Mercurial > repos > cristian > notos
changeset 0:1535ffddeff4 draft
planemo upload commit a7ac27de550a07fd6a3e3ea3fb0de65f3a10a0e6-dirty
| author | cristian |
|---|---|
| date | Thu, 07 Sep 2017 08:51:57 -0400 |
| parents | |
| children | cb8bac9d0d37 |
| files | CpGoe.pl CpGoe.xml Functions/Kernel_function_form.R Functions/density_pm.R KDEanalysis.r KDEanalysis.xml LICENSE readme.rst test-data/10_std_unif.txt test-data/9_seqs.fa tool_dependencies.xml |
| diffstat | 11 files changed, 2432 insertions(+), 0 deletions(-) [+] |
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--- /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; + } +} +
--- /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 @@ +<tool id="CpGoe" name="Calculate CpNo/e" version="1.0.0"> + <requirements> + <requirement type="package" version="5.8.0">perl</requirement> + </requirements> + <stdio> + <exit_code range="1:" /> + </stdio> + <command><![CDATA[ + perl ${__tool_directory__}/CpGoe.pl -f $fastafile -a $algo -c $context -o $outfile -m $minlength + ]]></command> + <inputs> + <param name="fastafile" type="data" format="fasta" /> + <param name="context" type="select" label="Select context to calculate the CpNo/e ratio" > + <option value="CpG" selected="selected">CpG</option> + <option value="CpA">CpA</option> + <option value="CpC">CpC</option> + <option value="CpT">CpT</option> + </param> + <param name="algo" type="select" label="Select algorithm to calculate the CpGo/e ratio" > + <option value="1">(CpG / (C * G)) * (L^2 / L-1)</option> + <option value="2">Gardiner-Garden and Frommer (CpG / (G + C))*L</option> + <option value="3">Zeng and Yi (CpG / L) / ((C + G)/L)^2</option> + <option value="4">Saxonov, Berg and Brutlag CpG / ((C + G)/2)^2</option> + </param> + <param name="minlength" type="integer" value="200" help="Minimum length of the sequence to calculate CpGo/e ratio" /> + </inputs> + <outputs> + <data name="outfile" format="tabular" /> + </outputs> + <tests> + <test> + <param name="fastafile" value="9_seqs.fa"/> + <output name="outfile1" file="9_seqs_cpgoe.csv"/> + </test> + </tests> + <help><![CDATA[ + CpGoe.pl -f fasta-file -a algo -c context -o output_file -m minlength + + Reads multi fasta files, writes name, length, CpGs and GpCs, CpGo/e ratios and other quantities into TAB file for each sequence. + + The available contexts (-c) are CpG, CpA, CpC, CpT. Default CpG. + + The algorithms (-a) available for -a are the following:: + + 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 + + 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. + ]]></help> + <citations> + <citation> + <citation type="doi">10.1101/180463</citation> + </citation> + </citations> +</tool>
--- /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)) + } +} + + +
--- /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]<value){ + p = c(p,j) + } + } + return(p) +} + + + +### Main functions ### + +# Estimate the kernel density and calculate the probability masses +# obs : data set +# num.points : number of points for the estimation of the kernel density +density_pm <- function(obs, num.points, p.bw = p.bw, threshold.modes = threshold.modes){ + + # fit model + ker = density(obs, bw = p.bw, n = num.points) + + # find peaks + p = peaks(ker$y) + + # find valleys + v = valleys(ker$y) + + # probability masses + pm = probability_mass(ker,v) + num_pm = length(pm) + + # number of pm < threshold.modes + p.del = test_pm(pm,threshold.modes) + + # delete modes with probability masses < threshold.modes + for(j in 1:num_pm){ + if(j %in% p.del){ + p[j] = NA + v[j+1] = NA + } + } + p = p[!is.na(p)] + v = v[!is.na(v)] + + # probability masses (without the ones < threshold.modes) + pm = probability_mass(ker,v) + num_pm = length(pm) + + # number