def run_igdiscover(infname, outfname, outdir):
if utils.output_exists(args, outfname):
return
prepare_igdiscover_outdir(outdir)
if args.n_random_queries is not None:
sub_infname = outdir + '/' + os.path.basename(infname.replace(utils.getsuffix(infname), '-n-random-queries-%d%s' % (args.n_random_queries, utils.getsuffix(infname))))
if os.path.exists(sub_infname):
print ' --n-random-queries: leaving existing fasta for igdiscover (hopefully it has %d queries)' % args.n_random_queries
else:
print ' --n-random-queries: writing new fasta for igdiscover (%d queries)' % args.n_random_queries
seqfos = utils.read_fastx(infname, n_random_queries=args.n_random_queries)
with open(sub_infname, 'w') as sub_infile:
for seqfo in seqfos:
sub_infile.write('>%s\n%s\n' % (seqfo['name'], seqfo['seq']))
infname = sub_infname
igdiscover_outfname = outdir + '/work/final/database/%s.fasta' % args.region.upper()
cmds = getpathcmd()
cmds += ['conda activate %s' % args.env_label]
cmds += ['cd %s' % outdir]
cmds += ['igdiscover init --db db --single-reads %s work' % infname] # prepares to run, putting files into <outdir>
cmds += ['cp %s work/' % os.path.basename(args.yamlfname)]
cmds += ['cd work']
cmds += ['igdiscover run']
utils.simplerun('\n'.join(cmds) + '\n', cmdfname=outdir + '/run.sh', print_time='igdiscover', debug=True)
template_gldir = args.glfo_dir # if args.glfo_dir is not None else 'data/germlines/ XXX human' # can probably delete this now that --glfo-dir is required (but leaving for now, to show how it used to be in case it comes up)
glfo = glutils.create_glfo_from_fasta(igdiscover_outfname, args.locus, args.region, template_gldir, simulation_germline_dir=args.simulation_germline_dir)
out_gldir = os.path.dirname(outfname).rstrip('/' + args.locus)
assert glutils.get_fname(out_gldir, args.locus, args.region) == outfname
glutils.write_glfo(out_gldir, glfo, debug=True)
def run_other_method(args, method):
if method not in ['tigger-default', 'tigger-tuned', 'igdiscover']: # really just to make it easier to search for this fcn
assert False
assert args.n_max_queries is None
if utils.output_exists(args, get_outfname(args, method)):
return
simfasta = utils.getprefix(args.simfname) + '.fa'
utils.csv_to_fasta(args.simfname, outfname=simfasta, overwrite=False, remove_duplicates=True)
cmd = './test/%s-run.py' % method.split('-')[0]
if method == 'tigger-tuned':
cmd += ' --tuned-tigger-params'
cmd += ' --infname ' + simfasta
cmd += ' --outfname ' + get_outfname(args, method)
if args.species != 'human':
cmd += ' --species %s' % args.species
if args.overwrite:
cmd += ' --overwrite'
if args.gls_gen:
cmd += ' --gls-gen'
cmd += ' --glfo-dir ' + partis_dir + '/' + args.default_germline_dir # the partis mehods have this as the default internally, but we want/have to set it explicitly here
else:
cmd += ' --glfo-dir ' + args.inf_glfo_dir
cmd += ' --simulation-germline-dir ' + args.outdir + '/germlines/simulation' # alleleclusterer is the only one that really uses this, but for now I want its dbg output to have the sim info
if method != 'igdiscover': # for now we're saving all the igdiscover output/intermediate files, so we write them to an output dir
cmd += ' --workdir ' + args.workdir + '/' + method
cmd += ' --n-procs ' + str(args.n_procs)
if args.slurm:
cmd += ' --slurm'
utils.simplerun(cmd, dryrun=args.dry_run)
def simulate():
rearrange()
glfo, naive_event_list, cpath = utils.read_output(naive_fname())
assert len(naive_event_list) == args.n_sim_events
outdirs = [
'%s/event-%d' % (simdir(), i) for i in range(len(naive_event_list))
]
for ievent, (naive_line,
outdir) in enumerate(zip(naive_event_list, outdirs)):
run_bcr_phylo(naive_line, outdir, ievent)
if utils.output_exists(
args, simfname(), outlabel='mutated simu', offset=4
): # i guess if it crashes during the plotting just below, this'll get confused
return
mutated_events = []
for ievent, (naive_line,
outdir) in enumerate(zip(naive_event_list, outdirs)):
mutated_events.append(
parse_bcr_phylo_output(glfo, naive_line, outdir, ievent))
print ' writing annotations to %s' % simfname()
utils.write_annotations(simfname(), glfo, mutated_events,
utils.simulation_headers)
import plotting
for outdir, event in zip(outdirs, mutated_events):
plotting.plot_bcr_phylo_simulation(outdir, event, args.extrastr,
args.metric_for_target_distance)
def run_other_method(args, method):
if method not in [
'tigger', 'igdiscover'
]: # really just to make it easier to search for this fcn
assert False
if utils.output_exists(args, get_outfname(args, method)):
return
simfasta = utils.getprefix(args.simfname) + '.fa'
utils.csv_to_fasta(args.simfname,
outfname=simfasta,
overwrite=False,
remove_duplicates=True)
