
1625
|
|
|
1626 mutationInfo = apply(rbind(GLcodons , Seqcodons),2,function(x){mutationType(c2s(x[1]),c2s(x[2]))})
|
|
|
1627 names(mutationInfo) = mutationPos
|
|
|
1628
|
|
|
1629 mutationInfoWhole = apply(rbind(GLcodons , SeqcodonsWhole),2,function(x){mutationType(c2s(x[1]),c2s(x[2]))})
|
|
|
1630 names(mutationInfoWhole) = mutationPos
|
|
|
1631
|
|
|
1632 mutationInfo <- mutationInfo[!is.na(mutationInfo)]
|
|
|
1633 mutationInfoWhole <- mutationInfoWhole[!is.na(mutationInfoWhole)]
|
|
|
1634
|
|
|
1635 if(any(!is.na(mutationInfo))){
|
|
|
1636
|
|
|
1637 #Filter based on Stop (at the codon level)
|
|
|
1638 if(seqWithStops==1){
|
|
|
1639 nucleotidesAtStopCodons = names(mutationInfoWhole[mutationInfoWhole!="Stop"])
|
|
|
1640 mutationInfo = mutationInfo[nucleotidesAtStopCodons]
|
|
|
1641 mutationInfoWhole = mutationInfo[nucleotidesAtStopCodons]
|
|
|
1642 }else{
|
|
|
1643 countStops = sum(mutationInfoWhole=="Stop")
|
|
|
1644 if(seqWithStops==2 & countStops==0) mutationInfo = NA
|
|
|
1645 if(seqWithStops==3 & countStops>0) mutationInfo = NA
|
|
|
1646 }
|
|
|
1647
|
|
|
1648 if(any(!is.na(mutationInfo))){
|
|
|
1649 #Filter mutations based on multipleMutation
|
|
|
1650 if(multipleMutation==1 & !is.na(mutationInfo)){
|
|
|
1651 mutationCodons = getCodonNumb(as.numeric(names(mutationInfoWhole)))
|
|
|
1652 tableMutationCodons <- table(mutationCodons)
|
|
|
1653 codonsWithMultipleMutations <- as.numeric(names(tableMutationCodons[tableMutationCodons>1]))
|
|
|
1654 if(any(codonsWithMultipleMutations)){
|
|
|
1655 #remove the nucleotide mutations in the codons with multiple mutations
|
|
|
1656 mutationInfo <- mutationInfo[!(mutationCodons %in% codonsWithMultipleMutations)]
|
|
|
1657 #replace those codons with Ns in the input sequence
|
|
|
1658 paramSeq[unlist(lapply(codonsWithMultipleMutations, getCodonNucs))] = "N"
|
|
|
1659 #matInput[inputMatrixIndex,1] <<- c2s(paramSeq)
|
|
|
1660 inputSeq <- c2s(paramSeq)
|
|
|
1661 }
|
|
|
1662 }
|
|
|
1663
|
|
|
1664 #Filter mutations based on the model
|
|
|
1665 if(any(mutationInfo)==T | is.na(any(mutationInfo))){
|
|
|
1666
|
|
|
1667 if(model==1 & !is.na(mutationInfo)){
|
|
|
1668 mutationInfo <- mutationInfo[mutationInfo=="S"]
|
|
|
1669 }
|
|
|
1670 if(any(mutationInfo)==T | is.na(any(mutationInfo))) return(list(mutationInfo,inputSeq))
|
|
|
1671 else return(list(NA,inputSeq))
|
|
|
1672 }else{
|
|
|
1673 return(list(NA,inputSeq))
|
|
|
1674 }
|
|
|
1675 }else{
|
|
|
1676 return(list(NA,inputSeq))
|
|
|
1677 }
|
|
|
1678
|
|
|
1679
|
|
|
1680 }else{
|
|
|
1681 return(list(NA,inputSeq))
|
|
|
1682 }
|
|
|
1683
|
|
|
1684
|
|
|
1685 }else{
|
|
|
1686 return (list(NA,inputSeq))
|
|
|
1687 }
|
|
|
1688 }
|
|
|
1689
|
|
|
1690 # triMutability Background Count
|
|
|
1691 buildMutabilityModel <- function( inputMatrixIndex, model=0 , multipleMutation=0, seqWithStops=0, stopMutations=0){
|
|
|
1692
|
|
|
1693 #rowOrigMatInput = matInput[inputMatrixIndex,]
|
|
|
1694 seqGL = gsub("-", "", matInput[inputMatrixIndex,2])
|
|
|
1695 seqInput = gsub("-", "", matInput[inputMatrixIndex,1])
|
|
|
1696 #matInput[inputMatrixIndex,] <<- cbind(seqInput,seqGL)
|
|
|
1697 tempInput <- cbind(seqInput,seqGL)
|
|
|
1698 seqLength = nchar(seqGL)
|
|
|
1699 list_analyzeMutationsFixed<- analyzeMutationsFixed(tempInput, model, multipleMutation, seqWithStops)
|
|
|
1700 mutationCount <- list_analyzeMutationsFixed[[1]]
|
|
|
1701 seqInput <- list_analyzeMutationsFixed[[2]]
|
|
|
1702 BackgroundMatrix = mutabilityMatrix
|
|
|
1703 MutationMatrix = mutabilityMatrix
|
|
|
1704 MutationCountMatrix = mutabilityMatrix
|
|
|
1705 if(!is.na(mutationCount)){
|
|
|
1706 if((stopMutations==0 & model==0) | (stopMutations==1 & (sum(mutationCount=="Stop")<length(mutationCount))) | (model==1 & (sum(mutationCount=="S")>0)) ){
|
|
|
1707
|