of pm<0.05 + p5 = test_pm(pm,0.05) + + # number of pm<0.10 + p10 = test_pm(pm,0.10) + + estimate = list(ker=ker,peaks=p,valleys=v,pm=pm,p5=p5,p10=p10) + + return(estimate) +} + + + +# Estimate the kernel density and calculate the probability masses, and do bootstrap +# obs : data set +# num.points : number of points for the estimation of the kernel density +density_pm_boot <- function(obs, num.points, p.bw = p.bw, threshold.modes = threshold.modes){ + + # fit model + ker = density(obs,bw = p.bw, n = num.points) + + # find peaks + p = peaks(ker$y) + + # find valleys + v = valleys(ker$y) + + # probability masses + pm = probability_mass(ker, v) + num_pm = length(pm) + + # number of pm < threshold.modes + p.del = test_pm(pm,threshold.modes) + + # delete modes with probability masses < threshold.modes + for(j in 1:num_pm){ + if(j %in% p.del){ + p[j] = 0 + v[j+1] = 0 + } + } + p = p[! p == 0] + v = v[! v == 0] + + # probability masses (without the ones < threshold.modes) + pm = probability_mass(ker,v) + num_pm = length(pm) + + estimate = list(ker=ker,peaks=p,valleys=v,pm=pm) + + return(estimate) +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/KDEanalysis.r Thu Sep 07 08:51:57 2017 -0400 @@ -0,0 +1,504 @@ +# Carry out analysis of CpGo/E data for Galaxy module +# Ingo Bulla +# 27 Jan 16 + +# load packages +pckg <- c("methods", "optparse") +for (p in pckg) { + if (!(p %in% rownames(installed.packages()))) { + stop( paste("R package", p , "is not installed"), call. = FALSE) + } +} +require(methods, quietly = TRUE) +require(optparse, quietly = TRUE) + +# determine directory where functions are located +cmdArgs <- commandArgs(trailingOnly = FALSE) +str <- "--file=" +match <- grep(str, cmdArgs) +if (length(match) == 0) { + stop("notos.r not set up to be called from R console") +} +path <- normalizePath( sub(str, "", cmdArgs[match]) ) +FCTN.DIR <- file.path(dirname(path), "Functions") + +source( file.path( FCTN.DIR, "Kernel_function_form.R") ) + + +MAX.CPGOE <- 10 # maximum value for CpGo/e ratios + + +# process outliers and return quantities characterizing the distribution +# obs: CpGo/e ratios +proc.outliers <- function(obs, frac.outl) { + ret <- list() + + # remove all zeros from sample + no.obs.raw <- length(obs) + ret[["prop.zero"]] <- sum(obs == 0) / no.obs.raw + obs <- obs[obs != 0] + if (length(obs) < 3) { + ret[["valid"]] <- FALSE + return(ret) + } + ret[["obs.nz"]] <- obs + + # replace very large values by a maximum value + obs <- sapply(obs, function(x) min(x, MAX.CPGOE)) + + # defining variables + # ... mean, median and standard deviation + ret[["mu.obs"]] <- mu.obs <- mean(obs) + ret[["me.obs"]] <- me.obs <- median(obs) + sd.obs <- sd(obs) + iqr.obs <- IQR(obs) + + # ... uppper and lower limits, based on mean +- k * sd, med. +- k * iqr, k = 2, ..., 4 + ul.mu <- mu.obs + (2 : 5) * sd.obs + ll.mu <- mu.obs - (2 : 5) * sd.obs + ul.me <- quantile(obs, 0.75) + (2 : 5) * iqr.obs + ll.me <- quantile(obs, 0.25) - (2 : 5) * iqr.obs + names(ul.mu) <- names(ll.mu) <- 2 : 5 + names(ul.me) <- names(ll.me) <- 2 : 5 + ret[["ul.mu"]] <- ul.mu + ret[["ll.mu"]] <- ll.mu + ret[["ul.me"]] <- ul.me + ret[["ll.me"]] <- ll.me + + # summary statistics and data output + # ... calculate proportion of data excluded when using different ranges + ret[["prop2"]] <- prop2 <- length(obs[obs < ll.me["2"] | ul.me["2"] < obs]) / no.obs.raw + ret[["prop3"]] <- prop3 <- length(obs[obs < ll.me["3"] | ul.me["3"] < obs]) / no.obs.raw + ret[["prop4"]] <- prop4 <- length(obs[obs < ll.me["4"] | ul.me["4"] < obs]) / no.obs.raw + ret[["prop5"]] <- prop5 <- length(obs[obs < ll.me["5"] | ul.me["5"] < obs]) / no.obs.raw + # ... choose k in Q1 / Q3 +- k * IQR such that no more than 1% of the data are excluded + v <- c(prop2, prop3, prop4, prop5) < frac.outl + + if (any(v)) { + excl.crit <- min(which(v)) + ret[["obs.cl"]] <- obs[!