cmd = './test/%s-run.py' % method
cmd += ' --infname ' + simfasta
cmd += ' --outfname ' + get_outfname(args, method)
if args.overwrite:
cmd += ' --overwrite'
if args.gls_gen:
cmd += ' --gls-gen'
cmd += ' --glfo-dir ' + partis_dir + '/data/germlines/human' # the partis mehods have this as the default internally, but we want/have to set it explicitly here
else:
cmd += ' --glfo-dir ' + args.inf_glfo_dir
if method != 'igdiscover': # for now we're saving all the igdiscover output/intermediate files, so we write them to an output dir
cmd += ' --workdir ' + args.workdir + '/' + method
cmd += ' --n-procs ' + str(args.n_procs)
utils.simplerun(cmd, dryrun=args.dry_run)
def run_partis(infname, outfname):
if utils.output_exists(args, outfname, offset=8):
return
aligned_gl_seqs = {} # keyed by seq so it's easy to check for duplicates
for r in utils.regions: # deduplicate before passing to partis
for seqfo in utils.read_fastx(get_glfname(r, aligned=True)):
if seqfo['seq'] in aligned_gl_seqs:
continue
aligned_gl_seqs[seqfo['seq']] = '|'.join(seqfo['infostrs'])
aligned_germline_fname = args.workdir + '/all-aligned-gl-seqs.fa'
with open(aligned_germline_fname, 'w') as merged_file:
for seq, gene in aligned_gl_seqs.items():
merged_file.write('>%s\n%s\n' % (gene, seq))
cmd = './bin/partis cache-parameters'
cmd += ' --infname ' + infname
cmd += ' --leave-default-germline'
cmd += ' --presto-output --only-smith-waterman'
cmd += ' --outfname ' + outfname
if args.glfo_dir is not None:
cmd += ' --initial-germline-dir ' + args.glfo_dir
cmd += ' --aligned-germline-fname ' + aligned_germline_fname
cmd += ' --n-procs ' + str(args.n_procs)
utils.simplerun(cmd, print_time='partis annotation')
os.remove(aligned_germline_fname)
def run_igdiscover(infname, outfname, outdir):
if utils.output_exists(args, outfname):
return
cmds = ['#!/bin/bash']
cmds += ['export PATH=%s:$PATH' % args.condapath]
cmds += [
'export PYTHONNOUSERSITE=True'
] # otherwise it finds the pip-installed packages in .local and breaks (see https://github.com/conda/conda/issues/448)
cmds += ['cd %s' % outdir]
cmds += ['igdiscover init --db db --single-reads %s work' % args.infname
] # prepares to run, putting files into <outdir>
cmds += ['cp %s work/' % os.path.basename(args.yamlfname)]
cmds += ['cd work']
cmds += ['igdiscover run']
cmdfname = outdir + '/run.sh'
with open(cmdfname, 'w') as cmdfile:
for cmd in cmds:
cmdfile.write(cmd + '\n')
subprocess.check_call(['chmod', '+x', cmdfname])
cmdfos = [{
'cmd_str': cmdfname,
'workdir': outdir,
'outfname': outdir + '/work/final/%s_usage.tab' % 'v'.upper()
}]
utils.simplerun(cmdfname, shell=True, print_time='igdiscover')
def run_bcr_phylo(naive_line, outdir, ievent, n_total_events, uid_str_len=None):
if utils.output_exists(args, bcr_phylo_fasta_fname(outdir), outlabel='bcr-phylo', offset=4):
return None
cmd = '%s/bin/simulator.py' % bcr_phylo_path
if args.run_help:
cmd += ' --help'
elif args.stype == 'neutral':
assert False # needs updating (well, maybe not, but I'm not thinking about it when I move the selection parameters to command line args)
cmd += ' --lambda %f --lambda0 %f' % (1.5, 0.365)
cmd += ' --n_final_seqs %d' % args.n_sim_seqs_per_generation
elif args.stype == 'selection':
cmd += ' --selection'
cmd += ' --lambda %f' % args.branching_parameter
cmd += ' --lambda0 %f' % args.base_mutation_rate
cmd += ' --selection_strength %f' % get_vpar_val('selection-strength', args.selection_strength)
cmd += ' --obs_times %s' % ' '.join(['%d' % get_vpar_val('obs-times', t) for t in args.obs_times])
cmd += ' --n_to_sample %s' % ' '.join('%d' % get_vpar_val('n-sim-seqs-per-generation', n) for n in args.n_sim_seqs_per_generation)
cmd += ' --metric_for_target_dist %s' % args.metric_for_target_distance
if args.paratope_positions is not None:
cmd += ' --paratope_positions %s' % args.paratope_positions
cmd += ' --target_dist %d' % args.target_distance
cmd += ' --target_count %d' % args.target_count
cmd += ' --carry_cap %d' % get_vpar_val('carry-cap', args.carry_cap)
if not args.dont_observe_common_ancestors:
cmd += ' --observe_common_ancestors'
if args.leaf_sampling_scheme is not None:
cmd += ' --leaf_sampling_scheme %s' % args.leaf_sampling_scheme
if args.n_target_clusters is not None:
cmd += ' --n_target_clusters %d' % args.n_target_clusters
# cmd += ' --target_cluster_distance 1'
if args.min_target_distance is not None:
cmd += ' --min_target_distance %d' % args.min_target_distance
else:
assert False
cmd += ' --debug %d' % args.debug
cmd += ' --n_tries 1000'
if args.context_depend == 0:
cmd += ' --no_context'
cmd += ' --no_plot'
if args.only_csv_plots:
cmd += ' --dont_write_hists'
cmd += ' --outbase %s/%s' % (outdir, args.extrastr)
