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pipelinePlotter.py
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pipelinePlotter.py
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#!/usr/bin/python
import subprocess
import os
from optparse import OptionParser
import sys
from rpy2 import *
import rpy2.robjects as robjects
###############################################################################
#
# pipelinePlotter.py
# Produce images plots from the parsed csv made in sam2PairPlotCSV
# Copyright (C) 2010 Adam Skarshewski, Michael Imelfort
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
###############################################################################
# Directionality
###############################################################################
# 0 DD <--- ---> if the first read disagrees with ref then the second is ahead and in the reverse orientation (wrt the first)
# 3 DA <--- <--- if the first read disagrees with ref then the second is ahead and in the same orientation
# 2 AD ---> <--- if the first read agrees with ref then the second is ahead and in the reverse orientation
# 3 AA ---> ---> if the first read agrees with ref then the second is ahead and in the same orientation
# 4 Single hit: this read agrees with it's ref
# 5 Single hit: this read disagrees with it's ref
# 6 Different references: this read agrees with it's ref
# 7 Different references: this read disagrees with it's ref
###############################################################################
#
# Stuff
#
def openRImageDevice(label, r):
png_filename = label + ".png"
r("png(filename='"+png_filename+"', width=1000, height=700);")
r("par( mfrow= c(4, 1));")
def closeRImageDevice(r):
r("dev.off();")
def plotPairs(fileName, r):
# set the transform to a log
r("transformation = function (X) { log(X) }")
#r("transformation = function (X) { X }")
r("par(mar=c(1, 4, 0, 2) + 0.1);")
# readin and transform the file
r("x = read.csv(\""+fileName+"\")")
print "{{{" + str(r("maxsert = max(x$insertsize) * 1.5")) + "}}}"
#r("scaling = (10^floor(log(max(x$position),10)-1))")
#r("scaffoldsize = max(x$position)")
#r("roundedscaffoldsize = scaling*(floor(max(x$position)/scaling)+1)")
# check we don't have any zeros which will kill the log transform
maxsert = r['maxsert']
print "Max insert is: {{" + str(maxsert) + "}}"
if(maxsert[0] == 0):
print "Max insert is 0"
sys.exit(1)
r("ylims=c(-5,transformation(maxsert))")
#################
# Data Containers
#################
r("opposed = list(position = x$position[x$direction == 0], insertsize = x$insertsize[x$direction == 0])")
r("facing = list(position = x$position[x$direction == 2], insertsize = x$insertsize[x$direction == 2])")
r("same = list(position = x$position[x$direction == 3], insertsize = x$insertsize[x$direction == 3])")
r("forwards = list(position = x$position[x$direction == 4], insertsize = x$insertsize[x$direction == 4])")
r("reverses = list(position = x$position[x$direction == 5], insertsize = x$insertsize[x$direction == 5])")
r("forwardjumpers = list(position = x$position[x$direction == 6], insertsize = x$insertsize[x$direction == 6])")
r("reversejumpers = list(position = x$position[x$direction == 7], insertsize = x$insertsize[x$direction == 7])")
# find the mode of the largest occurring insert type
r("op_len = length(opposed$position);")
r("fa_len = length(facing$position);")
r("sa_len = length(same$position);")
max = r['op_len']
max_str = "opposed"
fa = r['fa_len']
sa = r['sa_len']
if(fa[0] > max[0]):
max[0] = fa[0]
max_str = "facing"
if(sa[0] > max[0]):
max_str = "same"
r("ins_mode = mean("+max_str+"$insertsize)")
#################
# PowerPlot
#################
r("plot(c(0,roundedscaffoldsize), c(0,transformation(maxsert)), xlim=c(0,roundedscaffoldsize), ylim=ylims, pch='*', xlab='', ylab='(ln) Insert Size', axes='FALSE', col='white'); \
axis(2, c(0, floor(transformation(ins_mode))), labels=c('0',as.character(floor(ins_mode))), las='2'); \
axis(1, c(0, roundedscaffoldsize/5, 2*roundedscaffoldsize/5, 3*roundedscaffoldsize/5, 4*roundedscaffoldsize/5, roundedscaffoldsize)); \
grid(); \
points(opposed$position, transformation(opposed$insertsize), pch='.', col='red'); \
points(facing$position, transformation(facing$insertsize), pch='.', col='black'); \
points(same$position, transformation(same$insertsize), pch='.', col='blue'); \
points(forwards$position, transformation(forwards$insertsize) - 1, pch='>', col='purple'); \
points(reverses$position, transformation(reverses$insertsize) - 2, pch='<', col='orange'); \
points(forwardjumpers$position, transformation(forwardjumpers$insertsize) - 3, pch='>', col='green'); \
points(reversejumpers$position, transformation(reversejumpers$insertsize) - 4, pch='<', col='magenta');")
#
# insert size histogram
#
r("par(mar=c(2, 4, 1, 2) + 0.1);")
r("mean_mult = 2")
r("phil <- x$insertsize[which(x$insertsize < mean_mult*ins_mode)];")
r("phil <- phil[which(phil > 1)];")
r("hist(phil, breaks=200, col='red', xlab='', ylab='Insert Size freq.', main='', xlim=c(0,mean_mult*ins_mode));")
# r("hist(c(opposed$insertsize[opposed$insertsize], facing$insertsize[facing$insertsize]), breaks=100, col='red', xlab='', ylab='Insert Size freq.', main='', xlim=c(0,1.5*ins_mode)); \
# histobj = hist(facing$insertsize[facing$insertsize < 1000], breaks=100, plot = FALSE); \
# lines(histobj, col='black');")
#
# plot coverage
#
def plotCoverage(fileName, modeFileName, r):
r("par(mar=c(1, 4, 0, 2) + 0.1);")
r("cov_data = read.csv(\""+fileName+"\")")
r("max_cov = max(cov_data$coverage) + 5")
r("scaling = (10^floor(log(max(cov_data$position),10)-1))")
r("scaffoldsize = max(cov_data$position)")
r("roundedscaffoldsize = scaling*(floor(max(cov_data$position)/scaling)+1)")
r("mode_cov_data = read.csv(\""+modeFileName+"\");")
r("mode_cov = max(mode_cov_data$mode_coverage);")
r("mean_cov = mean(cov_data$coverage)")
# check we don't have any zeros which will kill the log transform
max_cov = r['max_cov']
if(max_cov[0] == 0):
print "Max coverage is 0!"
