samplestats.py 33.9 KB
Newer Older
Hoogenboom, Jerry's avatar
Hoogenboom, Jerry committed
1
2
3
4
5
#!/usr/bin/env python
"""
Compute various statistics for each sequence in the given sample data
file.

6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
Adds the following columns to the input data.  Some columns may be
omitted from the output if the input does not contain the required
columns.  In the column names below, 'X' is a placeholder for 'forward',
'reverse', and 'total', which refers to the strand of DNA for which the
statistics are calculated.  'Y' is a placeholder for 'corrected'
(statistics calculated on data after noise correction by e.g.,
BGCorrect), 'noise' (statistics calculated on the number of reads
attributed to noise), and 'add' (statistics calculated on the number
of reads recovered through noise correction).  Wherever the 'Y' part of
the column name is omitted, the values in the column are computed on
data prior to noise correction.

X_Y: The number of Y reads of this sequence on the X strand (this column
is not added by Samplestats, but should be present in the input).
X_Y_mp_sum: The value of X_Y, as a percentage of the sum of the X_Y of
the marker.
X_Y_mp_max: The value of X_Y, as a percentage of the maximum X_Y of the
marker.
forward_Y_pct: The number of Y reads on the forward strand, as a
percentage of the total number of Y reads of this sequence.
X_correction_pct: The difference between the values of X_corrected and
X, as a percentage of the value of X.
X_removed_pct: The value of X_noise, as a percentage of the value of X.
X_added_pct: The value of X_add, as a percentage of the value of X.
Hoogenboom, Jerry's avatar
Hoogenboom, Jerry committed
30
31
32
"""
import sys

33
34
from ..lib import add_sequence_format_args, add_input_output_args, \
                  get_input_output_files, get_column_ids
Hoogenboom, Jerry's avatar
Hoogenboom, Jerry committed
35
36
37
38

__version__ = "0.1dev"


39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
# Default values for parameters are specified below.

# Default minimum number of reads to mark as allele.
# This value can be overridden by the -n command line option.
_DEF_MIN_READS = 30

# Default minimum number of reads per strand to mark as allele.
# This value can be overridden by the -b command line option.
_DEF_MIN_PER_STRAND = 1

# Default minimum percentage of reads w.r.t. the highest allele of the
# marker to mark as allele.
# This value can be overridden by the -m command line option.
_DEF_MIN_PCT_OF_MAX = 5.

# Default minimum percentage of reads w.r.t. the marker's total number
# of reads to mark as allele.
# This value can be overridden by the -p command line option.
_DEF_MIN_PCT_OF_SUM = 3.

# Default minimum percentage of correction to mark as allele.
# This value can be overridden by the -c command line option.
_DEF_MIN_CORRECTION = 0

# Default minimum number of recovered reads as a percentage of the
# original number of reads to mark as allele.
# This value can be overridden by the -r command line option.
_DEF_MIN_RECOVERY = 0

68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
# Default minimum number of reads for filtering.
# This value can be overridden by the -N command line option.
_DEF_MIN_READS_FILT = 1

# Default minimum number of reads per strand for filtering.
# This value can be overridden by the -B command line option.
_DEF_MIN_PER_STRAND_FILT = 1

# Default minimum percentage of reads w.r.t. the highest allele of the
# marker for filtering.
# This value can be overridden by the -M command line option.
_DEF_MIN_PCT_OF_MAX_FILT = 0.

# Default minimum percentage of reads w.r.t. the marker's total number
# of reads for filtering.
# This value can be overridden by the -P command line option.
_DEF_MIN_PCT_OF_SUM_FILT = 0.

# Default minimum percentage of correction for filtering.
# This value can be overridden by the -C command line option.
_DEF_MIN_CORRECTION_FILT = 0

# Default minimum number of recovered reads as a percentage of the
# original number of reads for filtering.
# This value can be overridden by the -R command line option.
_DEF_MIN_RECOVERY_FILT = 0

COLUMN_ORDER = [
    "total_corrected",
    "total_corrected_mp_sum",
    "total_corrected_mp_max",
    "total_correction_pct",
    "forward_corrected_pct",
    "forward_corrected",
    "forward_corrected_mp_sum",
    "forward_corrected_mp_max",
    "forward_correction_pct",
    "reverse_corrected",
    "reverse_corrected_mp_sum",
    "reverse_corrected_mp_max",
    "reverse_correction_pct",
109

