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SaSii.R
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#-------REQUESTS-------------------------
#if (!require(Rcpp)) install.packages('Rcpp')
if (!require(readr)) install.packages('readr')
#----------------------------------------
# ------SOME GLOBAL VARIABLES------------
args <- commandArgs(trailingOnly = TRUE);
os = Sys.info()['sysname'];
path = '';
local_multi_he = 0; # temp variable for storage multilocus He.
local_multi_ho = 0; # temp variable for storage multilocus Ho.
localtime = format(Sys.time(), "%c");
less_frq_all = more_frq_all = 0;
less_frq_allname = more_frq_allname = '';
freqMin = 0;
he_diff = ho_diff = list();
n_size = 0;
str_type = nLines = first_column_data = is_gbs = indef = header_line = separator = '';
indef_locus = vector()
#----------------------------------------
#----SUBROTINES--------------------------
# Creates output file with results from script
save_array_output <- function(lines, mid_name){
output_name = paste0(mid_name,'.sso');
path = "results/";
file.create(paste0(path, output_name));
fileConn<-file(paste0(path, output_name));
writeLines(lines, fileConn);
close(fileConn);
}
#----------------------------------------
# Make adjustment in csv data
# For type 1 csv file, just changes second locus column name to {{locus_name}}.2 to aid in code optmization
# For type 2 csv file, create a new dataframe to change the individual allele order, just for padronization
csv_adjust <- function(csv_data){
locus = "";
# if(str_type == 2){
# csv_data = csv_data[-nrow(csv_data),];
# }
#Remove undesirable columns
if(first_column_data > 1){
csv_data = csv_data[,-1:-(first_column_data-1)];
}
if(is_gbs == 1 && indef != '-'){
csv_data[csv_data==indef] = "indef"; #Look for each indefined allele and changes its value
}
#If csv individual alleles is in just one line, copy the name of every locus adding ".2"
if(str_type == 1){
for(lines in names(csv_data)){
if(grepl('X', lines, fixed = TRUE)) {
names(csv_data)[names(csv_data) == lines] <- paste0(locus, '.2');
}else{locus = lines;}
}
}else{
new_data = data.frame(matrix(ncol = ncol(csv_data) * 2, nrow = 0)); #Creates a temp variable to change csv style
dplc_locus = array(unlist(rbind(names(csv_data),paste0(names(csv_data), ".2")))); #Duplicate locus columns
names(new_data) <- dplc_locus;
#For each individual, change alleles order into one line pattern
lapply(seq(1,nrow(csv_data), 2), function(indv){
new_indv = data.frame(matrix(data = unlist(rbind(csv_data[indv,],csv_data[indv+1,])), nrow = 1, ncol = ncol(csv_data)*2)); #Create a "list" of alternating values between line A and B
names(new_indv) <- dplc_locus;
new_data <<- rbind(new_data, new_indv); #Bind new generated row into the new csv_data
});
csv_data = new_data;
}
return (csv_data);
}
#----------------------------------------
#Create two matrixes with alleles and genotypes counted values
count_allele_gnot <- function(data){
a = lapply(loci_names, function(locus){
alleles = unlist(subset(data, TRUE, c(locus, paste0(locus,".2"))));
#Verify csv file to remove indefined alleles from counting
if(is_gbs == 1 && indef != '-'){
alleles = alleles[alleles != "indef"];
counted_alleles = table(droplevels(as.factor(alleles[alleles != "indef"])));
#print(paste0("teste",locus," ",counted_alleles))
}else{
counted_alleles = table(alleles);
}
if(length(counted_alleles) > 0){
allele_numbers[[locus]] <<- list(counted_alleles[is.numeric(counted_alleles)]);
counted_gnot = table(paste0(alleles[seq(1, length(alleles)/2)], "-", alleles[seq(length(alleles)/2 + 1, length(alleles))]));
gnot_numbers[[locus]] <<- list(counted_gnot);
}else{
indef_locus <<- append(indef_locus, locus)
allele_numbers <<- allele_numbers[names(allele_numbers) != locus];
gnot_numbers <<- gnot_numbers[names(gnot_numbers) != locus];
loci_names <<- loci_names[-which(locus == loci_names)]
}
});
}
#----------------------------------------
# Count alleles in population
allele_count <- function(allele_hash){
count = 0;
a = lapply(allele_hash, function(locus){
lapply(names(locus[[1]]), function(allele){
if(allele != indef){
count <<- count + 1;
}
})
});
return(count);
}
#----------------------------------------
#Counts alleles numbers for each locus to obtain allelic richness
allelic_richness <- function(allele_hash){
a = lapply(names(allele_rich), function(locus){
if(length(allele_hash[[locus]]) > 0 ){
allele_rich[[locus]] <<- length(allele_hash[[locus]]);
}else{
allele_rich <<- allele_rich[names(allele_rich) != locus];
}
});
}
#----------------------------------------
# Calculate frequencies for alleles and genotypes. Get counted alleles and genotypes divide by the sum of the counted alleles in locus
