parser = argparse.ArgumentParser()
parser.add_argument('rpkmfile')
parser.add_argument('nondiatable')
parser.add_argument('allelehits')
parser.add_argument('genelist')
parser.add_argument('-o', '--pdfout', default='ERCCsum_vs_biallelic_nonDia_variableminrpkm_v11.pdf')
parser.add_argument('--RNAamountfactor', default=1.0, type=float)
parser.add_argument('--lineparams', nargs=2, type=float)
parser.add_argument('--xfor20rpkm', default=21, type=float)
o = parser.parse_args()
ERCCvol_ul = 0.1/40000
ERCC_moleculenumber = calc_ERCC_moleculenumber('ERCC.txt', ERCCvol_ul) * o.RNAamountfactor
genelist_first = set(dr_tools.loadlist(o.genelist))
expra = dr_tools.loadexpr(o.allelehits, True)
expr = dr_tools.loadexpr(o.rpkmfile, False)
spikes_i = [i for i, ID in enumerate(expr['IDs']) if 'ERCC' in ID]
genes_i = [i for i, ID in enumerate(expr['IDs']) if 'ERCC' not in ID]
# pass 1: get cells per source
cells_per_source = defaultdict(list)
for p, sample, sample_i, cellsource in table_loader():
cells_per_source[cellsource].append(sample)
# middle step: get gene lists per cell source at RPKM cutoff
genelist_sources = defaultdict(dict)
for source, samples_source in cells_per_source.items():
#samples = set.union(*map(set, cells_per_source.values())) #new in v10 from v9
samples = samples_source
for ti, sym in enumerate(expr['symbols']):
from itertools import chain
if '__main__' == __name__:
parser = argparse.ArgumentParser()
parser.add_argument('infile')
parser.add_argument('outfile')
parser.add_argument('-m', '--maxgenes', type=int)
parser.add_argument('-S', '--maxgeneselection', choices=['max', 'mean', 'random'], default='max')
parser.add_argument('-t', '--transform', choices=['none', 'log10+0.3'], default='none')
parser.add_argument('-c', '--centering', choices=['none', 'mean'], default='none')
parser.add_argument('-s', '--samplelist')
parser.add_argument('-e', '--excludesample', nargs='+')
o = parser.parse_args()
# load input
expr = dr_tools.loadexpr(o.infile)
# select samples
if o.samplelist is not None:
samples = dr_tools.loadlist(o.samplelist)
else:
samples = expr.samples
if o.excludesample:
samples = [s for s in samples if s not in o.excludesample]
# select genes
genes_i = range(len(expr['symbols']))
if o.maxgenes is not None:
select_fn = {'max':max, 'mean':numpy.mean, 'random': (lambda v: random.random())}[o.maxgeneselection]
sort_list = [(select_fn([expr[s][i] for s in samples]), i) for i in genes_i]
sort_list.sort(reverse=True)
import argparse, dr_tools
if '__main__' == __name__:
parser = argparse.ArgumentParser()
parser.add_argument('rpkms', required=True)
parser.add_argument('allelehits', required=True)
parser.add_argument('--minrpkm', type=float, default=20)
o = parser.parse_args()
exprt = dr_tools.loadexpr(o.rpkms, False)
expra = dr_tools.loadexpr(o.allelehits, True)
samples = set(exprt.samples) & set(s.rsplit('_',1)[0] for s in expra.samples[::2])
count_per_gene = dict()
assert expra['symbols'] == exprt['symbols']
for ti, sym in
# not done
return num_c57only/num_both
if '__main__' == __name__:
parser = argparse.ArgumentParser()
parser.add_argument('-a', '--allelehits', required=True)
parser.add_argument('-gi', '--allowedgenes')
parser.add_argument('-ge', '--disallowedgenes', nargs='+', default=[])
parser.add_argument('-R', '--random_dots', type=int, default=1)
