import sys
try:
import wx
WX_VERSION = int(wx.version()[0])
hasWx = True
except Exception as e:
hasWx = False
WX_VERSION = 0
if hasWx:
import wx.xrc
from wx.lib.buttons import GenBitmapTextButton
from pubsub import pub
import wx.adv
import os
import time
import math
import random
import numpy
import scipy.stats
import datetime
import base
import pytransit.transit_tools as transit_tools
import pytransit.tnseq_tools as tnseq_tools
import pytransit.norm_tools as norm_tools
import pytransit.stat_tools as stat_tools
############# GUI ELEMENTS ##################
short_name = "rankproduct"
long_name = "Rank Product"
short_desc = "Rank Product test for determining conditional essentiality."
long_desc = "Differential Comparison based on ranks"
transposons = ["himar1", "tn5"]
columns = ["Orf","Name","Desc","Sites","Mean Ctrl","Mean Exp","log2FC","Obs RP","Expected RP","q-value"]
############# Analysis Method ##############
[docs]class RankProductAnalysis(base.TransitAnalysis):
def __init__(self):
base.TransitAnalysis.__init__(self, short_name, long_name, short_desc, long_desc, transposons, RankProductMethod, RankProductGUI, [RankProductFile])
################## FILE ###################
[docs]class RankProductFile(base.TransitFile):
def __init__(self):
base.TransitFile.__init__(self, "#RankProduct", columns)
############# GUI ##################
[docs]class RankProductGUI(base.AnalysisGUI):
[docs] def definePanel(self, wxobj):
self.wxobj = wxobj
rankproductPanel = wx.Panel( self.wxobj.optionsWindow, wx.ID_ANY, wx.DefaultPosition, wx.DefaultSize, wx.TAB_TRAVERSAL )
rankproductSizer = wx.BoxSizer( wx.VERTICAL )
rankproductLabel = wx.StaticText( rankproductPanel, wx.ID_ANY, u"rankproduct Options", wx.DefaultPosition, (160,-1), 0 )
rankproductLabel.SetFont( wx.Font( 10, wx.DEFAULT, wx.NORMAL, wx.BOLD) )
rankproductSizer.Add( rankproductLabel, 0, wx.ALL|wx.ALIGN_CENTER_HORIZONTAL, 5 )
rankproductLabelSizer = wx.BoxSizer( wx.VERTICAL )
mainSizer1 = wx.BoxSizer( wx.VERTICAL )
# SAMPLES
(rankproductSampleLabel, self.wxobj.rankproductSampleText, sampleSizer) = self.defineTextBox(rankproductPanel, u"Samples:", u"10000", "Number of samples to take when estimating the theoretical rankproduct distribution. Larger samples give more accurate estimates at the cost of computation time.")
mainSizer1.Add(sampleSizer, 1, wx.ALIGN_CENTER_HORIZONTAL|wx.EXPAND, 5 )
# NORMALIZATION
# Norm
rankproductNormChoiceChoices = [ u"TTR", u"nzmean", u"totreads", u'zinfnb', u'quantile', u"betageom", u"nonorm" ]
(rankproductNormLabel, self.wxobj.rankproductNormChoice, normSizer) = self.defineChoiceBox(rankproductPanel, u"Normalization:", rankproductNormChoiceChoices, "Choice of normalization method. The default choice, 'TTR', normalizes datasets to have the same expected count (while not being sensative to outliers). Read documentation for a description other methods. ")
mainSizer1.Add(normSizer, 1, wx.ALIGN_CENTER_HORIZONTAL|wx.EXPAND, 5 )
rankproductSizer.Add( mainSizer1, 1, wx.EXPAND, 5 )
rankproductButton = wx.Button( rankproductPanel, wx.ID_ANY, u"Run rankproduct", wx.DefaultPosition, wx.DefaultSize, 0 )
rankproductSizer.Add( rankproductButton, 0, wx.ALL|wx.ALIGN_CENTER_HORIZONTAL, 5 )
rankproductPanel.SetSizer( rankproductSizer )
rankproductPanel.Layout()
rankproductSizer.Fit( rankproductPanel )
#Connect events
rankproductButton.Bind( wx.EVT_BUTTON, self.wxobj.RunMethod )
self.panel = rankproductPanel
########## CLASS #######################
[docs]class RankProductMethod(base.DualConditionMethod):
"""
rankproduct
"""
def __init__(self,
ctrldata,
expdata,
annotation_path,
output_file,
normalization="TTR",
samples=10000,
adaptive=False,
doHistogram=False,
replicates="Sum",
LOESS=False,
ignoreCodon=True,
