def main(): if len(sys.argv) != 6: usage() cg_dir = sys.argv[1] helper.check_dir_exist(cg_dir) out_dir = sys.argv[2] helper.make_dir(out_dir) num_chromHMM_model = helper.get_command_line_integer(sys.argv[3]) num_score_bins = helper.get_command_line_integer(sys.argv[4]) cell_type_list_fn = sys.argv[5] ct_list = helper.get_list_from_line_seperated_file(cell_type_list_fn) helper.check_file_exist(cell_type_list_fn) print "Done getting command line arguments" calculate_summary_staistics_across_ct(cg_dir, out_dir, num_chromHMM_model, num_score_bins, ct_list) print "Done!"
def main():
num_mandatory_args = 8
if len(sys.argv) < num_mandatory_args:
usage()
train_segment_fn = sys.argv[1]
helper.check_file_exist(train_segment_fn)
all_ct_segment_folder = sys.argv[
2] # where the segmentation data of all cell types are combined, and stored in files corresponding to different regions in the genome.
if not os.path.isdir(all_ct_segment_folder):
print "all_ct_segment_folder IS NOT VALID: " + all_ct_segment_folder
usage()
predict_outDir = sys.argv[3]
helper.make_dir(predict_outDir)
response_ct = sys.argv[4]
try:
num_chromHMM_state = int(sys.argv[5])
assert num_chromHMM_state > 0, "num_chromHMM_state needs to be positive"
num_train_ct = int(sys.argv[6])
assert num_train_ct > 0, "num_train_ct needs to be positive"
except:
print "num_chromHMM_state or num_train_ct is not valid"
usage()
train_mode = sys.argv[7]
if len(sys.argv) != (num_train_ct + num_mandatory_args):
print "num_train_ct is different from the number of arguments passed into the program"
usage()
print "Done getting command line arguments"
train_cell_types = sys.argv[
num_mandatory_args:] # the rest of the arguments are the cell types that we use to train the model
# 1. Get the data of predictors and response for training
Xtrain_segment_df, Y_df = get_XY_segmentation_data(train_cell_types,
response_ct,
num_chromHMM_state,
train_segment_fn,
train_mode)
print "Done getting one hot data"
print Xtrain_segment_df.head()
print
print Y_df.head()
# 2. Get the regression machine
regression_machine = train_model(Xtrain_segment_df, Y_df,
num_chromHMM_state, train_mode)
print "Done training"
# 3. Based on the machine just created, process training data and then predict the segmentation at each position for the response_ct
predict_segmentation(all_ct_segment_folder, regression_machine,
predict_outDir, train_cell_types, response_ct,
num_chromHMM_state, train_mode)
print "Done predicting whole genome"
def main():
if len(sys.argv) != 4:
usage()
cell_type_folder = sys.argv[1]
if not os.path.isdir(cell_type_folder):
print "cell_type_folder DOES NOT EXIST"
usage()
ct_fn = sys.argv[2]
helper.check_file_exist(ct_fn)
ct_list = get_cell_types_of_interest(
ct_fn) # list of cell types of interests example: ['E003', 'E004']
output_fn = sys.argv[3]
helper.create_folder_for_file(ct_fn)
print "Done getting command line arguments"
# select regions on the genome that we will sample from
genome_sample_df = sample_genome_positions(
cell_type_folder, ct_list, output_fn
) # --> a dataframe of 3 columns: "chromosome", "start_bp", 'end_bp'
def main():
num_mandatory_args = 7
if len(sys.argv) < num_mandatory_args:
usage()
train_segment_fn = sys.argv[1]
helper.check_file_exist(train_segment_fn)
all_ct_posterior_folder = sys.argv[
2] # where the segmentation data of all cell types are combined, and stored in files corresponding to different regions in the genome.