# Ex: One locus has two alleles: allele_158 -> 2 and allele_202 -> 8. The sum of alleles is 10.
# So the respectives frequencies are: allele_158 -> 0.2 and allele_202 -> 0.8.
freq_calc <- function(numbers_hash){
freq_hash = array(rep(array(), loci_number), c(loci_number, 1)); #cria um array de tamanho loci
names(freq_hash) <- loci_names;
sorted_numbers_hash = sort(names(numbers_hash));
a = lapply(sorted_numbers_hash, function(locus){
sorted_locus_hash = sort(names(numbers_hash[[locus]][[1]]));
#Alleles is a list without lost data
good_data = as.character(unlist(
lapply(sorted_locus_hash,
function(x) x[(which(x != indef & x != paste0(x,"-",x)))])));
freq_hash[[locus]] <<- list(numbers_hash[[locus]][[1]][good_data]); #Creates a copy of the variable type
total_alleles = sum(numbers_hash[[locus]][[1]][good_data]);
freq_hash[[locus]] <<- numbers_hash[[locus]][[1]][good_data] / total_alleles; #Calculate the frequency for each element
});
return(freq_hash);
}
#C_FREQ_CALC
# Calculate frequencies for alleles and genotypes. Get counted alleles and genotypes divide by the sum of the counted alleles in locus
# Ex: One locus has two alleles: allele_158 -> 2 and allele_202 -> 8. The sum of alleles is 10.
# So the respectives frequencies are: allele_158 -> 0.2 and allele_202 -> 0.8.
# cppFunction('
# List freq_calc(List numbers_hash){
# CharacterVector locus_name(numbers_hash.names());
# List freq_hash = List::create();
# int total_alleles = 0;
# for(auto pLocus : locus_name){
# std::string locus = as<std::string>(pLocus);
# List alleles = as<List> (numbers_hash[locus])[0];
# CharacterVector alleles_name(alleles.names());
# NumericVector good_alleles = NumericVector::create();
# for(auto pAllele : alleles_name){
# std::string allele = as<std::string>(pAllele);
# if(allele != "0" && allele != "0-0") good_alleles.push_back(alleles[allele], allele);
# }
# total_alleles = sum(good_alleles);
# NumericVector res = good_alleles / total_alleles;
# res.names() = good_alleles.names();
# freq_hash.push_back(res, locus);
# }
# return freq_hash;
# }
# ')
#----------------------------------------
# Get smaller and bigger frequencies values from population, extrating their respectives locus and allele.
find_less_more <- function(freqs){
#Extract the position of the less freq allele
less_pos = match(min(unlist(freqs)), unlist(freqs))
less_locus = strsplit(names(unlist(freqs)[less_pos]), "\\.")[[1]][1];
less_allele = strsplit(names(unlist(freqs)[less_pos]), "\\.")[[1]][2];
#Extract the position of the more freq allele
more_pos = match(max(unlist(freqs)), unlist(freqs))
more_locus = strsplit(names(unlist(freqs)[more_pos]), "\\.")[[1]][1];
more_allele = strsplit(names(unlist(freqs)[more_pos]), "\\.")[[1]][2];
local_less = paste0(less_locus, "-", less_allele);
local_more = paste0(more_locus, "-", more_allele);
return (list(local_less, local_more));
}
#----------------------------------------
# Sort frequencies to look up smaller and bigger frequencies for resampling step
less_more_data <- function(freqs){
aux_array = strsplit(less_frq_all, '-')[[1]];
locus_l = aux_array[1]; allele_l = aux_array[2];
aux_array = strsplit(more_frq_all, '-')[[1]];
locus_m = aux_array[1]; allele_m = aux_array[2];
local_meanL = local_SDL = local_meanM = local_SDM = 0;
less_min = less_max = more_min = more_max = 0;
#Sort the frequencies of the locus_alleles
less_locus_freqs = sort(freqs[[locus_l]][[allele_l]][-1]);
more_locus_freqs = sort(freqs[[locus_m]][[allele_m]][-1]);
#Get frequencies of less and more locus_alleles
