|
3
|
1 import os;
|
|
|
2 import re;
|
|
|
3 import csv;
|
|
|
4 import sys;
|
|
|
5 import math;
|
|
|
6 import numpy as np;
|
|
|
7 import matplotlib;
|
|
|
8 matplotlib.use('Agg');
|
|
|
9 from pylab import *;
|
|
|
10
|
|
|
11 def extractGffFeatures(gffString):
|
|
|
12 # GFF v3
|
|
|
13 # 1. seqname - Must be a chromosome or scaffold.
|
|
|
14 # 2. source - The program that generated this feature.
|
|
|
15 # 3. feature - The name of this type of feature. Some examples of standard feature types are "CDS", "start_codon", "stop_codon", and "exon".
|
|
|
16 # 4. start - The starting position of the feature in the sequence. The first base is numbered 1.
|
|
|
17 # 5. end - The ending position of the feature (inclusive).
|
|
|
18 # 6. score - A score between 0 and 1000. If there is no score value, enter ".".
|
|
|
19 # 7. strand - Valid entries include '+', '-', or '.' (for don't know/care).
|
|
|
20 # 8. frame - If the feature is a coding exon, frame should be a number between 0-2 that represents the reading frame of the first base. If the feature is not a coding exon, the value should be '.'.
|
|
|
21 # 9. group - All lines with the same group are linked together into a single item.
|
|
|
22
|
|
|
23 res = [];
|
|
|
24
|
|
|
25 if len(gffString) > 1: # check for empty line (i.e. row in GFF file only contains \n)
|
|
|
26 try:
|
|
|
27 gffSeqName, gffSource, gffFeature, gffStart, gffEnd, gffScr, gffStrand, gffFrame, gffGroup = gffString.split('\t');
|
|
|
28 except:
|
|
|
29 return []; # exit, since we don't have 9 features
|
|
|
30
|
|
|
31 # handle the gffSeqName
|
|
|
32 try:
|
|
|
33 chrNo = int(re.findall(r'[0-9]+', gffSeqName)[0]);
|
|
|
34 except:
|
|
|
35 return [];
|
|
|
36
|
|
|
37 # handle gffStart
|
|
|
38 try:
|
|
|
39 probeStart = int(gffStart);
|
|
|
40 except ValueError:
|
|
|
41 return []; # exit, invalid gffStart
|
|
|
42
|
|
|
43 # handle gffEnd
|
|
|
44 try:
|
|
|
45 probeEnd = int(gffEnd);
|
|
|
46 except ValueError:
|
|
|
47 return []; # exit, invalid gffEnd
|
|
|
48
|
|
|
49 # handle gffScr
|
|
|
50 try:
|
|
|
51 probeScr = float(gffScr);
|
|
|
52 except ValueError:
|
|
|
53 return []; # exit, invalid gffScr
|
|
|
54
|
|
|
55 # if the input string was well-formed, return the extracted features
|
|
|
56 res = [chrNo, probeStart, probeEnd, probeScr];
|
|
|
57 return res; # return feature set (if any was found in this record)
|
|
|
58
|
|
|
59
|
|
|
60 # Define Constants
|
|
|
61 tickPlotAdj = 0.5; # adjust tick marks on x-axis such that they are exactly at the bin boundary (and not in the bin center)
|
|
|
62
|
|
|
63 # Initialize Variables
|
|
|
64 feaNames = [];
|
|
|
65 feaChrNoList = [];
|
|
|
66 feaStrandList = [];
|
|
|
67 feaStartList = [];
|
|
|
68 feaEndList = [];
|
|
|
69
|
|
|
70 gffChrNoList = [];
|
|
|
71 gffProbeCenterList = [];
|
|
|
72 gffMatScrList = [];
|
|
|
73
|
|
|
74 # get command line arguments
|
|
|
75 gffFileName = sys.argv[1];
|
|
|
76 feaFileName = sys.argv[2];
|
|
|
77 outFileFormat = sys.argv[3];
|
|
|
78 stepWidth = int(sys.argv[4]);
|
|
|
79 upStreamStretch = int(sys.argv[5]); # in bp
|
|
|
80 maxX = int(sys.argv[6]); # in bp, if -1 maxX <= max(feature length), see below