|
|
1708 fivermerStartPos = 1:(seqLength-4)
|
|
|
1709 fivemerLength <- length(fivermerStartPos)
|
|
|
1710 fivemerGL <- substr(rep(seqGL,length(fivermerStartPos)),(fivermerStartPos),(fivermerStartPos+4))
|
|
|
1711 fivemerSeq <- substr(rep(seqInput,length(fivermerStartPos)),(fivermerStartPos),(fivermerStartPos+4))
|
|
|
1712
|
|
|
1713 #Background
|
|
|
1714 for(fivemerIndex in 1:fivemerLength){
|
|
|
1715 fivemer = fivemerGL[fivemerIndex]
|
|
|
1716 if(!any(grep("N",fivemer))){
|
|
|
1717 fivemerCodonPos = fivemerCodon(fivemerIndex)
|
|
|
1718 fivemerReadingFrameCodon = substr(fivemer,fivemerCodonPos[1],fivemerCodonPos[3])
|
|
|
1719 fivemerReadingFrameCodonInputSeq = substr(fivemerSeq[fivemerIndex],fivemerCodonPos[1],fivemerCodonPos[3])
|
|
|
1720
|
|
|
1721 # All mutations model
|
|
|
1722 #if(!any(grep("N",fivemerReadingFrameCodon))){
|
|
|
1723 if(model==0){
|
|
|
1724 if(stopMutations==0){
|
|
|
1725 if(!any(grep("N",fivemerReadingFrameCodonInputSeq)))
|
|
|
1726 BackgroundMatrix[fivemer] <- (BackgroundMatrix[fivemer] + 1)
|
|
|
1727 }else{
|
|
|
1728 if( !any(grep("N",fivemerReadingFrameCodonInputSeq)) & translateCodonToAminoAcid(fivemerReadingFrameCodon)!="*" ){
|
|
|
1729 positionWithinCodon = which(fivemerCodonPos==3)#positionsWithinCodon[(fivemerCodonPos[1]%%3)+1]
|
|
|
1730 BackgroundMatrix[fivemer] <- (BackgroundMatrix[fivemer] + probNonStopMutations[fivemerReadingFrameCodon,positionWithinCodon])
|
|
|
1731 }
|
|
|
1732 }
|
|
|
1733 }else{ # Only silent mutations
|
|
|
1734 if( !any(grep("N",fivemerReadingFrameCodonInputSeq)) & translateCodonToAminoAcid(fivemerReadingFrameCodon)!="*" & translateCodonToAminoAcid(fivemerReadingFrameCodonInputSeq)==translateCodonToAminoAcid(fivemerReadingFrameCodon)){
|
|
|
1735 positionWithinCodon = which(fivemerCodonPos==3)
|
|
|
1736 BackgroundMatrix[fivemer] <- (BackgroundMatrix[fivemer] + probSMutations[fivemerReadingFrameCodon,positionWithinCodon])
|
|
|
1737 }
|
|
|
1738 }
|
|
|
1739 #}
|
|
|
1740 }
|
|
|
1741 }
|
|
|
1742
|
|
|
1743 #Mutations
|
|
|
1744 if(stopMutations==1) mutationCount = mutationCount[mutationCount!="Stop"]
|
|
|
1745 if(model==1) mutationCount = mutationCount[mutationCount=="S"]
|
|
|
1746 mutationPositions = as.numeric(names(mutationCount))
|
|
|
1747 mutationCount = mutationCount[mutationPositions>2 & mutationPositions<(seqLength-1)]
|
|
|
1748 mutationPositions = mutationPositions[mutationPositions>2 & mutationPositions<(seqLength-1)]
|
|
|
1749 countMutations = 0
|
|
|
1750 for(mutationPosition in mutationPositions){
|
|
|
1751 fivemerIndex = mutationPosition-2
|
|
|
1752 fivemer = fivemerSeq[fivemerIndex]
|
|
|
1753 GLfivemer = fivemerGL[fivemerIndex]
|
|
|
1754 fivemerCodonPos = fivemerCodon(fivemerIndex)
|
|
|
1755 fivemerReadingFrameCodon = substr(fivemer,fivemerCodonPos[1],fivemerCodonPos[3])
|
|
|
1756 fivemerReadingFrameCodonGL = substr(GLfivemer,fivemerCodonPos[1],fivemerCodonPos[3])
|
|
|
1757 if(!any(grep("N",fivemer)) & !any(grep("N",GLfivemer))){
|
|
|
1758 if(model==0){
|
|
|
1759 countMutations = countMutations + 1
|
|
|
1760 MutationMatrix[GLfivemer] <- (MutationMatrix[GLfivemer] + 1)
|
|
|
1761 MutationCountMatrix[GLfivemer] <- (MutationCountMatrix[GLfivemer] + 1)
|
|
|
1762 }else{
|
|
|
1763 if( translateCodonToAminoAcid(fivemerReadingFrameCodonGL)!="*" ){
|
|
|
1764 countMutations = countMutations + 1
|
|
|
1765 positionWithinCodon = which(fivemerCodonPos==3)
|
|
|
1766 glNuc = substr(fivemerReadingFrameCodonGL,positionWithinCodon,positionWithinCodon)
|
|
|
1767 inputNuc = substr(fivemerReadingFrameCodon,positionWithinCodon,positionWithinCodon)
|
|
|
1768 MutationMatrix[GLfivemer] <- (MutationMatrix[GLfivemer] + substitution[glNuc,inputNuc])
|
|
|
1769 MutationCountMatrix[GLfivemer] <- (MutationCountMatrix[GLfivemer] + 1)
|
|
|
1770 }
|
|
|
1771 }
|
|