less_min = less_locus_freqs[1];
less_max = less_locus_freqs[length(less_locus_freqs)];
more_min = more_locus_freqs[1];
more_max = more_locus_freqs[length(more_locus_freqs)];
#Calculate the mean and SD of locus
local_meanL = sprintf("%.5f", mean(less_locus_freqs));
local_meanM = sprintf("%.5f", mean(more_locus_freqs));
local_SDL = sprintf("%.5f", sd(less_locus_freqs));
local_SDM = sprintf("%.5f", sd(more_locus_freqs));
local_data = vector();
less_min = sprintf('%.5f', less_min);
less_max = sprintf('%.5f', less_max);
local_data <- append(local_data, paste0(n_size, "\t", paste0("lessfreq", less_frq_all), "\t", local_meanL, "\t", local_SDL, "\t", less_min, "\t", less_max));
more_min = sprintf('%.5f', more_min);
more_max = sprintf('%.5f', more_max);
local_data <- append(local_data, paste0(n_size, "\t", paste0("morefreq", more_frq_all), "\t", local_meanM, "\t", local_SDM, "\t", more_min, "\t", more_max));
return(local_data);
}
#----------------------------------------
# Calculate expected genotypes frquencies from HoH with allelic freq
expct_GNOT_freq <- function(alelic_freq){
EXPCT_GNOT_freq = array(rep(array(), length(orig_allele_numbers)), c(length(orig_allele_numbers), 1));
names(EXPCT_GNOT_freq) <- names(orig_allele_numbers); # Hash with expected genotypes frequencies
sorted_locus = sort(names(EXPCT_GNOT_freq));
a = lapply(sorted_locus, function(locus){
exp_gnot_freq = 0;
if(length(alelic_freq[[locus]]) > 0){
generated_genotypes = generate_genotypes(names(alelic_freq[[locus]]));
EXPCT_GNOT_freq[locus] <<- list(array(rep(array(), length(generated_genotypes)), c(length(generated_genotypes), 1)));
names(EXPCT_GNOT_freq[[locus]]) <<- generated_genotypes;
lapply(generated_genotypes, function(genotype){
aux_array = strsplit(genotype, '-')[[1]];
allele1 = aux_array[1]; allele2 = aux_array[2];
if(allele1 == allele2){
exp_gnot_freq = alelic_freq[[locus]][allele1] * alelic_freq[[locus]][allele2];
}else {
if (allele1 != allele2) {
exp_gnot_freq = 2 * (alelic_freq[[locus]][allele1] * alelic_freq[[locus]][allele2]);
}
}
EXPCT_GNOT_freq[[locus]][genotype] <<- exp_gnot_freq;
});
}else{
EXPCT_GNOT_freq <<- EXPCT_GNOT_freq[names(EXPCT_GNOT_freq) != locus];
}
});
return(EXPCT_GNOT_freq);
}
#----------------------------------------
# Create list of possible genotypes from an hash: allele => value
generate_genotypes <- function(allelic_freq_inlocus) {
generated_genotypes = array(); # Array to save genotypes list
sorted_allelic_freq_inlocus = sort(allelic_freq_inlocus);
a = lapply(sorted_allelic_freq_inlocus, function(allele1){
lapply(sorted_allelic_freq_inlocus, function(allele2) {
genotype = paste0(allele1, '-', allele2);
if(!is.na(generated_genotypes[1])){
generated_genotypes <<- append(generated_genotypes, genotype);
}else{
generated_genotypes <<- array(genotype);
}
});
sorted_allelic_freq_inlocus <<- sorted_allelic_freq_inlocus[-1];
});
return(generated_genotypes);
}
#----------------------------------------
# Find observed heterosygosity for data frame
ho_calc <- function(gnot_freq) {
sorted_gnot_freq = sort(names(gnot_freq));
a = lapply(sorted_gnot_freq, function(locus){
sorted_locus = sort(names(gnot_freq[[locus]]));
ho[[locus]] <<- 0;
lapply(sorted_locus, function(genotype){
aux_array = strsplit(genotype, '-')[[1]];
allele1 = aux_array[1]; allele2 = aux_array[2];
if(allele1 != allele2)
ho[[locus]] <<- ho[[locus]] + gnot_freq[[locus]][genotype];
});
});
}
#----------------------------------------
ho_range <- function(local_ho) {