|
|
|
81 tickSpacing = int(sys.argv[7]); # in bp
|
|
|
82 plotTitle = sys.argv[8];
|
|
|
83 outFileName = sys.argv[9];
|
|
|
84
|
|
|
85 # Read transcript information
|
|
|
86 feaFileReader = csv.reader(open(feaFileName, 'rb'), delimiter = '\t');
|
|
|
87 for row in feaFileReader:
|
|
|
88 feaName, feaChrNo, feaStrand, feaStart, feaEnd = row;
|
|
|
89 feaNames.append(feaName);
|
|
|
90 feaChrNoList.append(int(feaChrNo));
|
|
|
91 feaStrandList.append(int(feaStrand));
|
|
|
92 feaStartList.append(int(feaStart));
|
|
|
93 feaEndList.append(int(feaEnd));
|
|
|
94
|
|
|
95 # Read enrichment scores
|
|
|
96 gffFileHandle = open(gffFileName, "rU");
|
|
|
97 for row in gffFileHandle:
|
|
|
98 cache = extractGffFeatures(row);
|
|
|
99 if len(cache) == 4: # if curr row wasn't well-formed, skip row
|
|
|
100 chrNo, probeStart, probeEnd, probeScr = cache;
|
|
|
101 gffChrNoList.append(chrNo);
|
|
|
102 gffProbeCenterList.append(int(float(probeEnd - probeStart)/2.0 + probeStart)); # calc the center pos of each probe
|
|
|
103 gffMatScrList.append(probeScr);
|
|
|
104
|
|
|
105 # convert numerical lists into vectors
|
|
|
106 feaChrNos = np.array(feaChrNoList);
|
|
|
107 feaStrands = np.array(feaStrandList);
|
|
|
108 feaStarts = np.array(feaStartList);
|
|
|
109 feaEnds = np.array(feaEndList);
|
|
|
110
|
|
|
111 gfChrNos = np.array(gffChrNoList);
|
|
|
112 gfProbeLocs = np.array(gffProbeCenterList);
|
|
|
113 gfMatScrs = np.array(gffMatScrList);
|
|
|
114
|
|
|
115 # preallocate the results array, bins and 1 extra cols for feature length
|
|
|
116 avgFeatureMatScrs = np.empty((len(feaStarts),\
|
|
|
117 (int(math.ceil(float(max(feaEnds - feaStarts))/float(stepWidth)) + upStreamStretch/stepWidth + 1))),\
|
|
|
118 );
|
|
|
119 avgFeatureMatScrs[:] = np.NAN;
|
|
|
120
|
|
|
121 featureCounter = 0;
|
|
|
122
|
|
|
123 for chr in range(min(feaChrNos),max(feaChrNos) + 1):
|
|
|
124 #get all rows from the feature file that contain information about the i'th chromosome
|
|
|
125 currChrIdxs = (feaChrNos == chr);
|
|
|
126 currStrands = feaStrands[currChrIdxs];
|
|
|
127 currStarts = feaStarts[currChrIdxs];
|
|
|
128 currEnds = feaEnds[currChrIdxs];
|
|
|
129
|
|
|
130 #get all rows from ChIP-* data sets
|
|
|
131 currGfChrIdxs = (gfChrNos == chr);
|
|
|
132 currGfProbeLocs = gfProbeLocs[currGfChrIdxs];
|
|
|
133 currGfScrs = gfMatScrs[currGfChrIdxs];
|
|
|
134
|
|
|
135 # iterate over all features of the current chromosome
|
|
|
136 for j in range(0,len(currStarts)):
|
|
|
137 feaStrand = currStrands[j];
|
|
|
138 feaStart = currStarts[j];
|
|
|
139 feaEnd = currEnds[j];
|
|
|
140
|
|
|
141 #calc the corresponding probe positions for the feature start
|
|
|
142 if feaStrand == 1: # +strand
|
|
|
143 refFeaSt = 0;
|
|
|
144 while feaStart >= currGfProbeLocs[refFeaSt]:
|
|
|
145 refFeaSt += 1;
|
|
|
146 else: # -strand
|
|
|
147 refFeaSt = 0;
|
|
|
148 while feaEnd >= currGfProbeLocs[refFeaSt]:
|
|
|
149 refFeaSt += 1;
|
|
|
150 #now refFeaSt points to the correct probe according to strand orientation
|
|
|
151
|
|
|
152 #calc the avg probe scores per bin
|
|
|
153 if feaStrand == 1:
|
|
|
154 feaChunkStart = feaStart; # bp level pointer
