# HG changeset patch
# User cristian
# Date 1504788717 14400
# Node ID 1535ffddeff45a0912aaa1d5dcf1787d6362e254
planemo upload commit a7ac27de550a07fd6a3e3ea3fb0de65f3a10a0e6-dirty
diff -r 000000000000 -r 1535ffddeff4 CpGoe.pl
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/CpGoe.pl Thu Sep 07 08:51:57 2017 -0400
@@ -0,0 +1,368 @@
+#! /usr/bin/perl
+
+####################################
+#
+# CpGoe.pl -f fasta-file -o output_file -m minlength
+#
+# reads concatanated fasta files, writes name, length, CpGs and GpCs, CpGo/e ratios and other quantities into TAB file for each sequence
+#####################################
+
+use diagnostics;
+use strict;
+use Carp;
+use FileHandle;
+use File::Path;
+use File::Basename;
+use Data::Dumper;
+use Getopt::Std;
+
+my $VERSION = "1.0";
+$Getopt::Std::STANDARD_HELP_VERSION = 1;
+
+
+# Called when --help set as flag
+sub HELP_MESSAGE
+{
+ print "Description: CpGoe processes a FASTA file and outputs the CpGo/e ratios and - if specified - further quantities\n\n" .
+ "Usage: CpGoe.pl [OPTION] -f FASTA_FILE \n\n" .
+ "Options:\n" .
+ " -f FASTA_FILE Name of FASTA file containing the input sequences. REQUIRED.\n" .
+ " -o OUT_FILE name of output file containing the results\n" .
+ " -m MIN_LEN minimum length of sequences, shorter sequences are discarded\n" .
+ " -c CONTEXT Context to calculate the ratio (CpA, CpC, CpG, CpT) default CpG.\n" .
+ " -a ALGORITHM Algorithm used to calculate CpGo/e ratio. Default: 1\n" .
+ " 1 - (CpG / (C * G)) * (L^2 / L-1)\n" .
+ " 2 - (CpG / (C * G)) * (L^2 / L)\n" .
+ " 3 - (CpG / L) / (G + C)^2\n" .
+ " 4 - CpG / ( (C + G)/2 )^2\n" .
+ " -d detailed output, providing other quantities additional to the CpGo/e ratios\n" .
+ " -h output header line\n".
+ " -v verbose messages\n" ;
+ exit 0;
+}
+
+
+
+# Called when --version set as flag
+sub VERSION_MESSAGE
+{
+ print "CpGoe $VERSION\n";
+}
+
+
+
+# Command line parsing
+# ... read argument
+my %opts;
+getopts('f:o:m:c:a:dvh', \%opts);
+#if ($#ARGV != 0) {
+# print STDERR "Exactly one argument has to be provided, the name of the input FASTA file.\n" .
+# "Moreover, the options must be listed first, then the name of the input FASTA file.\n";
+# exit 1;
+#}
+my $fasta_fname;
+if (exists($opts{'f'})) {
+ $fasta_fname = $opts{'f'};
+} else {
+ HELP_MESSAGE
+}
+
+# ... read options
+my $out_fname;
+my $has_file_output;
+if (exists($opts{'o'})) {
+ $out_fname = $opts{'o'};
+ $has_file_output = 1;
+}
+else {
+ $has_file_output = 0;
+}
+
+my $min_len;
+if (exists($opts{'m'})) {
+ $min_len = $opts{'m'};
+}
+else {
+ $min_len = 1;
+}
+
+my $algo = 1;
+if (exists($opts{'a'})) {
+ $algo = $opts{'a'};
+}
+
+my $context = 'CpG';
+if (exists($opts{'c'})) {
+ $context = $opts{'c'};
+}
+
+my $is_verbose = exists($opts{'v'});
+my $has_header = exists($opts{'h'});
+my $is_detailed = exists($opts{'d'});
+
+
+
+# read input file and split into fasta sequences on the fly
+# ... check whether input FASTA file exists
+my $FASTA;
+if (-e $fasta_fname) {
+ if (-f $fasta_fname) {
+ my $res = open($FASTA, $fasta_fname);
+ if (!$res) {
+ print STDERR "could not open $fasta_fname\n";
+ exit 1;
+ }
+ }
+ else {
+ print STDERR "$fasta_fname is not a file\n";
+ exit 1;
+ }
+}
+else {
+ print STDERR "cannot open file $fasta_fname\n";
+ exit 1;
+}
+
+
+# ... determine which linebreak is used (linux / windows / mac)
+my $found_n = 0;
+my $found_r = 0;
+my $found_rn = 0;
+while ( defined( my $ch = getc($FASTA) ) ) {
+ if ($ch eq "\n") {
+ if ($found_r) {
+ $found_rn = 1;
+ $found_r = 0;
+ } else {
+ $found_n = 1;
+ }
+ last;
+ } elsif ($ch eq "\r") {
+ $found_r = 1;
+ } else {
+ if ($found_r) {
+ last;
+ }
+ }
+}
+close($FASTA);
+if ($found_r + $found_n + $found_rn != 1) {
+ print STDERR "something went wrong determining the linebreaks used in $fasta_fname\n";
+}
+
+
+# ... read in sequences
+my $old_linebreak = $/;
+if ($found_n) {
+ $/ = "\n";
+} elsif ($found_r) {
+ $/ = "\r";
+} else {
+ $/ = "\r\n";
+}
+my $res = open($FASTA, $fasta_fname);
+if (!$res) {
+ print STDERR "could not open $fasta_fname\n";
+ exit 1;
+}
+
+my @names = (); # names of the sequences
+my @seqs = (); # sequences
+my $is_first = 1;
+while ( <$FASTA> ) {
+ chomp;
+ if (/^[^#]/) {
+ if ( /^>(\S+)/) {
+ #s/^>|\s+$//g; # remove leading '>' and trailing whitespaces
+ #s/\s/_/g; # replace spaces by underscores
+ push(@names, $1);
+ push(@seqs, "");
+ $is_first = 0;
+ }
+ else {
+ if ($is_first) {
+ print STDERR "first non-comment line of FASTA file " . $fasta_fname . " does not start with \'>\'\n";
+ exit 1;
+ }
+ else {
+ s/[\-\.]*//g; # remove dashes and dots
+ $seqs[-1] .= $_;
+ }
+ }
+ }
+}
+$res = close($FASTA);
+if (!$res) {
+ print STDERR "could not close $fasta_fname\n";
+ exit 1;
+}
+$/ = $old_linebreak;
+
+# print Dumper(@names) . "\n";
+# print Dumper(@seqs) . "\n";
+
+
+# ... check sequences
+# ... ... are there any sequence names?