parser.add_argument('-s', '--samplelist', required=True, nargs='+')
parser.add_argument('-n', default=4, type=int)
parser.add_argument('-o', '--figure', default='poolN.pdf')
parser.add_argument('-S', '--subtract_allelerand', action='store_true')
parser.add_argument('-r', '--allelerand_skew', action='store_true')
o = parser.parse_args()
expra = dr_tools.loadexpr(o.allelehits, True)
if o.allowedgenes:
allowed_genes = set(dr_tools.loadlist(o.allowedgenes))
else:
allowed_genes = None
disallowed_genes = set()
for filename in o.disallowedgenes:
disallowed_genes.update(set(dr_tools.loadlist(filename)))
random.seed(0)
samples_n = dict((samplelist, [random.sample(dr_tools.loadlist(samplelist, ignore='#'), o.n) for di in range(o.random_dots)]) for samplelist in o.samplelist)
samples_all = [sa.split('_c57only')[0] for sa in expra.samples[::2]]
allelerand_skew = dict((gi, ratio(expra, gi, samples_all)) for gi in range(len(expra['symbols'])))
opts = argparse.ArgumentParser()
opts.add_argument('inf')
opts.add_argument('rpkmf_total')
opts.add_argument('min_rpkm', type=float)
opts.add_argument('--filter', nargs='+')
opts.add_argument('-f',
'--figf',
default='plot_monoallelic_by_cell_minrpkm.pdf')
opts.add_argument('-gi', '--genelistf_include', nargs='+')
opts.add_argument('-ge', '--genelistf_exclude', nargs='+')
opts.add_argument('--castfather', action='store_true')
opts.add_argument('--infercross', action='store_true')
opts.add_argument('--alg2', action='store_true')
o = opts.parse_args()
expr = dr_tools.loadexpr([o.inf], counts=True)
exprt = dr_tools.loadexpr([o.rpkmf_total], counts=False)
allowed_gene_i = gene_i_by_listf(o.genelistf_include,
expr) if o.genelistf_include else None
excluded_gene_i = gene_i_by_listf(o.genelistf_exclude,
expr) if o.genelistf_exclude else None
def rpkm(Ai, sample):
Ti = exprt.ID_to_index[expr['IDs'][Ai]]
return exprt[sample][Ti]
for p in dr_tools.splitlines(o.inf):
if p[0] == '#samples':
samples = p[1:]
break
raise
# sort the columns
sample_order = [name for num_out,name in sorted((num(name), name) for name in sample_values)]
# add in removal of e.g. midblast_2-19,midblast_2-20,midblast_2-22
if o.samplenames:
with open(o.samplenames, 'r') as infh:
requested_samples = set(line.split()[0] for line in infh)
sample_order = [name for name in sample_order if name in requested_samples]
if requested_samples - set(sample_order):
print 'Missing:\n' + '\n'.join(list(requested_samples - set(sample_order)))
# change ID column
if o.rpkmf_getID:
expr = dr_tools.loadexpr(o.rpkmf_getID)
symbol_to_IDs = dict(zip(expr['symbols'],expr['IDs']))
#IDs = [symbol_to_IDs.get(sym, prevID) for prevID, sym in zip(IDs, symbols)]
IDs = [symbol_to_IDs.get(sym, 'NA') for prevID, sym in zip(IDs, symbols)]
if o.rpkmf_genes:
symbols_set = dict((s,i) for i,s in enumerate(symbols))
new_sample_values = dict()
for name in sample_order:
new_sample_values[name] = []
for i, symbol in enumerate(expr['symbols']):
if symbol in symbols_set:
new_sample_values[name].append(sample_values[name][symbols_set[symbol]])
else:
new_sample_values[name].append('0 0')
sample_values = new_sample_values
symbols = expr['symbols']
import argparse, dr_tools, os
if '__main__' == __name__:
parser = argparse.ArgumentParser()
parser.add_argument('rpkmf_alleles', nargs='?')