110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
    "total",
    "total_mp_sum",
    "total_mp_max",
    "forward_pct",
    "forward",
    "forward_mp_sum",
    "forward_mp_max",
    "reverse",
    "reverse_mp_sum",
    "reverse_mp_max",

    "total_noise",
    "total_noise_mp_sum",
    "total_noise_mp_max",
    "total_removed_pct",
    "forward_noise_pct",
    "forward_noise",
    "forward_noise_mp_sum",
    "forward_noise_mp_max",
    "forward_removed_pct",
    "reverse_noise",
    "reverse_noise_mp_sum",
    "reverse_noise_mp_max",
    "reverse_removed_pct",

    "total_add",
    "total_add_mp_sum",
    "total_add_mp_max",
    "total_added_pct",
    "forward_add_pct",
    "forward_add",
    "forward_add_mp_sum",
    "forward_add_mp_max",
    "forward_added_pct",
    "reverse_add",
    "reverse_add_mp_sum",
    "reverse_add_mp_max",
    "reverse_added_pct"
]


def compute_stats(infile, outfile, min_reads,
152
                  min_per_strand, min_pct_of_max, min_pct_of_sum,
153
154
155
156
                  min_correction, min_recovery, filter_action, min_reads_filt,
                  min_per_strand_filt, min_pct_of_max_filt,
                  min_pct_of_sum_filt, min_correction_filt, min_recovery_filt):
    # Check presence of required columns.
Hoogenboom, Jerry's avatar
Hoogenboom, Jerry committed
157
    column_names = infile.readline().rstrip("\r\n").split("\t")
158
    get_column_ids(column_names, "marker", "sequence", "forward", "reverse",
159
160
        "total")
    if "flags" not in column_names:
161
        column_names.append("flags")
Hoogenboom, Jerry's avatar
Hoogenboom, Jerry committed
162
163

    # Add columns for which we have the required data.
164
165
166
167
168
    if "total_corrected" in column_names:
        column_names.append("total_corrected_mp_sum")
        column_names.append("total_corrected_mp_max")
        column_names.append("total_correction_pct")
        if "forward_corrected" in column_names:
Hoogenboom, Jerry's avatar
Hoogenboom, Jerry committed
169
            column_names.append("forward_corrected_pct")
170
171
172
    if "forward_corrected" in column_names:
        column_names.append("forward_corrected_mp_sum")
        column_names.append("forward_corrected_mp_max")
Hoogenboom, Jerry's avatar
Hoogenboom, Jerry committed
173
        column_names.append("forward_correction_pct")
174
175
176
    if "reverse_corrected" in column_names:
        column_names.append("reverse_corrected_mp_sum")
        column_names.append("reverse_corrected_mp_max")
Hoogenboom, Jerry's avatar
Hoogenboom, Jerry committed
177
        column_names.append("reverse_correction_pct")
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
    column_names.append("total_mp_sum")
    column_names.append("total_mp_max")
    column_names.append("forward_pct")
    column_names.append("forward_mp_sum")
    column_names.append("forward_mp_max")
    column_names.append("reverse_mp_sum")
    column_names.append("reverse_mp_max")
    if "total_noise" in column_names:
        column_names.append("total_noise_mp_sum")
        column_names.append("total_noise_mp_max")
        column_names.append("total_removed_pct")
        if "forward_noise" in column_names:
            column_names.append("forward_noise_pct")
    if "forward_noise" in column_names:
        column_names.append("forward_noise_mp_sum")
        column_names.append("forward_noise_mp_max")
        column_names.append("forward_removed_pct")
    if "reverse_noise" in column_names:
        column_names.append("reverse_noise_mp_sum")
        column_names.append("reverse_noise_mp_max")
        column_names.append("reverse_removed_pct")
    if "total_add" in column_names:
        column_names.append("total_add_mp_sum")
        column_names.append("total_add_mp_max")
        column_names.append("total_added_pct")
        if "forward_add" in column_names:
            column_names.append("forward_add_pct")
    if "forward_add" in column_names:
        column_names.append("forward_add_mp_sum")
        column_names.append("forward_add_mp_max")
        column_names.append("forward_added_pct")
    if "reverse_add" in column_names:
        column_names.append("reverse_add_mp_sum")
        column_names.append("reverse_add_mp_max")
        column_names.append("reverse_added_pct")

    # Build a column number lookup dictionary.
    ci = {column_names[i]: i for i in range(len(column_names))}
Hoogenboom, Jerry's avatar
Hoogenboom, Jerry committed
216
217
218
219