|
1772 }
|
|
|
1773 }
|
|
|
1774
|
|
|
1775 seqMutability = MutationMatrix/BackgroundMatrix
|
|
|
1776 seqMutability = seqMutability/sum(seqMutability,na.rm=TRUE)
|
|
|
1777 #cat(inputMatrixIndex,"\t",countMutations,"\n")
|
|
|
1778 return(list("seqMutability" = seqMutability,"numbMutations" = countMutations,"seqMutabilityCount" = MutationCountMatrix, "BackgroundMatrix"=BackgroundMatrix))
|
|
|
1779
|
|
|
1780 }
|
|
|
1781 }
|
|
|
1782
|
|
|
1783 }
|
|
|
1784
|
|
|
1785 #Returns the codon position containing the middle nucleotide
|
|
|
1786 fivemerCodon <- function(fivemerIndex){
|
|
|
1787 codonPos = list(2:4,1:3,3:5)
|
|
|
1788 fivemerType = fivemerIndex%%3
|
|
|
1789 return(codonPos[[fivemerType+1]])
|
|
|
1790 }
|
|
|
1791
|
|
|
1792 #returns probability values for one mutation in codons resulting in R, S or Stop
|
|
|
1793 probMutations <- function(typeOfMutation){
|
|
|
1794 matMutationProb <- matrix(0,ncol=3,nrow=125,dimnames=list(words(alphabet = c(NUCLEOTIDES,"N"), length=3),c(1:3)))
|
|
|
1795 for(codon in rownames(matMutationProb)){
|
|
|
1796 if( !any(grep("N",codon)) ){
|
|
|
1797 for(muPos in 1:3){
|
|
|
1798 matCodon = matrix(rep(s2c(codon),3),nrow=3,ncol=3,byrow=T)
|
|
|
1799 glNuc = matCodon[1,muPos]
|
|
|
1800 matCodon[,muPos] = canMutateTo(glNuc)
|
|
|
1801 substitutionRate = substitution[glNuc,matCodon[,muPos]]
|
|
|
1802 typeOfMutations = apply(rbind(rep(codon,3),apply(matCodon,1,c2s)),2,function(x){mutationType(c2s(x[1]),c2s(x[2]))})
|
|
|
1803 matMutationProb[codon,muPos] <- sum(substitutionRate[typeOfMutations==typeOfMutation])
|
|
|
1804 }
|
|
|
1805 }
|
|
|
1806 }
|
|
|
1807
|
|
|
1808 return(matMutationProb)
|
|
|
1809 }
|
|
|
1810
|
|
|
1811
|
|
|
1812
|
|
|
1813
|
|
|
1814 #Mapping Trinucleotides to fivemers
|
|
|
1815 mapTriToFivemer <- function(triMutability=triMutability_Literature_Human){
|
|
|
1816 rownames(triMutability) <- triMutability_Names
|
|
|
1817 Fivemer<-rep(NA,1024)
|
|
|
1818 names(Fivemer)<-words(alphabet=NUCLEOTIDES,length=5)
|
|
|
1819 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)))
|
|
|
1820 Fivemer<-Fivemer/sum(Fivemer)
|
|
|
1821 return(Fivemer)
|
|
|
1822 }
|
|
|
1823
|
|
|
1824 collapseFivemerToTri<-function(Fivemer,Weights=MutabilityWeights,position=1,NUC="A"){
|
|
|
1825 Indices<-substring(names(Fivemer),3,3)==NUC
|
|
|
1826 Factors<-substring(names(Fivemer[Indices]),(4-position),(6-position))
|
|
|
1827 tapply(which(Indices),Factors,function(i)weighted.mean(Fivemer[i],Weights[i],na.rm=TRUE))
|
|
|
1828 }
|
|
|
1829
|
|
|
1830
|
|
|
1831
|
|
|
1832 CountFivemerToTri<-function(Fivemer,Weights=MutabilityWeights,position=1,NUC="A"){
|
|
|
1833 Indices<-substring(names(Fivemer),3,3)==NUC
|
|
|
1834 Factors<-substring(names(Fivemer[Indices]),(4-position),(6-position))
|
|
|
1835 tapply(which(Indices),Factors,function(i)sum(Weights[i],na.rm=TRUE))
|
|
|
1836 }
|
|
|
1837
|
|
|
1838 #Uses the real counts of the mutated fivemers
|
|
|
1839 CountFivemerToTri2<-function(Fivemer,Counts=MutabilityCounts,position=1,NUC="A"){
|
|
|
1840 Indices<-substring(names(Fivemer),3,3)==NUC
|
|
|
1841 Factors<-substring(names(Fivemer[Indices]),(4-position),(6-position))
|
|
|
1842 tapply(which(Indices),Factors,function(i)sum(Counts[i],na.rm=TRUE))
|
|
|
1843 }
|
|
|
1844
|
|
|
1845 bootstrap<-function(x=c(33,12,21),M=10000,alpha=0.05){
|
|
|
1846 N<-sum(x)
|
|
|
1847 if(N){
|
|
|
1848 p<-x/N
|
|
|
1849 k<-length(x)-1
|
|
|
1850 tmp<-rmultinom(M, size = N, prob=p)
|
|
|
1851 tmp_p<-apply(tmp,2,function(y)y/N)
|
|
|
1852 (apply(tmp_p,1,function(y)quantile(y,c(alpha/2/k,1-alpha/2/k))))
|
|
|
1853 }
|
|
|
1854 else return(matrix(0,2,length(x)))
|
|
|
1855 }
|
|
|
1856
|
|
|
1857
|
|
|
1858
|
|
|
1859
|
|
|
1860 bootstrap2<-function(x=c(33,12,21),n=10,M=10000,alpha=0.05){
|
|
|
1861
|
|
|
1862 N<-sum(x)
|
|
|