sys.exit(1)
r("ylims=c(0,max_cov);")
r("plot(c(0,roundedscaffoldsize), c(0,max_cov), xlim=c(0,roundedscaffoldsize), ylim=ylims, pch='*', xlab='', ylab='Vertical Coverage', axes='FALSE', yaxt='n', col='white'); \
axis(1, c(0, roundedscaffoldsize/5, 2*roundedscaffoldsize/5, 3*roundedscaffoldsize/5, 4*roundedscaffoldsize/5, roundedscaffoldsize)); \
axis(2, c(0, floor(max_cov/3), floor(2*max_cov/3), floor(max_cov))); \
grid(); \
lines(cov_data$position, cov_data$coverage, pch='.', col='#FF034A'); \
lines(c(0,roundedscaffoldsize), c(mode_cov, mode_cov), col='black'); \
lines(c(0,roundedscaffoldsize), c(mean_cov, mean_cov), col='green')")
def plotKmerHeatMap(fileName, r):
# first we need to find out how many colums we have in the
# file and then use these ar labels
csv_file = open(fileName, 'r')
line = csv_file.readline()
line = line.replace('"','')
line = line.rstrip('\n')
cols = line.rsplit(',')
csv_file.close()
num_cols = len(cols);
# we need this so we can see the bottom axis ticks
r("par(mar=c(4, 4, 0, 2) + 0.1);")
# read in and transform the file
#r("transformation = function (X) { log(X) }")
r("transformation = function (X) { X }")
r("hm_data = read.csv(\""+fileName+"\")")
#r("scaling = (10^floor(log(max(hm_data$position),10)-1))")
#r("scaffoldsize = max(hm_data$position)")
#r("roundedscaffoldsize = scaling*(floor(max(hm_data$position)/scaling)+1)")
# get nice green-red and red-fade pallettes
r("rgbs_R <- cbind(255, seq(255,0, length=256), 0) / 255; \
rgbs_G <- cbind(seq(0,255, length=256), 255, 0) / 255; \
rgbs_RG = (rgbs_R * rgbs_G ); \
cols_RG <- rgb(rgbs_RG[,1], rgbs_RG[,2], rgbs_RG[,3]); \
rgbs_R <- cbind(255, seq(255,32, length=256), 0) / 255; \
cols_R <- rgb(rgbs_R[,1], rgbs_R[,2], rgbs_R[,3]);")
r("plot(c(0,roundedscaffoldsize), c(0,"+str(num_cols)+"), xlim=c(0,roundedscaffoldsize), ylim=c(0,"+str(num_cols)+"), pch='*', xlab='', ylab='Kmer abundance', axes='FALSE', col='white');")
r("grid();")
#return
for i in range (1, num_cols - 1):
r("total_len = length(hm_data$position)")
r("bob = hm_data$position-hm_data$position+"+str(i)+"")