sorted_local_ho = sort(names(local_ho));
a = lapply(sorted_local_ho, function(locus){
ho_for_range[[locus]] <<- append(ho_for_range[[locus]], ho[[locus]]);
});
sorted_ho = sort(names(ho));
ho_array_local = vector();
a = lapply(sorted_ho, function(locus){
ho_array_local <<- append(ho_array_local, ho[[locus]]);
});
mean = sprintf("%.5f", mean(ho_array_local)); SD = sprintf("%.5f", sd(ho_array_local));
local_multi_ho <<- mean;
}
#----------------------------------------
# Get random individuals from population for sampling
random_sampling <- function(n) {
count = n;
total_lines = total_individuals;
sample = vector();
sorted_numbers = sample(1:nrow(csv_data), n);
#sorted_numbers = rev(1:n)#sample(rev(1:n), n);
sample = csv_data[sorted_numbers,];
return(sample);
}
#----------------------------------------
# Calculate important information for each sample
sample_calcs <- function(sample) {
allele_rich <<- gnot_numbers <<- allele_numbers <<- array(rep(array(), length(orig_allele_numbers)), c(length(orig_allele_numbers), 1)); #cria um array de tamanho loci
names(allele_rich) <<- names(gnot_numbers) <<- names(allele_numbers) <<- names(orig_allele_numbers); #cria o nome da coluna
count_allele_gnot(sample);
allelic_richness(allele_numbers);
allele_freq <<- freq_calc(allele_numbers);
gnot_freq = freq_calc(gnot_numbers);
EXPCT_GNOT_freq = expct_GNOT_freq(allele_freq);
he_calc(allele_freq, n_size, '2');
multi_he = local_multi_he;
he_mean <<- append(he_mean, as.numeric(multi_he));
he_diff <<- het_diff_calc(he_orig, he_diff, he);
mean_freq_prep(allele_freq);
sorted_orig_gnot_freq = sort(names(orig_gnot_freq));
ho_calc(gnot_freq);
ho_range(ho);
multi_ho = local_multi_ho;
ho_mean <<- append(ho_mean, as.numeric(multi_ho));
ho_diff <<- het_diff_calc(ho_orig, ho_diff, ho);
nei_fst <<- calc_nei_fst(multi_ht, multi_he_orig, multi_he, n_size);
local_nei_d = nei_d(allele_freq);
neiD_array <<- local_nei_d;
local_rogers_d = rogers_d(allele_freq);
rogersD_array <<- local_rogers_d;
}
#----------------------------------------
mixed_calcs <- function(sample) {
allele_rich <<- gnot_numbers <<- allele_numbers <<- array(rep(array(), length(orig_allele_numbers)), c(length(orig_allele_numbers), 1)); #cria um array de tamanho loci
names(allele_rich) <<- names(gnot_numbers) <<- names(allele_numbers) <<- names(orig_allele_numbers); #cria o nome da coluna
count_allele_gnot(sample);
allele_freq <<- freq_calc(allele_numbers);
gnot_freq = freq_calc(gnot_numbers);
EXPCT_GNOT_freq = expct_GNOT_freq(allele_freq);
he_calc(allele_freq, n_size, '1');
mix_he = he;
multi_ht <<- local_multi_he;
return(mix_he);
}
#----------------------------------------
#Needs a HoH with allelic frequencies
he_calc <- function(local_hash, number_id, type) {
somatory = 0;
r = 0; #Number o loci
sorted_local_hash = sort(names(local_hash));
a = lapply(sorted_local_hash, function(locus){
somatory = sum(local_hash[[locus]] ** 2);
he_simple = 1 - somatory;
he[[locus]] <<- he_simple * ((2*number_id)/((2*number_id)-1));
if(type == '2') {
he_for_range[[locus]] <<- append(he_for_range[[locus]], he[[locus]]);
}
});
local_multi_he <<- 0;
he_array_local = vector();
sorted_he = sort(names(he));
a = lapply(sorted_he, function(locus){
he_array_local <<- append(he_array_local, he[[locus]]);
});
mean = sprintf("%.5f", mean(he_array_local)); SD = sprintf("%.5f", sd(he_array_local));
local_multi_he <<- mean;
}
#----------------------------------------
#Calcs Nei Fst (1973). Needs $Multi_Ht, $Multi_He_ORIGINAL, $Multi_He, $n_size.