parser.add_argument('rpkmf_total')
o = parser.parse_args()
exprt = dr_tools.loadexpr(o.rpkmf_total, counts=False)
counts = dr_tools.loadexpr(o.rpkmf_total, counts=True)
if o.rpkmf_alleles:
expra = dr_tools.loadexpr(o.rpkmf_alleles, counts=True)
AiD = dict((ti, expra.ID_to_index[ID]) for ti, ID in enumerate(exprt['IDs']) if ID in expra.ID_to_index)
for s in exprt.samples:
if s+'_castonly' not in expra.samples: continue
with open(s + '_expression.txt', 'w') as outfh:
print >>outfh, dr_tools.join('#Gene_symbol', 'Refseq_IDs', 'RPKM', 'reads', 'CAST_hits', 'C57_hits')
for ti in range(len(exprt['IDs'])):
if ti in AiD:
ai = AiD[ti]
cast = int(expra[s+'_castonly'][ai])
c57 = int(expra[s+'_c57only'][ai])
else:
cast = 0
c57 = 0
rpkm = exprt[s][ti]
reads = int(round(counts[s][ti]))
parser.add_argument('-o', '--sample_list_prefix')
o = parser.parse_args()
header, markers, marker_order = parse_table(o.tableS4)
gene_to_marker = dict(dr_tools.splitlines(o.to_cytof_markers))
marker_order = [m for m in marker_order if m in gene_to_marker.values()]
if not o.shuffle_patterns:
pop_cytof_pattern = dict(
(pop, [markers[m][popi] for m in marker_order])
for popi, pop in enumerate(header))
else:
pop_cytof_pattern = dict(
(pop, random.shuffle([markers[m][popi] for m in marker_order]))
for popi, pop in enumerate(header))
exprt = dr_tools.loadexpr(o.rpkmfile)
random.seed(0)
midexpr_symi_all_D = dict()
for symi, sym in enumerate(exprt['symbols']):
if sym not in gene_to_marker:
raise Exception(dr_tools.join(sym, 'sym'))
if gene_to_marker[sym] not in markers:
raise Exception(dr_tools.join(gene_to_marker[sym], 'cytof'))
midexpr_symi_all_D[gene_to_marker[sym]] = (numpy.mean(
[exprt[s][symi] for s in exprt.samples]), symi)
midexpr_symi_all = [midexpr_symi_all_D[m] for m in marker_order]
sym_order = [midexpr_symi_all_D[m][1] for m in marker_order]
pop_counts = dict((pop, 0) for pop in pop_cytof_pattern)
pop_samples = defaultdict(list)
import argparse, dr_tools, os
if '__main__' == __name__:
parser = argparse.ArgumentParser()
parser.add_argument('rpkmf_alleles', nargs='?')
parser.add_argument('rpkmf_total')
o = parser.parse_args()
exprt = dr_tools.loadexpr(o.rpkmf_total, counts=False)
counts = dr_tools.loadexpr(o.rpkmf_total, counts=True)
if o.rpkmf_alleles:
expra = dr_tools.loadexpr(o.rpkmf_alleles, counts=True)
AiD = dict((ti, expra.ID_to_index[ID])
for ti, ID in enumerate(exprt['IDs'])
if ID in expra.ID_to_index)
for s in exprt.samples:
if s + '_castonly' not in expra.samples: continue
with open(s + '_expression.txt', 'w') as outfh:
print >> outfh, dr_tools.join('#Gene_symbol', 'Refseq_IDs',
'RPKM', 'reads', 'CAST_hits',
'C57_hits')
for ti in range(len(exprt['IDs'])):
if ti in AiD:
ai = AiD[ti]
cast = int(expra[s + '_castonly'][ai])
c57 = int(expra[s + '_c57only'][ai])
else:
cast = 0
]
# add in removal of e.g. midblast_2-19,midblast_2-20,midblast_2-22
if o.samplenames:
with open(o.samplenames, 'r') as infh:
requested_samples = set(line.split()[0] for line in infh)
sample_order = [
name for name in sample_order if name in requested_samples
]
if requested_samples - set(sample_order):
print 'Missing:\n' + '\n'.join(
list(requested_samples - set(sample_order)))
# change ID column
if o.rpkmf_getID:
expr = dr_tools.loadexpr(o.rpkmf_getID)
symbol_to_IDs = dict(zip(expr['symbols'], expr['IDs']))
#IDs = [symbol_to_IDs.get(sym, prevID) for prevID, sym in zip(IDs, symbols)]
IDs = [
symbol_to_IDs.get(sym, 'NA') for prevID, sym in zip(IDs, symbols)
]
if o.rpkmf_genes:
symbols_set = dict((s, i) for i, s in enumerate(symbols))
new_sample_values = dict()
for name in sample_order:
new_sample_values[name] = []
for i, symbol in enumerate(expr['symbols']):
if symbol in symbols_set:
new_sample_values[name].append(
sample_values[name][symbols_set[symbol]])
else:
else:
conc_attomolul += float(p[Mix1_i])
return conc_attomolul * before_dilution_vol_ul * 602214.12927