1863 k<-length(x)
|
|
|
1864 y<-rep(1:k,x)
|
|
|
1865 tmp<-sapply(1:M,function(i)sample(y,n))
|
|
|
1866 if(n>1)tmp_p<-sapply(1:M,function(j)sapply(1:k,function(i)sum(tmp[,j]==i)))/n
|
|
|
1867 if(n==1)tmp_p<-sapply(1:M,function(j)sapply(1:k,function(i)sum(tmp[j]==i)))/n
|
|
|
1868 (apply(tmp_p,1,function(z)quantile(z,c(alpha/2/(k-1),1-alpha/2/(k-1)))))
|
|
|
1869 }
|
|
|
1870
|
|
|
1871
|
|
|
1872
|
|
|
1873 p_value<-function(x=c(33,12,21),M=100000,x_obs=c(2,5,3)){
|
|
|
1874 n=sum(x_obs)
|
|
|
1875 N<-sum(x)
|
|
|
1876 k<-length(x)
|
|
|
1877 y<-rep(1:k,x)
|
|
|
1878 tmp<-sapply(1:M,function(i)sample(y,n))
|
|
|
1879 if(n>1)tmp_p<-sapply(1:M,function(j)sapply(1:k,function(i)sum(tmp[,j]==i)))
|
|
|
1880 if(n==1)tmp_p<-sapply(1:M,function(j)sapply(1:k,function(i)sum(tmp[j]==i)))
|
|
|
1881 tmp<-rbind(sapply(1:3,function(i)sum(tmp_p[i,]>=x_obs[i])/M),
|
|
|
1882 sapply(1:3,function(i)sum(tmp_p[i,]<=x_obs[i])/M))
|
|
|
1883 sapply(1:3,function(i){if(tmp[1,i]>=tmp[2,i])return(-tmp[2,i])else return(tmp[1,i])})
|
|
|
1884 }
|
|
|
1885
|
|
|
1886 #"D:\\Sequences\\IMGT Germlines\\Human_SNPless_IGHJ.FASTA"
|
|
|
1887 # Remove SNPs from IMGT germline segment alleles
|
|
|
1888 generateUnambiguousRepertoire <- function(repertoireInFile,repertoireOutFile){
|
|
|
1889 repertoireIn <- read.fasta(repertoireInFile, seqtype="DNA",as.string=T,set.attributes=F,forceDNAtolower=F)
|
|
|
1890 alleleNames <- sapply(names(repertoireIn),function(x)strsplit(x,"|",fixed=TRUE)[[1]][2])
|
|
|
1891 SNPs <- tapply(repertoireIn,sapply(alleleNames,function(x)strsplit(x,"*",fixed=TRUE)[[1]][1]),function(x){
|
|
|
1892 Indices<-NULL
|
|
|
1893 for(i in 1:length(x)){
|
|
|
1894 firstSeq = s2c(x[[1]])
|
|
|
1895 iSeq = s2c(x[[i]])
|
|
|
1896 Indices<-c(Indices,which(firstSeq[1:320]!=iSeq[1:320] & firstSeq[1:320]!="." & iSeq[1:320]!="." ))
|
|
|
1897 }
|
|
|
1898 return(sort(unique(Indices)))
|
|
|
1899 })
|
|
|
1900 repertoireOut <- repertoireIn
|
|
|
1901 repertoireOut <- lapply(names(repertoireOut), function(repertoireName){
|
|
|
1902 alleleName <- strsplit(repertoireName,"|",fixed=TRUE)[[1]][2]
|
|
|
1903 geneSegmentName <- strsplit(alleleName,"*",fixed=TRUE)[[1]][1]
|
|
|
1904 alleleSeq <- s2c(repertoireOut[[repertoireName]])
|
|
|
1905 alleleSeq[as.numeric(unlist(SNPs[geneSegmentName]))] <- "N"
|
|
|
1906 alleleSeq <- c2s(alleleSeq)
|
|
|
1907 repertoireOut[[repertoireName]] <- alleleSeq
|
|
|
1908 })
|
|
|
1909 names(repertoireOut) <- names(repertoireIn)
|
|
|
1910 write.fasta(repertoireOut,names(repertoireOut),file.out=repertoireOutFile)
|
|
|
1911
|
|
|
1912 }
|
|
|
1913
|
|
|
1914
|
|
|
1915
|
|
|
1916
|
|
|
1917
|
|
|
1918
|
|
|
1919 ############
|
|
|
1920 groupBayes2 = function(indexes, param_resultMat){
|
|
|
1921
|
|
|
1922 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])}))
|
|
|
1923 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])}))
|
|
|
1924 #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])}))
|
|
|
1925 #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])}))
|
|
|
1926 #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])}))
|
|
|
1927 #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])}))
|
|
|
1928 return ( list("BayesGDist_Focused_CDR"=BayesGDist_Focused_CDR,
|
|
|
1929 "BayesGDist_Focused_FWR"=BayesGDist_Focused_FWR) )
|
|
|
1930 #"BayesGDist_Local_CDR"=BayesGDist_Local_CDR,
|
|
|
1931 #"BayesGDist_Local_FWR" = BayesGDist_Local_FWR))
|
|
|
1932 # "BayesGDist_Global_CDR" = BayesGDist_Global_CDR,
|
|
|
1933 # "BayesGDist_Global_FWR" = BayesGDist_Global_FWR) )
|
|
|
1934
|
|
|
1935
|
|
|
1936 }
|
|
|
1937
|
|
|
1938
|
|
|
1939 calculate_bayesG <- function( x=array(), N=array(), p=array(), max_sigma=20, length_sigma=4001){
|
|
|
1940 G <- max(length(x),length(N),length(p))
|
|
|
1941 x=array(x,dim=G)
|
|
|
1942 N=array(N,dim=G)