    # Read data.
    data = {}
    for line in infile:
220
        row = line.rstrip("\r\n").split("\t")
221
        marker = row[ci["marker"]]
222
223
224
225
226
227
228
229
230
231
232
        row[ci["forward"]] = int(row[ci["forward"]])
        row[ci["reverse"]] = int(row[ci["reverse"]])
        row[ci["total"]] = int(row[ci["total"]])
        for i in (ci[column] for column in (
                "forward_corrected", "reverse_corrected", "total_corrected",
                "forward_noise", "reverse_noise", "total_noise",
                "forward_add", "reverse_add", "total_add") if column in ci):
            row[i] = float(row[i])
        if len(row) == ci["flags"]:
            row.append([])
        else:
233
            row[ci["flags"]] = map(str.strip, row[ci["flags"]].split(","))
Hoogenboom, Jerry's avatar
Hoogenboom, Jerry committed
234
235
        if marker not in data:
            data[marker] = []
236
        data[marker].append(row)
Hoogenboom, Jerry's avatar
Hoogenboom, Jerry committed
237
238

    # Compute statistics.
239
240
    if filter_action != "off":
        filtered = {marker: [] for marker in data}
Hoogenboom, Jerry's avatar
Hoogenboom, Jerry committed
241
    for marker in data:
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
        if "total_corrected" in ci:
            marker_total_corrected_sum = sum(
                row[ci["total_corrected"]] for row in data[marker])
            marker_total_corrected_max = max(
                row[ci["total_corrected"]] for row in data[marker])
        if "forward_corrected" in ci:
            marker_forward_corrected_sum = sum(
                row[ci["forward_corrected"]] for row in data[marker])
            marker_forward_corrected_max = max(
                row[ci["forward_corrected"]] for row in data[marker])
        if "reverse_corrected" in ci:
            marker_reverse_corrected_sum = sum(
                row[ci["reverse_corrected"]] for row in data[marker])
            marker_reverse_corrected_max = max(
                row[ci["reverse_corrected"]] for row in data[marker])
        marker_total_sum = sum(row[ci["total"]] for row in data[marker])
        marker_total_max = max(row[ci["total"]] for row in data[marker])
        marker_forward_sum = sum(row[ci["forward"]] for row in data[marker])
        marker_forward_max = max(row[ci["forward"]] for row in data[marker])
        marker_reverse_sum = sum(row[ci["reverse"]] for row in data[marker])
        marker_reverse_max = max(row[ci["reverse"]] for row in data[marker])
        if "total_noise" in ci:
            marker_total_noise_sum = sum(
                row[ci["total_noise"]] for row in data[marker])
            marker_total_noise_max = max(
                row[ci["total_noise"]] for row in data[marker])
        if "forward_noise" in ci:
            marker_forward_noise_sum = sum(
                row[ci["forward_noise"]] for row in data[marker])
            marker_forward_noise_max = max(
                row[ci["forward_noise"]] for row in data[marker])
        if "reverse_noise" in ci:
            marker_reverse_noise_sum = sum(
                row[ci["reverse_noise"]] for row in data[marker])
            marker_reverse_noise_max = max(
                row[ci["reverse_noise"]] for row in data[marker])
        if "total_add" in ci:
            marker_total_add_sum = sum(
                row[ci["total_add"]] for row in data[marker])
            marker_total_add_max = max(
                row[ci["total_add"]] for row in data[marker])
        if "forward_add" in ci:
            marker_forward_add_sum = sum(
                row[ci["forward_add"]] for row in data[marker])
            marker_forward_add_max = max(
                row[ci["forward_add"]] for row in data[marker])
        if "reverse_add" in ci:
            marker_reverse_add_sum = sum(
                row[ci["reverse_add"]] for row in data[marker])
            marker_reverse_add_max = max(
                row[ci["reverse_add"]] for row in data[marker])
Hoogenboom, Jerry's avatar
Hoogenboom, Jerry committed
293
        for row in data[marker]:
294
295
296
297
298
299
300
            if "total_corrected" in ci:
                row.append(100.*row[ci["total_corrected"]] /
                    marker_total_corrected_sum
                    if marker_total_corrected_sum else 0)
                row.append(100.*row[ci["total_corrected"]] /
                    marker_total_corrected_max
                    if marker_total_corrected_max else 0)
301
                row.append(
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
                    100.*row[ci["total_corrected"]]/row[ci["total"]]-100
                    if row[ci["total"]]
                    else ((row[ci["total_corrected"]]>0)*200-100
                        if row[ci["total_corrected"]] else 0))
                if "forward_corrected" in ci:
                    row.append(100.*(
                        row[ci["forward_corrected"]]/row[ci["total_corrected"]]
                        if row[ci["total_corrected"]]
                        else row[ci["forward_corrected"]] > 0))
            if "forward_corrected" in ci:
                row.append(100.*row[ci["forward_corrected"]] /
                    marker_forward_corrected_sum