if '__main__' == __name__:
parser = argparse.ArgumentParser()
parser.add_argument('rpkmfile')
parser.add_argument('nondiatable')
parser.add_argument('allelehits')
o = parser.parse_args()
ERCCvol_ul = 4e-7
ERCC_moleculenumber = calc_ERCC_moleculenumber('ERCC.txt', ERCCvol_ul)
expr = dr_tools.loadexpr(o.rpkmfile, False)
spikes_i = [i for i, ID in enumerate(expr['IDs']) if 'ERCC' in ID]
genes_i = [i for i, ID in enumerate(expr['IDs']) if 'ERCC' not in ID]
xarr = defaultdict(list)
for p in dr_tools.splitlines(o.nondiatable):
if p[0] == '#sample':
index_cellsource = p.index('cell.type')
else:
sample = p[0]
if sample == 'BQx46_indD_EmbryoMEF_BxC':
continue # degraded sample
try:
ERCC_rpkmsum = sum(expr[sample][spike] for spike in spikes_i)
if ERCC_rpkmsum < 100: continue
o = parser.parse_args()
plotted_variable_index = {
'mono%': 3,
'z': 1,
'num_genes': 0,
'error': 2,
'info_genes': 4
}[o.plotted_variable]
# suffixes of sample names in expression file
S2 = '_c57only'
S1 = '_castonly'
# load
expra = dr_tools.loadexpr(o.allelehits_file, True)
samples = [s.split(S2)[0] for s in expra.samples[::2]]
if o.minrpkm:
exprt = dr_tools.loadexpr(o.rpkm_file, False)
samples = [s for s in samples if s in exprt.samples]
global done_c
done_c = dict()
# pairs end in the same capital letter
# skip the _wronglane samples and the non-split cells
extract_short_name = extract_short_name2 if o.fibroblastnames else extract_short_name1
pair_letters = list(
set(remove_digits(extract_short_name(name))
for name in samples) - set(['']))
from __future__ import division
import argparse, dr_tools, numpy, pylab, random
if '__main__' == __name__:
parser = argparse.ArgumentParser()
parser.add_argument('-r', '--rpkms', required=True)
parser.add_argument('-m', '--minrpkm', default=20, type=float)
parser.add_argument('-M', '--maxrpkm', type=float)
parser.add_argument('-gi', '--allowedgenes')
parser.add_argument('-ge', '--disallowedgenes', nargs='+')
o = parser.parse_args()
exprt = dr_tools.loadexpr(o.rpkms)
allowedgenes = set(dr_tools.loadlist(o.allowedgenes)) if o.allowedgenes else None
if o.disallowedgenes:
disallowedgenes = set()
for filename in o.disallowedgenes:
disallowedgenes.update(set(dr_tools.loadlist(filename)))
else:
disallowedgenes = None
samples = exprt.samples
for ti, sym in enumerate(exprt['symbols']):
meanexpr = numpy.mean([exprt[s][ti] for s in samples])
if meanexpr < o.minrpkm: continue
if o.maxrpkm is not None and meanexpr >= o.maxrpkm: continue
if disallowedgenes and sym in disallowedgenes: continue
if allowedgenes and sym not in allowedgenes: continue
print sym
if '__main__' == __name__:
opts = argparse.ArgumentParser()
opts.add_argument('rpkmf_alleles')
opts.add_argument('--filter', nargs='+')
opts.add_argument('-M', '--method', default='monoallelic', choices=['m', 'monoallelic', 'monoallelic_norm', 'monoallelic_norm2', 'c57overlap', 'castoverlap', 'c57overlap_assym', 'castoverlap_assym', 'numsamemono', 'numsameC57', 'numsameCAST', 'numsamemono_norm', 'numsameC57_norm', 'numsameCAST_norm', 'spearman', 'pearson', 'braycurtis', 'canberra', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'euclidean', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule', 'numsamemono100', 'numsamemono100_norm'])
opts.add_argument('-L', '--linkage', default='complete', choices=['single', 'average', 'complete', 'linkage', 'weighted', 'centroid', 'median', 'ward'])
opts.add_argument('-s', '--bootstrap', type=int)
opts.add_argument('-S', '--states', default='3state', choices=['3state', 'fraction', 'diff', 'monoallelic'])
opts.add_argument('--fig', default='which_allele_tree.pdf')
opts.add_argument('-r', '--rpkmf_total')
opts.add_argument('-t', '--threshold_rpkm', help='requires --rpkmf_total', type=float)
opts.add_argument('-R', '--randomize', action='store_true')
o = opts.parse_args()