|
|
|
1943 p=array(p,dim=G)
|
|
|
1944
|
|
|
1945 indexOfZero = N>0 & p>0
|
|
|
1946 N = N[indexOfZero]
|
|
|
1947 x = x[indexOfZero]
|
|
|
1948 p = p[indexOfZero]
|
|
|
1949 G <- length(x)
|
|
|
1950
|
|
|
1951 if(G){
|
|
|
1952
|
|
|
1953 cons<-array( dim=c(length_sigma,G) )
|
|
|
1954 if(G==1) {
|
|
|
1955 return(calculate_bayes(x=x[G],N=N[G],p=p[G],max_sigma=max_sigma,length_sigma=length_sigma))
|
|
|
1956 }
|
|
|
1957 else {
|
|
|
1958 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)
|
|
|
1959 listMatG <- convolutionPowersOfTwoByTwos(cons,length_sigma=length_sigma)
|
|
|
1960 y<-calculate_bayesGHelper(listMatG,length_sigma=length_sigma)
|
|
|
1961 return( y/sum(y)/(2*max_sigma/(length_sigma-1)) )
|
|
|
1962 }
|
|
|
1963 }else{
|
|
|
1964 return(NA)
|
|
|
1965 }
|
|
|
1966 }
|
|
|
1967
|
|
|
1968
|
|
|
1969 calculate_bayesGHelper <- function( listMatG,length_sigma=4001 ){
|
|
|
1970 matG <- listMatG[[1]]
|
|
|
1971 groups <- listMatG[[2]]
|
|
|
1972 i = 1
|
|
|
1973 resConv <- matG[,i]
|
|
|
1974 denom <- 2^groups[i]
|
|
|
1975 if(length(groups)>1){
|
|
|
1976 while( i<length(groups) ){
|
|
|
1977 i = i + 1
|
|
|
1978 resConv <- weighted_conv(resConv, matG[,i], w= {{2^groups[i]}/denom} ,length_sigma=length_sigma)
|
|
|
1979 #cat({{2^groups[i]}/denom},"\n")
|
|
|
1980 denom <- denom + 2^groups[i]
|
|
|
1981 }
|
|
|
1982 }
|
|
|
1983 return(resConv)
|
|
|
1984 }
|
|
|
1985
|
|
|
1986 weighted_conv<-function(x,y,w=1,m=100,length_sigma=4001){
|
|
|
1987 lx<-length(x)
|
|
|
1988 ly<-length(y)
|
|
|
1989 if({lx<m}| {{lx*w}<m}| {{ly}<m}| {{ly*w}<m}){
|
|
|
1990 if(w<1){
|
|
|
1991 y1<-approx(1:ly,y,seq(1,ly,length.out=m))$y
|
|
|
1992 x1<-approx(1:lx,x,seq(1,lx,length.out=m/w))$y
|
|
|
1993 lx<-length(x1)
|
|
|
1994 ly<-length(y1)
|
|
|
1995 }
|
|
|
1996 else {
|
|
|
1997 y1<-approx(1:ly,y,seq(1,ly,length.out=m*w))$y
|
|
|
1998 x1<-approx(1:lx,x,seq(1,lx,length.out=m))$y
|
|
|
1999 lx<-length(x1)
|
|
|
2000 ly<-length(y1)
|
|
|
2001 }
|
|
|
2002 }
|
|
|
2003 else{
|
|
|
2004 x1<-x
|
|
|
2005 y1<-approx(1:ly,y,seq(1,ly,length.out=floor(lx*w)))$y
|
|
|
2006 ly<-length(y1)
|
|
|
2007 }
|
|
|
2008 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
|
|
|
2009 tmp[tmp<=0] = 0
|
|
|
2010 return(tmp/sum(tmp))
|
|
|
2011 }
|
|
|
2012
|
|
|
2013 ########################
|
|
|
2014
|
|
|
2015
|
|
|
2016
|
|
|
2017
|
|
|
2018 mutabilityMatrixONE<-rep(0,4)
|
|
|
2019 names(mutabilityMatrixONE)<-NUCLEOTIDES
|
|
|
2020
|
|
|
2021 # triMutability Background Count
|
|
|
2022 buildMutabilityModelONE <- function( inputMatrixIndex, model=0 , multipleMutation=0, seqWithStops=0, stopMutations=0){
|
|
|
2023
|
|
|
2024 #rowOrigMatInput = matInput[inputMatrixIndex,]
|
|
|
2025 seqGL = gsub("-", "", matInput[inputMatrixIndex,2])
|
|
|
2026 seqInput = gsub("-", "", matInput[inputMatrixIndex,1])
|
|
|
2027 matInput[inputMatrixIndex,] <<- c(seqInput,seqGL)
|
|
|
2028 seqLength = nchar(seqGL)
|
|
|
2029 mutationCount <- analyzeMutations(inputMatrixIndex, model, multipleMutation, seqWithStops)
|
|
|
2030 BackgroundMatrix = mutabilityMatrixONE
|
|
|
2031 MutationMatrix = mutabilityMatrixONE
|
|
|
2032 MutationCountMatrix = mutabilityMatrixONE
|
|
|
2033 if(!is.na(mutationCount)){
|
|
|
2034 if((stopMutations==0 & model==0) | (stopMutations==1 & (sum(mutationCount=="Stop")<length(mutationCount))) | (model==1 & (sum(mutationCount=="S")>0)) ){
|
|
|
2035
|
|
|
2036 # ONEmerStartPos = 1:(seqLength)
|
|
|
2037 # ONEmerLength <- length(ONEmerStartPos)
|
|
|
2038 ONEmerGL <- s2c(seqGL)
|
|
|
2039 ONEmerSeq <- s2c(seqInput)
|
|
|
2040
|
|
|
2041 #Background
|
|
|
2042 for(ONEmerIndex in 1:seqLength){
|
|
|
2043 ONEmer = ONEmerGL[ONEmerIndex]
|
|
|
2044 if(ONEmer!="N"){
|
|
|
2045 ONEmerCodonPos = getCodonPos(ONEmerIndex)
|
|
|
2046 ONEmerReadingFrameCodon = c2s(ONEmerGL[ONEmerCodonPos])