# find the y limits this will be the maximum of the fist column...
r("max_y = transformation(max(hm_data$"+cols[i]+") + 10)")
r("col_scaler=255/max_y")
r("scaled_col_ids = hm_data$"+cols[i]+" * col_scaler")
r("plot_cols = cols_R[scaled_col_ids]")
r("points(hm_data$position, bob, pch='|', col=rep(plot_cols, each=1, length.out=length(hm_data$position)))")
r("col_scaler=255/5")
r("scaled_col_ids = hm_data$"+cols[num_cols-1]+" * col_scaler")
r("plot_cols = cols_RG[scaled_col_ids]")
r("points(hm_data$position, bob+1, pch='|', col=rep(plot_cols, each=1, length.out=length(hm_data$position)))")
# draw the purdy axis
r("axis(1, c(0, roundedscaffoldsize/5, 2*roundedscaffoldsize/5, 3*roundedscaffoldsize/5, 4*roundedscaffoldsize/5, roundedscaffoldsize))")
r("axis(2, c(1, 2, 3, 4,5), labels=c('3','4','8','12','GC'), las='2');")
def plotGC(fileName, r):
# first we need to find out how many colums we have in the
# file and then use these ar labels
csv_file = open(fileName, 'r')
line = csv_file.readline()
line = line.replace('"','')
line = line.rstrip('\n')
cols = line.rsplit(',')
csv_file.close()
num_cols = len(cols);
# we need this so we can see the bottom axis ticks
r("par(mar=c(4, 4, 0, 2) + 0.1);")
# read in and transform the file
#r("transformation = function (X) { log(X) }")
r("transformation = function (X) { X }")
r("hm_data = read.csv(\""+fileName+"\")")
#r("scaling = (10^floor(log(max(hm_data$position),10)-1))")
#r("scaffoldsize = max(hm_data$position)")
#r("roundedscaffoldsize = scaling*(floor(max(hm_data$position)/scaling)+1)")
# get nice green-red and red-fade pallettes
r("rgbs_R <- cbind(255, seq(255,0, length=256), 0) / 255; \
rgbs_G <- cbind(seq(0,255, length=256), 255, 0) / 255; \
rgbs_RG = (rgbs_R * rgbs_G ); \
cols_RG <- rgb(rgbs_RG[,1], rgbs_RG[,2], rgbs_RG[,3]); \
rgbs_R <- cbind(255, seq(255,32, length=256), 0) / 255; \
cols_R <- rgb(rgbs_R[,1], rgbs_R[,2], rgbs_R[,3]);")
r("plot(c(0,roundedscaffoldsize), c(0,"+str(num_cols)+"), xlim=c(0,roundedscaffoldsize), ylim=c(0,"+str(num_cols)+"), pch='*', xlab='', ylab='Kmer abundance', axes='FALSE', col='white');")
r("grid();")
#return
for i in range (1, num_cols - 1):
r("total_len = length(hm_data$position)")
r("bob = hm_data$position-hm_data$position+"+str(i)+"")
# find the y limits this will be the maximum of the fist column...
r("max_y = transformation(max(hm_data$"+cols[i]+") + 10)")
r("col_scaler=255/max_y")
r("scaled_col_ids = hm_data$"+cols[i]+" * col_scaler")
r("plot_cols = cols_R[scaled_col_ids]")
r("points(hm_data$position, bob, pch='|', col=rep(plot_cols, each=1, length.out=length(hm_data$position)))")
r("col_scaler=255/5")
r("scaled_col_ids = hm_data$"+cols[num_cols-1]+" * col_scaler")
r("plot_cols = cols_RG[scaled_col_ids]")
r("points(hm_data$position, bob+1, pch='|', col=rep(plot_cols, each=1, length.out=length(hm_data$position)))")
# draw the purdy axis
r("axis(1, c(0, roundedscaffoldsize/5, 2*roundedscaffoldsize/5, 3*roundedscaffoldsize/5, 4*roundedscaffoldsize/5, roundedscaffoldsize))")
r("axis(2, c(1, 2, 3, 4,5), labels=c('3','4','8','12','GC'), las='2');")
#
# Entry sub. Parse vars and call parseSamBam
#
if __name__ == '__main__':
# intialise the options parser
parser = OptionParser("\n\n %prog -c csvFileName [-l image label]")
parser.add_option("-p", "--pair_fileName", type="string", dest="pairCSVFileName", help="Specify a name for the pair plot CSV file")
parser.add_option("-c", "--cov_fileName", type="string", dest="covCSVFileName", help="Specify a name for the coverage CSV file")
parser.add_option("-m", "--mode_cov_fileName", type="string", dest="modeCovCSVFileName", help="Specify a name for the mode coverage CSV file")
parser.add_option("-H", "--heatmap_fileName", type="string", dest="heatCSVFileName", help="Specify a name for the heat map CSV file")
parser.add_option("-l", "--image_label", type="string", dest="imageLabel", help="Specify a label for the image")
# get and check options
(opts, args) = parser.parse_args()
if (opts.pairCSVFileName is None):
print ('You need to specify a .csv file to parse for paired plotting')
parser.print_help()
sys.exit(1)
if (opts.covCSVFileName is None):
print ('You need to specify a .csv file to parse for coverage plotting')
parser.print_help()
sys.exit(1)
if (opts.modeCovCSVFileName is None):
print ('You need to specify a .csv file to parse for mode coverage plotting')
parser.print_help()
sys.exit(1)
if (opts.heatCSVFileName is None):
print ('You need to specify a .csv file to parse for heatmap plotting')
parser.print_help()
sys.exit(1)
# override defaults
if(opts.imageLabel is None):
label = 'ADD_LABEL'
else:
label = opts.imageLabel
# do stuff
r = robjects.r
openRImageDevice(label, r)
plotCoverage(opts.covCSVFileName, opts.modeCovCSVFileName, r)
plotKmerHeatMap(opts.heatCSVFileName, r)
plotPairs(opts.pairCSVFileName, r)
closeRImageDevice(r)