def create_precision_report_row(
classification: float, gt_conf: float = 0, sample: str = "sample1"
) -> pd.Series:
ref_probe_header = ProbeHeader()
pandora_probe_header = ProbeHeader(gt_conf=gt_conf)
data = {
"sample": sample,
"query_probe_header": str(pandora_probe_header),
"ref_probe_header": str(ref_probe_header),
"classification": classification,
}
return pd.Series(data=data)
def create_classifier_with_two_entries(cls: Type) -> Type[Classifier]:
flag = 0
cigar = "56M"
nm = "NM:i:0"
md = "MD:Z:56"
mapq = 60
pos = 1
query_header = ProbeHeader(interval=ProbeInterval(12, 17))
sequence = "AAAAAAAAAAACGGCTCGCATAGACACGACGACGACACGTACGATCGATCAGTCAT"
ref_header = ProbeHeader(
chrom="GC00000422_2",
sample="CFT073",
pos=603,
interval=ProbeInterval(25, 32),
svtype="PH_SNPs",
gt_conf=89.5987,
)
header = create_sam_header(str(ref_header), 64)
contents = str(header) + "\n"
record1 = pysam.AlignedSegment.fromstring(
f"{query_header}\t{flag}\t{ref_header}\t{pos}\t{mapq}\t{cigar}\t*\t0\t0\t{sequence}\t*\t{nm}\t{md}\tAS:i:43\tXS:i:32",
header,
)
contents += record1.to_string() + "\n"
flag = 2048
cigar = "43M"
nm = "NM:i:1"
md = "MD:Z:21T21"
mapq = 0
pos = 5
query_header = ProbeHeader(chrom="3", pos=14788, interval=ProbeInterval(21, 22))
sequence = "CGCGAAAGCCCTGACCATCTGCACCGTGTCTGACCACATCCGC"
header = create_sam_header(str(ref_header), 57)
record2 = pysam.AlignedSegment.fromstring(
f"{query_header}\t{flag}\t{ref_header}\t{pos}\t{mapq}\t{cigar}\t*\t0\t0\t{sequence}\t*\t{nm}\t{md}\tAS:i:43\tXS:i:32",
header,
)
contents += record2.to_string() + "\n"
sam = create_tmp_sam(contents)
return cls(sam)
def create_incorrect_primary_sam_record() -> pysam.AlignedSegment:
flag = 0
cigar = "56M"
nm = "NM:i:1"
md = "MD:Z:12T43"
mapq = 60
pos = 1
query_header = ProbeHeader(interval=ProbeInterval(12, 13))
ref_header = ProbeHeader(
chrom="GC00000422_2",
sample="CFT073",
pos=603,
interval=ProbeInterval(25, 32),
svtype="PH_SNPs",
gt_conf=89.5987,
)
sequence = "AAAAAAAAAAACGGCTCGCATAGACACGACGACGACACGTACGATCGATCAGTCAT"
header = create_sam_header(str(ref_header), 64)
record = pysam.AlignedSegment.fromstring(
f"{query_header}\t{flag}\t{ref_header}\t{pos}\t{mapq}\t{cigar}\t*\t0\t0\t{sequence}\t*\t{nm}\t{md}\tAS:i:43\tXS:i:32",
header,
)
return record
def create_correct_supplementary_sam_record() -> pysam.AlignedSegment:
flag = 2048
cigar = "43M"
nm = "NM:i:1"
md = "MD:Z:19T23"
mapq = 0
pos = 5
query_header = ProbeHeader(chrom="3", pos=14788, interval=ProbeInterval(21, 22))
ref_header = ProbeHeader(
chrom="GC00000422_2",
sample="CFT073",
pos=603,
interval=ProbeInterval(25, 32),
svtype="PH_SNPs",
gt_conf=89.5987,
)
sequence = "CGCGAAAGCCCTGACCATCTGCACCGTGTCTGACCACATCCGC"
header = create_sam_header(str(ref_header), 57)
record = pysam.AlignedSegment.fromstring(
f"{query_header}\t{flag}\t{ref_header}\t{pos}\t{mapq}\t{cigar}\t*\t0\t0\t{sequence}\t*\t{nm}\t{md}\tAS:i:43\tXS:i:32",
header,
)
return record
def create_recall_report_row(
truth_probe_header:str, classification: AlignmentAssessment, gt_conf: float = 0, sample: str = "sample1", with_gt_conf=False
) -> pd.Series:
vcf_probe_header = ProbeHeader(gt_conf=gt_conf)
data = {
"sample": sample,
"query_probe_header": str(truth_probe_header),
"ref_probe_header": str(vcf_probe_header),
"classification": classification.value,
"good_eval": classification.value in ["primary_correct", "secondary_correct", "supplementary_correct"],
"PVID": None,
"NB_ALL": None,
"ALL_ID": None,
"NB_DIFF_ALL_SEQ": None,
"ALL_SEQ_ID": None,
"NB_OF_SAMPLES": None,
}
if with_gt_conf:
data["GT_CONF"] = gt_conf
return pd.Series(data=data)