cmd += ' --random_seed %d' % (args.seed + ievent)
if uid_str_len is not None:
cmd += ' --uid_str_len %d' % uid_str_len
cmd += ' --naive_seq %s' % naive_line['naive_seq']
if not os.path.exists(outdir):
os.makedirs(outdir)
cfo = None
if args.n_procs == 1:
utils.run_ete_script(cmd, ete_path) # NOTE kind of hard to add a --dry-run option, since we have to loop over the events we made in rearrange()
else:
cmd, _ = utils.run_ete_script(cmd, ete_path, return_for_cmdfos=True, tmpdir=outdir)
cfo = {'cmd_str' : cmd, 'workdir' : outdir, 'outfname' : bcr_phylo_fasta_fname(outdir)}
return cfo
def run_changeo(infname, igblast_outfname, outfname):
if utils.output_exists(args, outfname, offset=8):
return
glfnames = [get_glfname(r, aligned=True) for r in utils.regions]
cmd = args.changeo_path + '/bin/MakeDb.py igblast'
cmd += ' -i %s -s %s -r %s --regions --scores' % (igblast_outfname, infname, ' '.join(glfnames))
utils.simplerun(cmd, print_time='changeo')
def cache_parameters():
if utils.output_exists(args,
param_dir() + '/hmm/hmms',
outlabel='parameters',
offset=4):
return
cmd = './bin/partis cache-parameters --infname %s --parameter-dir %s --n-procs %d --seed %d' % (
simfname(), param_dir(), args.n_procs, args.seed)
utils.simplerun(cmd, debug=True) #, dryrun=True)
def run_partis_parameter_cache(args, method):
if utils.output_exists(args, get_outfname(args, method)):
return
paramdir = args.outdir + '/' + method
plotdir = args.outdir + '/' + method + '/plots'
# remove any old sw cache files
sw_cachefiles = glob.glob(paramdir + '/sw-cache-*.csv')
if len(sw_cachefiles) > 0:
for cachefname in sw_cachefiles:
check_call(['rm', '-v', cachefname])
sw_cache_gldir = cachefname.replace('.csv', '-glfo')
if os.path.exists(
sw_cache_gldir
): # if stuff fails halfway through, you can get one but not the other
glutils.remove_glfo_files(sw_cache_gldir, args.locus)
# os.rmdir(sw_cache_gldir)
# generate germline set and cache parameters
cmd_str = args.partis_path + ' cache-parameters --infname ' + args.simfname + ' --only-smith-waterman'
cmd_str += ' --initial-germline-dir %s' % args.default_germline_dir
if method == 'partis':
cmd_str += ' --debug-allele-finding' # --always-find-new-alleles'
cmd_str += ' --is-simu --simulation-germline-dir ' + args.outdir + '/germlines/simulation' # alleleclusterer is the only one that really uses this, but for now I want its dbg output to have the sim info
if args.allele_cluster:
cmd_str += ' --allele-cluster'
if args.kmeans_allele_cluster:
cmd_str += ' --kmeans-allele-cluster'
elif method == 'full':
cmd_str += ' --leave-default-germline'
else:
assert False
if args.species != 'human':
cmd_str += ' --species %s' % args.species
cmd_str += ' --n-procs ' + str(args.n_procs)
if args.n_max_queries is not None:
cmd_str += ' --n-max-queries ' + str(
args.n_max_queries
) # NOTE do *not* use --n-random-queries, since it'll change the cluster size distribution
if args.slurm:
cmd_str += ' --batch-system slurm'
if not args.gls_gen: # otherwise it uses the default (full) germline dir
cmd_str += ' --initial-germline-dir ' + args.inf_glfo_dir # --dont-remove-unlikely-alleles
cmd_str += ' --parameter-dir ' + paramdir
cmd_str += ' --plotdir ' + plotdir
if args.seed is not None:
cmd_str += ' --seed ' + str(args.seed)
if args.plot_and_fit_absolutely_everything is not None:
cmd_str += ' --plot-and-fit-absolutely-everything ' + str(
args.plot_and_fit_absolutely_everything)
utils.simplerun(cmd_str, dryrun=args.dryrun)
def rearrange():
if utils.output_exists(args,
naive_fname(),
outlabel='naive simu',
offset=4):
return
cmd = './bin/partis simulate --simulate-from-scratch --mutation-multiplier 0.0001 --n-leaves 1 --constant-number-of-leaves' # tends to get in infinite loop if you actually pass 0. (yes, I should fix this)
cmd += ' --debug %d --seed %d --outfname %s --n-sim-events %d' % (int(
args.debug), args.seed, naive_fname(), args.n_sim_events)
utils.simplerun(cmd, debug=True)
def run_bcr_phylo(naive_line, outdir, ievent, n_total_events):
if utils.output_exists(args,
bcr_phylo_fasta_fname(outdir),
outlabel='bcr-phylo',
offset=4):
return
cmd = '%s/bin/simulator.py' % bcr_phylo_path
if args.run_help:
cmd += ' --help'
elif args.stype == 'neutral':
assert False # needs updating (well, maybe not, but I'm not thinking about it when I move the selection parameters to command line args)
cmd += ' --lambda %f --lambda0 %f' % (1.5, 0.365)
cmd += ' --n_final_seqs %d' % args.n_sim_seqs_per_generation
elif args.stype == 'selection':
cmd += ' --selection'
cmd += ' --lambda %f' % args.branching_parameter
cmd += ' --lambda0 %f' % args.base_mutation_rate
cmd += ' --selection_strength %f' % get_vpar_val(