# load expression data
expr_alleles = dr_tools.loadexpr([o.rpkmf_alleles], counts=True)
samples_alleles = sorted([e for e in expr_alleles if e not in ('IDs', 'symbols')])
if o.rpkmf_total is not None:
expr_total = dr_tools.loadexpr([o.rpkmf_total], counts=False)
exprt_samples = set(expr_total.samples)
character_matrix = [] # 2D, values from state()
samplenames = []
for s1, s2 in zip(samples_alleles[::2], samples_alleles[1::2]):
if o.filter is not None and not any(part in s1.rsplit('_',1)[0] for part in o.filter): continue
samplename = s1.rsplit('_',1)[0]
# check that sample labels are consistent
if samplename != s2.rsplit('_',1)[0] and samplename in expr_total:
continue
parser.add_argument('-o', '--figure', default='pair_overlap6.pdf')
parser.add_argument('-v', '--plotted_variable', default='mono%', choices=['mono%', 'z', 'num_genes', 'error', 'info_genes'])
parser.add_argument('--ylim', default=[0,1], type=float, nargs=2)
parser.add_argument('-s', '--shiftpairs', type=int, nargs='?', const=1)
parser.add_argument('-F', '--fibroblastnames', action='store_true')
parser.add_argument('-S', '--separatelines', action='store_true')
o = parser.parse_args()
plotted_variable_index = {'mono%':3, 'z': 1, 'num_genes': 0, 'error': 2, 'info_genes':4}[o.plotted_variable]
# suffixes of sample names in expression file
S2 = '_c57only'
S1 = '_castonly'
# load
expra = dr_tools.loadexpr(o.allelehits_file, True)
samples = [s.split(S2)[0] for s in expra.samples[::2]]
if o.minrpkm:
exprt = dr_tools.loadexpr(o.rpkm_file, False)
samples = [s for s in samples if s in exprt.samples]
global done_c
done_c = dict()
# pairs end in the same capital letter
# skip the _wronglane samples and the non-split cells
extract_short_name = extract_short_name2 if o.fibroblastnames else extract_short_name1
pair_letters = list(set(remove_digits(extract_short_name(name)) for name in samples) - set(['']))
if o.pairletters: pair_letters = [l for l in pair_letters if l.strip('-_') in o.pairletters]
print pair_letters
def w(gi, Y_k, Y_r, N_k, N_r):
return (N_k - Y_k[gi])/N_k/Y_k[gi] + (N_r - Y_r[gi])/N_r/Y_r[gi]
def A(gi, Y_k, Y_r, N_k, N_r):
return 0.5*log2(Y_k[gi]/N_k * Y_r[gi]/N_r) if Y_k[gi] > 0 else -10000
if '__main__' == __name__:
parser = argparse.ArgumentParser()
parser.add_argument('infile')
parser.add_argument('outfile')
parser.add_argument('--ref_samples', nargs='+', metavar='samplename')
parser.add_argument('--copy_counts', action='store_true', help='does not work with stdin as input')
parser.add_argument('--run_on_counts', action='store_true')
o = parser.parse_args()
expr_in = dr_tools.loadexpr(o.infile, counts=o.run_on_counts)
ref_samples = expr_in.samples if o.ref_samples is None else o.ref_samples
Y_r = [numpy.mean([expr_in[s][gi] for s in ref_samples]) for gi in range(len(expr_in['symbols']))]
N_r = sum(Y_r)
expr_out = dr_tools.Parsed_rpkms([], False)
normalization_factors = []
for s in expr_in.samples:
Y_k = expr_in[s]
N_k = sum(Y_k)
nonzero = [gi for gi in range(len(expr_in['symbols'])) if Y_k[gi] > 0 and Y_r[gi] > 0]
A_distr = sorted((A(gi, Y_k, Y_r, N_k, N_r), gi) for gi in nonzero)
M_distr = sorted((M(gi, Y_k, Y_r, N_k, N_r), gi) for gi in nonzero)
from __future__ import division
import argparse, dr_tools, numpy, pylab, random
if '__main__' == __name__:
parser = argparse.ArgumentParser()
parser.add_argument('-r', '--rpkms', required=True)
parser.add_argument('-m', '--minrpkm', default=20, type=float)
parser.add_argument('-M', '--maxrpkm', type=float)
parser.add_argument('-gi', '--allowedgenes')
parser.add_argument('-ge', '--disallowedgenes', nargs='+')
o = parser.parse_args()
exprt = dr_tools.loadexpr(o.rpkms)
allowedgenes = set(dr_tools.loadlist(
o.allowedgenes)) if o.allowedgenes else None
if o.disallowedgenes:
disallowedgenes = set()
for filename in o.disallowedgenes:
disallowedgenes.update(set(dr_tools.loadlist(filename)))
else:
disallowedgenes = None