+if ($#names < 0) {
+ print STDERR "FASTA file $fasta_fname is empty\n";
+ exit 1;
+}
+
+# ... ... are there empty sequences?}
+my $str = "";
+my $err = 0;
+my $num = 0;
+my $MAX = 50; # maximum number of notifications about an empty sequence
+for (my $i = 0; $i <= $#names; ++$i) {
+ if ($seqs[$i] eq "") {
+ if ($num < $MAX) {
+ $str .= "Sequence " . $names[$i] . " in FASTA file $fasta_fname is empty\n";
+ }
+ $err = 1;
+ ++$num;
+ }
+}
+if ($err) {
+ print STDERR "$str";
+ if ($num > $MAX) {
+ print STDERR "$num empty sequences in total in FASTA file $fasta_fname \n";
+ }
+ exit 1;
+}
+
+
+# ... ... check for illegal characters in sequences
+for (my $i = 0; $i <= $#names; ++$i) {
+ my $str = $seqs[$i];
+ $str =~ s/[abcdghkmnrstuvwy]//gi;
+ if (length($str) > 0) {
+ $str = join '', sort { $a cmp $b } split(//, $str);
+ print STDERR "Sequence " . $names[$i] . " of FASTA file " . $fasta_fname . " contains illegal characters: ";
+ for (my $j = 0; $j <= length($str); ++$j) {
+ my $out = ($j == 0);
+ my $curr = substr($str, $j, 1);
+ if (!$out) {
+ my $prev = substr($str, $j - 1, 1) ;
+ $out = ($curr ne $prev);
+ }
+ if ($out) {
+ print STDERR $curr;
+ }
+ }
+ print STDERR "\n";
+ exit 1;
+ }
+}
+
+
+# ... output
+if ($is_verbose) {
+ print $#names + 1 . " sequence(s) read.\n";
+}
+
+
+
+# output quantities
+# ... open output file
+my $OUT;
+if ($has_file_output) {
+ if ((-e $out_fname) && !(-f $out_fname)) {
+ print STDERR "$out_fname exists and is not a file\n";
+ exit 1;
+ }
+ if (!open($OUT, ">$out_fname")) {
+ print STDERR "cannot create file $out_fname\n";
+ exit 1;
+ }
+} else {
+ $OUT = *STDOUT;
+}
+
+# ... print header
+if ($has_header) {
+ print $OUT "#name\tlength\tCpGs\tGpCs\tCs\tGs\tNs\tCpG o\/e\n";
+}
+
+
+
+# ... for each sequence calculate CpGo/e ratios and related quantities:
+# - length of the sequence
+# - CpGs present in the sequence
+# - GpCs present in the sequence
+# - Cs the number of C present in the sequence
+# - Gs the number of G present in the sequence
+# - CpG o/e ratio of the sequence
+my $num_short = 0; # number of sequences which are too short
+for my $i (0 .. $#names) {
+ my @ar = ();
+ @ar = split('\|', $names[$i]);
+ my $seqname = $ar[1];
+ my $num_N = () = ( $seqs[$i] =~ m/N/gi );
+ my $len = length($seqs[$i]);
+ my $l = $len - $num_N;
+ if ($l >= $min_len) {
+ my ($num_G, $num_CG);
+ if ($context eq 'CpG') {
+ $num_G = () = ( $seqs[$i] =~ m/G/gi );
+ $num_CG = () = ( $seqs[$i] =~ m/CG/gi );
+ } elsif ($context eq 'CpA') {
+ $num_G = () = ( $seqs[$i] =~ m/A/gi );
+ $num_CG = () = ( $seqs[$i] =~ m/CA/gi );
+ } elsif ($context eq 'CpC') {
+ $num_G = () = ( $seqs[$i] =~ m/C/gi );
+ $num_CG = () = ( $seqs[$i] =~ m/CC/gi );
+ } elsif ($context eq 'CpT') {
+ $num_G = () = ( $seqs[$i] =~ m/T/gi );
+ $num_CG = () = ( $seqs[$i] =~ m/CT/gi );
+ } else {
+ $num_G = 0;
+ $num_CG = 0;
+ }
+ my $num_C = () = ( $seqs[$i] =~ m/C/gi );
+ my $num_TG = () = ( $seqs[$i] =~ m/TG/gi );
+ my $CpGoe;
+ if ( ($num_G == 0) || ($num_C == 0) || ($l == 1) || ($num_CG == 0) ) {
+ $CpGoe = 0;
+ }
+ else {
+ if ($algo == 1) {
+ my $x = $num_CG / ($num_C * $num_G);
+ my $y = $l**2 / ($l - 1);
+ $CpGoe = $x * $y;
+ } elsif ($algo == 2) {
+ # cf.Gardiner-Garden and Frommer
+ $CpGoe = ($num_CG/($num_C * $num_G))*$l;
+ } elsif ($algo == 3) {
+ # cf. Zeng and Yi
+ $CpGoe = ($num_CG / $l)/(($num_C + $num_G)/$l)**2;
+ } elsif ($algo == 4) {
+ # cf. Saxonov, Berg and Brutlag
+ $CpGoe = $num_CG / (($num_C + $num_G)/2)**2;
+ }
+ }
+ print $OUT $names[$i] . "\t";
+ if ($is_detailed) {
+ if ($algo == 3) {
+ print $OUT $len . "\t" . $num_CG . "\t" . $num_TG . "\t" .$num_C. "\t" .$num_G. "\t" .$num_N. "\t";
+ } else {
+ print $OUT $len . "\t" . $num_CG . "\t" . $num_C . "\t" .$num_G. "\t" .$num_N. "\t";
+ }
+ }
+ print $OUT $CpGoe . "\n";
+ } else {
+ ++$num_short;
+ }
+}
+if ($is_verbose) {
+ print $num_short . " sequence(s) discarded due to short length.\n";
+}
+
+if ($has_file_output) {
+ my $res = close($OUT);
+ if (!$res) {
+ print STDERR "could not close $out_fname\n";
+ exit 1;
+ }
+}
+
diff -r 000000000000 -r 1535ffddeff4 CpGoe.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/CpGoe.xml Thu Sep 07 08:51:57 2017 -0400
@@ -0,0 +1,57 @@
+
+
+ perl
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ (CpG / (C * G)) * (L^2 / L-1)
+ 2 => (CpG / (C * G)) * L
+ 3 => (CpG / L) / ((C + G) / L)^2
+ 4 => (CpG / (C + G)/2)^2
+
+ Where L represents the length of the sequence, CpG represents the count of CG dinucleotide, C and G represent the count for the respective bases and TG represents the number of TG dinucleotides.
+ ]]>
+
+
+ 10.1101/180463
+
+
+
diff -r 000000000000 -r 1535ffddeff4 Functions/Kernel_function_form.R
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/Functions/Kernel_function_form.R Thu Sep 07 08:51:57 2017 -0400
@@ -0,0 +1,311 @@
+##############################################
+# KDE function for single observation sequence #
+##############################################
+
+# load packages
+require(moments, quietly = TRUE)
+require(doParallel, quietly = TRUE)
+
+
+# get names for the columns of the table characterizing the peaks
+col.names.peaks <- function()
+{
+ v <- c("Number of modes", "Number of modes (5% excluded)",
+ "Number of modes (10% excluded)", "Skewness",
+ "Mode skewness", "Nonparametric skewness", "Q50 skewness",
+ "Absolute Q50 mode skewness", "Absolute Q80 mode skewness",
+ "Peak 1", "Probability Mass 1",
+ "Peak 2", "Probability Mass 2",
+ "Peak 3", "Probability Mass 3",
+ "Peak 4", "Probability Mass 4",
+ "Peak 5", "Probability Mass 5",
+ "Peak 6", "Probability Mass 6",
+ "Peak 7", "Probability Mass 7",
+ "Peak 8", "Probability Mass 8",
+ "Peak 9", "Probability Mass 9",
+ "Peak 10", "Probability Mass 10",
+ "Warning close modes",
+ "Number close modes",
+ "Modes (close modes excluded)",
+ "SD", "IQR 80", "IQR 90",
+ "Total number of sequences")
+ return(v)
+}
+
+
+# get names for the columns of the table containing the boostrap results
+col.names.bs <- function()
+{
+ v <- c("Number of modes (NM)",
+ "% of samples with same NM",
+ "% of samples with more NM",
+ "% of samples with less NM",
+ "no. of samples with same NM",
+ "% BS samples excluded by prob. mass crit.",
+ "Warning CI")
+ return(v)
+}
+
+
+# plot KDE for one set up CpGo/e ratios
+# obs: CpGo/e ratios
+# bs.cis: Is bootstrap done?
+# t.name: Title of the plot
+# t.sub: Text that is added below the title
+# t.legend: Is a legend printed?