                    if marker_forward_corrected_sum else 0)
                row.append(100.*row[ci["forward_corrected"]] /
                    marker_forward_corrected_max
                    if marker_forward_corrected_max else 0)
318
                row.append(
319
320
321
322
323
324
325
326
327
328
329
                    100.*row[ci["forward_corrected"]]/row[ci["forward"]]-100
                    if row[ci["forward"]]
                    else ((row[ci["forward_corrected"]]>0)*200-100
                        if row[ci["forward_corrected"]] else 0))
            if "reverse_corrected" in ci:
                row.append(100.*row[ci["reverse_corrected"]] /
                    marker_reverse_corrected_sum
                    if marker_reverse_corrected_sum else 0)
                row.append(100.*row[ci["reverse_corrected"]] /
                    marker_reverse_corrected_max
                    if marker_reverse_corrected_max else 0)
330
                row.append(
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
                    100.*row[ci["reverse_corrected"]]/row[ci["reverse"]]-100
                    if row[ci["reverse"]]
                    else ((row[ci["reverse_corrected"]]>0)*200-100
                        if row[ci["reverse_corrected"]] else 0))
            row.append(100.*row[ci["total"]]/marker_total_sum
                if marker_total_sum else 0)
            row.append(100.*row[ci["total"]]/marker_total_max
                if marker_total_max else 0)
            row.append(100.*(1.*row[ci["forward"]]/row[ci["total"]]
                if row[ci["total"]] else row[ci["forward"]] > 0))
            row.append(100.*row[ci["forward"]]/marker_forward_sum
                if marker_forward_sum else 0)
            row.append(100.*row[ci["forward"]]/marker_forward_max
                if marker_forward_max else 0)
            row.append(100.*row[ci["reverse"]]/marker_reverse_sum
                if marker_reverse_sum else 0)
            row.append(100.*row[ci["reverse"]]/marker_reverse_max
                if marker_reverse_max else 0)
            if "total_noise" in ci:
                row.append(100.*row[ci["total_noise"]]/marker_total_noise_sum
                    if marker_total_noise_sum else 0)
                row.append(100.*row[ci["total_noise"]]/marker_total_noise_max
                    if marker_total_noise_max else 0)
                row.append(100.*row[ci["total_noise"]]/row[ci["total"]]
                    if row[ci["total"]] else 0)
                if "forward_noise" in ci:
                    row.append(100.*(
                        row[ci["forward_noise"]]/row[ci["total_noise"]]
                        if row[ci["total_noise"]]
                        else row[ci["forward_noise"]] > 0))
            if "forward_noise" in ci:
                row.append(100.*row[ci["forward_noise"]]/
                    marker_forward_noise_sum
                    if marker_forward_noise_sum else 0)
                row.append(100.*row[ci["forward_noise"]]/
                    marker_forward_noise_max
                    if marker_forward_noise_max else 0)
                row.append(100.*row[ci["forward_noise"]]/row[ci["forward"]]
                    if row[ci["forward"]] else 0)
            if "reverse_noise" in ci:
                row.append(100.*row[ci["reverse_noise"]]/
                    marker_reverse_noise_sum
                    if marker_reverse_noise_sum else 0)
                row.append(100.*row[ci["reverse_noise"]]/
                    marker_reverse_noise_max
                    if marker_reverse_noise_max else 0)
                row.append(100.*row[ci["reverse_noise"]]/row[ci["reverse"]]
                    if row[ci["reverse"]] else 0)
            if "total_add" in ci:
                row.append(100.*row[ci["total_add"]]/marker_total_add_sum
                    if marker_total_add_sum else 0)
                row.append(100.*row[ci["total_add"]]/marker_total_add_max
                    if marker_total_add_max else 0)
                row.append(100.*row[ci["total_add"]]/row[ci["total"]]
                    if row[ci["total"]] else 0)
                if "forward_add" in ci:
                    row.append(100.*(
                        row[ci["forward_add"]]/row[ci["total_add"]]
                        if row[ci["total_add"]]
                        else row[ci["forward_add"]] > 0))
            if "forward_add" in ci:
                row.append(100.*row[ci["forward_add"]]/marker_forward_add_sum
                    if marker_forward_add_sum else 0)
                row.append(100.*row[ci["forward_add"]]/marker_forward_add_max
                    if marker_forward_add_max else 0)
                row.append(100.*row[ci["forward_add"]]/row[ci["forward"]]
                    if row[ci["forward"]] else 0)
            if "reverse_add" in ci:
                row.append(100.*row[ci["reverse_add"]]/marker_reverse_add_sum
                    if marker_reverse_add_sum else 0)
                row.append(100.*row[ci["reverse_add"]]/marker_reverse_add_max
                    if marker_reverse_add_max else 0)
                row.append(100.*row[ci["reverse_add"]]/row[ci["reverse"]]
                    if row[ci["reverse"]] else 0)
Hoogenboom, Jerry's avatar
Hoogenboom, Jerry committed
405