(obs < ll.me[excl.crit] | ul.me[excl.crit] < obs)] + ret[["used"]] <- paste(2 : 5, "iqr", sep = "")[excl.crit] + } else { + ret[["obs.cl"]] <- obs[!(obs < ll.me[4] | ul.me[4] < obs)] + ret[["used"]] <- "limited to 5 * iqr" + } + ret[["valid"]] <- TRUE + return(ret) +} + + +# Read CpGo/e ratios from file +# warn: issue warning if necessary +read.CpGoe <- function(fname, warn) { + # read input file line by line, split by whitespaces, assign last substring to CpGo/e ratios + # ... remove comments and trailing whitespaces + print(fname) + v <- read.table(fname, fill = TRUE, col.names = c("seq", "val")) + obs <- v$val + + obs <- obs[!is.na(obs)] + return(obs) +} + + +# process command line arguments +# expected arguments: +# - names of the species (each as a separate argument) +# - names of CpGo/e files of the species (each as a separate argument) +# ... parse arguments +option_list <- list(make_option(c("-o", "--frac-outl"), type = "double", default = 0.01, + help = "maximum fraction of CpGo/e ratios excluded as outliers [default %default]"), + make_option(c("-d", "--min-dist"), type = "double", default = 0.2, + help = "minimum distance between modes, modes that are closer are joined [default %default]"), + make_option(c("-c", "--conf-level"), type = "double", default = 0.95, + help = "level of the confidence intervals of the mode positions [default %default]"), + make_option(c("-m", "--mode-mass"), type = "double", default = 0.05, + help = "minimum probability mass of a mode [default %default]"), + make_option(c("-b", "--band-width"), type = "double", default = 1.06, + help = "bandwidth constant for kernels [default %default]"), + make_option(c("-B", "--bootstrap"), action="store_true", default = FALSE, + help = "calculate confidence intervals of mode positions using bootstrap [default %default]"), + make_option(c("-r", "--bootstrap-reps"), type = "integer", default = 1500, + help = "number of bootstrap repetitions [default %default]"), + make_option(c("-H", "--outlier-hist-file"), type = "character", default = "outliers_hist.pdf", + help = "name of the output file for the outlier histograms [default %default]"), + make_option(c("-C", "--cutoff-file"), type = "character", default = "outliers_cutoff.csv", + help = "name of the output file for the outlier cutoff [default %default]"), + make_option(c("-k", "--kde-file"), type = "character", default = "KDE.pdf", + help = "name of the output file for the KDE [default %default]"), + make_option(c("-v", "--valley-file"), type = "character", default = "valleys.csv", + help = "name of the output file with the values for valleys of the KDE [default %default]"), + make_option(c("-p", "--peak-file"), type = "character", default = "modes_basic_stats.csv", + help = "name of the output file describing the peaks of the KDE [default %default]"), + make_option(c("-s", "--bootstrap-file"), type = "character", default = "modes_bootstrap.csv", + help = "name of the output file for the bootstrap results [default %default]"), + make_option(c("-u", "--summary-file"), type = "character", default = "summary.csv", + help = "name of the summary file for the KDE results [default %default]"), + make_option(c("-f", "--no-warning-few-seqs"), action = "store_true", default = FALSE, + help = paste("suppress warning in case the input file only contains few values ", + "[default %default]", sep = ""))) + +op <- OptionParser(usage = "notos.r [options] spc_name_1 ... spc_name_N CpGoe_file_name_1 ... CpGoe_file_name_N", + description = paste("\nDescription: Notos generates a histogram and a kernel density estimator from files containing CpGo/e ratios. ", + "Moreover, it