+plot.KDE <- function(obs, t.name, bs.cis = FALSE, bstrp.reps = 1500, conf.lev = 0.95, t.sub = NULL, t.legend = TRUE, min.dist = 0.2, mode.mass = 0.01, band.width = 1.06)
+{
+ # determine directory where functions are located
+ cmdArgs <- commandArgs(trailingOnly = FALSE)
+ str <- "--file="
+ match <- grep(str, cmdArgs)
+ if (length(match) == 0) {
+ FCTN.DIR <- "../Galaxy/Functions"
+ } else {
+ path <- normalizePath( sub(str, "", cmdArgs[match]) )
+ FCTN.DIR <- file.path(dirname(path), "Functions")
+ }
+
+ # part 1: initialize parameters etc
+ # ---------------------------------
+
+ # table with names and number of peaks
+ v <- col.names.peaks()
+ tab1.m <- data.frame(matrix(NA, nrow = 1, ncol = length(v)))
+ names(tab1.m) <- col.names.peaks()
+ # parameters to set in any case
+ num.points <- 10 ^ 4 # number of points where to estimate the density
+ p.bw <- "nrd" # algorithm for the bandwidth selection, "nrd" for Scott's bandwith
+ use.seed <- TRUE
+ threshold.modes <- mode.mass
+ threshold.bs.ci <- 0.2 # only changes of +- threshold.bs.ci% in prob. mass is allowed for entering the CI calculation
+ # bootstrap with optional parameters
+ if (bs.cis) {
+ ncpus = max(1, detectCores()) # number of processsors
+ cl <- makeCluster(ncpus)
+ registerDoParallel(cl)
+ # table for the bootstrap
+ tab2.m <- data.frame(matrix(NA, nrow = 1, ncol = 7))
+ names(tab2.m) <- col.names.bs()
+ }
+
+
+ # part 2: estimate the KD and calculate probability masses
+ # --------------------------------------------------------
+
+ source( file.path(FCTN.DIR, "density_pm.R") )
+ estimate <- density_pm(obs, num.points, p.bw = band.width * sd(obs) / length(obs)^0.2, threshold.modes = threshold.modes)
+ ker <- estimate$ker # kernel density
+ p <- estimate$peaks
+ v <- estimate$valleys
+ pm <- estimate$pm
+
+ # select for each peak a line type
+ num.pm <- length(p) # number of peaks
+ p5 <- estimate$p5
+ p10 <- estimate$p10
+ lty = rep(1, num.pm) #pm > 0.10
+ if (length(p10) > 0) { #pm < 0.10
+ lty[p10] <- 2
+ }
+ if (length(p5) > 0) { #pm < 0.05
+ lty[p5] <- 4
+ }
+ p.lty <- lty
+
+ # part 3: bootstrap
+ # -----------------
+
+ if (bs.cis == TRUE) {
+ # do bootstrapping
+ estimateB = foreach(j = 1 : bstrp.reps ) %dopar% {
+ if (use.seed == TRUE) {set.seed(j)}
+ source( file.path(FCTN.DIR, "density_pm.R") ) # As doParallel is used, the source code has to be included here
+ obs.boot = obs[sample(seq(obs), replace = TRUE)]
+ density_pm_boot(obs.boot, num.points = num.points, p.bw = band.width * sd(obs) / length(obs)^0.2, threshold.modes = threshold.modes)
+ }
+ stopCluster(cl)
+
+ # calculate CIs based on samples where the number of peaks of the KDE coincide with the original data
+ # and the probability mass of the peaks of the sample don not divert to strongly from the original data
+ # ... extract modes and pm for samples
+ num.pmB <- sapply(lapply(estimateB, "[[", "peaks"), length) # no of peaks before cleaning
+ num.peaks.ok <- num.pmB == num.pm # samples with same number of peaks before cleaning
+ pB <- t(sapply( estimateB[num.peaks.ok], "[[", "peaks") )
+ pmB <- t(sapply( estimateB[num.peaks.ok], "[[", "pm") )
+ if (num.pm == 1) { # put into right matrix form
+ pB <- t(pB)
+ pmB <- t(pmB)
+ }
+
+ # ... check if prob. mass changes too strongly for any mode
+ t.pmB <- t(pmB)
+ keep.ub <- !apply(pm * (1 + threshold.bs.ci) < t.pmB, 2, any)
+ keep.lb <- !apply(pm * (1 - threshold.bs.ci) > t.pmB, 2, any)
+ mass.ok <- keep.ub & keep.lb
+ pB.cl <- as.matrix( pB[mass.ok, ] )
+
+ # ... determine CIs
+ q <- (1 - conf.lev) / 2
+ p.CI <- apply(pB.cl, 2, quantile, probs = c(q, 1 - q))
+ }
+
+
+ # part 4: plots
+ # -------------
+
+ #t.breaks <- seq(0, max(obs)*1.05, by = 0.03)
+ t.breaks = 50
+ hist_data <- hist(obs, breaks = t.breaks, plot = FALSE)
+ hist(obs, breaks = t.breaks, prob = TRUE, main = t.name,
+ # sub = paste("Gaussian kernel with band width", band.width),
+ xlab = "CpG o/e ratio",
+ col = grey(0.9), border = grey(0.6)) #, xlim = c(-0.05, max(obs)*1.1))
+ if (!is.null(t.sub)) {
+ mtext(t.sub)
+ }
+ if (bs.cis == TRUE) { # CI
+ j <- 1 : ncol(p.CI)
+ rect(ker$x[p.CI[1, j]], 0, ker$x[p.CI[2, j]],
+ 15, density = 20, angle = 45 + (j - 1) * 90, col = "blue")
+ }
+ lines(ker, col = "red", lwd = 2) # density
+
+ # vertical lines at peaks
+ x.pos = ker$x[p]
+ ok <- c()
+ close <- c()
+ for (i in 1:length(x.pos)) {
+ b <- TRUE
+ if (i > 1) {
+ if (x.pos[i] - x.pos[i - 1] < min.dist) {
+ b <- FALSE
+ }
+ }
+ if (i < length(x.pos)) {
+ if (x.pos[i + 1] - x.pos[i] < min.dist) {
+ b <- FALSE
+ }
+ }
+
+ if (b) {
+ ok <- c(ok, i)
+ } else {
+ close <- c(close, i)
+ }
+ }
+ # peaks
+ if (length(ok) > 0) {
+ abline(v = ker$x[p][ok], col = "blue", lwd = 3, lty = p.lty)
+ }
+ if (length(close) > 0) {
+ abline(v = ker$x[p][close], col = "orange", lwd = 3, lty = p.lty)
+ }
+ # valleys
+ vals = ""
+ if (length(x.pos)>1) {
+# ker$x[v][c(-1, -length(ker$x[v]))]
+ vals = ker$x[v][c(-1, -length(ker$x[v]))]
+ abline(v = vals, col = "black", lwd = 1)
+ }
+
+ # legend
+ if(t.legend == TRUE) {
+ t.sym <- expression(""<="", ""<"", "">="")
+ thr = threshold.modes
+ if (thr >= 0.1) {
+ md.labs <- substitute(paste("Mode with PM ", sym3, " ", thr, sep = ""), list(thr = thr, sym3 = t.sym[[3]]))
+ } else {
+ md.labs <- substitute(paste("Mode with PM ", sym3, " 0.1", sep = ""), list(sym3 = t.sym[[3]]))
+ if (thr >= 0.05) {
+ md.labs <- c( md.labs, substitute(paste("Mode with ", thr, " ", sym1, " PM ", sym2, " 0.1", sep = ""), list(thr = thr, sym1 = t.sym[[1]], sym2 = t.sym[[2]])) )
+ } else {
+ md.labs <- c( md.labs, substitute(paste("Mode with 0.05 ", sym1, " PM ", sym2, " 0.1", sep = ""), list(sym1 = t.sym[[1]], sym2 = t.sym[[2]])),
+ substitute(paste("Mode with ", thr, sym1, " PM ", sym2, " 0.05"), list(thr = thr, sym1 = t.sym[[1]], sym2 = t.sym[[2]])))
+ }
+ }
+ legend("topright",
+ c(expression("Estimated density"), md.labs),
+ lty = c(1, 1, 2, 4), lwd = c(2, 3, 3, 3),
+ col = c("red", "blue", "blue", "blue"), bg = "white")
+ }
+
+
+ # part 5: return results
+ # ----------------------
+
+ # part 5 a): results table 1
+ # tbd (maybe): introduce maximum number of modes (10 right now)
+ tab1.m[1, "Number of modes"] <- num.pm
+ tab1.m[1, 2] = num.pm - length(estimate$p5)
+ tab1.m[1, 3] = num.pm - length(estimate$p10)
+ for(j in 1 : 10){
+ if(num.pm < j){
+ tab1.m[1, j * 2 + 8] = c(" ")
+ tab1.m[1, j * 2 + 9] = c(" ")
+ } else{
+ tab1.m[1, j * 2 + 8] = ker$x[p][j]
+ tab1.m[1, j * 2 + 9] = pm[j]
+ }
+ }
+
+ # fill table 1 with descriptives
+ tab1.m[1, "Skewness"] <- skewness(obs)
+ mode <- ker$x[ which.max(ker$y) ]
+ tab1.m[1, "Mode skewness"] <- (mean(obs) - mode) / sd(obs)
+ tab1.m[1, "Nonparametric skewness"] <- (mean(obs) - median(obs)) / sd(obs)
+ q <- quantile(obs, c(0.25, 0.5, 0.75))
+ tab1.m[1, "Q50 skewness"] <- (q[3] + q[1] - 2 * q[2]) / (q[3] - q[1])
+ tab1.m[1, "Absolute Q50 mode skewness"] <- (q[3] + q[1]) / 2 - mode
+ q <- quantile(obs, c(0.1, 0.5, 0.9))
+ tab1.m[1, "Absolute Q80 mode skewness"] <- (q[3] + q[1]) / 2 - mode
+ tab1.m[1, "SD"] <- sd(obs)
+ tab1.m[1, "IQR 80"] <- diff(quantile(obs, c(0.1, 0.9)))
+ tab1.m[1, "IQR 90"] <- diff(quantile(obs, c(0.05, 0.95)))