|
|
|
155 refChunkStart = refFeaSt; # probe level pointer
|
|
|
156 refChunkEnd = refFeaSt; # probe level pointer
|
|
|
157
|
|
|
158 # determine values for the upstream bins
|
|
|
159 for k in range(1,upStreamStretch/stepWidth + 1):
|
|
|
160 feaChunkEnd = feaStart - k * stepWidth; # bp level pointer
|
|
|
161
|
|
|
162 # find the correct ref for the current feaChunkEnd; catch potential IndexOutOfBoundEx when upstream region too close to chr boundary
|
|
|
163 try:
|
|
|
164 while feaChunkEnd <= currGfProbeLocs[refChunkEnd]:
|
|
|
165 refChunkEnd -= 1;
|
|
|
166 except:
|
|
|
167 pass; # don't handle the exception further
|
|
|
168
|
|
|
169 #calc probe mean
|
|
|
170 avgFeatureMatScrs[featureCounter, upStreamStretch/stepWidth - k] = np.mean(currGfScrs[refChunkEnd:refChunkStart + 1]);
|
|
|
171
|
|
|
172 #adjust pointers
|
|
|
173 refChunkStart = refChunkEnd;
|
|
|
174
|
|
|
175 # determine values for bins in feature body
|
|
|
176 for k in range(1, int(math.ceil(float(feaEnd - feaStart)/float(stepWidth)) + 1)):
|
|
|
177 feaChunkEnd = feaStart + k*stepWidth; # bp level pointer
|
|
|
178
|
|
|
179 try:
|
|
|
180 while feaChunkEnd >= currGfProbeLocs[refChunkEnd]:
|
|
|
181 refChunkEnd += 1;
|
|
|
182 except:
|
|
|
183 pass; # don't handle the exception further
|
|
|
184
|
|
|
185 # calc probe mean
|
|
|
186 avgFeatureMatScrs[featureCounter, k - 1 + upStreamStretch/stepWidth] = np.mean(currGfScrs[refChunkStart:refChunkEnd + 1]);
|
|
|
187
|
|
|
188 # adjust chunk pointers
|
|
|
189 refChunkStart = refChunkEnd;
|
|
|
190
|
|
|
191 # write feature length in last col of the array
|
|
|
192 avgFeatureMatScrs[featureCounter, -1] = feaEnd - feaStart;
|
|
|
193 featureCounter += 1;
|
|
|
194 else: # -1 strand
|
|
|
195 feaChunkStart = feaEnd; # bp level pointer
|
|
|
196 refChunkStart = refFeaSt; # probe level pointer
|
|
|
197 refChunkEnd = refFeaSt; # probe level pointer
|
|
|
198
|
|
|
199 # determine values for the upStream region bins
|
|
|
200 for k in range(1,upStreamStretch/stepWidth + 1):
|
|
|
201 feaChunkEnd = feaEnd + k*stepWidth; # bp level pointer
|
|
|
202
|
|
|
203 # find the ref for the current feaChunkEnd, catch potential IndexOutOfBoundEx when upstream region too close to chr boundary
|
|
|
204 try:
|
|
|
205 while feaChunkEnd >= currGfProbeLocs[refChunkEnd]:
|
|
|
206 refChunkEnd += 1;
|
|
|
207 except:
|
|
|
208 pass;
|
|
|
209
|
|
|
210 # calc probe mean
|
|
|
211 avgFeatureMatScrs[featureCounter, upStreamStretch/stepWidth - k] = np.mean(currGfScrs[refChunkStart:refChunkEnd+1]);
|
|
|
212
|
|
|
213 # adjust chunk pointers
|
|
|
214 refChunkStart = refChunkEnd;
|
|
|
215
|
|
|
216 # determine values for bins in feature body
|
|
|
217 for k in range(1, int(math.ceil(float(feaEnd - feaStart)/float(stepWidth)) + 1)):
|
|
|
218 feaChunkEnd = feaEnd - k*stepWidth; # bp level pointer
|
|
|
219
|
|
|
220 try:
|
|
|
221 while feaChunkEnd <= currGfProbeLocs[refChunkEnd]:
|
|
|
222 refChunkEnd -= 1;
|
|
|
223 except:
|
|
|
224 pass;
|
|
|
225
|
|
|
226 # calc probe mean
|
|
|
227 avgFeatureMatScrs[featureCounter, k-1 + upStreamStretch/stepWidth] = np.mean(currGfScrs[refChunkEnd:refChunkStart+1]);
|
|
|
228
|
|
|