'selection-strength', args.selection_strength)
cmd += ' --obs_times %s' % ' '.join(
['%d' % get_vpar_val('obs-times', t) for t in args.obs_times])
cmd += ' --n_to_sample %s' % ' '.join(
'%d' % get_vpar_val('n-sim-seqs-per-generation', n)
for n in args.n_sim_seqs_per_generation)
cmd += ' --metric_for_target_dist %s' % args.metric_for_target_distance
cmd += ' --target_dist %d' % args.target_distance
cmd += ' --target_count %d' % args.target_count
cmd += ' --carry_cap %d' % get_vpar_val('carry-cap', args.carry_cap)
if not args.dont_observe_common_ancestors:
cmd += ' --observe_common_ancestors'
# cmd += ' --n_target_clusters 1'
# cmd += ' --target_cluster_distance 1'
# cmd += ' --observe_based_on_affinity' # implementation in bcr-phylo needs some work
else:
assert False
cmd += ' --debug %d' % args.debug
cmd += ' --n_tries 30'
cmd += ' --no_context'
cmd += ' --no_plot'
cmd += ' --outbase %s/%s' % (outdir, args.extrastr)
cmd += ' --random_seed %d' % (args.seed + ievent)
if n_total_events > 1: # if the final sample's going to contain many trees, it's worth making the uids longer so there's fewer collisions/duplicates
cmd += ' --uid_str_len 7'
cmd += ' --naive_seq %s' % naive_line['naive_seq']
if not os.path.exists(outdir):
os.makedirs(outdir)
utils.run_ete_script(
cmd, ete_path
) # NOTE kind of hard to add a --dry-run option, since we have to loop over the events we made in rearrange()
def cache_parameters():
if utils.output_exists(args, param_dir() + '/hmm/hmms', outlabel='parameters', offset=4):
return
cmd = './bin/partis cache-parameters --infname %s --parameter-dir %s --seed %d --no-indels' % (simfname(), param_dir(), args.seed) # forbid indels because in the very rare cases when we call them, they're always wrong, and then they screw up the simultaneous true clonal seqs option
if args.n_procs > 1:
cmd += ' --n-procs %d' % args.n_procs
if args.slurm:
cmd += ' --batch-system slurm'
if args.n_max_queries is not None:
cmd += ' --n-max-queries %d' % args.n_max_queries
utils.simplerun(cmd, debug=True) #, dryrun=True)
def partition():
if utils.output_exists(args,
partition_fname(),
outlabel='partition',
offset=4):
return
cmd = './bin/partis partition --n-final-clusters 1 --write-additional-cluster-annotations 0:5 --is-simu --get-tree-metrics --infname %s --parameter-dir %s --plotdir %s --n-procs %d --outfname %s --seed %d' % (
simfname(), param_dir(), infdir() + '/plots', args.n_procs,
partition_fname(), args.seed)
if args.lb_tau is not None:
cmd += ' --lb-tau %f' % args.lb_tau
utils.simplerun(cmd, debug=True) #, dryrun=True)
def rearrange():
if utils.output_exists(args, naive_fname(), outlabel='naive simu', offset=4):
return
cmd = './bin/partis simulate --simulate-from-scratch --mutation-multiplier 0.0001 --n-leaves 1 --constant-number-of-leaves' # tends to get in infinite loop if you actually pass 0. (yes, I should fix this)
cmd += ' --debug %d --seed %d --outfname %s --n-sim-events %d' % (int(args.debug), args.seed, naive_fname(), args.n_sim_events)
if args.restrict_available_genes:
cmd += ' --only-genes IGHV1-18*01:IGHJ1*01'
if args.n_procs > 1:
cmd += ' --n-procs %d' % args.n_procs
if args.slurm:
cmd += ' --batch-system slurm'
utils.simplerun(cmd, debug=True)
def run_igdiscover(infname, outfname, outdir):
if utils.output_exists(args, outfname):
return
prepare_igdiscover_outdir(outdir)
if args.n_random_queries is not None:
sub_infname = outdir + '/' + os.path.basename(
infname.replace(
utils.getsuffix(infname), '-n-random-queries-%d%s' %
(args.n_random_queries, utils.getsuffix(infname))))
if os.path.exists(sub_infname):
print ' --n-random-queries: leaving existing fasta for igdiscover (hopefully it has %d queries)' % args.n_random_queries
else:
print ' --n-random-queries: writing new fasta for igdiscover (%d queries)' % args.n_random_queries
seqfos = utils.read_fastx(infname,
n_random_queries=args.n_random_queries)
with open(sub_infname, 'w') as sub_infile:
for seqfo in seqfos:
sub_infile.write('>%s\n%s\n' %
(seqfo['name'], seqfo['seq']))
infname = sub_infname
igdiscover_outfname = outdir + '/work/final/database/%s.fasta' % args.region.upper(
)
cmds = ['#!/bin/bash']
cmds += ['export PATH=%s:$PATH' % args.condapath]
cmds += [
'export PYTHONNOUSERSITE=True'
] # otherwise it finds the pip-installed packages in .local and breaks (see https://github.com/conda/conda/issues/448)
cmds += ['cd %s' % outdir]
cmds += ['igdiscover init --db db --single-reads %s work' % infname
] # prepares to run, putting files into <outdir>
cmds += ['cp %s work/' % os.path.basename(args.yamlfname)]
cmds += ['cd work']
cmds += ['igdiscover run']
utils.simplerun('\n'.join(cmds) + '\n',
cmdfname=outdir + '/run.sh',
print_time='igdiscover',