+ tab1.m[1, "Total number of sequences"] = length(obs)
+
+ # check if any peak is closer than a given threshold to any other
+ num.close.modes <- sum(diff(ker$x[p]) < min.dist)
+ if ( any(diff(ker$x[p]) < min.dist) && (num.pm > 1) ) {
+ tab1.m[1, "Warning close modes"] <- "Modes too close"
+ tab1.m[1, "Number close modes"] <- num.close.modes
+ tab1.m[1, "Modes (close modes excluded)"] <- num.pm - num.close.modes
+ } else {
+ tab1.m[1, "Modes (close modes excluded)"] <- num.pm
+ }
+
+ # part 5 b): results table 2
+ if (bs.cis == TRUE) {
+ ker <- lapply( estimateB, "[[", "ker")
+ peaks <- lapply( estimateB, "[[", "peaks")
+ num.peaks <- c()
+ for (i in 1:length(peaks)) {
+ curr.peaks <- ker[[i]]$x[ peaks[[i]] ]
+ num.cl <- sum(diff(curr.peaks) < min.dist)
+ num.peaks <- c(num.peaks, length(curr.peaks) - num.cl)
+ }
+
+ # fill table 2 with stats on number of modes in bs samples
+ num <- num.pm - num.close.modes
+ tab2.m[1, "Number of modes (NM)"] <- num
+ tab2.m[1, "% of samples with same NM"] <- 100 * sum(num.peaks == num) / bstrp.reps # equal
+ tab2.m[1, "% of samples with more NM"] <- 100 * sum(num.peaks > num) / bstrp.reps # more
+ tab2.m[1, "% of samples with less NM"] <- 100 * sum(num.peaks < num) / bstrp.reps # less
+ if (num.pm > 1) {
+ tab2.m[1, "Warning CI"] <- "CI's may be unreliable"
+ }
+
+ tab2.m[1, "no. of samples with same NM"] <- sum(num.peaks == num)
+ tab2.m[1, "% BS samples excluded by prob. mass crit."] <- (1 - sum(mass.ok) / sum(num.peaks.ok)) * 100
+ }
+
+ # return the results
+ if (bs.cis){
+ return(list(tab.des = tab1.m, tab.bs = tab2.m, valleys = vals))
+ } else {
+ return(list(tab.des = tab1.m, valleys = vals))
+ }
+}
+
+
+
diff -r 000000000000 -r 1535ffddeff4 Functions/density_pm.R
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/Functions/density_pm.R Thu Sep 07 08:51:57 2017 -0400
@@ -0,0 +1,141 @@
+### Auxiliary functions ###
+
+# Finds the peak
+peaks <- function(x,partial=TRUE){
+ if (partial){ #includes the first and the last element
+ which(diff(c(TRUE,diff(x)>=0,FALSE))<0)
+ }else {
+ which(diff(diff(x)>=0)<0)+1
+ }
+}
+
+
+#Function that finds the valleys
+valleys <- function(x,partial=TRUE){
+ if (partial){ #includes the first and the last element
+ which(diff(c(FALSE,diff(x)>0,TRUE))>0)
+ }else {
+ which(diff(diff(x)>0)>0)+1
+ }
+}
+
+
+#Function that calculates the probability masses
+#ker: kernel density
+#v: valleys
+probability_mass <- function(ker,v){
+ require(sfsmisc, quietly = TRUE)
+ ker$y[which(ker$x<0)] = 0
+ pm = c()
+ for(j in 1:(length(v)-1)){
+ pm[j] = integrate.xy(ker$x,ker$y,ker$x[v[j]],ker$x[v[j+1]], use.spline = FALSE)
+ }
+ pm = pm/sum(pm)
+ return(pm)
+}
+
+
+#Function that tests if pm < value
+#pm: probability masses
+test_pm <- function(pm,value){
+ p = c()
+ num_pm = length(pm)
+ for(j in 1:num_pm){
+ if(pm[j]= 1)) {
+ stop("The maximum fraction of CpGo/e ratios excluded as outliers has to be greater than zero and less than one")
+}
+if (frac.outl >= 0.2) {
+ warning("The maximum fraction of CpGo/e ratios excluded as outliers has been set to a rather large value, resulting in the removal of many CpGo/e ratios")
+}
+
+
+# ... check numerical arguments
+# ... ... check minimum distance between modes
+min.dist <- args$options$`min-dist`
+if (min.dist < 0) {
+ stop("The minimum distance between modes has to be equal to or larger than zero")
+}
+if (min.dist >= 0.4) {
+ warning("The minimum distance between modes has been set to a rather large value, resulting in a strong reduction of the number of modes")
+}
+
+# ... ... check confidence level
+conf.lev <- args$options$`conf-level`
+if ((conf.lev <= 0) || (conf.lev >= 1)) {
+ stop("The level of the confidence intervals of the mode positions has to be larger than zero and smaller than one.")
+}
+if (conf.lev >= 0.995) {
+ warning("The level of the confidence intervals of the mode positions has been set to a rather high value, resulting in very broad confidence intervals")
+}
+
+# ... ... check minimum probability mass of a mode
+mode.mass <- args$options$`mode-mass`
+if ((mode.mass < 0) || (mode.mass >= 1)) {
+ stop("The minimum probability mass of a mode has to be larger than or equal to zero and smaller than one.")
+}
+if (mode.mass >= 0.3) {
+ warning("The minimum probability mass of a mode has been set to a rather large value, resulting in the elemination of a high number of modes.")
+}
+
+# ... ... check bandwidth constant
+band.width <- args$options$`band-width`
+if (band.width <= 0) {
+ stop("The bandwidth constant has to be positive")
+}
+if (band.width >= 5) {
+ warning("The bandwidth constant has to been set to a rather large value, resulting in a strong smoothing")
+}
+
+# ... ... check number of boostrap repetitions
+bstrp.reps <- args$options$`bootstrap-reps`
+if (bstrp.reps != round(bstrp.reps)) {
+ stop("The number of boostrap repetitions has to be a positive integer")
+}
+if (bstrp.reps <= 0) {
+ stop("The number of boostrap repetitions has to be positive")
+}
+if (bstrp.reps >= 10000) {
+ warning("The number of boostrap repetitions has been set to a rather large value, resulting in a long running time")
+}
+
+# ... check file name arguments
+# ... ... check histogram output file name
+outlier.hist.fname <- args$options$`outlier-hist-file`
+if ( file.exists(outlier.hist.fname) && (file.info(outlier.hist.fname)$isdir) ) {
+ stop(paste("File name for the outlier histogram output refers to a directory:", outlier.hist.fname))
+}
+v <- strsplit(outlier.hist.fname, split = ".", fixed = TRUE)[[1]]
+if ((length(v) == 1) || (v[ length(v) ] != "pdf")) {
+ warning(paste("File name for the outlier histogram output does not have a .pdf extension:", outlier.hist.fname))
+}
+g <- gregexpr(pattern ='/', outlier.hist.fname)[[1]]
+if (as.vector(g)[1] != -1) {
+ v <- as.vector(g)
+ d <- substr(outlier.hist.fname, 1, v[length(v)])
+ if (!file.exists(d)) {
+ stop(paste("Path to file for the outlier histogram output is not valid:", outlier.hist.fname))
+ }
+}
+
+# ... ... check outlier cutoff output file name
+cutoff.fname <- args$options$`cutoff-file`
+if ( file.exists(cutoff.fname) && (file.info(cutoff.fname)$isdir) ) {
+ stop(paste("File name for the outlier cutoff table output refers to a directory:", cutoff.fname))
+}
+v <- strsplit(cutoff.fname, split = ".", fixed = TRUE)[[1]]
+if (length(v) == 1) {
+ stop(paste("File name for the outlier cutoff table output does not have a file extension:", cutoff.fname))
+}
+#if (v[ length(v) ] != "xlsx") {
+# warning(paste("File name for the outlier cutoff table output does not have a .xlsx extension:", cutoff.fname))
+#}
+g <- gregexpr(pattern ='/', cutoff.fname)[[1]]
+if (as.vector(g)[1] != -1) {
+ v <- as.vector(g)
+ d <- substr(cutoff.fname, 1, v[length(v)])
+ if (!file.exists(d)) {
+ stop(paste("Path to file for the outlier cutoff is not valid:", cutoff.fname))
+ }
+}
+
+# ... ... check KDE output file name
+kde.fname <- args$options$`kde-file`
+if ( file.exists(kde.fname) && (file.info(kde.fname)$isdir) ) {
+ stop(paste("File name for the KDE output refers to a directory:", kde.fname))
+}
+v <- strsplit(kde.fname, split = ".", fixed = TRUE)[[1]]