406
407
408
409
410
            # The 'No data' lines are fine like this.
            if row[ci["sequence"]] == "No data":
                row[ci["flags"]] = ",".join(row[ci["flags"]])
                continue

411
412
413
414
415
416
417
418
419
420
421
            # Get the values we will filter on.
            total_added = row[ci["total"]] if "total_corrected" not in ci \
                else row[ci["total_corrected"]]
            pct_of_sum = row[ci["total_mp_sum"]] if "total_corrected_mp_sum" \
                not in ci else row[ci["total_corrected_mp_sum"]]
            pct_of_max = row[ci["total_mp_max"]] if "total_corrected_mp_max" \
                not in ci else row[ci["total_corrected_mp_max"]]
            correction = 0 if "total_correction_pct" not in ci \
                else row[ci["total_correction_pct"]]
            recovery = 0 if "total_added_pct" not in ci \
                else row[ci["total_added_pct"]]
422
            min_strand = min(
423
424
425
426
427
                row[ci["forward"]] if "forward_corrected" not in ci
                    else row[ci["forward_corrected"]],
                row[ci["reverse"]] if "reverse_corrected" not in ci
                    else row[ci["reverse_corrected"]])

428
            # Check if this sequence should be filtered out.
429
            # Always filter/combine existing 'Other sequences'.
430
            if filter_action != "off" and (
431
                    row[ci["sequence"]] == "Other sequences" or (
432
433
434
                    total_added < min_reads_filt or
                    pct_of_max < min_pct_of_max_filt or
                    pct_of_sum < min_pct_of_sum_filt or
435
                    (correction < min_correction_filt and
436
                    recovery < min_recovery_filt) or
437
                    min_strand < min_per_strand_filt)):
438
439
440
                filtered[marker].append(row)

            # Check if this sequence is an allele.
441
            elif (row[ci["sequence"]] != "Other sequences" and
442
                    total_added >= min_reads and
443
444
                    pct_of_max >= min_pct_of_max and
                    pct_of_sum >= min_pct_of_sum and
445
                    (correction >= min_correction or
446
                    recovery >= min_recovery) and
447
                    min_strand >= min_per_strand):
448
449
450
451
452
453
454
455
456
457
458
                row[ci["flags"]].append("allele")
            row[ci["flags"]] = ",".join(row[ci["flags"]])

    # Reorder columns.
    new_order = {}
    for i in range(len(COLUMN_ORDER)-1, -1, -1):
        if COLUMN_ORDER[i] in ci:
            new_order[len(ci) - len(new_order) - 1] = ci[COLUMN_ORDER[i]]
    for i in range(len(column_names)-1, -1, -1):
        if column_names[i] not in COLUMN_ORDER:
            new_order[len(ci) - len(new_order) - 1] = i
459