debug=True)
template_gldir = args.glfo_dir if args.glfo_dir is not None else 'data/germlines/human'
glfo = glutils.create_glfo_from_fasta(
igdiscover_outfname,
args.locus,
args.region,
template_gldir,
simulation_germline_dir=args.simulation_germline_dir)
out_gldir = os.path.dirname(outfname).rstrip('/' + args.locus)
assert glutils.get_fname(out_gldir, args.locus, args.region) == outfname
glutils.write_glfo(out_gldir, glfo, debug=True)
def write_locus_file(locus, ofos, lpair=None, extra_str=' '):
ofn = utils.paired_fn(args.outdir, locus=locus, lpair=lpair)
if utils.output_exists(args, ofn, leave_zero_len=len(ofos)==0, offset=4): # NOTE not really sure this does anything (or if i want it) now that I'm cleaning/looking for the whole dir at the start of this script
return
if not os.path.exists(os.path.dirname(ofn)):
os.makedirs(os.path.dirname(ofn))
if len(ofos) == 0:
# print '%s%s: nothing to write' % (extra_str, locus)
open(ofn, 'w').close()
return
print '%s%s: %d to %s/%s' % (extra_str, locus, len(ofos), os.path.basename(os.path.dirname(ofn)), os.path.basename(ofn))
with open(ofn, 'w') as lfile:
for sfo in ofos:
lfile.write('>%s\n%s\n' % (sfo['name'], sfo['seq']))
def rearrange():
if utils.output_exists(args,
naive_fname(),
outlabel='naive simu',
offset=4):
return
cmd = './bin/partis simulate --simulate-from-scratch --mutation-multiplier 0.0001 --n-leaves 1 --constant-number-of-leaves' # tends to get in infinite loop if you actually pass 0. (yes, I should fix this)
cmd += ' --debug %d --seed %d --outfname %s --n-sim-events %d' % (int(
args.debug), args.seed, naive_fname(), args.n_sim_events)
if args.n_procs > 1 and args.n_sim_events % args.n_procs == 0: # if --n-procs is not divisble by --n-sim-events, partis simulate doesn't give you exactly the number you asked for
cmd += ' --n-procs %d' % args.n_procs
if args.slurm:
cmd += ' --batch-system slurm'
utils.simplerun(cmd, debug=True)
def partition():
if utils.output_exists(args,
partition_fname(),
outlabel='partition',
offset=4):
return
cmd = './bin/partis partition --simultaneous-true-clonal-seqs --is-simu --infname %s --parameter-dir %s --n-procs %d --outfname %s --seed %d' % (
simfname(), param_dir(), args.n_procs, partition_fname(), args.seed)
# --write-additional-cluster-annotations 0:5 # I don't think there was really a good reason for having this
if not args.dont_get_tree_metrics:
cmd += ' --get-tree-metrics --plotdir %s' % (infdir() + '/plots')
if args.lb_tau is not None:
cmd += ' --lb-tau %f' % args.lb_tau
utils.simplerun(cmd, debug=True) #, dryrun=True)
def simulate(args):
if utils.output_exists(args, args.simfname):
return
cmd_str = args.partis_path + ' simulate --n-sim-events ' + str(args.n_sim_events) + ' --outfname ' + args.simfname + ' --n-leaves ' + str(args.n_leaves) + ' --rearrange-from-scratch --shm-parameter-dir ' + partis_dir + '/data/recombinator/scratch-parameters'
if args.n_leaf_distribution is None:
cmd_str += ' --constant-number-of-leaves'
else:
cmd_str += ' --n-leaf-distribution ' + args.n_leaf_distribution
if args.mut_mult is not None:
cmd_str += ' --mutation-multiplier ' + str(args.mut_mult)
if args.root_mrca_weibull_parameter is not None:
cmd_str += ' --root-mrca-weibull-parameter ' + str(args.root_mrca_weibull_parameter)
cmd_str += ' --n-procs ' + str(args.n_procs)
if args.slurm:
cmd_str += ' --batch-system slurm --subsimproc'
allele_prevalence_fname = args.workdir + '/allele-prevalence-freqs.csv'
# figure what genes we're using
if args.gls_gen:
assert args.sim_v_genes is None and args.allele_prevalence_freqs is None
sglfo = glutils.read_glfo(args.default_germline_dir, locus=args.locus)
glutils.remove_v_genes_with_bad_cysteines(sglfo)
glutils.generate_germline_set(sglfo, args.n_genes_per_region, args.n_sim_alleles_per_gene, args.min_allele_prevalence_freq, allele_prevalence_fname, new_allele_info=args.new_allele_info, dont_remove_template_genes=args.dont_remove_template_genes, debug=True)
cmd_str += ' --allele-prevalence-fname ' + allele_prevalence_fname
else:
sglfo = glutils.read_glfo(args.default_germline_dir, locus=args.locus, only_genes=(args.sim_v_genes + args.dj_genes))
added_snp_names = glutils.generate_new_alleles(sglfo, args.new_allele_info, debug=True, remove_template_genes=(not args.dont_remove_template_genes)) # NOTE template gene removal is the default for glutils.generate_germline_set
if args.allele_prevalence_freqs is not None:
if not utils.is_normed(args.allele_prevalence_freqs):
raise Exception('--allele-prevalence-freqs %s not normalized' % args.allele_prevalence_freqs)
if len(args.allele_prevalence_freqs) != len(sglfo['seqs']['v']): # already checked when parsing args, but, you know...