determines the number of modes of the CpGo/e ratio for each input file. The input files ", + "can either be composed of \n", + "1) CpGo/e ratios separated by linebreaks or\n", + "2) sequence names and CpGo/e ratios with each sequence name put on a separate line together with its CpGo/e ratio ", + "and sequence and CpGo/e being separated by whitespaces on each line.", sep = ""), + option_list = option_list) +args <- parse_args(op, positional_arguments = c(2, Inf)) +num.args <- length(args$args) +use.bstrp <- args$options$`bootstrap` +supp.warn.few <- args$options$`no-warning-few-seqs` + + +# ... check number of arguments +# ... ... check number of mandatory arguments +if (num.args < 2) { + stop("One species name and one file containing CpGo/e ratios have to be provided") +} + +# ... ... check whether number of mandatory arguments is even +if (num.args %% 2 != 0) { + stop("Number of arguments has to be even") +} + +# ... ... check maximum fraction of CpGo/e ratios excluded as outliers +frac.outl <- args$options$`frac-outl` +if ((frac.outl <= 0) || (frac.outl >= 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) +}
--- /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 @@ +<tool id="notos" name="Analyse CpGo/e ratios" version="1.0.0"> + <requirements> + <requirement type="package" version="3.2.1">R</requirement> + <requirement type="package" version="1.0.0">notos</requirement> + </requirements> + <stdio> + <exit_code range="1:" /> + </stdio> + <command><![CDATA[ + Rscript ${__tool_directory__}/KDEanalysis.r + -o $fracOutl + -d $minDist + -c $confLevel + -m $modeMass + -b $bandWidth + #if $bootstrap == "TRUE" + -B + #end if + -r $BSrep + #if $nofew == "TRUE" + -f + #end if + "$species" + $cpgoe + ]]></command> + <inputs> + <param name="species" size="30" type="text" value="my_species" label="Species name" /> + <param name="cpgoe" type="data" format="tabular" /> + <param name="fracOutl" type="float" value="0.01" help="maximum fraction of CpGo/e ratios excluded as outliers" label= "max fraction of CpGo/e ratios excluded as outliers" /> + <param name="minDist" type="float" value="0.2" help="minimum distance between modes. Modes that are closer are joined" label= "min distance between modes to join" /> + <param name="confLevel" type="float" value="0.95" help="level of the confidence intervals of the mode positions" label= "level of the confidence intervals of the mode positions" /> + <param name="modeMass" type="float" value="0.05" help="minimum probability mass of a mode " label= "minimum probability mass of a mode" /> + <param name="bandWidth" type="float" value="1.06" help="bandwidth constant for kernels " label= "bandwidth constant for kernels" /> + <param name="bootstrap" type="boolean" truevalue="TRUE" falsevalue="FALSE" help="calculate confidence intervals of mode positions using bootstrap" label= "bootstrap confidence intervals?" /> + <param name="BSrep" type="integer" value="1500" help="number of bootstrap repetitions " label= "number of bootstrap repetitions " /> + <param name="nofew" type="boolean" truevalue="TRUE" falsevalue="FALSE" help="Do not print warning message when few datapoint are given." label= "Supress few datapoint warning" /> + </inputs> + <outputs> + <data name="summary" format="tabular" from_work_dir="summary.csv" label="Summary on ${on_string}" /> + <data name="outlierHist" format="pdf" from_work_dir="outliers_hist.pdf" label="Outlier Histogram on ${on_string}"/> + <data name="cutoff" format="tabular" from_work_dir="outliers_cutoff.csv" label="Outlier cutoff on ${on_string}" /> + <data name="peaks" format="tabular" from_work_dir="modes_basic_stats.csv" label="Peaks on ${on_string}" /> + <data name="kde" format="pdf" from_work_dir="KDE.pdf" label="KDE analysis on ${on_string}" /> + </outputs> + <tests> + <test> + <param name="cpgoe" value="10_std_unif.txt"/> + <output name="cutoff1" file="outliers_cutoff.csv"/> + <!