NTerminus=0.0,
CTerminus=0.0, wxobj=None):
base.DualConditionMethod.__init__(self, short_name, long_name, short_desc, long_desc, ctrldata, expdata, annotation_path, output_file, normalization=normalization, replicates=replicates, LOESS=LOESS, NTerminus=NTerminus, CTerminus=CTerminus, wxobj=wxobj)
self.samples = samples
self.adaptive = adaptive
self.doHistogram = doHistogram
[docs] @classmethod
def fromGUI(self, wxobj):
""" """
#Get Annotation file
annotationPath = wxobj.annotation
if not transit_tools.validate_annotation(annotationPath):
return None
#Get selected files
ctrldata = wxobj.ctrlSelected()
expdata = wxobj.expSelected()
if not transit_tools.validate_both_datasets(ctrldata, expdata):
return None
#Validate transposon types
if not transit_tools.validate_transposons_used(ctrldata+expdata, transposons):
return None
#Read the parameters from the wxPython widgets
ignoreCodon = True
samples = int(wxobj.rankproductSampleText.GetValue())
normalization = wxobj.rankproductNormChoice.GetString(wxobj.rankproductNormChoice.GetCurrentSelection())
replicates="Sum"
adaptive = False
doHistogram = False
NTerminus = float(wxobj.globalNTerminusText.GetValue())
CTerminus = float(wxobj.globalCTerminusText.GetValue())
LOESS = False
#Get output path
defaultFileName = "rankproduct_output.dat"
defaultDir = os.getcwd()
output_path = wxobj.SaveFile(defaultDir, defaultFileName)
if not output_path: return None
output_file = open(output_path, "w")
return self(ctrldata,
expdata,
annotationPath,
output_file,
normalization,
samples,
adaptive,
doHistogram,
replicates,
LOESS,
ignoreCodon,
NTerminus,
CTerminus, wxobj)
[docs] @classmethod
def fromargs(self, rawargs):
(args, kwargs) = transit_tools.cleanargs(rawargs)
ctrldata = args[0].split(",")
expdata = args[1].split(",")
annotationPath = args[2]
output_path = args[3]
output_file = open(output_path, "w")
normalization = kwargs.get("n", "TTR")
samples = int(kwargs.get("s", 100))
adaptive = kwargs.get("a", False)
doHistogram = kwargs.get("h", False)
replicates = kwargs.get("r", "Sum")
LOESS = kwargs.get("l", False)
ignoreCodon = True
NTerminus = float(kwargs.get("iN", 0.00))
CTerminus = float(kwargs.get("iC", 0.00))
return self(ctrldata,
expdata,
annotationPath,
output_file,
normalization,
samples,
adaptive,
doHistogram,
replicates,
LOESS,
ignoreCodon,
NTerminus,
CTerminus)
[docs] def Run(self):
self.transit_message("Starting rankproduct Method")
start_time = time.time()
Kctrl = len(self.ctrldata)
Kexp = len(self.expdata)
#Get orf data
self.transit_message("Getting Data")
(data, position) = transit_tools.get_validated_data(self.ctrldata+self.expdata, wxobj=self.wxobj)
if self.normalization != "none":
self.transit_message("Normalizing using: %s" % self.normalization)
(data, factors) = norm_tools.normalize_data(data, self.normalization, self.ctrldata+self.expdata, self.annotation_path)
Gctrl= tnseq_tools.Genes(self.ctrldata + self.expdata, self.annotation_path, ignoreCodon=self.ignoreCodon, nterm=self.NTerminus, cterm=self.CTerminus, data=data[:Kctrl,:], position=position)
Gexp= tnseq_tools.Genes(self.ctrldata + self.expdata, self.annotation_path, ignoreCodon=self.ignoreCodon, nterm=self.NTerminus, cterm=self.CTerminus, data=data[Kctrl:,:], position=position)
Ngenes = len(Gctrl)
# Get the average counts for all the genes, in each replicate
meanCtrl = numpy.zeros((Kctrl, Ngenes))
meanExp = numpy.zeros((Kexp, Ngenes))
for i in range(Ngenes):
if numpy.any(Gctrl[i].reads):
meanCtrl[:,i] = numpy.mean(Gctrl[i].reads,1)
else:
meanCtrl[:,i] = numpy.zeros(Kctrl)
#
if numpy.any(Gexp[i].reads):
meanExp[:,i] = numpy.mean(Gexp[i].reads,1)
else:
meanExp[:,i] = numpy.zeros(Kexp)