+if ((length(v) == 1) || (v[ length(v) ] != "pdf")) {
+ warning(paste("File name for the KDE output does not have a .pdf extension:", kde.fname))
+}
+g <- gregexpr(pattern ='/', kde.fname)[[1]]
+if (as.vector(g)[1] != -1) {
+ v <- as.vector(g)
+ d <- substr(kde.fname, 1, v[length(v)])
+ if (!file.exists(d)) {
+ stop(paste("Path to file for the KDE output is not valid:", kde.fname))
+ }
+}
+
+
+# ... ... check peak descriptives output file name
+peak.fname <- args$options$`peak-file`
+if ( file.exists(peak.fname) && (file.info(peak.fname)$isdir) ) {
+ stop(paste("File name for the peak descriptives refers to a directory:", peak.fname))
+}
+v <- strsplit(peak.fname, split = ".", fixed = TRUE)[[1]]
+if ((length(v) == 1) || (v[ length(v) ] != "csv")) {
+ warning(paste("File name for the peak descriptives does not have a .csv extension:", peak.fname))
+}
+g <- gregexpr(pattern ='/', peak.fname)[[1]]
+if (as.vector(g)[1] != -1) {
+ v <- as.vector(g)
+ d <- substr(peak.fname, 1, v[length(v)])
+ if (!file.exists(d)) {
+ stop(paste("Path to file for the peak descriptives is not valid:", peak.fname))
+ }
+}
+
+# ... ... check bootstrap results output file name
+bstrp.fname <- args$options$`bootstrap-file`
+if ( file.exists(bstrp.fname) && (file.info(bstrp.fname)$isdir) ) {
+ stop(paste("File name for the bootstrap results refers to a directory:", bstrp.fname))
+}
+v <- strsplit(bstrp.fname, split = ".", fixed = TRUE)[[1]]
+if ((length(v) == 1) || (v[ length(v) ] != "csv")) {
+ warning(paste("File name for the bootstrap results does not have a .csv extension:", bstrp.fname))
+}
+g <- gregexpr(pattern ='/', bstrp.fname)[[1]]
+if (as.vector(g)[1] != -1) {
+ v <- as.vector(g)
+ d <- substr(bstrp.fname, 1, v[length(v)])
+ if (!file.exists(d)) {
+ stop(paste("Path to file for the bootstrap results is not valid:", bstrp.fname))
+ }
+}
+
+# ... ... check summary results output file name
+summ.fname <- args$options$`summary-file`
+if ( file.exists(summ.fname) && (file.info(summ.fname)$isdir) ) {
+ stop(paste("File name for the bootstrap results refers to a directory:", summ.fname))
+}
+v <- strsplit(summ.fname, split = ".", fixed = TRUE)[[1]]
+if ((length(v) == 1) || (v[ length(v) ] != "csv")) {
+ warning(paste("File name for the bootstrap results does not have a .csv extension:", summ.fname))
+}
+g <- gregexpr(pattern ='/', summ.fname)[[1]]
+if (as.vector(g)[1] != -1) {
+ v <- as.vector(g)
+ d <- substr(summ.fname, 1, v[length(v)])
+ if (!file.exists(d)) {
+ stop(paste("Path to file for the bootstrap results is not valid:", summ.fname))
+ }
+}
+
+
+# ... ... check CpGo/e input file names
+num.spec <- num.args / 2
+spec.names <- args$args[1:num.spec]
+cpgoe.fnames <- args$args[(num.spec + 1):num.args]
+for (i in 1:length(cpgoe.fnames)) {
+ if (!file.exists(cpgoe.fnames[i])) {
+ stop(paste("CpGo/e file does not exist:", cpgoe.fnames[i]))
+ }
+ if (file.info(cpgoe.fnames[i])$isdir) {
+ stop(paste("CpGo/e file name refers to a directory:", cpgoe.fnames[i]))
+ }
+}
+
+valleys.fname <- args$options$`valley-file`
+
+# remove outliers and output histograms
+# ... set up table with cutoff quantities
+tab.des <- data.frame(matrix(NA, nrow = num.spec, ncol = 6))
+names(tab.des) <- c("prop.zero", "prop.out.2iqr", "prop.out.3iqr",
+ "prop.out.4iqr", "prop.out.5iqr", "used")
+rownames(tab.des) <- spec.names
+
+# ... set up figure
+t.height <- 6
+t.width <- 20
+pdf(outlier.hist.fname, height = t.height,width = t.width, paper = "special")
+par(mfrow = c(1, 3), mgp = c(2, 0.5, 0), mar = c(4.0, 3.0, 1.5, 1))
+tmp.fnames <- c()
+
+# ... iterate through species
+for (i in 1:num.spec) {
+ fname <- cpgoe.fnames[i]
+ obs <- read.CpGoe(fname, TRUE)
+
+
+ # check CpGo/e ratios
+ for (j in 1:length(obs)) {
+ # is format legal?
+ val <- as.numeric( obs[j] )
+ err.str <- paste("Observation", i, "in", fname)
+ if (!is.finite(val)) {
+ stop(paste(err.str, "could not be converted to a number:", obs[j]))
+ }
+
+ # is ratio too small / large?
+ if (val < 0) {
+ stop(paste(err.str, "is negative:", val))
+ } else {
+ if (val > MAX.CPGOE) {
+ warning(paste(err.str , "is suspiciously large:", val, "\nthis value is replaced by", MAX.CPGOE))
+ }
+ }
+ }
+
+ # process outliers and store the results
+ obs.org <- obs
+ l <- proc.outliers(obs, frac.outl)
+ if (!l[["valid"]]) {
+ stop( paste("Too few values in", fname, "(less than 3) after removal of zeros"), call. = FALSE )
+ }
+ tab.des[i, "prop.zero"] <- l[["prop.zero"]]
+ mu.obs <- l[["mu.obs"]]
+ me.obs <- l[["me.obs"]]
+ ul.mu <- l[["ul.mu"]]
+ ll.mu <- l[["ll.mu"]]
+ ul.me <- l[["ul.me"]]
+ ll.me <- l[["ll.me"]]
+ tab.des[i, "prop.out.2iqr"] <- l[["prop2"]]
+ tab.des[i, "prop.out.3iqr"] <- l[["prop3"]]
+ tab.des[i, "prop.out.4iqr"] <- l[["prop4"]]
+ tab.des[i, "prop.out.5iqr"] <- l[["prop5"]]
+ obs.cl <- l[["obs.cl"]]
+ obs.nz <- l[["obs.nz"]]
+ tab.des[i, "used"] <- l[["used"]]
+ tab.des[i, "no.obs.raw"] <- length(obs.org)
+ tab.des[i, "no.obs.nozero"] <- length(obs.nz)
+ tab.des[i, "no.obs.clean"] <- length(obs.cl)
+ usedindex <- substr(l[["used"]],1,1)
+ # Histograms
+ # ... histogram 1: original data with zeros
+ t.breaks <- seq(0, max(obs.org) + 1, by = 0.03)
+ t.xlim <- c(0, ul.me["5"] + 0.1)
+ hist(obs.org, breaks = t.breaks, xlim = t.xlim, xlab = "CpG o/e", main = "",
+ sub = "Original data", prob = TRUE,
+ col = grey(0.9), border = grey(0.6))
+ mtext(paste(spec.names[i]), side = 3, adj = 0)
+
+
+ # ... histogram 3: median / iqr based
+ t.lty <- rep(3, 4)
+ t.lty[usedindex] <- 1
+
+ hist(obs.nz, breaks = t.breaks, xlim = t.xlim, xlab = "CpG o/e", main = "",
+ sub = "Data without zeros, Q1/3 +- k*IQR, k=2,...,5", prob = TRUE,
+ col = grey(0.9), border = grey(0.6))
+ abline(v = me.obs, col = 'blue', lwd = 2)
+ abline(v = c(ll.me, ul.me), col = "red", lty = rep(t.lty, 2))
+
+ # ... histogram 4: cleaned data
+ hist(obs.cl, breaks = t.breaks, xlim = t.xlim, xlab = "CpG o/e", main = "",
+ sub = "Cleaned data", prob = TRUE,
+ col = grey(0.9), border = grey(0.6))
+ abline(v = me.obs, col = 'blue', lwd = 2)
+ abline(v = c(ll.me[usedindex], ul.me[usedindex]), col = "red")
+}
+invisible(dev.off())
+
+# output cutoff quantities
+write.table(tab.des, file = cutoff.fname, sep = "\t", col.names=NA)
+
+# plot KDE and output quantities characterizing the peaks and the bootstrap results
+# ... table with quantities characterizing the peaks
+v <- col.names.peaks()
+tab1.m <- data.frame(matrix(NA, nrow = num.spec, ncol = length(v)))
+names(tab1.m) <- col.names.peaks()
+rownames(tab1.m) <- spec.names
+
+# ... table for the bootstrap
+tab2.m <- data.frame(matrix(NA, nrow = num.spec, ncol = 7))
+names(tab2.m) <- col.names.bs()
+rownames(tab2.m) <- spec.names
+
+# summary table
+sum1.m <- data.frame(matrix(NA, nrow = num.spec, ncol = 13))
+names(sum1.m) <- c("Modes", "Skewness", "Variance", "Modes too close", "Peak1", "Peak2", "Peak3", "Peak4", "Peak5", "Peak6", "Peak7", "Peak8", "Peak9")
+rownames(sum1.m) <- spec.names
+
+# ... plotting
+t.height <- 6
+t.width <- 20
+pdf(kde.fname, height = t.height,width = t.width, paper = "special")
+for (i in 1:num.spec) {
+ # read in GcGo/e ratios