-- + <output name="outlierHist" file="outliers_hist.pdf"/> + <output name="kde" file="KDE.pdf"/> + --> + </test> + </tests> + <help><![CDATA[ +Model the distribution of CpG o/e ratios using Kernel Density Estimation. + +Parameters:: + + "-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 peaks.csv] + "-s", "--bootstrap-file" Name of the output file with bootstrap values [default "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] + + + ]]></help> + <citations> + <citation> + <citation type="doi">10.1101/180463</citation> + </citation> + </citations> +</tool>
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--- /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) | ++-------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------+
--- /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
--- /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
--- /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 @@ +<?xml version="1.0"?> +<tool_dependency> + <package name="R" version="3.2.1"> + <repository changeset_revision="d9f7d84125b7" name="package_r_3_2_1" owner="iuc" prior_installation_required="True" toolshed="https://toolshed.g2.bx.psu.edu" /> + </package> + <package name="libxml2" version="2.9.1"> + <repository changeset_revision="45b16a3ab504" name="package_libxml2_2_9_1" owner="iuc" prior_installation_required="True" toolshed="https://toolshed.g2.bx.psu.edu" /> + </package> + <package name="notos" version="1.0.0"> + <install version="1.0"> + <actions> + <action type="setup_r_environment"> + <repository changeset_revision="d9f7d84125b7" name="package_r_3_2_1" owner="iuc" toolshed="https://toolshed.g2.bx.psu.edu"> + <package name="R" version="3.2.1" /> + </repository> + <!-- libxml2 needs to be sourced after R because R also was compiled against a different version of libxml2 --> + <repository changeset_revision="45b16a3ab504" name="package_libxml2_2_9_1" owner="iuc" toolshed="https://toolshed.g2.bx.psu.edu"> + <package name="libxml2" version="2.9.1" /> + </repository> + <package sha256sum="f920baa2a0ef7082155c8b666851af0c77534af8b2ca0cd067e7d56fdf3ec501"> + https://github.com/cche/KDEAnalysis_deps/blob/master/getopt_1.20.0.tar.gz?raw=true + </package> + <package sha256sum="bca93c8646b731758f1cc888ee6c25e8c1ecf2364d7f556489bd879413d20abd"> + https://github.com/cche/KDEAnalysis_deps/blob/master/optparse_1.3.2.tar.gz?raw=true + </package> + <package sha256sum="ae4ea23385776eb0c06c992a3da6b0256a6c84558c1061034c5a1fbdd43d05b8"> + https://github.com/cche/KDEAnalysis_deps/blob/master/iterators_1.0.8.tar.gz?raw=true + </package> + <package sha256sum="1ef03f770f726a62e3753f2402eb26b226245958fa99d570d003fc9e47d35881"> + https://github.com/cche/KDEAnalysis_deps/blob/master/foreach_1.4.3.tar.gz?raw=true + </package> + <package sha256sum="70024b6950025cc027022ee409f382e5ad3680c0a25bcd404bfc16418be8add5"> + https://github.com/cche/KDEAnalysis_deps/blob/master/doParallel_1.0.10.tar.gz?raw=true + </package> + <package sha256sum="2a3b81e60dafdd092d2bdd3513d7038855ca7d113dc71df1229f7518382a3e39"> + https://github.com/cche/KDEAnalysis_deps/blob/master/moments_0.14.tar.gz?raw=true + </package> + <package sha256sum="7f430cf3ebb95bac806fbf093fb1e2112deba47416a93be8d5d1064b76bc0015"> + https://github.com/cche/KDEAnalysis_deps/blob/master/sfsmisc_1.1-0.tar.gz?raw=true + </package> + </action> + <action type="set_environment"> + <environment_variable action="append_to" name="R_LIBS">$INSTALL_DIR</environment_variable> + </action> + </actions> + </install> + <readme> + ***CHANGE THIS***** + </readme> + </package> +</tool_dependency>