229 # adjust chunk pointers
|
|
|
230 refChunkStart = refChunkEnd;
|
|
|
231
|
|
|
232 # write feature length in last col of the array
|
|
|
233 avgFeatureMatScrs[featureCounter, -1] = feaEnd - feaStart;
|
|
|
234 featureCounter += 1;
|
|
|
235
|
|
|
236
|
|
|
237 # assign names and data format to all cols of the results array and sort array by feature length (last column)
|
|
|
238 sortByLengthCol = 'col' + str(np.size(avgFeatureMatScrs,1)-1);
|
|
|
239 matDtype = {'names':['col%i'%i for i in range(np.size(avgFeatureMatScrs,1))],'formats':(np.size(avgFeatureMatScrs,1))*[np.float]};
|
|
|
240 avgFeatureMatScrs.dtype = matDtype;
|
|
|
241 avgFeatureMatScrs.sort(axis = 0, order = sortByLengthCol);
|
|
|
242
|
|
|
243 # generate colormap
|
|
|
244 BYcmapData = {'red': ((0.0, 0.0, 0.094), (0.5, 0.0, 0.0), (1.0, 0.992, 1.0)), 'green': ((0.0, 0.0,0.658), (0.5, 0.0, 0.0), (1.0, 0.996, 1.0)), 'blue': ((0.0, 1.0, 0.828), (0.5, 0.0, 0.0), (1.0, 0.0, 0.0))};
|
|
|
245 BYcmap = matplotlib.colors.LinearSegmentedColormap('BYcmap', BYcmapData,9);
|
|
|
246
|
|
|
247 lowerHinge = matplotlib.mlab.prctile(gfMatScrs, 25.0);
|
|
|
248 upperHinge = matplotlib.mlab.prctile(gfMatScrs, 75.0);
|
|
|
249 cmLim = (upperHinge - lowerHinge) * 1.5 + upperHinge; # upper inner fence
|
|
|
250
|
|
|
251 # remove custom array dtype used for sorting and plot avgScrs (without the last col which stores the length info)
|
|
|
252 imgplot = imshow(avgFeatureMatScrs.view(np.float)[:,0:-1], aspect='auto', interpolation='nearest', origin='lower', vmin = -cmLim, vmax = cmLim);
|
|
|
253 axvline(x = int(float(upStreamStretch)/float(stepWidth)) - tickPlotAdj, color = 'w', linewidth = 1);
|
|
|
254
|
|
|
255 # if maxX wasn't set by hand, set it to max feature length and set x-coord accordingly
|
|
|
256 if maxX == -1:
|
|
|
257 maxX = max(feaEnds - feaStarts);
|
|
|
258 imgplot.axes.set_xbound(-tickPlotAdj, int(float(maxX)/float(stepWidth)) + int(float(upStreamStretch)/float(stepWidth)) - tickPlotAdj);
|
|
|
259 else:
|
|
|
260 imgplot.axes.set_xbound(-tickPlotAdj, int(float(maxX)/float(stepWidth)) - tickPlotAdj);
|
|
|
261
|
|
|
262 xTicks = [k * int(float(tickSpacing)/float(stepWidth)) + int(float(upStreamStretch)/float(stepWidth)) - tickPlotAdj for k in range(0, int(math.ceil(float(maxX)/float(tickSpacing)))+1)];
|
|
|
263 xTicks.insert(-1,-tickPlotAdj);
|
|
|
264 xTicksLabel = [k * tickSpacing for k in range(0, int(math.ceil(float(maxX)/float(tickSpacing)))+1)];
|
|
|
265 xTicksLabel.insert(-1, str(-upStreamStretch));
|
|
|
266 imgplot.axes.set_xticks(xTicks);
|
|
|
267 imgplot.axes.set_xticklabels(xTicksLabel);
|
|
|
268
|
|
|
269 imgplot.axes.set_title(plotTitle);
|
|
|
270 imgplot.axes.set_xlabel('Distance from feature start (bp)');
|
|
|
271 imgplot.axes.set_ylabel('');
|
|
|
272 imgplot.axes.set_yticks([]);
|
|
|
273
|
|
|
274 imgplot.set_cmap(BYcmap);
|
|
|
275 colorbar();
|
|
|
276 show();
|
|
|
277
|
|
|
278 savefig('chromatra_l_tmp_plot', format=outFileFormat);
|
|
|
279 data = file('chromatra_l_tmp_plot', 'rb').read();
|
|
|
280 fp = open(outFileName, 'wb');
|
|
|
281 fp.write(data);
|
|
|
282 fp.close();
|
|
|
283 os.remove('chromatra_l_tmp_plot');
|