# Calculate a logFC2 between Experimental and Control
# Then calculates it's rank, and observed rankProduct
logFC2 = numpy.log2((meanExp+0.0001)/(meanCtrl+0.0001))
rank = numpy.array([scipy.stats.rankdata(Lvec) for Lvec in logFC2])
obsRP = numpy.power(numpy.prod(rank,0), 1.0/Kctrl)
permutations = numpy.zeros((self.samples, Ngenes))
tempranks = scipy.array([numpy.arange(1,Ngenes+1) for rep in range(Kctrl)])
for s in range(self.samples):
rankperm = numpy.array([numpy.random.permutation(tr) for tr in tempranks])
permutations[s] = numpy.power(numpy.prod(rankperm,0), 1.0/Kctrl)
rankRP = numpy.argsort(obsRP) + 1
#rankproduct
data = []
count = 0
self.progress_range(Ngenes)
for i,gene in enumerate(Gctrl):
count+=1
meanctrl = numpy.mean(Gctrl[i].reads)
meanexp = numpy.mean(Gexp[i].reads)
log2fc = numpy.log2((meanexp+0.0001)/(meanctrl+0.0001))
countbetter = numpy.sum(permutations <= obsRP[i])
pval = countbetter/float(self.samples*Ngenes)
e_val = countbetter/float(self.samples)
q_paper = e_val/float(rankRP[i])
data.append([gene.orf, gene.name, gene.desc, gene.n, meanctrl, meanexp, log2fc, obsRP[i], e_val, q_paper, pval])
# Update Progress
text = "Running rankproduct Method... %5.1f%%" % (100.0*count/Ngenes)
self.progress_update(text, count)
#
self.transit_message("") # Printing empty line to flush stdout
self.transit_message("Performing Benjamini-Hochberg Correction")
data.sort()
q_bh = stat_tools.BH_fdr_correction([row[-1] for row in data])
self.output.write("#RankProduct\n")
if self.wxobj:
members = sorted([attr for attr in dir(self) if not callable(getattr(self,attr)) and not attr.startswith("__")])
memberstr = ""
for m in members:
memberstr += "%s = %s, " % (m, getattr(self, m))
self.output.write("#GUI with: ctrldata=%s, annotation=%s, output=%s\n" % (",".join(self.ctrldata).encode('utf-8'), self.annotation_path.encode('utf-8'), self.output.name.encode('utf-8')))
else:
self.output.write("#Console: python %s\n" % " ".join(sys.argv))
self.output.write("#Data: %s\n" % (",".join(self.ctrldata).encode('utf-8')))
self.output.write("#Annotation path: %s\n" % self.annotation_path.encode('utf-8'))
self.output.write("#Time: %s\n" % (time.time() - start_time))
self.output.write("#%s\n" % (columns))
for i,row in enumerate(data):
(orf, name, desc, n, mean1, mean2, log2FCgene, obsRPgene, e_val, q_paper, pval) = row
self.output.write("%s\t%s\t%s\t%d\t%1.1f\t%1.1f\t%1.2f\t%1.8f\t%1.1f\t%1.8f\n" % (orf, name, desc, n, mean1, mean2,log2FCgene, obsRPgene, e_val, q_paper))
self.output.close()
self.transit_message("Adding File: %s" % (self.output.name))
self.add_file(filetype="RankProduct")
self.finish()
self.transit_message("Finished rankproduct Method")
[docs] @classmethod
def usage_string(self):
return """python %s rankproduct <comma-separated .wig control files> <comma-separated .wig experimental files> <annotation .prot_table or GFF3> <output file> [Optional Arguments]
Optional Arguments:
-s <integer> := Number of samples. Default: -s 100
-n <string> := Normalization method. Default: -n TTR
-h := Output histogram of the permutations for each gene. Default: Turned Off.
-a := Perform adaptive rankproduct. Default: Turned Off.
-l := Perform LOESS Correction; Helps remove possible genomic position bias. Default: Turned Off.
-iN <float> := Ignore TAs occuring at given fraction of the N terminus. Default: -iN 0.0
-iC <float> := Ignore TAs occuring at given fraction of the C terminus. Default: -iC 0.0
""" % (sys.argv[0])
if __name__ == "__main__":
(args, kwargs) = transit_tools.cleanargs(sys.argv)
#TODO: Figure out issue with inputs (transit requires initial method name, running as script does not !!!!)
G = RankProductMethod.fromargs(sys.argv[1:])
G.console_message("Printing the member variables:")
G.print_members()
print ""
print "Running:"
G.Run()