raise Exception('--allele-prevalence-freqs %d not the same length as sglfo %d' % (len(args.allele_prevalence_freqs), len(sglfo['seqs']['v'])))
gene_list = sorted(sglfo['seqs']['v']) if len(added_snp_names) == 0 else list(set(args.sim_v_genes)) + added_snp_names
prevalence_freqs = {'v' : {g : f for g, f in zip(gene_list, args.allele_prevalence_freqs)}, 'd' : {}, 'j' : {}}
glutils.write_allele_prevalence_freqs(prevalence_freqs, allele_prevalence_fname)
cmd_str += ' --allele-prevalence-fname ' + allele_prevalence_fname
glutils.write_glfo(args.outdir + '/germlines/simulation', sglfo)
cmd_str += ' --initial-germline-dir ' + args.outdir + '/germlines/simulation'
# glutils.print_glfo(sglfo)
# run simulation
if args.seed is not None:
cmd_str += ' --seed ' + str(args.seed)
utils.simplerun(cmd_str, dryrun=args.dry_run)
def run_tigger(infname, outfname, outdir):
if utils.output_exists(args, outfname, offset=8):
return
rcmds = ['library(tigger)', 'library(dplyr)']
# rcmds += ['data(sample_db, germline_ighv)']
db_name = 'annotations'
gls_name = 'gls'
rcmds += ['%s = read.csv("%s", sep="\t")' % (db_name, infname)]
rcmds += ['%s = readIgFasta("%s")' % (gls_name, get_glfname('v', aligned=True))]
tigger_outfname = outdir + '/tigger.fasta'
rcmds += ['novel_df = findNovelAlleles(%s, %s, germline_min=2, nproc=%d)' % (db_name, gls_name, args.n_procs)] #
rcmds += ['geno = inferGenotype(%s, find_unmutated = FALSE, germline_db = %s, novel_df = novel_df)' % (db_name, gls_name)]
rcmds += ['genotype_seqs = genotypeFasta(geno, %s, novel_df)' % (gls_name)]
rcmds += ['writeFasta(genotype_seqs, "%s")' % tigger_outfname]
cmdfname = args.workdir + '/tigger-in.cmd'
with open(cmdfname, 'w') as cmdfile:
cmdfile.write('\n'.join(rcmds) + '\n')
cmdstr = 'R --slave -f ' + cmdfname
utils.simplerun(cmdstr, shell=True, print_time='tigger')
# post-process tigger .fa
gldir = args.glfo_dir if args.glfo_dir is not None else 'data/germlines/human'
glfo = glutils.read_glfo(gldir, args.locus)
tigger_alleles = set()
for seqfo in utils.read_fastx(tigger_outfname):
seq = seqfo['seq'].replace(utils.gap_chars[0], '') # it should be just dots...
tigger_alleles.add(seqfo['name'])
if seqfo['name'] not in glfo['seqs'][args.region]:
newfo = {'gene' : seqfo['name'], 'seq' : seq}
use_template_for_codon_info = False
if '+' in newfo['gene']:
newfo['template-gene'] = newfo['gene'].split('+')[0]
use_template_for_codon_info = True
glutils.add_new_allele(glfo, newfo, use_template_for_codon_info=use_template_for_codon_info, debug=True)
elif glfo['seqs'][args.region][seqfo['name']] != seq:
print '%s different sequences in glfo and tigger output for %s:\n %s\n %s' % (utils.color('red', 'error'), seqfo['name'], glfo['seqs'][args.region][seqfo['name']], seqfo['seq'])
for gene in glfo['seqs'][args.region]: # remove them afterwards so we can use existing ones to get codon info
if gene not in tigger_alleles:
glutils.remove_gene(glfo, gene)
out_gldir = os.path.dirname(outfname).rstrip('/' + args.locus)
assert glutils.get_fname(out_gldir, args.locus, args.region) == outfname
glutils.write_glfo(out_gldir, glfo)
os.remove(cmdfname)
def cache_parameters():
if utils.output_exists(args,
ifname('params'),
outlabel='parameters',
offset=4):
return
cmd = './bin/partis cache-parameters --seed %d --no-indels' % args.seed # forbid indels because in the very rare cases when we call them, they're always wrong, and then they screw up the simultaneous true clonal seqs option
fstr = ' --paired-loci --paired-indir %s --paired-outdir %s' if args.paired_loci else ' --infname %s --parameter-dir %s'
cmd += fstr % (spath('mutated'), ipath('params'))
if args.n_procs > 1:
cmd += ' --n-procs %d' % args.n_procs
if args.slurm:
cmd += ' --batch-system slurm'
if args.n_max_queries is not None:
cmd += ' --n-max-queries %d' % args.n_max_queries
utils.simplerun(cmd, debug=True, dryrun=args.dry_run)
def run_igblast(infname, outfname):
if utils.output_exists(args, outfname, offset=8):
return
if args.glfo_dir is not None:
print '%s --glfo-dir isn\'t getting plugged in to igblast/changeo (would need to rebuild igblast db)' % utils.color('red', 'warning')
cmd = './igblastn'
cmd += ' -germline_db_V human_gl_V -germline_db_D human_gl_V -germline_db_J human_gl_J'
cmd += ' -auxiliary_data optional_file/human_gl.aux'
cmd += ' -domain_system imgt -ig_seqtype Ig -organism human -outfmt \'7 std qseq sseq btop\''
cmd += ' -num_threads %d' % args.n_procs
cmd += ' -query ' + infname + ' -out ' + outfname
cmd = 'cd %s; %s' % (args.igbdir, cmd)
utils.simplerun(cmd, shell=True, print_time='igblast')
def run_performance_plot(args, method):
perf_outdir = get_outfname(args, method, annotation_performance_plots=True)
if utils.output_exists(args, perf_outdir):
return
cmd_str = args.partis_path + ' cache-parameters --infname ' + args.simfname + ' --plot-annotation-performance'
cmd_str += ' --is-simu --simulation-germline-dir ' + args.outdir + '/germlines/simulation'
cmd_str += ' --initial-germline-dir ' + get_outfname(args, method, return_parent_gl_dir=True) # i.e. use the inferred glfo from <method>