samples = exprt.samples
for ti, sym in enumerate(exprt['symbols']):
meanexpr = numpy.mean([exprt[s][ti] for s in samples])
if meanexpr < o.minrpkm: continue
if o.maxrpkm is not None and meanexpr >= o.maxrpkm: continue
if disallowedgenes and sym in disallowedgenes: continue
if allowedgenes and sym not in allowedgenes: continue
print sym
choices=['max', 'mean', 'random'],
default='max')
parser.add_argument('-t',
'--transform',
choices=['none', 'log10+0.3'],
default='none')
parser.add_argument('-c',
'--centering',
choices=['none', 'mean'],
default='none')
parser.add_argument('-s', '--samplelist')
parser.add_argument('-e', '--excludesample', nargs='+')
o = parser.parse_args()
# load input
expr = dr_tools.loadexpr(o.infile)
# select samples
if o.samplelist is not None:
samples = dr_tools.loadlist(o.samplelist)
else:
samples = expr.samples
if o.excludesample:
samples = [s for s in samples if s not in o.excludesample]
# select genes
genes_i = range(len(expr['symbols']))
if o.maxgenes is not None:
select_fn = {
'max': max,
'mean': numpy.mean,
from __future__ import division
import argparse, pylab, dr_tools
from scipy import stats
if '__main__' == __name__:
parser = argparse.ArgumentParser()
parser.add_argument('rpkmfile')
parser.add_argument('diatable')
parser.add_argument('--dim', type=float, default=3)
o = parser.parse_args()
expr = dr_tools.loadexpr(o.rpkmfile, True)
spikes_i = [i for i, ID in enumerate(expr['IDs']) if 'ERCC' in ID]
xarr = []
yarr = []
for p in dr_tools.splitlines(o.diatable):
if p[0] == '#sample':
index_dia = [p.index('cytoplasm.length'), p.index('cytoplasm.width')]
else:
sample = p[0]
ERCC_readsum = sum(expr[sample][spike] for spike in spikes_i)
sample_i = expr.samples.index(sample)
mRNA_readsum = expr.normalizationreads[sample_i]
try:
width = float(p[index_dia[1]])
length = float(p[index_dia[0]])
except ValueError:
continue
xarr.append((width*length)**(o.dim/2))
parser.add_argument('-o', '--figure', default='pool_n.pdf')
parser.add_argument('--nonrandom_n1', action='store_true')
o = parser.parse_args()
if o.random_seed is not None: random.seed(o.random_seed)
allowedgenes = set(dr_tools.loadlist(
o.allowedgenes)) if o.allowedgenes else None
if o.disallowedgenes:
disallowedgenes = set()
for filename in o.disallowedgenes:
disallowedgenes.update(set(dr_tools.loadlist(filename)))
else:
disallowedgenes = None
expra = dr_tools.loadexpr(o.allelehits, True)
c57fraction = 0.5
for clonal_group in dr_tools.loadlist(o.clonal_groups):
if not o.clonal_group in clonal_group: continue
samples = [s.rsplit('_', 1)[0] for s in expra.samples[::2]]
samples = [
s for s in samples if any(
s.startswith(clonal_group_start)
or s.startswith('pool.' + clonal_group_start)
for clonal_group_start in clonal_group.split('\t'))
]
xarr_n = []
yarr_mono = []
xarr_ctrl_n = []
if '__main__' == __name__:
opts = argparse.ArgumentParser()
opts.add_argument('rpkmf_alleles')
opts.add_argument(
'--genePred',
default=
'/mnt/crick/danielr/twocellstage/mouse/annotation/mm9_refGene_31Jul2011_norandom.txt'
)
opts.add_argument('--filter', nargs='+')
opts.add_argument('-o', '--figurefile', default='monoallelic_by_chr.pdf')
args = opts.parse_args()
# load expression data
expr_alleles = dr_tools.loadexpr([args.rpkmf_alleles], counts=True)
samples_alleles = sorted(
e for e in expr_alleles
if e not in ('IDs', 'symbols') and (args.filter is None or any(
part in e for part in args.filter)))
# sort the genes by position
# only include transcripts which are the first ID in the entry of the rpkm file
allowed_IDs = set(IDs.split('+')[0] for IDs in expr_alleles['IDs'])
genes_per_chr = dict()
ID_to_gene = dict()
for p in dr_tools.splitlines(args.genePred):
ID = p[1]
if ID in allowed_IDs:
chromosome = p[2]
if 'random' in chromosome: continue
danielr@rna ~/casthybrid/one_chr_reads $ python allele_independence.py -i ~/casthybrid/snp_positions/allelecounts_from_pileup/v17S15_genomic_refseq_autosomes.txt --stages blast