Hoogenboom, Jerry's avatar
Hoogenboom, Jerry committed
460
    # Write results.
461
462
    outfile.write("\t".join(
        column_names[new_order[i]] for i in range(len(column_names))) + "\n")
463
    for marker in sorted(data):
464
465
466
        if filter_action == "combine":
            have_combined = False
            combined = [""] * len(column_names)
467
468
            combined[ci["marker"]] = marker
            combined[ci["sequence"]] = "Other sequences"
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
            for i in (ci[column] for column in COLUMN_ORDER if column in ci):
                # Set known numeric columns to 0.
                combined[i] = 0

        for row in data[marker]:
            if filter_action == "combine" and row in filtered[marker]:
                have_combined = True
                if "total_corrected" in ci:
                    combined[ci["total_corrected"]] += \
                        row[ci["total_corrected"]]
                    combined[ci["total_corrected_mp_sum"]] += \
                        row[ci["total_corrected_mp_sum"]]
                    combined[ci["total_corrected_mp_max"]] += \
                        row[ci["total_corrected_mp_max"]]
                if "forward_corrected" in ci:
                    combined[ci["forward_corrected"]] += \
                        row[ci["forward_corrected"]]
                    combined[ci["forward_corrected_mp_sum"]] += \
                        row[ci["forward_corrected_mp_sum"]]
                    combined[ci["forward_corrected_mp_max"]] += \
                        row[ci["forward_corrected_mp_max"]]
                if "reverse_corrected" in ci:
                    combined[ci["reverse_corrected"]] += \
                        row[ci["reverse_corrected"]]
                    combined[ci["reverse_corrected_mp_sum"]] += \
                        row[ci["reverse_corrected_mp_sum"]]
                    combined[ci["reverse_corrected_mp_max"]] += \
                        row[ci["reverse_corrected_mp_max"]]
                combined[ci["total"]] += row[ci["total"]]
                combined[ci["total_mp_sum"]] += row[ci["total_mp_sum"]]
                combined[ci["total_mp_max"]] += row[ci["total_mp_max"]]
                combined[ci["forward"]] += row[ci["forward"]]
                combined[ci["forward_mp_sum"]] += row[ci["forward_mp_sum"]]
                combined[ci["forward_mp_max"]] += row[ci["forward_mp_max"]]
                combined[ci["reverse"]] += row[ci["reverse"]]
                combined[ci["reverse_mp_sum"]] += row[ci["reverse_mp_sum"]]
                combined[ci["reverse_mp_max"]] += row[ci["reverse_mp_max"]]
                if "total_noise" in ci:
                    combined[ci["total_noise"]] += row[ci["total_noise"]]
                    combined[ci["total_noise_mp_sum"]] += \
                        row[ci["total_noise_mp_sum"]]
                    combined[ci["total_noise_mp_max"]] += \
                        row[ci["total_noise_mp_max"]]
                if "forward_noise" in ci:
                    combined[ci["forward_noise"]] += row[ci["forward_noise"]]
                    combined[ci["forward_noise_mp_sum"]] += \
                        row[ci["forward_noise_mp_sum"]]
                    combined[ci["forward_noise_mp_max"]] += \
                        row[ci["forward_noise_mp_max"]]
                if "reverse_noise" in ci:
                    combined[ci["reverse_noise"]] += row[ci["reverse_noise"]]
                    combined[ci["reverse_noise_mp_sum"]] += \
                        row[ci["reverse_noise_mp_sum"]]
                    combined[ci["reverse_noise_mp_max"]] += \
                        row[ci["reverse_noise_mp_max"]]
                if "total_add" in ci:
                    combined[ci["total_add"]] += row[ci["total_add"]]
                    combined[ci["total_add_mp_sum"]] += \
                        row[ci["total_add_mp_sum"]]
                    combined[ci["total_add_mp_max"]] += \
                        row[ci["total_add_mp_max"]]
                if "forward_add" in ci:
                    combined[ci["forward_add"]] += row[ci["forward_add"]]
                    combined[ci["forward_add_mp_sum"]] += \
                        row[ci["forward_add_mp_sum"]]
                    combined[ci["forward_add_mp_max"]] += \
                        row[ci["forward_add_mp_max"]]
                if "reverse_add" in ci:
                    combined[ci["reverse_add"]] += row[ci["reverse_add"]]
                    combined[ci["reverse_add_mp_sum"]] += \
                        row[ci["reverse_add_mp_sum"]]
                    combined[ci["reverse_add_mp_max"]] += \
                        row[ci["reverse_add_mp_max"]]
            elif filter_action == "off" or row not in filtered[marker]:
                for i in (ci[col] for col in COLUMN_ORDER if col in ci
                        and col not in ("total", "forward", "reverse")):
                    row[i] = "%#.3g" % row[i]
                outfile.write("\t".join(map(str,
                    (row[new_order[i]] for i in range(len(row))))) + "\n")