cmd_str += ' --parameter-dir ' + perf_outdir + '/dummy-parameter-dir'
cmd_str += ' --only-overall-plots --plotdir ' + perf_outdir
cmd_str += ' --only-smith-waterman --leave-default-germline --dont-write-parameters' # i.e. we really want to annotate, not cache parameters, but then it'd look for a parameter dir
cmd_str += ' --n-procs ' + str(args.n_procs)
if args.n_max_queries is not None:
cmd_str += ' --n-max-queries ' + str(args.n_max_queries) # NOTE do *not* use --n-random-queries, since it'll change the cluster size distribution
if args.slurm:
cmd_str += ' --batch-system slurm'
if args.seed is not None:
cmd_str += ' --seed ' + str(args.seed)
utils.simplerun(cmd_str, dryrun=args.dry_run)
def rearrange():
if utils.output_exists(
args, naive_fname('igh'), outlabel='naive simu', offset=4
): # just look for the merged igh file, since it's about the last to be written (and both paired subdirs may not be there)
return
cmd = './bin/partis simulate --simulate-from-scratch --mutation-multiplier 0.0001 --n-leaves 1 --constant-number-of-leaves' # tends to get in infinite loop if you actually pass 0. (yes, I should fix this)
cmd += ' --debug %d --seed %d --n-sim-events %d' % (int(
args.debug), args.seed, args.n_sim_events)
if args.paired_loci:
cmd += ' --paired-loci --paired-outdir %s' % spath('naive')
else:
cmd += ' --outfname %s' % spath('naive')
if args.restrict_available_genes:
assert not args.paired_loci
cmd += ' --only-genes IGHV1-18*01:IGHJ1*01'
if args.n_procs > 1:
cmd += ' --n-procs %d' % args.n_procs
if args.slurm:
cmd += ' --batch-system slurm'
utils.simplerun(cmd, dryrun=args.dry_run, debug=True)
def simulate():
rearrange()
glfo, naive_event_list, cpath = utils.read_output(naive_fname())
assert len(naive_event_list) == args.n_sim_events
outdirs = ['%s/event-%d' % (simdir(), i) for i in range(len(naive_event_list))]
start = time.time()
cmdfos = []
if args.n_procs > 1:
print ' starting %d events' % len(naive_event_list)
uid_str_len = 6 + int(math.log(len(naive_event_list), 10)) # if the final sample's going to contain many trees, it's worth making the uids longer so there's fewer collisions/duplicates
for ievent, (naive_line, outdir) in enumerate(zip(naive_event_list, outdirs)):
if args.n_sim_events > 1 and args.n_procs == 1:
print ' %s %d' % (utils.color('blue', 'ievent'), ievent)
cfo = run_bcr_phylo(naive_line, outdir, ievent, len(naive_event_list), uid_str_len=uid_str_len) # if n_procs > 1, doesn't run, just returns cfo
if cfo is not None:
print ' %s %s' % (utils.color('red', 'run'), cfo['cmd_str'])
cmdfos.append(cfo)
if args.n_procs > 1 and len(cmdfos) > 0:
utils.run_cmds(cmdfos, shell=True, n_max_procs=args.n_procs, batch_system='slurm' if args.slurm else None, allow_failure=True, debug='print')
print ' bcr-phylo run time: %.1fs' % (time.time() - start)
if utils.output_exists(args, simfname(), outlabel='mutated simu', offset=4): # i guess if it crashes during the plotting just below, this'll get confused
return
start = time.time()
mutated_events = []
for ievent, (naive_line, outdir) in enumerate(zip(naive_event_list, outdirs)):
mutated_events.append(parse_bcr_phylo_output(glfo, naive_line, outdir, ievent))
print ' parsing time: %.1fs' % (time.time() - start)
print ' writing annotations to %s' % simfname()
utils.write_annotations(simfname(), glfo, mutated_events, utils.simulation_headers)
if not args.only_csv_plots:
import lbplotting
for outdir, event in zip(outdirs, mutated_events):
lbplotting.plot_bcr_phylo_simulation(outdir, event, args.extrastr, lbplotting.metric_for_target_distance_labels[args.metric_for_target_distance])
def run_igblast(infname, outfname):
if utils.output_exists(args, outfname, offset=8):
return
if args.glfo_dir is not None:
print '%s --glfo-dir isn\'t getting plugged in to igblast/changeo (would need to rebuild igblast db)' % utils.color(
'red', 'warning')
if args.n_random_queries is not None:
sub_infname = os.path.dirname(outfname) + '/' + os.path.basename(
infname.replace(
utils.getsuffix(infname), '-n-random-queries-%d%s' %
(args.n_random_queries, utils.getsuffix(infname))))
if os.path.exists(sub_infname):
print ' --n-random-queries: leaving existing fasta for igblast (hopefully it has %d queries)' % args.n_random_queries
else:
print ' --n-random-queries: writing new fasta for igblast (%d queries)' % args.n_random_queries
seqfos = utils.read_fastx(infname,
n_random_queries=args.n_random_queries)
with open(sub_infname, 'w') as sub_infile:
for seqfo in seqfos:
sub_infile.write('>%s\n%s\n' %
(seqfo['name'], seqfo['seq']))
infname = sub_infname
cmds = ['#!/bin/bash']
cmds += ['cd %s/%s' % (args.igbdir, args.locus)]
cmds += ['export PATH=%s:$PATH' % args.condapath]
cmds += ['igblastn']
for tmpreg in utils.regions:
cmds[-1] += ' -germline_db_%s %s%s-unaligned.fasta' % (
tmpreg.upper(), args.locus, tmpreg)
cmds[-1] += ' -auxiliary_data optional_file/%s_gl.aux' % args.species
cmds[
-1] += ' -domain_system imgt -ig_seqtype Ig -organism %s -outfmt \'7 std qseq sseq btop\'' % args.species