|
|
|
2047 ONEmerReadingFrameCodonInputSeq = c2s(ONEmerSeq[ONEmerCodonPos] )
|
|
|
2048
|
|
|
2049 # All mutations model
|
|
|
2050 #if(!any(grep("N",ONEmerReadingFrameCodon))){
|
|
|
2051 if(model==0){
|
|
|
2052 if(stopMutations==0){
|
|
|
2053 if(!any(grep("N",ONEmerReadingFrameCodonInputSeq)))
|
|
|
2054 BackgroundMatrix[ONEmer] <- (BackgroundMatrix[ONEmer] + 1)
|
|
|
2055 }else{
|
|
|
2056 if( !any(grep("N",ONEmerReadingFrameCodonInputSeq)) & translateCodonToAminoAcid(ONEmerReadingFrameCodonInputSeq)!="*"){
|
|
|
2057 positionWithinCodon = which(ONEmerCodonPos==ONEmerIndex)#positionsWithinCodon[(ONEmerCodonPos[1]%%3)+1]
|
|
|
2058 BackgroundMatrix[ONEmer] <- (BackgroundMatrix[ONEmer] + probNonStopMutations[ONEmerReadingFrameCodon,positionWithinCodon])
|
|
|
2059 }
|
|
|
2060 }
|
|
|
2061 }else{ # Only silent mutations
|
|
|
2062 if( !any(grep("N",ONEmerReadingFrameCodonInputSeq)) & translateCodonToAminoAcid(ONEmerReadingFrameCodonInputSeq)!="*" & translateCodonToAminoAcid(ONEmerReadingFrameCodonInputSeq)==translateCodonToAminoAcid(ONEmerReadingFrameCodon) ){
|
|
|
2063 positionWithinCodon = which(ONEmerCodonPos==ONEmerIndex)
|
|
|
2064 BackgroundMatrix[ONEmer] <- (BackgroundMatrix[ONEmer] + probSMutations[ONEmerReadingFrameCodon,positionWithinCodon])
|
|
|
2065 }
|
|
|
2066 }
|
|
|
2067 }
|
|
|
2068 }
|
|
|
2069 }
|
|
|
2070
|
|
|
2071 #Mutations
|
|
|
2072 if(stopMutations==1) mutationCount = mutationCount[mutationCount!="Stop"]
|
|
|
2073 if(model==1) mutationCount = mutationCount[mutationCount=="S"]
|
|
|
2074 mutationPositions = as.numeric(names(mutationCount))
|
|
|
2075 mutationCount = mutationCount[mutationPositions>2 & mutationPositions<(seqLength-1)]
|
|
|
2076 mutationPositions = mutationPositions[mutationPositions>2 & mutationPositions<(seqLength-1)]
|
|
|
2077 countMutations = 0
|
|
|
2078 for(mutationPosition in mutationPositions){
|
|
|
2079 ONEmerIndex = mutationPosition
|
|
|
2080 ONEmer = ONEmerSeq[ONEmerIndex]
|
|
|
2081 GLONEmer = ONEmerGL[ONEmerIndex]
|
|
|
2082 ONEmerCodonPos = getCodonPos(ONEmerIndex)
|
|
|
2083 ONEmerReadingFrameCodon = c2s(ONEmerSeq[ONEmerCodonPos])
|
|
|
2084 ONEmerReadingFrameCodonGL =c2s(ONEmerGL[ONEmerCodonPos])
|
|
|
2085 if(!any(grep("N",ONEmer)) & !any(grep("N",GLONEmer))){
|
|
|
2086 if(model==0){
|
|
|
2087 countMutations = countMutations + 1
|
|
|
2088 MutationMatrix[GLONEmer] <- (MutationMatrix[GLONEmer] + 1)
|
|
|
2089 MutationCountMatrix[GLONEmer] <- (MutationCountMatrix[GLONEmer] + 1)
|
|
|
2090 }else{
|
|
|
2091 if( translateCodonToAminoAcid(ONEmerReadingFrameCodonGL)!="*" ){
|
|
|
2092 countMutations = countMutations + 1
|
|
|
2093 positionWithinCodon = which(ONEmerCodonPos==ONEmerIndex)
|
|
|
2094 glNuc = substr(ONEmerReadingFrameCodonGL,positionWithinCodon,positionWithinCodon)
|
|
|
2095 inputNuc = substr(ONEmerReadingFrameCodon,positionWithinCodon,positionWithinCodon)
|
|
|
2096 MutationMatrix[GLONEmer] <- (MutationMatrix[GLONEmer] + substitution[glNuc,inputNuc])
|
|
|
2097 MutationCountMatrix[GLONEmer] <- (MutationCountMatrix[GLONEmer] + 1)
|
|
|
2098 }
|
|
|
2099 }
|
|
|
2100 }
|
|
|
2101 }
|
|
|
2102
|
|
|
2103 seqMutability = MutationMatrix/BackgroundMatrix
|
|
|
2104 seqMutability = seqMutability/sum(seqMutability,na.rm=TRUE)
|
|
|
2105 #cat(inputMatrixIndex,"\t",countMutations,"\n")
|
|
|
2106 return(list("seqMutability" = seqMutability,"numbMutations" = countMutations,"seqMutabilityCount" = MutationCountMatrix, "BackgroundMatrix"=BackgroundMatrix))
|
|
|
2107 # tmp<-list("seqMutability" = seqMutability,"numbMutations" = countMutations,"seqMutabilityCount" = MutationCountMatrix)
|
|
|
2108 }
|
|
|
2109 }
|
|
|
2110
|
|
|
2111 ################
|
|
|
2112 # $Id: trim.R 989 2006-10-29 15:28:26Z ggorjan $
|
|
|
2113
|
|
|
2114 trim <- function(s, recode.factor=TRUE, ...)