        # Add combined row for this marker.
        if filter_action == "combine" and have_combined:
            if "total_corrected" in ci:
                combined[ci["total_correction_pct"]] = (
                    100.*combined[ci["total_corrected"]]/
                        combined[ci["total"]]-100
                    if combined[ci["total"]]
                    else ((combined[ci["total_corrected"]]>0)*200-100
                        if combined[ci["total_corrected"]] else 0))
                if "forward_corrected" in ci:
                    combined[ci["forward_corrected_pct"]] = 100.*(
                        combined[ci["forward_corrected"]]/
                            combined[ci["total_corrected"]]
                        if combined[ci["total_corrected"]]
                        else combined[ci["forward_corrected"]] > 0)
            if "forward_corrected" in ci:
                combined[ci["forward_correction_pct"]] = (
                    100.*combined[ci["forward_corrected"]]/
                        combined[ci["forward"]]-100
                    if combined[ci["forward"]]
                    else ((combined[ci["forward_corrected"]]>0)*200-100
                        if combined[ci["forward_corrected"]] else 0))
            if "reverse_corrected" in ci:
                combined[ci["reverse_correction_pct"]] = (
                    100.*combined[ci["reverse_corrected"]]/
                        combined[ci["reverse"]]-100
                    if combined[ci["reverse"]]
                    else ((combined[ci["reverse_corrected"]]>0)*200-100
                        if combined[ci["reverse_corrected"]] else 0))
            combined[ci["forward_pct"]] = 100.*(
                1.*combined[ci["forward"]]/combined[ci["total"]]
                if combined[ci["total"]] else combined[ci["forward"]] > 0)
            if "total_noise" in ci:
                combined[ci["total_removed_pct"]] = (
                    100.*combined[ci["total_noise"]]/combined[ci["total"]]
                    if combined[ci["total"]] else 0)
                if "forward_noise" in ci:
                    combined[ci["forward_noise_pct"]] = 100.*(
                        combined[ci["forward_noise"]]/
                            combined[ci["total_noise"]]
                        if combined[ci["total_noise"]]
                        else combined[ci["forward_noise"]] > 0)
            if "forward_noise" in ci:
                combined[ci["forward_removed_pct"]] = (
                    100.*combined[ci["forward_noise"]]/combined[ci["forward"]]
                    if combined[ci["forward"]] else 0)
            if "reverse_noise" in ci:
                combined[ci["reverse_removed_pct"]] = (
                    100.*combined[ci["reverse_noise"]]/combined[ci["reverse"]]
                    if combined[ci["reverse"]] else 0)
            if "total_add" in ci:
600
                combined[ci["total_added_pct"]] = (
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
                    100.*combined[ci["total_add"]]/combined[ci["total"]]
                    if combined[ci["total"]] else 0)
                if "forward_add" in ci:
                    combined[ci["forward_add_pct"]] = 100.*(
                        combined[ci["forward_add"]]/combined[ci["total_add"]]
                        if combined[ci["total_add"]]
                        else combined[ci["forward_add"]] > 0)
            if "forward_add" in ci:
                combined[ci["forward_added_pct"]] = (
                    100.*combined[ci["forward_add"]]/combined[ci["forward"]]
                    if combined[ci["forward"]] else 0)
            if "reverse_add" in ci:
                combined[ci["reverse_added_pct"]] = (
                    100.*combined[ci["reverse_add"]]/combined[ci["reverse"]]
                    if combined[ci["reverse"]] else 0)

            for i in (ci[column] for column in COLUMN_ORDER if column in ci
                    and column not in ("total", "forward", "reverse")):
                combined[i] = "%#.3g" % combined[i]
            outfile.write("\t".join(map(str,
                (combined[new_order[i]] for i in range(len(combined))))) +"\n")
Hoogenboom, Jerry's avatar
Hoogenboom, Jerry committed
622
623
624
625
626
#compute_stats