+ obs <- read.CpGoe(cpgoe.fnames[i], FALSE)
+ l <- proc.outliers(obs, frac.outl)
+ obs.cl <- l[["obs.cl"]]
+
+ # check number of values
+ fname <- cpgoe.fnames[i]
+ if (length(obs.cl) < 3) {
+ stop( paste("Too few values in", fname, "(less than 3) after removal of outliers and zeros"), call. = FALSE )
+ }
+ if (!supp.warn.few & length(obs.cl) < 250) {
+ warning( paste(fname, " contains only few values (", length(obs.cl), ") after removal of outliers and zeros, which may lead to unreliable results", sep = ""), call. = FALSE )
+ }
+
+ # plotting
+ l <- plot.KDE(obs.cl, t.name = spec.names[i], bs.cis = use.bstrp, bstrp.reps = bstrp.reps, conf.lev = conf.lev,
+ min.dist = min.dist, mode.mass = mode.mass, band.width = band.width)
+ tab1.m[i, ] <- l$tab.des
+ sum1.m[i, ] <- l$tab.des[c(1, 4, 33, 30, 10+(2*0:8))]
+ if (use.bstrp) {
+ tab2.m[i, ] <- l$tab.bs
+ }
+ valleys = l$valleys
+}
+invisible(dev.off())
+#sessionInfo()
+
+# ... output quantities in tables
+write.table(sum1.m, file = summ.fname, sep = "\t", col.names = NA)
+write.table(tab1.m, file = peak.fname, sep = "\t", col.names=NA)
+write.table(valleys, file = valleys.fname, sep = "\t", col.names=NA)
+if (use.bstrp) {
+ write.table(tab2.m, file = bstrp.fname, sep = "\t", col.names=NA)
+}
diff -r 000000000000 -r 1535ffddeff4 KDEanalysis.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/KDEanalysis.xml Thu Sep 07 08:51:57 2017 -0400
@@ -0,0 +1,80 @@
+
+
+ R
+ notos
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 10.1101/180463
+
+
+
diff -r 000000000000 -r 1535ffddeff4 LICENSE
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/LICENSE Thu Sep 07 08:51:57 2017 -0400
@@ -0,0 +1,674 @@
+ GNU GENERAL PUBLIC LICENSE
+ Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc.
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+ Preamble
+
+ The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
+
+ The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works. By contrast,
+the GNU General Public License is intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users. We, the Free Software Foundation, use the
+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors. You can apply it to
+your programs, too.
+
+ When we speak of free software, we are referring to freedom, not
+price. Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
+want it, that you can change the software or use pieces of it in new
+free programs, and that you know you can do these things.
+
+ To protect your rights, we need to prevent others from denying you
+these rights or asking you to surrender the rights. Therefore, you have
+certain responsibilities if you distribute copies of the software, or if
+you modify it: responsibilities to respect the freedom of others.
+
+ For example, if you distribute copies of such a program, whether
+gratis or for a fee, you must pass on to the recipients the same
+freedoms that you received. You must make sure that they, too, receive
+or can get the source code. And you must show them these terms so they
+know their rights.
+
+ Developers that use the GNU GPL protect your rights with two steps:
+(1) assert copyright on the software, and (2) offer you this License
+giving you legal permission to copy, distribute and/or modify it.
+
+ For the developers' and authors' protection, the GPL clearly explains
+that there is no warranty for this free software. For both users' and
+authors' sake, the GPL requires that modified versions be marked as
+changed, so that their problems will not be attributed erroneously to
+authors of previous versions.
+
+ Some devices are designed to deny users access to install or run
+modified versions of the software inside them, although the manufacturer
+can do so. This is fundamentally incompatible with the aim of
+protecting users' freedom to change the software. The systematic
+pattern of such abuse occurs in the area of products for individuals to
+use, which is precisely where it is most unacceptable. Therefore, we
+have designed this version of the GPL to prohibit the practice for those
+products. If such problems arise substantially in other domains, we
+stand ready to extend this provision to those domains in future versions
+of the GPL, as needed to protect the freedom of users.
+
+ Finally, every program is threatened constantly by software patents.
+States should not allow patents to restrict development and use of
+software on general-purpose computers, but in those that do, we wish to
+avoid the special danger that patents applied to a free program could
+make it effectively proprietary. To prevent this, the GPL assures that
+patents cannot be used to render the program non-free.
+
+ The precise terms and conditions for copying, distribution and
+modification follow.
+
+ TERMS AND CONDITIONS
+
+ 0. Definitions.
+
+ "This License" refers to version 3 of the GNU General Public License.
+
+ "Copyright" also means copyright-like laws that apply to other kinds of
+works, such as semiconductor masks.
+
+ "The Program" refers to any copyrightable work licensed under this
+License. Each licensee is addressed as "you". "Licensees" and
+"recipients" may be individuals or organizations.
+
+ To "modify" a work means to copy from or adapt all or part of the work
+in a fashion requiring copyright permission, other than the making of an
+exact copy. The resulting work is called a "modified version" of the
+earlier work or a work "based on" the earlier work.
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diff -r 000000000000 -r 1535ffddeff4 readme.rst
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/readme.rst Thu Sep 07 08:51:57 2017 -0400
@@ -0,0 +1,173 @@
+Notos
+=====
+
+Notos is a suite that calculates CpN o/e ratios (e.g., the commonly used CpG o/e ratios) for a set of nucleotide sequences and uses Kernel Density Estimation (KDE) to model the obtained distribution.
+
+It consists of two programs, CpGoe.pl is used to calculate the CpN o/e ratios and KDEanalysis.r estimates the model.
+In the following, these two programs are described briefly.
+
+CpGoe.pl
+--------
+
+
+This program will calculate CpN o/e ratios on nucleotide multifasta files.
+For each sequence that is found in the file it will output the sequence name followed by the CpN o/e ratio, where N can be any of the nucletides A, C, G or T, into a TAB separated file.
+
+An example call would be:
+
+ perl CpGoe.pl -f input_species.fasta -a 1 -c CpG -o input_species_cpgoe.csv -m 200
+
+
+The available contexts (-c) are CpG, CpA, CpC, CpT. Default is CpG.
+
+The available algorithms (-a) for calculating the CpNo/e ratio are the following (here shown for CpG o/e)::
+
+ 1 => (CpG / (C * G)) * (L^2 / L-1)
+ 2 => (CpG / (C * G)) * L
+ 3 => (CpG / L) / ((C + G) / L)^2
+ 4 => (CpG / (C + G)/2)^2
+
+Here L denotes the length of the sequence, CpG represents the count of CG dinucleotide, C and G represent the count for the respective bases.
+
+KDEanalysis.r
+-------------
+
+This program carries out two steps.
+First, the data preparation step, mainly to remove data artifacts.
+Secondly, the mode detection step, which is baesd on a KDE modelling approach.