|
|
|
2115 UseMethod("trim", s)
|
|
|
2116
|
|
|
2117 trim.default <- function(s, recode.factor=TRUE, ...)
|
|
|
2118 s
|
|
|
2119
|
|
|
2120 trim.character <- function(s, recode.factor=TRUE, ...)
|
|
|
2121 {
|
|
|
2122 s <- sub(pattern="^ +", replacement="", x=s)
|
|
|
2123 s <- sub(pattern=" +$", replacement="", x=s)
|
|
|
2124 s
|
|
|
2125 }
|
|
|
2126
|
|
|
2127 trim.factor <- function(s, recode.factor=TRUE, ...)
|
|
|
2128 {
|
|
|
2129 levels(s) <- trim(levels(s))
|
|
|
2130 if(recode.factor) {
|
|
|
2131 dots <- list(x=s, ...)
|
|
|
2132 if(is.null(dots$sort)) dots$sort <- sort
|
|
|
2133 s <- do.call(what=reorder.factor, args=dots)
|
|
|
2134 }
|
|
|
2135 s
|
|
|
2136 }
|
|
|
2137
|
|
|
2138 trim.list <- function(s, recode.factor=TRUE, ...)
|
|
|
2139 lapply(s, trim, recode.factor=recode.factor, ...)
|
|
|
2140
|
|
|
2141 trim.data.frame <- function(s, recode.factor=TRUE, ...)
|
|
|
2142 {
|
|
|
2143 s[] <- trim.list(s, recode.factor=recode.factor, ...)
|
|
|
2144 s
|
|
|
2145 }
|
|
|
2146 #######################################
|
|
|
2147 # Compute the expected for each sequence-germline pair by codon
|
|
|
2148 getExpectedIndividualByCodon <- function(matInput){
|
|
|
2149 if( any(grep("multicore",search())) ){
|
|
|
2150 facGL <- factor(matInput[,2])
|
|
|
2151 facLevels = levels(facGL)
|
|
|
2152 LisGLs_MutabilityU = mclapply(1:length(facLevels), function(x){
|
|
|
2153 computeMutabilities(facLevels[x])
|
|
|
2154 })
|
|
|
2155 facIndex = match(facGL,facLevels)
|
|
|
2156
|
|
|
2157 LisGLs_Mutability = mclapply(1:nrow(matInput), function(x){
|
|
|
2158 cInput = rep(NA,nchar(matInput[x,1]))
|
|
|
2159 cInput[s2c(matInput[x,1])!="N"] = 1
|
|
|
2160 LisGLs_MutabilityU[[facIndex[x]]] * cInput
|
|
|
2161 })
|
|
|
2162
|
|
|
2163 LisGLs_Targeting = mclapply(1:dim(matInput)[1], function(x){
|
|
|
2164 computeTargeting(matInput[x,2],LisGLs_Mutability[[x]])
|
|
|
2165 })
|
|
|
2166
|
|
|
2167 LisGLs_MutationTypes = mclapply(1:length(matInput[,2]),function(x){
|
|
|
2168 #print(x)
|
|
|
2169 computeMutationTypes(matInput[x,2])
|
|
|
2170 })
|
|
|
2171
|
|
|
2172 LisGLs_R_Exp = mclapply(1:nrow(matInput), function(x){
|
|
|
2173 Exp_R <- rollapply(as.zoo(1:readEnd),width=3,by=3,
|
|
|
2174 function(codonNucs){
|
|
|
2175 RPos = which(LisGLs_MutationTypes[[x]][,codonNucs]=="R")
|
|
|
2176 sum( LisGLs_Targeting[[x]][,codonNucs][RPos], na.rm=T )
|
|
|
2177 }
|
|
|
2178 )
|
|
|
2179 })
|
|
|
2180
|
|
|
2181 LisGLs_S_Exp = mclapply(1:nrow(matInput), function(x){
|
|
|
2182 Exp_S <- rollapply(as.zoo(1:readEnd),width=3,by=3,
|
|
|
2183 function(codonNucs){
|
|
|
2184 SPos = which(LisGLs_MutationTypes[[x]][,codonNucs]=="S")
|
|
|
2185 sum( LisGLs_Targeting[[x]][,codonNucs][SPos], na.rm=T )
|
|
|
2186 }
|
|
|
2187 )
|
|
|
2188 })
|
|
|
2189
|
|
|
2190 Exp_R = matrix(unlist(LisGLs_R_Exp),nrow=nrow(matInput),ncol=readEnd/3,T)
|
|
|
2191 Exp_S = matrix(unlist(LisGLs_S_Exp),nrow=nrow(matInput),ncol=readEnd/3,T)
|
|
|
2192 return( list( "Expected_R"=Exp_R, "Expected_S"=Exp_S) )
|
|
|
2193 }else{
|
|
|
2194 facGL <- factor(matInput[,2])
|
|
|
2195 facLevels = levels(facGL)
|
|
|
2196 LisGLs_MutabilityU = lapply(1:length(facLevels), function(x){
|
|
|
2197 computeMutabilities(facLevels[x])
|
|
|
2198 })
|
|
|
2199 facIndex = match(facGL,facLevels)
|
|
|
2200
|
|
|
2201 LisGLs_Mutability = lapply(1:nrow(matInput), function(x){
|
|
|
2202 cInput = rep(NA,nchar(matInput[x,1]))
