def test_100x_attn_model():
# test data creation
create_data(ms_list, vector_folder, 50, 16, None, tumor_bam, normal_bam,
"tumor", "normal")
assert os.path.isfile("./test_dir/tumor_normal_-1_data.npy")
assert os.path.isfile("./test_dir/tumor_normal_-1_locations.npy")
run_eval(
"../model/mi_msi_v0_4_0_100x_attn.model",
vector_folder,
False,
2,
True,
"both",
vector_folder,
"test-run",
50,
0.95,
True,
)
assert os.path.isfile("./test_dir/tumor_normal_results.npy")
assert os.path.isfile("./test_dir/test-run_results.txt")
def batch_test():
# Update case list with full path to bam file
global case_list
file_path = os.path.dirname(os.path.abspath(case_list))
cases = pd.read_csv(case_list, sep="\t", header=0)
cases["Tumor_Bam"] = cases["Tumor_Bam"].apply(
lambda x: os.path.join(file_path, x))
cases["Normal_Bam"] = cases["Normal_Bam"].apply(
lambda x: os.path.join(file_path, x))
cases.to_csv(vector_folder + "/test_case_list.txt",
header=True,
sep="\t",
index=False)
case_list = vector_folder + "/test_case_list.txt"
# test data creation
create_data(ms_list, vector_folder, 50, 16, case_list, None, None, None,
None)
assert os.path.isfile("./test_dir/tumor1_normal1_-1_data.npy")
assert os.path.isfile("./test_dir/tumor1_normal1_-1_locations.npy")
assert os.path.isfile("./test_dir/tumor2_normal2_-1_data.npy")
assert os.path.isfile("./test_dir/tumor2_normal2_-1_locations.npy")
assert os.path.isfile("./test_dir/tumor3_-1_data.npy")
assert os.path.isfile("./test_dir/tumor3_-1_locations.npy")
run_eval(
"../model/mi_msi_v0_4_0_100x.model",
vector_folder,
False,
2,
True,
"both",
vector_folder,
"test-run",
50,
0.95,
False,
)
assert os.path.isfile("./test_dir/tumor1_normal1_results.npy")
assert os.path.isfile("./test_dir/tumor2_normal2_results.npy")
assert os.path.isfile("./test_dir/tumor3_results.npy")
assert os.path.isfile("./test_dir/test-run_results.txt")
def main():
parser = argparse.ArgumentParser(description="MiMSI Analysis")
parser.add_argument(
"--version",
action="store_true",
default=False,
help="Display current version of MiMSI",
)
parser.add_argument(
"--no-cuda",
action="store_true",
default=False,
help=
"Disables CUDA for use off GPU, if this is not specified the utility will check availability of torch.cuda",
)
parser.add_argument(
"--model",
default=ROOT_DIR + "/model/mimsi_mskcc_impact_200.model",
help=
"name of the saved model weights to load (default: model/mimsi_mskcc_impact_200.model)",
)
parser.add_argument(
"--save",
action="store_true",
default=False,
help=
"save the results of the evaluation to a numpy array or a tsv text file",
)
parser.add_argument(
"--save-format",
choices=["tsv", "npy", "both"],
default="tsv",
help=
"save the results of the evaluation to a numpy array or as summary in a tsv text file or both",
)
parser.add_argument("--seed",
type=int,
default=2,
metavar="S",
help="Random Seed (default: 2)")
parser.add_argument(
"--microsatellites-list",
default=ROOT_DIR + "/utils/microsatellites.list",
help=
"The list of microsatellites to check in the tumor/normal pair (default: utils/microsatellites.list)",
)
parser.add_argument(
"--save-location",
default="./mimsi_results",
help=
"The location on the filesystem to save the converted vectors and final results (default: Current_working_directory/mimsi_results/). WARNING: Exisitng files in this directory in the formats *_locations.npy and *_data.npy will be deleted!",
)
parser.add_argument(
"--cores",
default=16,
help="Number of cores to utilize in parallel (default: 16)",
)
parser.add_argument(
"--coverage",
default=100,
help=
"Required coverage for both the tumor and the normal. Any coverage in excess of this limit will be randomly downsampled",
)
parser.add_argument(
"--confidence-interval",
default=0.95,
help=
"Confidence interval for the estimated MSI Score reported in the tsv output file (default: 0.95)",
)
parser.add_argument(
"--use-attention",
action="store_true",
default=False,
help=
"Use attention pooling rather than average pooling to aggregate sample embeddings (default: False)",
)
single_sample_group = parser.add_argument_group("Single Sample Mode")
single_sample_group.add_argument("--tumor-bam",
help="Tumor bam file for conversion")
single_sample_group.add_argument(
"--normal-bam", help="Matched normal bam file for conversion")
single_sample_group.add_argument(
"--case-id",
default="TestCase",
help=
"Unique identifier for the single sample/case submitted. This will be the filename for any saved results (default: TestCase)",
)
single_sample_group.add_argument("--norm-case-id",
default=None,
help="Normal case name (default: None)")
batch_mode_group = parser.add_argument_group("Batch Mode")
batch_mode_group.add_argument(
"--case-list",
default="",
help=
"Case List for generating sample vectors in bulk, if specified all other input file args will be ignored",
)
batch_mode_group.add_argument(
"--name",
default="BATCH",
help=
"name of the run submitted using --case-list, this will be the filename for any saved results in the tsv format (default: BATCH)",
)
args = parser.parse_args()
if args.version:
print("MiMSI Case Analysis CLI version - " +
pkg_resources.require("MiMSI")[0].version)
return
case_list, tumor_bam, normal_bam, case_id, norm_case_id, ms_list, save_loc, cores, saved_model, no_cuda, seed, save, save_format, name, covg, confidence, use_attention = (
args.case_list,
args.tumor_bam,
args.normal_bam,
args.case_id,
args.norm_case_id,
args.microsatellites_list,
args.save_location,
args.cores,
args.model,
args.no_cuda,
args.seed,
args.save,
args.save_format,
args.name,
args.coverage,
args.confidence_interval,
args.use_attention,
)
cuda = not no_cuda and torch.cuda.is_available()
# Resolve args
if case_list:
print("Case list is provided. Running in batch mode!")
elif all([tumor_bam, normal_bam]):
print("Running in single sample mode!")
else:
print(
"Either a case_list (batch mode) or a tumor_bam/normal_bam pairs (single sample mode) is required."
)
return
if save_loc == "./mimsi_results":
try:
save_loc = os.getcwd() + "/mimsi_results"
except OSError as e:
print(
"Cannot create directory to save intermediate files and final results!"
)
raise
try:
# is_labled is false since this is an evaluation pipeline, 50 is the coverage
create_data(
ms_list,
save_loc,
covg,
cores,
case_list,
tumor_bam,
normal_bam,
case_id,
norm_case_id,
)
except Exception:
raise
try:
run_eval(saved_model, save_loc, cuda, seed, save, save_format,
save_loc, name, covg, confidence, use_attention)
except Exception:
raise
print("Analysis Complete!")