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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,212 @@ | ||
| """ | ||
| The pychip submodule is designed for working with annotation or manifest | ||
| files from the Axiom (Thermo Fisher Scientific) and Infinium (Illumina) | ||
| array platforms. | ||
| """ | ||
|
|
||
| import re | ||
| import pandas as pd | ||
|
|
||
| class AxiomFrame: | ||
| """ | ||
| Class for storing Axiom annotation data. | ||
| Parameters | ||
| ---------- | ||
| meta : list | ||
| List of metadata lines. | ||
| df : pandas.DataFrame | ||
| DataFrame containing annotation data. | ||
| """ | ||
| def __init__(self, meta, df): | ||
| self._meta = meta | ||
| self._df = df.reset_index(drop=True) | ||
|
|
||
| @property | ||
| def meta(self): | ||
| """list : List of metadata lines.""" | ||
| return self._meta | ||
|
|
||
| @meta.setter | ||
| def meta(self, value): | ||
| self._meta = value | ||
|
|
||
| @property | ||
| def df(self): | ||
| """pandas.DataFrame : DataFrame containing annotation data.""" | ||
| return self._df | ||
|
|
||
| @df.setter | ||
| def df(self, value): | ||
| self._df = value.reset_index(drop=True) | ||
|
|
||
| @classmethod | ||
| def from_file(cls, fn): | ||
| """ | ||
| Construct AxiomFrame from a CSV file. | ||
| Parameters | ||
| ---------- | ||
| fn : str | ||
| CSV file (compressed or uncompressed). | ||
| Returns | ||
| ------- | ||
| AxiomFrame | ||
| AxiomFrame object. | ||
| """ | ||
| if fn.startswith('~'): | ||
| fn = os.path.expanduser(fn) | ||
|
|
||
| if fn.endswith('.gz'): | ||
| f = gzip.open(fn, 'rt') | ||
| else: | ||
| f = open(fn) | ||
|
|
||
| meta = [] | ||
| n = 0 | ||
| for line in f: | ||
| if line.startswith('#'): | ||
| meta.append(line) | ||
| n += 1 | ||
| f.close() | ||
|
|
||
| df = pd.read_csv(fn, skiprows=n) | ||
|
|
||
| return cls(meta, df) | ||
|
|
||
| def to_vep(self): | ||
| """ | ||
| Convert AxiomFrame to the Ensembl VEP format. | ||
| Returns | ||
| ------- | ||
| pandas.DataFrame | ||
| Variants in Ensembl VEP format. | ||
| """ | ||
| print(self.df.shape) | ||
| df = self.df[self.df.Chromosome != '---'] | ||
| print(df.shape) | ||
| def one_row(r): | ||
| result = [] | ||
| nucleotides = ['A', 'C', 'G', 'T'] | ||
| chrom = r['Chromosome'] | ||
| ref = r['Ref Allele'] | ||
| strand = r['Strand'] | ||
| start = r['Physical Position'] | ||
| end = r['Position End'] | ||
| for alt in r['Alt Allele'].split(' // '): | ||
| if ref in nucleotides and alt in nucleotides: # SNV | ||
| pass | ||
| elif alt == '-': # DEL I | ||
| pass | ||
| elif len(alt) == len(ref): # MNV | ||
| pass | ||
| elif len(alt) < len(ref) and ref.startswith(alt): # DEL II | ||
| start += len(alt) | ||
| ref = ref[len(alt):] | ||
| alt = '-' | ||
| elif ref == '-': # INS I | ||
| start += 1 | ||
| end = start - 1 | ||
| elif len(alt) > len(ref) and alt.startswith(ref): # INS II | ||
| diff = len(alt) - len(ref) | ||
| start += diff | ||
| end = start - 1 | ||
| ref = '-' | ||
| alt = alt[diff:] | ||
| else: | ||
| pass | ||
| line = [chrom, start, end, f'{ref}/{alt}', strand] | ||
| result.append('|'.join([str(x) for x in line])) | ||
| return ','.join(result) | ||
| s = df.apply(one_row, axis=1) | ||
| s = ','.join(s) | ||
| data = [x.split('|') for x in s.split(',')] | ||
| df = pd.DataFrame(data).drop_duplicates() | ||
| df.iloc[:, 1] = df.iloc[:, 1].astype(int) | ||
| df.iloc[:, 2] = df.iloc[:, 2].astype(int) | ||
| df = df.sort_values(by=[0, 1]) | ||
| return df | ||
|
|
||
| class InfiniumFrame: | ||
| """ | ||
| Class for storing Infinium manifest data. | ||
| Parameters | ||
| ---------- | ||
| df : pandas.DataFrame | ||
| DataFrame containing manifest data. | ||
| """ | ||
| def __init__(self, df): | ||
| self._df = df.reset_index(drop=True) | ||
|
|
||
| @property | ||
| def df(self): | ||
| """pandas.DataFrame : DataFrame containing manifest data.""" | ||
| return self._df | ||
|
|
||
| @df.setter | ||
| def df(self, value): | ||
| self._df = value.reset_index(drop=True) | ||
|
|
||
| @classmethod | ||
| def from_file(cls, fn): | ||
| """ | ||
| Construct InfiniumFrame from a CSV file. | ||
| Parameters | ||
| ---------- | ||
| fn : str | ||
| CSV file (compressed or uncompressed). | ||
| Returns | ||
| ------- | ||
| InfiniumFrame | ||
| InfiniumFrame object. | ||
| """ | ||
| if fn.startswith('~'): | ||
| fn = os.path.expanduser(fn) | ||
|
|
||
| if fn.endswith('.gz'): | ||
| f = gzip.open(fn, 'rt') | ||
| else: | ||
| f = open(fn) | ||
|
|
||
| lines = f.readlines() | ||
| f.close() | ||
|
|
||
| for i, line in enumerate(lines): | ||
| if line.startswith('[Assay]'): | ||
| start = i | ||
| headers = lines[i+1].strip().split(',') | ||
| elif line.startswith('[Controls]'): | ||
| end = i | ||
|
|
||
| lines = lines[start+2:end] | ||
| lines = [x.strip().split(',') for x in lines] | ||
|
|
||
| df = pd.DataFrame(lines, columns=headers) | ||
|
|
||
| return cls(df) | ||
|
|
||
| def to_vep(self): | ||
| """ | ||
| Convert InfiniumFrame to the Ensembl VEP format. | ||
| Returns | ||
| ------- | ||
| pandas.DataFrame | ||
| Variants in Ensembl VEP format. | ||
| """ | ||
| df = self.df[(self.df.Chr != 'XY') & (self.df.Chr != '0')] | ||
| def one_row(r): | ||
| pos = r.MapInfo | ||
| matches = re.findall(r'\[([^\]]+)\]', r.SourceSeq) | ||
| if not matches: | ||
| raise ValueError(f'Something went wrong: {r}') | ||
| a1, a2 = matches[0].split('/') | ||
| data = pd.Series([r.Chr, r.MapInfo, a1, a2]) | ||
| return data | ||
| df = df.apply(one_row, axis=1) | ||
| return df |