|
|
|
2203 cInput[s2c(matInput[x,1])!="N"] = 1
|
|
|
2204 LisGLs_MutabilityU[[facIndex[x]]] * cInput
|
|
|
2205 })
|
|
|
2206
|
|
|
2207 LisGLs_Targeting = lapply(1:dim(matInput)[1], function(x){
|
|
|
2208 computeTargeting(matInput[x,2],LisGLs_Mutability[[x]])
|
|
|
2209 })
|
|
|
2210
|
|
|
2211 LisGLs_MutationTypes = lapply(1:length(matInput[,2]),function(x){
|
|
|
2212 #print(x)
|
|
|
2213 computeMutationTypes(matInput[x,2])
|
|
|
2214 })
|
|
|
2215
|
|
|
2216 LisGLs_R_Exp = lapply(1:nrow(matInput), function(x){
|
|
|
2217 Exp_R <- rollapply(as.zoo(1:readEnd),width=3,by=3,
|
|
|
2218 function(codonNucs){
|
|
|
2219 RPos = which(LisGLs_MutationTypes[[x]][,codonNucs]=="R")
|
|
|
2220 sum( LisGLs_Targeting[[x]][,codonNucs][RPos], na.rm=T )
|
|
|
2221 }
|
|
|
2222 )
|
|
|
2223 })
|
|
|
2224
|
|
|
2225 LisGLs_S_Exp = lapply(1:nrow(matInput), function(x){
|
|
|
2226 Exp_S <- rollapply(as.zoo(1:readEnd),width=3,by=3,
|
|
|
2227 function(codonNucs){
|
|
|
2228 SPos = which(LisGLs_MutationTypes[[x]][,codonNucs]=="S")
|
|
|
2229 sum( LisGLs_Targeting[[x]][,codonNucs][SPos], na.rm=T )
|
|
|
2230 }
|
|
|
2231 )
|
|
|
2232 })
|
|
|
2233
|
|
|
2234 Exp_R = matrix(unlist(LisGLs_R_Exp),nrow=nrow(matInput),ncol=readEnd/3,T)
|
|
|
2235 Exp_S = matrix(unlist(LisGLs_S_Exp),nrow=nrow(matInput),ncol=readEnd/3,T)
|
|
|
2236 return( list( "Expected_R"=Exp_R, "Expected_S"=Exp_S) )
|
|
|
2237 }
|
|
|
2238 }
|
|
|
2239
|
|
|
2240 # getObservedMutationsByCodon <- function(listMutations){
|
|
|
2241 # numbSeqs <- length(listMutations)
|
|
|
2242 # obsMu_R <- matrix(0,nrow=numbSeqs,ncol=readEnd/3,dimnames=list(c(1:numbSeqs),c(1:(readEnd/3))))
|
|
|
2243 # obsMu_S <- obsMu_R
|
|
|
2244 # temp <- mclapply(1:length(listMutations), function(i){
|
|
|
2245 # arrMutations = listMutations[[i]]
|
|
|
2246 # RPos = as.numeric(names(arrMutations)[arrMutations=="R"])
|
|
|
2247 # RPos <- sapply(RPos,getCodonNumb)
|
|
|
2248 # if(any(RPos)){
|
|
|
2249 # tabR <- table(RPos)
|
|
|
2250 # obsMu_R[i,as.numeric(names(tabR))] <<- tabR
|
|
|
2251 # }
|
|
|
2252 #
|
|
|
2253 # SPos = as.numeric(names(arrMutations)[arrMutations=="S"])
|
|
|
2254 # SPos <- sapply(SPos,getCodonNumb)
|
|
|
2255 # if(any(SPos)){
|
|
|
2256 # tabS <- table(SPos)
|
|
|
2257 # obsMu_S[i,names(tabS)] <<- tabS
|
|
|
2258 # }
|
|
|
2259 # }
|
|
|
2260 # )
|
|
|
2261 # return( list( "Observed_R"=obsMu_R, "Observed_S"=obsMu_S) )
|
|
|
2262 # }
|
|
|
2263
|
|
|
2264 getObservedMutationsByCodon <- function(listMutations){
|
|
|
2265 numbSeqs <- length(listMutations)
|
|
|
2266 obsMu_R <- matrix(0,nrow=numbSeqs,ncol=readEnd/3,dimnames=list(c(1:numbSeqs),c(1:(readEnd/3))))
|
|
|
2267 obsMu_S <- obsMu_R
|
|
|
2268 temp <- lapply(1:length(listMutations), function(i){
|
|
|
2269 arrMutations = listMutations[[i]]
|
|
|
2270 RPos = as.numeric(names(arrMutations)[arrMutations=="R"])
|
|
|
2271 RPos <- sapply(RPos,getCodonNumb)
|
|
|
2272 if(any(RPos)){
|
|
|
2273 tabR <- table(RPos)
|
|
|
2274 obsMu_R[i,as.numeric(names(tabR))] <<- tabR
|
|
|
2275 }
|
|
|
2276
|
|
|
2277 SPos = as.numeric(names(arrMutations)[arrMutations=="S"])
|
|
|
2278 SPos <- sapply(SPos,getCodonNumb)
|
|
|
2279 if(any(SPos)){
|
|
|
2280 tabS <- table(SPos)
|
|
|
2281 obsMu_S[i,names(tabS)] <<- tabS
|
|
|
2282 }
|
|
|
2283 }
|
|
|
2284 )
|
|
|
2285 return( list( "Observed_R"=obsMu_R, "Observed_S"=obsMu_S) )
|
|
|
2286 }
|
|
|
2287
|
