aboutsummaryrefslogtreecommitdiff
path: root/wqflask/maintenance/convert_geno_to_bimbam.py
blob: 455227057fb2335d280225e2606612eec4cefac6 (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
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
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
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
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
#!/usr/bin/python

"""
Convert .geno files to json

This file goes through all of the genofiles in the genofile directory (.geno)
and converts them to json files that are used when running the marker regression
code

"""

from __future__ import print_function, division, absolute_import
import sys
sys.path.append("..")
import os
import glob
import traceback
import gzip

import simplejson as json

from pprint import pformat as pf

class EmptyConfigurations(Exception): pass

class Marker(object):
    def __init__(self):
        self.name = None
        self.chr = None
        self.cM = None
        self.Mb = None
        self.genotypes = []

class ConvertGenoFile(object):

    def __init__(self, input_file, output_files):
        self.input_file = input_file
        self.output_files = output_files

        self.mb_exists = False
        self.cm_exists = False
        self.markers = []

        self.latest_row_pos = None
        self.latest_col_pos = None

        self.latest_row_value = None
        self.latest_col_value = None

    def convert(self):
        self.haplotype_notation = {
            '@mat': "1",
            '@pat': "0",
            '@het': "0.5",
            '@unk': "NA"
            }

        self.configurations = {}
        self.input_fh = open(self.input_file)

        self.process_csv()

    def process_csv(self):
        for row in self.process_rows():
            row_items = row.split("\t")

            this_marker = Marker()
            this_marker.name = row_items[1]
            this_marker.chr = row_items[0]
            if self.cm_exists and self.mb_exists:
                this_marker.cM = row_items[2]
                this_marker.Mb = row_items[3]
                genotypes = row_items[4:]
            elif self.cm_exists:
                this_marker.cM = row_items[2]
                genotypes = row_items[3:]
            elif self.mb_exists:
                this_marker.Mb = row_items[2]
                genotypes = row_items[3:]
            else:
                genotypes = row_items[2:]
            for item_count, genotype in enumerate(genotypes):
                if genotype.upper().strip() in self.configurations:
                    this_marker.genotypes.append(self.configurations[genotype.upper().strip()])
                else:
                    this_marker.genotypes.append("NA")

            self.markers.append(this_marker.__dict__)

        self.write_to_bimbam()    

    def write_to_bimbam(self):
        with open(self.output_files[0], "w") as geno_fh:
            for marker in self.markers:
                geno_fh.write(marker['name'])
                geno_fh.write(", X, Y")
                geno_fh.write(", " + ", ".join(marker['genotypes']))
                geno_fh.write("\n")

        with open(self.output_files[1], "w") as pheno_fh:
            for sample in self.sample_list:
                pheno_fh.write("1\n")

        with open(self.output_files[2], "w") as snp_fh:
            for marker in self.markers:
                if self.mb_exists:
                    snp_fh.write(marker['name'] +", " + str(int(float(marker['Mb'])*1000000)) + ", " + marker['chr'] + "\n")
                else:
                    snp_fh.write(marker['name'] +", " + str(int(float(marker['cM'])*1000000)) + ", " + marker['chr'] + "\n")

    def get_sample_list(self, row_contents):
        self.sample_list = []
        if self.mb_exists:
            if self.cm_exists:
                self.sample_list = row_contents[4:]
            else:
                self.sample_list = row_contents[3:]
        else:
            if self.cm_exists:
                self.sample_list = row_contents[3:]
            else:
                self.sample_list = row_contents[2:]
    
    def process_rows(self):
        for self.latest_row_pos, row in enumerate(self.input_fh):
            self.latest_row_value = row
            # Take care of headers
            if not row.strip():
                continue
            if row.startswith('#'):
                continue
            if row.startswith('Chr'):
                if 'Mb' in row.split():
                    self.mb_exists = True
                if 'cM' in row.split():
                    self.cm_exists = True
                self.get_sample_list(row.split())
                continue
            if row.startswith('@'):
                key, _separater, value = row.partition(':')
                key = key.strip()
                value = value.strip()
                if key in self.haplotype_notation:
                    self.configurations[value] = self.haplotype_notation[key]
                continue
            if not len(self.configurations):
                raise EmptyConfigurations
            yield row

    @classmethod
    def process_all(cls, old_directory, new_directory):
        os.chdir(old_directory)
        for input_file in glob.glob("*"):
            if not input_file.endswith(('geno', '.geno.gz')):
                continue
            group_name = ".".join(input_file.split('.')[:-1])
            geno_output_file = os.path.join(new_directory, group_name + "_geno.txt")
            pheno_output_file = os.path.join(new_directory, group_name + "_pheno.txt")
            snp_output_file = os.path.join(new_directory, group_name + "_snps.txt")
            output_files = [geno_output_file, pheno_output_file, snp_output_file]
            print("%s -> %s" % (
                os.path.join(old_directory, input_file), geno_output_file))
            convertob = ConvertGenoFile(input_file, output_files)
            try:
                convertob.convert()
            except EmptyConfigurations as why:
                print("  No config info? Continuing...")
                continue
            except Exception as why:
                print("  Exception:", why)
                print(traceback.print_exc())
                print("    Found in row %s at tabular column %s" % (convertob.latest_row_pos,
                                                                convertob.latest_col_pos))
                print("    Column is:", convertob.latest_col_value)
                print("    Row is:", convertob.latest_row_value)
                break

if __name__=="__main__":
    Old_Geno_Directory = """/home/zas1024/genotype_files/genotype/"""
    New_Geno_Directory = """/home/zas1024/genotype_files/genotype/bimbam/"""
    #Input_File = """/home/zas1024/gene/genotype_files/genotypes/BXD.geno"""
    #Output_File = """/home/zas1024/gene/wqflask/wqflask/pylmm/data/bxd.snps"""
    #convertob = ConvertGenoFile("/home/zas1024/gene/genotype_files/genotypes/SRxSHRSPF2.geno", "/home/zas1024/gene/genotype_files/new_genotypes/SRxSHRSPF2.json")
    #convertob.convert()
    ConvertGenoFile.process_all(Old_Geno_Directory, New_Geno_Directory)
    #ConvertGenoFiles(Geno_Directory)