calc_nei_fst <- function(ht, hsA, hsB, nB) {
nA = total_individuals;
nei_fst = 0;
upper1a = ((as.numeric(nA) * as.numeric(hsA)) + (as.numeric(nB) * as.numeric(hsB))) / (as.numeric(nA) + as.numeric(nB));
upper1b = as.numeric(ht) - upper1a;
nei_fst = upper1b / as.numeric(ht);
return (nei_fst);
}
#----------------------------------------
#Nei distance (1972)
nei_d <- function(freq2) {
freq1 = orig_allele_freq;
jx = jy = jxy = l = 0;
jx_array = jy_array = jxy_array = vector();
sorted_freq1 = sort(names(freq1));
a = lapply(sorted_freq1, function(locus) {
if(length(freq2[[locus]]) > 0){ #In case of GBS data, random individuals might cause NULL loci
l <<- l + 1;
sorted_locus = sort(names(freq1[[locus]]));
lapply(sorted_locus, function(allele) {
if (is.na(freq2[[locus]][allele])){
freq2[[locus]][allele] <<- 0;
}
jx <<- freq1[[locus]][allele] ** 2;
jx_array <<- append(jx_array, jx);
jy <<- freq2[[locus]][allele] ** 2;
jy_array <<- append(jy_array, jy);
});
}
});
a = lapply(sorted_freq1, function(locus) {
sorted_locus = sort(names(freq1[[locus]]));
if(length(freq2[[locus]]) > 0){
lapply(sorted_locus, function(allele) {
if(is.na(freq2[[locus]][allele]) || freq2[[locus]][allele] == 0){ jxy <<- 0;}
else {
jxy <<- freq1[[locus]][allele] * freq2[[locus]][allele];
}
jxy_array <<- append(jxy_array, jxy);
});
}
});
jx = sum(jx_array)/l;
jy = sum(jy_array)/l;
jxy = sum(jxy_array)/l;
gx = jx;
gy = jy;
neiD = jxy / sqrt(gx * gy);
neiD = log(neiD);
neiD = 0 - neiD;
return(neiD);
}
#----------------------------------------
# Calculares modified Rogers distance. Needs HoH of resample allele frequencies. Roger (1972), Wright (1978), GOodman and Stuber (1983)
rogers_d <- function(freq2) {
freq1 = orig_allele_freq;
diff_sum = 0; l = 0;
diff_sum_array = vector();
sorted_freq1 = sort(names(freq1));
a = lapply(sorted_freq1, function(locus){
l <<- l + 1;
diff_array = vector();
sorted_locus = sort(names(freq1[[locus]]));
lapply(sorted_locus, function(allele) {
#if(is.na(freq2[[locus]][allele]) || freq2[[locus]][allele] == 0) freq2[[locus]][allele] <<- 0; #Treat cases where allele do not exist
diff = freq1[[locus]][allele] - freq2[[locus]][allele];
diff = diff ** 2;
diff_array <<- append(diff_array, diff);
});
diff_sum = sum(diff_array);
diff_sum_array <<- append(diff_sum_array, diff_sum);
});
rogersD = sqrt(sum(diff_sum_array));
rogersD = rogersD / sqrt(2 * l);
return (rogersD);
}
#----------------------------------------
mean_freq_prep <- function(freqs) {
sorted_orig_allele_freq = sort(names(orig_allele_freq));
a = lapply(sorted_orig_allele_freq, function(locus){
sorted_locus = sort(names(orig_allele_freq[[locus]]));
#print(locus);
#print(allele_freq[[locus]]);
lapply(sorted_locus, function(allele){
if(length(allele_freq[[locus]]) > 0){
if(is.na(allele_freq[[locus]][allele])){
allele_freq[[locus]][allele] <<- 0;
}
mean_allele_freq[[locus]][allele] <<- list(append(unlist(mean_allele_freq[[locus]][allele]), allele_freq[[locus]][allele]));
}
});
});
}
#----------------------------------------
freq_diff_calc <- function(freqs) {
mean_freqs = freqs;
sorted_freqs = sort(names(freqs));
freqs_differences = vector();
for(locus in sorted_freqs) {
sorted_locus = sort(names(freqs[[locus]]));
for(allele in sorted_locus) {
mean_freqs[[locus]][allele] = sprintf('%.5f', sum(freqs[[locus]][[allele]][-1])/length(freqs[[locus]][[allele]][-1]));
diff = orig_allele_freq[[locus]][allele] - as.double(mean_freqs[[locus]][[allele]]);
diff = abs(diff);
if(allele < 100) {allele = paste0('_', allele);}
freqs_differences <- append(freqs_differences, paste0(n_size, "\t", paste0(locus, ".", allele), "\t", sprintf('%.5f', diff)));
}
}
return (freqs_differences);
}
#----------------------------------------
freq_5 <- function(freq_hash) {
five_percent_hash = array(rep(array(), length(orig_allele_numbers)), c(length(orig_allele_numbers), 1));
names(five_percent_hash) <- names(orig_allele_numbers); # HoH to save alleles with freq higher than freqMin
five_percent_n = 0;
sorted_freq_hash = sort(names(five_percent_hash));
for(locus in sorted_freq_hash) {
if(length(freq_hash[[locus]]) > 0 ){
sorted_locus = sort(names(freq_hash[[locus]]));
for(key in sorted_locus){