helper.check_dir_exist(all_ct_posterior_folder)
predict_outDir = sys.argv[3]
helper.make_dir(predict_outDir)
response_ct = sys.argv[4]
try:
num_chromHMM_state = int(sys.argv[5])
assert num_chromHMM_state > 0, "num_chromHMM_state needs to be positive"
num_train_ct = int(sys.argv[6])
assert num_train_ct > 0, "num_train_ct needs to be positive"
except:
print "num_chromHMM_state or num_train_ct is not valid"
usage()
if len(sys.argv) != (num_train_ct + num_mandatory_args):
print "num_train_ct is different from the number of arguments passed into the program"
usage()
print "Done getting command line arguments"
train_cell_types = sys.argv[
num_mandatory_args:] # the rest of the arguments are the cell types that we use to train the model
# 1. Get the data of predictors and response for training
Xtrain_segment_df, Y_df = get_XY_segmentation_data(
train_cell_types, response_ct, num_chromHMM_state, train_segment_fn
) # Xtrain_segment_df: example colnames: 'E047_S16', 'E047_S17' --> posterior probabilities of each of the state in each cell type that are used to train
# Y_df --> example colnames 'E047' --> state numbers 1 --> 18 of each position used to train data for the response cell type
print "Done getting one hot data"
print Xtrain_segment_df.head()
print
print Y_df.head()
# 2. Get the regression machine
regression_machine = train_multinomial_logistic_regression(
Xtrain_segment_df, Y_df, num_chromHMM_state)
print "Done training"
# 3. Based on the machine just created, process training data and then predict the segmentation at each position for the response_ct
predict_segmentation(all_ct_posterior_folder, regression_machine,
predict_outDir, train_cell_types, response_ct,
num_chromHMM_state)
print "Done predicting whole genome"
def main(): if len(sys.argv) != 7: usage() cg_dir = sys.argv[1] helper.check_dir_exist(cg_dir) out_dir = sys.argv[2] helper.make_dir(out_dir) state_annotation_fn = sys.argv[3] helper.check_file_exist(state_annotation_fn) state_annot_df = read_state_annot_fn(state_annotation_fn) ct_list_fn = sys.argv[4] helper.check_file_exist(ct_list_fn) ct_list = helper.get_list_from_line_seperated_file(ct_list_fn) num_chromHMM_state = helper.get_command_line_integer(sys.argv[5]) igv_track_name = sys.argv[6] print "Done getting command line arguments" get_average_state_assign_matrix(cg_dir, ct_list, num_chromHMM_state, out_dir) print "Done getting the representative state semgentation for the cellg group" draw_genome_pos_list = ['chr5_15'] # create_igv_format_bed(out_dir, state_annot_df, draw_genome_pos_list, igv_track_name) print "Done!"
def main():
if len(sys.argv) != 7:
usage()
train_sampled_data_fn = sys.argv[1]
helper.check_file_exist(train_sampled_data_fn)
outDir = sys.argv[2]
helper.make_dir(outDir)
all_ct_posterior_folder = sys.argv[3]
helper.check_dir_exist(all_ct_posterior_folder)
num_chromHMM_state = helper.get_command_line_integer(sys.argv[4])
validate_ct = sys.argv[5]
all_ct_list_fn = sys.argv[6]
print "Done getting command line arguments"
# get all cell types
ct_list = get_all_train_ct_list(all_ct_list_fn, validate_ct)
print ct_list
# call all cell types
call_cross_validation_functions(validate_ct, ct_list, outDir,
train_sampled_data_fn,
all_ct_posterior_folder,
num_chromHMM_state)
def main():
if len(sys.argv) != 8:
usage()
train_sampled_data_fn = sys.argv[1]
helper.check_file_exist(train_sampled_data_fn)
outDir = sys.argv[2]
helper.make_dir(outDir)
all_ct_segment_folder = sys.argv[3]
helper.check_dir_exist(all_ct_segment_folder)
num_chromHMM_state = helper.get_command_line_integer(sys.argv[4])
validate_ct = sys.argv[5]
train_mode = sys.argv[6]
all_ct_list_fn = sys.argv[7]
print "Done getting command line arguments"
# get the list of all genomic positions used to segment the genome for our model training (we exclude chromosome Y in all analysis)
gen_pos_list = get_genomic_positions_list(all_ct_segment_folder)
# get all cell types
ct_list = get_all_train_ct_list(all_ct_list_fn, validate_ct)
# call all cell types
call_cross_validation_functions(validate_ct, ct_list, outDir,
train_sampled_data_fn,
all_ct_segment_folder, num_chromHMM_state,
gen_pos_list, train_mode)