cmds[-1] += ' -num_threads %d' % utils.auto_n_procs()
cmds[-1] += ' -query ' + infname + ' -out ' + outfname
utils.simplerun('\n'.join(cmds) + '\n', cmdfname=args.workdir + '/run.sh')
def partition():
if utils.output_exists(args,
ifname('partition'),
outlabel='partition',
offset=4):
return
cmd = './bin/partis partition --simultaneous-true-clonal-seqs --is-simu --seed %d' % args.seed
fstr = ' --paired-loci --paired-indir %s --paired-outdir %s' if args.paired_loci else (
' --infname %%s --parameter-dir %s --outfname %%s' % ipath('params'))
cmd += fstr % (spath('mutated'), ipath('partition'))
# --write-additional-cluster-annotations 0:5 # I don't think there was really a good reason for having this
if not args.dont_get_tree_metrics:
cmd += ' --get-selection-metrics --plotdir %s' % (
'paired-outdir' if args.paired_loci else ipath('plots'))
if args.lb_tau is not None:
cmd += ' --lb-tau %f' % args.lb_tau
if args.n_procs > 1:
cmd += ' --n-procs %d' % args.n_procs
if args.slurm:
cmd += ' --batch-system slurm'
if args.n_max_queries is not None:
cmd += ' --n-max-queries %d' % args.n_max_queries
utils.simplerun(cmd, debug=True, dryrun=args.dry_run)
def run_tigger(infname, outfname, outdir):
if utils.output_exists(args, outfname, offset=8):
return
rcmds = [
'library(ggplot2)', 'library(tigger, warn.conflicts=FALSE)',
'library(dplyr, warn.conflicts=FALSE)'
]
# rcmds += ['data(sample_db, germline_ighv)']
db_name = 'annotations'
gls_name = 'gls'
rcmds += ['%s = read.csv("%s", sep="\t")' % (db_name, infname)]
rcmds += [
'%s = readIgFasta("%s")' % (gls_name, get_glfname('v', aligned=True))
]
tigger_outfname = outdir + '/tigger.fasta'
find_novel_argstr = '%s, %s, nproc=%d' % (db_name, gls_name,
utils.auto_n_procs())
if args.tuned_tigger_params:
germline_min = 5 # only analyze genes which correspond to at least this many V calls (default 200)
min_seqs = 5 # minimum number of total sequences
j_max = 0.95 # of sequences which align perfectly (i.e. zero mutation?) to a new allele, no more than this fraction can correspond to each junction length + j gene combination (default 0.15)
find_novel_argstr += ', germline_min=%d, min_seqs=%d, j_max=%f' % (
germline_min, min_seqs, j_max)
rcmds += ['novel_df = findNovelAlleles(%s)' % find_novel_argstr]
# rcmds += ['sessionInfo()']
rcmds += ['print(novel_df)']
rcmds += [
'geno = inferGenotype(%s, find_unmutated = TRUE, germline_db = %s, novel_df = novel_df)'
% (db_name, gls_name)
]
rcmds += ['genotype_seqs = genotypeFasta(geno, %s, novel_df)' % (gls_name)]
rcmds += ['writeFasta(genotype_seqs, "%s")' % tigger_outfname]
cmdfname = args.workdir + '/tigger-in.cmd'
with open(cmdfname, 'w') as cmdfile:
cmdfile.write('\n'.join(rcmds) + '\n')
cmdstr = 'R --slave -f ' + cmdfname
cmdfo = {'cmd_str': cmdstr, 'logdir': args.workdir, 'env': os.environ}
proc = utils.run_cmd(cmdfo)
while proc.poll() is None:
time.sleep(0.01)
if proc.returncode != 0: # damn thing crashes if it thinks the sample size is small
with open(args.workdir + '/err') as ferr:
errstr = ''.join(ferr.readlines())
if 'Not enough sample sequences were assigned to any germline' in errstr:
with open(tigger_outfname, 'w') as dummy_outfasta:
dummy_outfasta.write('')
else:
subprocess.check_call(['cat', args.workdir + '/out'])
subprocess.check_call(['cat', args.workdir + '/err'])
sys.exit(proc.returncode)
for oe in ['err', 'out']:
with open(args.workdir + '/' + oe) as oefile:
print ''.join(oefile.readlines())
os.remove(args.workdir + '/' + oe)
# post-process tigger .fa
template_gldir = args.glfo_dir if args.glfo_dir is not None else 'data/germlines/human'
glfo = glutils.create_glfo_from_fasta(
tigger_outfname,
args.locus,
args.region,
template_gldir,
simulation_germline_dir=args.simulation_germline_dir)
out_gldir = os.path.dirname(outfname).rstrip('/' + args.locus)
assert glutils.get_fname(out_gldir, args.locus, args.region) == outfname
glutils.write_glfo(out_gldir, glfo)
os.remove(cmdfname)
'--droplet-id-separator',
default='_',
help=
'everything in the sequence id before this character is treated as the droplet id, e.g. for the default, the uid AAACGGGCAAGCGAGT-1_contig_2 has a droplet id of AAACGGGCAAGCGAGT-1'
)
parser.add_argument('--overwrite', action='store_true')
parser.add_argument(
'--n-max-queries',
type=int,
default=-1,
help=
'Maximum number of query sequences to read from input file, starting from beginning of file'
)
args = parser.parse_args()
if utils.output_exists(args, args.outfname, offset=4, debug=False):
print ' extract-pairing-info.py output exists and --overwrite was not set, so not doing anything: %s' % args.outfname
sys.exit(0)
seqfos = utils.read_fastx(args.infname, n_max_queries=args.n_max_queries)
droplet_ids = {}
for sfo in seqfos:
did = utils.get_droplet_id(sfo['name'])
if did not in droplet_ids:
droplet_ids[did] = []
droplet_ids[did].append(sfo['name'])
print ' read %d sequences with %d droplet ids' % (len(seqfos),
len(droplet_ids))
count_info = {}
for dlist in droplet_ids.values():