def add_arguments(parser):
    add_input_output_args(parser, True, True, False)
627
    intergroup = parser.add_argument_group("interpretation options",
628
629
        "sequences that match the -c or -y option (or both) and all of the "
        "other settings are marked as 'allele'")
630
    intergroup.add_argument('-n', '--min-reads', metavar="N", type=float,
631
632
        default=_DEF_MIN_READS,
        help="the minimum number of reads (default: %(default)s)")
633
634
    intergroup.add_argument('-b', '--min-per-strand', metavar="N", type=float,
        default=_DEF_MIN_PER_STRAND,
635
636
637
638
639
640
641
642
643
644
645
646
647
        help="the minimum number of reads in both orientations (default: "
             "%(default)s)")
    intergroup.add_argument('-m', '--min-pct-of-max', metavar="PCT",
        type=float, default=_DEF_MIN_PCT_OF_MAX,
        help="the minimum percentage of reads w.r.t. the highest allele of "
             "the marker (default: %(default)s)")
    intergroup.add_argument('-p', '--min-pct-of-sum', metavar="PCT",
        type=float, default=_DEF_MIN_PCT_OF_SUM,
        help="the minimum percentage of reads w.r.t. the marker's total "
             "number of reads (default: %(default)s)")
    intergroup.add_argument('-c', '--min-correction', metavar="PCT",
        type=float, default=_DEF_MIN_CORRECTION,
        help="the minimum change in read count due to correction by e.g., "
648
649
             "bgcorrect (default: %(default)s)")
    intergroup.add_argument('-y', '--min-recovery', metavar="PCT",
650
651
        type=float, default=_DEF_MIN_RECOVERY,
        help="the minimum number of reads that was recovered thanks to "
652
653
             "noise correction (by e.g., bgcorrect), as a percentage of the "
             "original number of reads (default: %(default)s)")
654
655
656
    filtergroup = parser.add_argument_group("filtering options",
        "sequences that match the -C or -Y option (or both) and all of the "
        "other settings are retained, all others are filtered")
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
    filtergroup.add_argument('-a', '--filter-action', metavar="ACTION",
        choices=("off", "combine", "delete"), default="off",
        help="filtering mode: 'off', disable filtering; 'combine', replace "
             "filtered sequences by a single line with aggregate values per "
             "marker; 'delete', remove filtered sequences without leaving a "
             "trace (default: %(default)s)")
    filtergroup.add_argument('-N', '--min-reads-filt', metavar="N", type=float,
        default=_DEF_MIN_READS_FILT,
        help="the minimum number of reads (default: %(default)s)")
    filtergroup.add_argument('-B', '--min-per-strand-filt', metavar="N",
        type=float, default=_DEF_MIN_PER_STRAND_FILT,
        help="the minimum number of reads in both orientations (default: "
             "%(default)s)")
    filtergroup.add_argument('-M', '--min-pct-of-max-filt', metavar="PCT",
        type=float, default=_DEF_MIN_PCT_OF_MAX_FILT,
        help="the minimum percentage of reads w.r.t. the highest allele of "
             "the marker (default: %(default)s)")
    filtergroup.add_argument('-P', '--min-pct-of-sum-filt', metavar="PCT",
        type=float, default=_DEF_MIN_PCT_OF_SUM_FILT,
        help="the minimum percentage of reads w.r.t. the marker's total "
             "number of reads (default: %(default)s)")
    filtergroup.add_argument('-C', '--min-correction-filt', metavar="PCT",
        type=float, default=_DEF_MIN_CORRECTION_FILT,
        help="the minimum change in read count due to correction by e.g., "
             "bgcorrect (default: %(default)s)")
    filtergroup.add_argument('-Y', '--min-recovery-filt', metavar="PCT",
        type=float, default=_DEF_MIN_RECOVERY_FILT,
        help="the minimum number of reads that was recovered thanks to "
             "noise correction (by e.g., bgcorrect), as a percentage of the "
             "original number of reads (default: %(default)s)")
Hoogenboom, Jerry's avatar
Hoogenboom, Jerry committed
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
#add_arguments


def run(args):
    gen = get_input_output_files(args, True, True)
    if not gen:
        raise ValueError("please specify an input file, or pipe in the output "
                         "of another program")

    for tag, infiles, outfile in gen:
        # TODO: Aggregate data from all infiles of each sample.
        if len(infiles) > 1:
            raise ValueError(
                "multiple input files for sample '%s' specified " % tag)
        infile = sys.stdin if infiles[0] == "-" else open(infiles[0], "r")
702
        compute_stats(infile, outfile,
703
704
                      args.min_reads, args.min_per_strand, args.min_pct_of_max,
                      args.min_pct_of_sum, args.min_correction,
705
706
707
708
                      args.min_recovery, args.filter_action,
                      args.min_reads_filt, args.min_per_strand_filt,
                      args.min_pct_of_max_filt, args.min_pct_of_sum_filt,
                      args.min_correction_filt, args.min_recovery_filt)
Hoogenboom, Jerry's avatar
Hoogenboom, Jerry committed
709
710
711
        if infile != sys.stdin:
            infile.close()
#run