+
+Example basic usage on command line:
+
+ Rscript ~/src/github/notos/KDEanalysis.r "Input species" input_species_cpgoe.csv
+
+
+In the above case "Input species" will be used to name the graphs that are generated as well as an identifier for each sample.
+It has to be surrounded by " if the name of the species contains spaces.
+The input of KDEanalysis.r is of the same format as the output of CpGoe.pl.
+
+Any of the following parameters can be used
+
++--------+---------------------+-----------------------------------------------------------------------------------------+
+| Option | Long option | Description |
++--------+---------------------+-----------------------------------------------------------------------------------------+
+| -o | --frac-outl | maximum fraction of CpGo/e ratios excluded as outliers [default 0.01] |
++--------+---------------------+-----------------------------------------------------------------------------------------+
+| -d | --min-dist | minimum distance between modes, modes that are closer are joined [default 0.2] |
++--------+---------------------+-----------------------------------------------------------------------------------------+
+| -c | --conf-level | level of the confidence intervals of the mode positions [default 0.95] |
++--------+---------------------+-----------------------------------------------------------------------------------------+
+| -m | --mode-mass | minimum probability mass of a mode [default 0.05] |
++--------+---------------------+-----------------------------------------------------------------------------------------+
+| -b | --band-width | bandwidth constant for kernels [default 1.06] |
++--------+---------------------+-----------------------------------------------------------------------------------------+
+| -B | --bootstrap | calculate confidence intervals of mode positions using bootstrap. |
++--------+---------------------+-----------------------------------------------------------------------------------------+
+| -r | --bootstrap-reps | number of bootstrap repetitions [default 1500] |
++--------+---------------------+-----------------------------------------------------------------------------------------+
+| -p | --peak-file | name of the output file describing the peaks of the KDE [default modes_basic_stats.csv] |
++--------+---------------------+-----------------------------------------------------------------------------------------+
+| -s | --bootstrap-file | Name of the output file with bootstrap values [default "modes_bootstrap.csv"] |
++--------+---------------------+-----------------------------------------------------------------------------------------+
+| -H | --outlier-hist-file | Outliers histogram file [default outliers_hist.pdf] |
++--------+---------------------+-----------------------------------------------------------------------------------------+
+| -C | --cutoff-file | Outliers cutoff file [default outliers_cutoff.csv] |
++--------+---------------------+-----------------------------------------------------------------------------------------+
+| -k | --kde-file | Kernel density estimation graph [default KDE.pdf] |
++--------+---------------------+-----------------------------------------------------------------------------------------+
+
+Of special interest is the -B parameter that will trigger the bootstrap calculations.
+Default settings have been thoroughly calibrated through extensive testing, so we would advice to modify them only if you know what you are doing.
+
+Output: Both the data preparation and the mode detection step return results in form of CSV files and figures to the user.
+The two figures illustrate the results of the data cleaning and mode detection step, respectively.
+The contents of the CSV files is described in the following.
+
+1. outliers_cutoff.csv. The columns of this file contain
+
++---------------+----------------------------------------------------------------------------------------------------------------+
+| Column | description |
++---------------+----------------------------------------------------------------------------------------------------------------+
+| Name | name of the file analyzed |
++---------------+----------------------------------------------------------------------------------------------------------------+
+| prop.zero | proportion of observations equal to zero excluded (relative to original sample) |
++---------------+----------------------------------------------------------------------------------------------------------------+
+| prop.out.2iqr | proportion of values equal excluded if 2 * IQR was used, relative to sample after exclusion of zeros (0 - 100) |
++---------------+----------------------------------------------------------------------------------------------------------------+
+| prop.out.3iqr | proportion of values equal excluded if 3 * IQR was used, relative to sample after exclusion of zeros (0 - 100) |
++---------------+----------------------------------------------------------------------------------------------------------------+
+| prop.out.4iqr | proportion of values equal excluded if 4 * IQR was used, relative to sample after exclusion of zeros (0 - 100) |
++---------------+----------------------------------------------------------------------------------------------------------------+
+| prop.out.5iqr | proportion of values equal excluded if 5 * IQR was used, relative to sample after exclusion of zeros (0 - 100) |
++---------------+----------------------------------------------------------------------------------------------------------------+
+| used | IQR used for exclusion of outliers / extreme values |
++---------------+----------------------------------------------------------------------------------------------------------------+
+| no.obs.raw | number of observations in the original sample |
++---------------+----------------------------------------------------------------------------------------------------------------+
+| no.obs.nozero | number of observations in sample after excluding values equal to zero |
++---------------+----------------------------------------------------------------------------------------------------------------+
+| no.obs.clean | number of observations in sample after excluding outliers / extreme values |
++---------------+----------------------------------------------------------------------------------------------------------------+
+
+2. modes_basic_stats.csv. We use the following notation: sigma - standard deviation, mu - mean, nu - median, Mo - mode, Q_i - the i-th quartile, q_s - the s % quantile. The columns of this file contain
+
++-----------------------------------+------------------------------------------------------------------------------------+
+| Column | description |
++-----------------------------------+------------------------------------------------------------------------------------+
+| Name | name of the file analyzed |
++-----------------------------------+------------------------------------------------------------------------------------+
+| Number of modes | number of modes without applying any exclusion criterion |
++-----------------------------------+------------------------------------------------------------------------------------+
+| Number of modes (5% excluded) | number of modes after exclusion of those with less then 5% probability mass |
++-----------------------------------+------------------------------------------------------------------------------------+
+| Number of modes (10% excluded) | number of modes after exclusion of those with less then 10% probability mass |
++-----------------------------------+------------------------------------------------------------------------------------+
+| Skewness | Pearson's moment coefficient of skewness E(X-mu/sigma)^3 |
++-----------------------------------+------------------------------------------------------------------------------------+
+| Mode skewness | Pearson's first skewness coefficient (mu - Mo)/sigma |
++-----------------------------------+------------------------------------------------------------------------------------+
+| Nonparametric skew | (mu - nu)/sigma |
++-----------------------------------+------------------------------------------------------------------------------------+
+| Q50 skewness | Bowley's measure of skewness / Yule's coefficient (Q_3 + Q_1 - 2Q_2) / (Q_3 - Q_1) |
++-----------------------------------+------------------------------------------------------------------------------------+
+| Absolute Q50 mode skewness | (Q_3 + Q_1) / 2 - Mo |
++-----------------------------------+------------------------------------------------------------------------------------+