if(as.numeric(freq_hash[[locus]][key]) >= freqMin){
if(is.na(five_percent_hash[[locus]])){
five_percent_hash[[locus]] = c(key);
}else{
five_percent_hash[[locus]] = list(c(unlist(five_percent_hash[[locus]]), key));
}
five_percent_n = five_percent_n + 1; #writes in local variable with same name as global one
}
}
}else{
five_percent_hash = five_percent_hash[names(five_percent_hash) != locus];
}
}
return(list(five_percent_hash, five_percent_n)); #returning hash as reference
}
#----------------------------------------
freq <- function(freq_hash) {
hash = array(rep(array(), length(orig_allele_numbers)), c(length(orig_allele_numbers), 1));
names(hash) <- names(orig_allele_numbers); # HoH to save alleles with freq higher than freqMin
n = 0;
sorted_freq_hash = sort(names(hash));
for(locus in sorted_freq_hash) {
if(length(freq_hash[[locus]]) > 0 ){
sorted_locus = sort(names(freq_hash[[locus]]));
for(key in sorted_locus){
if(is.na(hash[[locus]])){
hash[[locus]] = c(key);
}else{
hash[[locus]] = list(c(unlist(hash[[locus]]), key));
}
n = n + 1; #writes in local variable with same name as global one
}
}else{
hash = hash[names(hash) != locus];
}
}
return(list(hash, n)); #returning hash as reference
}
#----------------------------------------
freq_rare <- function(freq_hash) {
hash = array(rep(array(), length(orig_allele_numbers)), c(length(orig_allele_numbers), 1));
names(hash) <- names(orig_allele_numbers); # HoH to save alleles with freq higher than freqMin
n = 0;
sorted_freq_hash = sort(names(hash));
for(locus in sorted_freq_hash) {
if(length(freq_hash[[locus]]) > 0 ){
sorted_locus = sort(names(freq_hash[[locus]]));
for(key in sorted_locus){
if(as.numeric(freq_hash[[locus]][key]) < freqMin){
if(is.na(hash[[locus]])){
hash[[locus]] = c(key);
}else{
hash[[locus]] = list(c(unlist(hash[[locus]]), key));
}
n = n + 1; #writes in local variable with same name as global one
}
}
}else{
hash = hash[names(hash) != locus];
}
}
return(list(hash, n)); #returning hash as reference
}
#----------------------------------------
count_freq_sample <- function(freq_hash, sample_local, type) {
if(type == "five_percent"){
reference_hash = orig_five_percent;
local_ref_count = five_percent_n;
}else if(type == "all_percent"){
reference_hash = orig_percent;
local_ref_count = n;
}else{
reference_hash = orig_percent_rare;
local_ref_count = n_rare;
}
general_local_count = 0;
local_array = array();
sorted_freq_hash = sort(names(freq_hash));
a = lapply(sorted_freq_hash, function(locus) {
local_array = reference_hash[[locus]][[1]];
sorted_freq_locus = sort(names(freq_hash[[locus]]));
local_count = 0;
lapply(sorted_freq_locus, function(key){
if((freq_hash[[locus]][key] != 0) && (key %in% local_array)) {
local_count <<- local_count + 1;
general_local_count <<- general_local_count + 1;
}
});
local_count = local_count / length(local_array);
sample_local[[locus]] <<- append(sample_local[[locus]], local_count);
})
general_local_count = general_local_count / local_ref_count;
return (list(general_local_count, sample_local));
}
#----------------------------------------
r_input_HoHoA_array <- function(target_hash1) {
array_final = list(paste0('N', '\t', 'ID', '\t', 'repeat_values'));
sorted_target_hash1 = sort(names(target_hash1));
for(local_n in sorted_target_hash1) {
sorted_local_n = sort(names(local_n));
for(array_ in sorted_local_n) {
array_final <- append(array_final, paste0(local_n, '\t', array_, '\t', target_hash1[[local_n]][[array_]]));
}
}
return(array_final);
}
#----------------------------------------
make_path_files <- function(localfile, n){
file.create(paste0("fig", n, ".dat"));
#file.create(paste0("results/", localfile,".sso"));
dput(paste0("results/", localfile,".sso"),
file = paste0("fig", n, ".dat"));
}
#----------------------------------------
plot_graphs <- function() {
source("SaSii_plots.R");
}
#----------------------------------------
het_diff_calc <- function(hash, local_hash, orig_hash) {
sorted_orig_hash = sort(names(orig_hash));
for(locus in sorted_orig_hash) {
local_hash[[locus]] <- append(local_hash[[locus]], abs(orig_hash[[locus]] - hash[[locus]]));
}
return(local_hash);
}
#----------------------------------------