to allele_independence_blastocyst.pdf
'''
if '__main__' == __name__:
opts = argparse.ArgumentParser()
opts.add_argument('-i', '--inf', nargs='+', required=True)
opts.add_argument('--stages', nargs='+', help='when there is not a genomewide maternal bias')
opts.add_argument('--exclude', nargs='+', help='remove from stages', default=[])
opts.add_argument('--sim', action='store_true')
opts.add_argument('-o', '--figure', default='allele_independence.pdf')
opts.add_argument('--plotstyle', default=['mean_graph'], choices=['mean_graph', 'boxplot', 'mean_sem', 'violin', 'std', 'sayN', 'sayY'], nargs='+')
opts.add_argument('--minN', default=0, type=int)
o = opts.parse_args()
expra = dr_tools.loadexpr(o.inf, counts=True)
sample_pairs = pairs(expra, o.stages, o.exclude)
bins = [Bin(num_cells, len(sample_pairs), o.sim) for num_cells in range(len(sample_pairs)+1)]
for gene_i in range(len(expra['symbols'])):
num_mono = sum((expra[s_pat][gene_i]>0)^(expra[s_mat][gene_i]>0) for s_pat,s_mat in sample_pairs)
num_bi = sum((expra[s_pat][gene_i]>0)and(expra[s_mat][gene_i]>0) for s_pat,s_mat in sample_pairs)
num_silent = sum((expra[s_pat][gene_i]==0)and(expra[s_mat][gene_i]==0) for s_pat,s_mat in sample_pairs)
bins[num_silent].add(num_bi)
if o.sim:
while any(len(b.exp_frac_bi) < 10000 for b in bins):
r = random.random()**2
sim_states = [(random.random() < r, random.random() < r) for p in sample_pairs]
allowedgenes = set()
for genelistf in genelistf_arr:
allowedgenes |= set(dr_tools.loadlist(genelistf))
return set(i for i,sym in enumerate(expr['symbols']) if sym in allowedgenes)
if '__main__' == __name__:
opts = argparse.ArgumentParser()
opts.add_argument('inf')
opts.add_argument('--filter', nargs='+')
opts.add_argument('-f', '--figf', default='plot_monoallelic_by_cell.pdf')
opts.add_argument('-gi', '--genelistf_include', nargs='+')
opts.add_argument('-ge', '--genelistf_exclude', nargs='+')
o = opts.parse_args()
expr = dr_tools.loadexpr([o.inf], counts=True)
#samples = sorted([e for e in expr if e not in ('IDs', 'symbols')])
allowed_gene_i = gene_i_by_listf(o.genelistf_include, expr) if o.genelistf_include else None
excluded_gene_i = gene_i_by_listf(o.genelistf_exclude, expr) if o.genelistf_exclude else None
for p in dr_tools.splitlines(o.inf):
if p[0] == '#samples': samples = p[1:]; break
fractions = [] # maternal only + paternal only
mfractions = [] # maternal only
fractions_all3 = [] # maternal+parternal+biallelic
labels = []
for s1, s2 in zip(samples[::2], samples[1::2]):
#if not 'Mix 1' in p[Mix1_i]: raise Exception
else:
conc_attomolul += float(p[Mix1_i])
return conc_attomolul * before_dilution_vol_ul * 602214.12927
if '__main__' == __name__:
parser = argparse.ArgumentParser()
parser.add_argument('rpkmfile')
parser.add_argument('nondiatable')
parser.add_argument('allelehits')
o = parser.parse_args()
ERCCvol_ul = 4e-7
ERCC_moleculenumber = calc_ERCC_moleculenumber('ERCC.txt', ERCCvol_ul)
expr = dr_tools.loadexpr(o.rpkmfile, False)
spikes_i = [i for i, ID in enumerate(expr['IDs']) if 'ERCC' in ID]
genes_i = [i for i, ID in enumerate(expr['IDs']) if 'ERCC' not in ID]
xarr = defaultdict(list)
for p in dr_tools.splitlines(o.nondiatable):
if p[0] == '#sample':
index_cellsource = p.index('cell.type')
else:
sample = p[0]
if sample == 'BQx46_indD_EmbryoMEF_BxC': continue # degraded sample
try:
ERCC_rpkmsum = sum(expr[sample][spike] for spike in spikes_i)
if ERCC_rpkmsum < 100: continue
sample_i = expr.samples.index(sample)
pairs = list(itertools.combinations(samples, 2))
if len(pairs) > nmax:
return random.sample(pairs, nmax)
else:
return pairs
if '__main__' == __name__:
parser = argparse.ArgumentParser()
parser.add_argument('-r', '--rpkmfile', nargs='+', required=True)
parser.add_argument('-s', '--samplelist', nargs='+', required=True, action='append')