+| Absolute Q80 mode skewness | (q_90 + q_10) / 2 - Mo |
++-----------------------------------+------------------------------------------------------------------------------------+
+| Peak i, i = 1,..., 10 | location of peak i |
++-----------------------------------+------------------------------------------------------------------------------------+
+| Probability Mass i, i = 1,..., 10 | probability mass assigned to peak i |
++-----------------------------------+------------------------------------------------------------------------------------+
+| Warning close modes | flag indicating that modes lie too close. The default threshold is 0.2 |
++-----------------------------------+------------------------------------------------------------------------------------+
+| Number close modes | number of modes lying too close, given the threshold |
++-----------------------------------+------------------------------------------------------------------------------------+
+| Modes (close modes excluded) | number of modes after exclusion of modes that are too close |
++-----------------------------------+------------------------------------------------------------------------------------+
+| SD | sample standard deviation sigma |
++-----------------------------------+------------------------------------------------------------------------------------+
+| IQR 80 | 80% distance between the 90 % and 10 % quantile |
++-----------------------------------+------------------------------------------------------------------------------------+
+| IQR 90 | 90% distance between the 95 % and 5 % quantile |
++-----------------------------------+------------------------------------------------------------------------------------+
+| Total number of sequences | total number of sequences / CpG o/e ratios used for this analysis step |
++-----------------------------------+------------------------------------------------------------------------------------+
+
+3. modes_bootstrap.csv. The columns of this optional file resulting from the bootstrap procedure contains:
+
++-------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+
+| Column | description |
++-------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+
+| Name | name of the file analyzed |
++-------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+
+| Number of modes (NM) | number of modes detected for the original sample |
++-------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+
+| % of samples with same NM | proportion of bootstrap samples with the same number of modes (0 - 100) |
++-------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+
+| % of samples with more NM | proportion of bootstrap samples a higher number of modes (0 - 100) |
++-------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+
+| % of samples with less NM | proportion of bootstrap samples a lower number of modes (0 - 100) |
++-------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+
+| no. of samples with same NM | number of bootstrap samples with the same number of modes |
++-------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+
+| % BS samples excluded by prob.~mass crit. | proportion of bootstrap samples excluded due to strong deviations from the probability masses determined for the original sample (0 - 100) |
++-------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+
diff -r 000000000000 -r 1535ffddeff4 test-data/10_std_unif.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/10_std_unif.txt Thu Sep 07 08:51:57 2017 -0400
@@ -0,0 +1,11 @@
+# 10 values that are uniformly distributed on [0,1]
+0.16603589
+0.77443410
+0.98018264
+0.05113997
+0.29865607
+0.45602460
+0.75859942
+0.44160652
+0.04546894
+0.05510023
diff -r 000000000000 -r 1535ffddeff4 test-data/9_seqs.fa
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/9_seqs.fa Thu Sep 07 08:51:57 2017 -0400
@@ -0,0 +1,62 @@
+>seq1
+aatttatagctcttaaaaaaaagtataaccctacctaccctggacgtacgatagagggatttagagacc
+tttcttccacgttagaacgacgacgtaaggacgaaagggaggtttctttttcgccttacgccgaggcgt
+aaagggtggggtattttcacgcgttcgtggtacggtagaaatagggtcacatcttccacgttagaagtag
+aagtagaagtaggtcgtctacacgcggaagtctgcctatttattcctcttacctagacgtagacccgcgt
+ctacttgttctcgggtgtagatgtactatcccgcgaacggtcgtaggcctaactagacggtacctatcctat
+>seq2
+cgcttcggagaactagacgtggaaaacgagcggcggtagaaacgtaggaataggtgtaccgtcgtaccctc
+tggacgacctaggtctgtctatgtattctatctcctccggtagacgtagaaaaacgaatactcggtctatcc
+tcgggcagacatagacgcgtccacctccgaatctaaacagacgggtatacgataaatctacgtcgtcggaga
+cccgacgaacgaccatggaggtccgcgtagaatcgcgtcgatgtacccgtcgcggcggatccccgccggagg
+agtggtcacggttggtacttcctcgacgcgatctatcgagcgggtaaacaactacgaaggaaaaaaaagcg
+agcggtattccctatatgggttttaactatttatataggtataaaaacgtgacgcgtaacggtcagaaa
+>seq3
+aatacgacatcgatcgtagataggattagttacacgccttgagggtaggcgtacgaaggacaag
+gatatcgtcctcgagtagtttgccgttctttaccttaagactgtctctcgccgaaagatcagacgttctctt
+acacatttcacccacttggacggttatgtctagactcttcacacattccgtgtcgaccagggtagatccgat
+aatatgactgcgtctcgtcgcttgagttatacgcgggacaagcgccgcggttccgtacgtcgtttcggttac
+gttgacgcgactgcatttgatatctacggacacttcacacgtgtcgagtagggcgagcgcctctgtacggtt
+catagacacgggattcgtatctctccaaggagcgtcgctggacagtcggacgtagacgtggcgtcctttgac
+>seq4
+gtgttcggatatcgccttaacggtaggacatcccgtaaatccgaacgtgacggagagtcctcctccgacgat
+cgtgtggtaagtacttccgatcacgaaaccggttagaaacgattcgtcgttttcgtcgtactcgtattcctc
+gctaagatcgtcgggctgctccgtacccgtcgcgccatcgtcttcgttttcgtcttctgtatacgcggattc
+aatagtcgtacgcagaccctgacacggcgtgtcgcttccggtagtcctctttgtaattttggccacgacgtc
+tattcccatgtatcggacatcttcgccttgtctgcagatgcccttcgtcgcgagggtcgcggccaggcacgc
+gaataatacacacaggcgtttcatgatagagttcttctatacgcccac
+>seq5
+gttttattataattcgacttttttttaagaatttaactagaagaaaatccgtaatctatgaaacgagtcta
+catcgtttgggtgagggaaagagacggaggcgtgtaccccaatggtattccacgaagtcgtcgacctcctcg
+ggatgattactgtcgctgtactcgatatctacgttcacgtcggacaggcaccctcctatcacggtaatcgtt
+tccgaatgagataggacgccgaccccgtcgctcgtcctcggtatagtacgcacggcgtctccctcgacgcac
+cgtatctctatattaagcgtacacattttggtcgtattctggtacctatccatcccggtgaagaatcctccc
+gcgccctcgctgccgctcgttccgtagtattccgtgtggaagacgggatcacaatccgtgtgatttagcgta
+>seq6
+accgagaactccgacgttttaaccacttcgttgggtccggcggtcgtcgtacaagacgtgtcgtttacggga
+tataggttgaattccacgctgatgtagttaaacgacgaggtacacgtctccgtggaggatacggcgtcggaa
+tacgtgtaccgaggacattttgtgcagagcacgtcgcccgtacgcgtatgtccggagacgccataccccgcg
+ggacacttcgttttgggagcgcatatcctacacccctcctgtcctttcaacagacaatagttccccgcagaa
+cagtcgcagactctatcgcgggttttatcacacgattgagactcggataggtggcctgtgcaccgccctcga
+caacttacgcacgcgggagcgtggttcgtactcgccgtaaaggtttcgttcttgcacggagaacataccgtg
+>seq7
+tcggacccgggtccgcataacctagaggcgtacgacccgggaggacaggaggtacaacacagtccgtccttt
+tcgtagtcgttccctctacattttcctcgatccgcgccatacggggcaccgcccccgtatacgcacgcgacg
+tacgcgagtagtagcgttaaacgaaacatgatacatataccgtagttatttttatattttacacgaagag
+aaaaagacgtagaaggtgccaattagagatgccaattagagagataaaaaccgaattagagaggagcc
+aattaggagccgaattagagaggagccaattaggagccgaattagagaggag
+ccaattagtcgtcacgacgttcctgagagcaaaggcgttgtaaacgaatattaatcacggacctgagatgcg
+>seq8
+ggacctcgtagtcggtgatcaatctcatgaacaactcgatcactctcaccgaggtggggataggatcgtcgt
+acccccagtactcggcgacccgcgccaataagccaatgatggcgcaggcctcggtgatgtaactgtcgtgac
+gcaacatagctcgaatgagagattcggaactgtcgacgtgttcgtacgagcatcggacgagccaacgcacgt
+tgtcacgaaacacgatctccgcgtcccttttgaaagactcgaacacggcaaacaagcgacccgccgattccc
+tatccctgaacggggtagccgaccatctgagatagtcctgaaccgccagaatcacgctgtcgtccgcgccga
+attcgttcctacgcacatagaaagtcatggtcgcaggaatgaaaggaaacgttcgagaatggctcgaacacc
+>seq9
+gttaagtagtgtgttattttttcatttattctaaagtctccatcatgtcgaagaatcccttcttcaacggat
+aggacgagggttgtttagtgttgttattacactcgtagc
+gtttaaacgggttcggtatcttcatgaaatcgaacatgccgtttaattcgttcgcgtttaagggttccttgg
+gaatggggggacaatcggccgggacaacgacccggtcgtcgtccttcttgggatcgggttcgcaca
+cgggattgagttccccgaacatgaaagacacgagtcggttgatgggatacatcagcagcgtatagacgctgt
+acacgaacagattgcatagtctcgccaggtaaaatacgcacaacatacatagtcgcgcgatgctatttacca
diff -r 000000000000 -r 1535ffddeff4 tool_dependencies.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/tool_dependencies.xml Thu Sep 07 08:51:57 2017 -0400
@@ -0,0 +1,51 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ https://github.com/cche/KDEAnalysis_deps/blob/master/getopt_1.20.0.tar.gz?raw=true
+
+
+ https://github.com/cche/KDEAnalysis_deps/blob/master/optparse_1.3.2.tar.gz?raw=true
+
+
+ https://github.com/cche/KDEAnalysis_deps/blob/master/iterators_1.0.8.tar.gz?raw=true
+
+
+ https://github.com/cche/KDEAnalysis_deps/blob/master/foreach_1.4.3.tar.gz?raw=true
+
+
+ https://github.com/cche/KDEAnalysis_deps/blob/master/doParallel_1.0.10.tar.gz?raw=true
+
+
+ https://github.com/cche/KDEAnalysis_deps/blob/master/moments_0.14.tar.gz?raw=true
+
+
+ https://github.com/cche/KDEAnalysis_deps/blob/master/sfsmisc_1.1-0.tar.gz?raw=true
+
+
+
+ $INSTALL_DIR
+
+
+
+
+ ***CHANGE THIS*****
+
+
+