het_diff_push <- function(hash) {
array = vector();
sorted_hash = sort(names(hash));
for(locus in sorted_hash) {
hash[[locus]] <- hash[[locus]][-1]; # take of '5' from beggining of hash VERIFICAR ISSO
het_men_diff = sprintf("%.5f", mean(hash[[locus]])); het_sd_diff = sd(hash[[locus]]);
array <- append(array, paste0(n_size, "\t", locus, "\t", het_men_diff, "\t", ifelse(is.na(het_sd_diff), 0, sprintf("%.5f", het_sd_diff))));
}
return (array);
}
#----------------------------------------
#----MAIN CODE---------------------------
print("SaSii - Sample_Size Impact , 'R Beta version ', (2021)");
print(paste("Running in:", os));
if(file.exists("config.txt")){
config = readLines("config.txt", n = 13);
}else{
config = NA;
}
#Check if config file was read and set variables with input from file
if(!is.na(config[1])){
path = strsplit(config[1], " ")[[1]][2];
str_type = as.integer(strsplit(config[2], " ")[[1]][2]);
indef = strsplit(config[3], " ")[[1]][2];
first_line_data = as.integer(strsplit(config[4], " ")[[1]][2]);
n_minimum = as.integer(strsplit(config[5], " ")[[1]][2]);
repeat_N = as.integer(strsplit(config[6], " ")[[1]][2]);
freqMin = as.numeric(strsplit(config[7], " ")[[1]][2]);
separator = as.character(strsplit(config[8], " ")[[1]][2]);
nLines = as.numeric(strsplit(config[9], " ")[[1]][2]);
first_column_data = as.integer(strsplit(config[10], " ")[[1]][2]);
is_gbs = as.integer(strsplit(config[11], " ")[[1]][2]);
header_line = as.integer(strsplit(config[12], " ")[[1]][2]);
max_indv_pop = as.integer(strsplit(config[13], " ")[[1]][2]);
}else{ #get parameters from user, checking if is interactive mode or not
if(length(args) == 0){
cat("Put your input file path: ");
if(interactive()){
path = as.character(readline());
}else{
path = as.character(readLines("stdin", n=1));
}
}else{
path = args;
}
if(interactive()){
cat("Structure type (1 line or 2 lines data) [default = 1]: ");
str_type = as.character(readline());
cat("The file data is gbs? (1 or 0) [default = 0]: ");
is_gbs = as.character(readline());
cat("Indefined character [default = -9]: ");
if(is_gbs == 1) indef = as.character(readline());
cat("Data begin at line [default = 1]: ");
first_line_data = as.character(readline());
cat("Choose multiple number of sample size (n) for resamples [default = 5]: ");
n_minimum = as.character(readline());
cat("Number of resampling for each N class [default = 50]: ");
repeat_N = as.character(readline());
cat("Minimal frequence of alleles to be preserved [default = 0.05]: ");
freqMin = as.character(readline());
cat("Csv file separator (as ASCII number) [default = 59 (;)]: ");
separator = as.character(readline());
cat("Number of lines to read [default = 50]: ");
nLines = as.character(readline());
cat("Number of the column which data is represented (according to header) [default = 1]: ");
first_column_data = as.character(readline());
cat("What is the header line number? (0, if not present) [default = 1]: ");
header_line = as.character(readline());
cat("Max number of individuals to plot? [default = 60]: ");
max_indv_pop = as.character(readline());
}else{
cat("Structure type (1 line or 2 lines data) [default = 1]: ");
str_type = as.character(readLines("stdin", n=1));
cat("The file data is gbs? (1 or 0) [default = 0]: ");
is_gbs = as.character(readLines("stdin", n=1));
cat("Indefined character [default = -9]: ");
if(is_gbs == 1) indef = as.character(readLines("stdin", n=1));
cat("Data begin at line [default = 1]: ");
first_line_data = as.character(readLines("stdin", n=1));
cat("Choose multiple number of sample size (n) for resamples [default = 5]: ");
n_minimum = as.character(readLines("stdin", n=1));
cat("Number of resampling for each N class [default = 50]: ");
repeat_N = as.character(readLines("stdin", n=1));
cat("Minimal frequence of alleles to be preserved [default = 0.05]: ");
freqMin = as.character(readLines("stdin", n=1));
cat("Csv file separator (as ASCII number) [default = 59 (;)]: ");
separator = as.character(readLines("stdin", n=1));
cat("Number of lines to read [default = 50]: ");
nLines = as.character(readLines("stdin", n=1));
cat("Number of the column which data is represented (according to header) [default = 1]: ");
first_column_data = as.character(readLines("stdin", n=1));
cat("What is the header line number? (0, if not present) [default = 1]: ");
header_line = as.character(readLines("stdin", n=1));
cat("Max number of individuals to plot? [default = 60]: ");
max_indv_pop = as.character(readLines("stdin", n=1));
}
}
if(path == "help" | path == "ajuda"){
print("Help file not exist at this version.\nDone.");
quit();
} else{
if(path == "exit") {
print("Done."); quit();
} else {
if(path == '' && path == '\n'){
print("WARNING: You passed no input file.\n Try: sample-size-simulationsXX.pl inputfile.txt");
quit();
}
}
}
if(str_type == '') {
str_type = 1;
print("Using str_type = 1.");
}else{
str_type = as.integer(str_type);
if(str_type != 1 && str_type != 2) {
print("Str_type must be 1 or 2.\n");
print("Try Again.");
quit();
}
}
if(nLines == '') {
nLines = 50;
print("Using number of lines = 50.");
}else{
nLines = as.integer(nLines);
}
if(first_column_data == '') {
first_column_data = 1;
print("Using first column number = 1.");
}else{
first_column_data = as.integer(first_column_data);
}
if(is_gbs == '') {
is_gbs = 0;
print("Defined data as gbs data = 0.");
}else{
is_gbs = as.integer(is_gbs);
}
if(is_gbs == 1 && indef == '') {
indef = "-9";
print("Indefined allele setted as '-9'.");
}else{
indef = as.character(indef);
}
if(header_line == '') {
header_line = 1;
print("Using number of lines = 50.");
}else{
header_line = as.integer(header_line);
}
if(max_indv_pop == '') {
max_indv_pop = 60;
print("Using max number of individuals to plot = 60.");
}else{
max_indv_pop = as.integer(max_indv_pop);
}
if(separator == '') {
separator = 59;
print(paste("Using csv separator:", intToUtf8(separator), " (59)."));
}else{
separator = as.integer(separator);
}
#Check if results folder exists, if do, delete it.
if(dir.exists("results")){
unlink("results", recursive = TRUE);
}
dir.create("results");
#Check if exists any temp file and deletes it.
if(!is.null(list.files(paste(pattern = ".dat")))){
print("Cleaning temp files....");
unlink(paste0("fig","*",".dat"));
}
# Defining the begining of data.---------------
if(first_line_data == '' | is.na(as.integer(first_line_data))){
first_line_data = 1;
}
first_line_data = as.integer(first_line_data) - 1;
csv_data = "";
head = TRUE;
if(str_type == 1){
rows = nLines-first_line_data;
}else{
rows = nLines-first_line_data-1;
}
if(first_line_data > 1){
head = FALSE;
rows = nLines-first_line_data;
}
csv_data = csv_adjust(read.csv(file = path, sep = intToUtf8(separator), nrows = rows, skip = first_line_data, header = head)); #mudar o nome
if(header_line > 1){
locus = strsplit(readLines(path, n = 1), intToUtf8(separator))[[1]];
if(first_column_data > 1){
locus = locus[-c(1,2)];
}
if(str_type == 2){
locus = array(unlist(rbind(locus,paste0(locus, ".2")))); #Duplicate locus columns
}
names(csv_data) <- locus;
}
#if(is.na(ploid)) {
ploid = 2; #May be a future change for multiploid species
#print(paste0("Using ", ploid, "-ploid"));
#}
loci_names = names(csv_data); # Selects all loci names which are countable (important for GBS data)
loci_names <- sort(loci_names[seq(1,length(loci_names), ploid)]); #Selects just one value for each locus(sorted)
total_loci = length(names(csv_data))/ploid;
total_individuals = nrow(csv_data);
print(paste("Your input data has", total_loci, "loci", "and", total_individuals, "individuals"));
if(n_minimum == '') {
n_minimum = 5;
print("Using N = 5.");
}else{
n_minimum = as.integer(n_minimum);
if(n_minimum > total_individuals) {
print("Your N is higher than the total number of individuals in the original population!");
print("Try Again.");
quit();
}else{
if(n_minimum < 5){
n_minimum = 5;
print("N should not be less than 5!");
print("Using N = 5.");
}
}
}
if(repeat_N == '') {
repeat_N = 50;
print("Using 50 repeats.");
}else{
repeat_N = as.integer(repeat_N);
if(repeat_N > 10000){
print("Number of repetitions can not exceed 10.000!!!");
quit();
}
}
if(freqMin == ''){
freqMin = 0.05;
}else{
if(freqMin > 0.90){
freqMin = 0.90;
}else{
if(freqMin < 0.0001){
freqMin = 0.0001;
}
}
}
print(paste("Using", freqMin, "as minimal frequence."));
cat ("Press enter to continue\n");
if(interactive()){
readline();