parser.add_argument('-o', '--figure', default='correlation.pdf')
parser.add_argument('-n', '--names', action='append')
parser.add_argument('-m', '--maxpergroup', type=int, default=300000000)
o = parser.parse_args()
expr = dr_tools.loadexpr(o.rpkmfile)
boxplot_values = []
labels = []
for samplelistgroup, name in itertools.izip_longest(o.samplelist, o.names):
if samplelistgroup is None: raise Exception
if name is None: label = ''
else: label = name + '\n'
rho_values = []
samples_used = set()
possible_pairs = 0
for samplelistfile in samplelistgroup:
samples = set(dr_tools.loadlist(samplelistfile))
rho_values.extend([stats.spearmanr(expr[s1], expr[s2])[0] for s1, s2 in maxpairs(samples, o.maxpergroup)])
samples_used.update(samples)
possible_pairs += len(samples) * (len(samples)-1) // 2
boxplot_values.append(rho_values)
from __future__ import division
import argparse, dr_tools, numpy
from collections import defaultdict
if '__main__' == __name__:
parser = argparse.ArgumentParser()
parser.add_argument('sample_and_chromosome_list')
parser.add_argument('output_file')
parser.add_argument('-A', '--annotationfile')
parser.add_argument('-a', '--allelehits')
o = parser.parse_args()
exprr = dr_tools.loadexpr(o.allelehits, False)
expra = dr_tools.loadexpr(o.allelehits, True)
chrom_to_IDs = defaultdict(set)
for p in dr_tools.splitlines(o.annotationfile):
chrom = p[2]
sym = p[12]
ID = p[1]
chrom_to_IDs[chrom].add(ID)
samples_set = set(expra.samples)
with open(o.sample_and_chromosome_list) as infh:
for line in infh:
p = line.split()
chrom = p[1]
s_c57 = p[0]+'_c57only'
s_cast = p[0]+'_castonly'
if p[0] not in samples_set: continue
def MAfraction(expra, sample):
count_bi, count_mono = 0, 0
for sym, c57, cast in zip(expra['symbols'], expra[sample+'_c57only'], expra[sample+'_castonly']):
if c57 and cast: count_bi += 1
elif c57 or cast: count_mono += 1
return count_mono/(count_bi + count_mono)
if '__main__' == __name__:
parser = argparse.ArgumentParser()
parser.add_argument('allelehits')
parser.add_argument('diatable')
parser.add_argument('--dim', type=float, default=3)
o = parser.parse_args()
expra = dr_tools.loadexpr(o.allelehits, True)
xarr = []
yarr = []
for p in dr_tools.splitlines(o.diatable):
if p[0] == '#sample':
index_dia = [p.index('cytoplasm.length'), p.index('cytoplasm.width')]
else:
sample = p[0]
try:
width = float(p[index_dia[1]])
length = float(p[index_dia[0]])
except ValueError:
continue
opts.add_argument('-s', '--bootstrap', type=int)
opts.add_argument('-S',
'--states',
default='3state',
choices=['3state', 'fraction', 'diff', 'monoallelic'])
opts.add_argument('--fig', default='which_allele_tree.pdf')
opts.add_argument('-r', '--rpkmf_total')
opts.add_argument('-t',
'--threshold_rpkm',
help='requires --rpkmf_total',
type=float)
opts.add_argument('-R', '--randomize', action='store_true')
o = opts.parse_args()
# load expression data
expr_alleles = dr_tools.loadexpr([o.rpkmf_alleles], counts=True)
samples_alleles = sorted(
[e for e in expr_alleles if e not in ('IDs', 'symbols')])
if o.rpkmf_total is not None:
expr_total = dr_tools.loadexpr([o.rpkmf_total], counts=False)
exprt_samples = set(expr_total.samples)
character_matrix = [] # 2D, values from state()
samplenames = []
for s1, s2 in zip(samples_alleles[::2], samples_alleles[1::2]):
if o.filter is not None and not any(part in s1.rsplit('_', 1)[0]
for part in o.filter):
continue
samplename = s1.rsplit('_', 1)[0]
# check that sample labels are consistent
opts.add_argument('-f', '--figurefile', default='monoallelic_at_chr.png')
opts.add_argument('-w', '--maxwhite', type=int)
opts.add_argument('--allowallwhite', action='store_true')
opts.add_argument('--allowedgenes')
opts.add_argument('--disallowedgenes')
opts.add_argument('--verticalborder', action='store_true')
opts.add_argument('--stageline', action='store_true')
opts.add_argument('--embryoline', action='store_true')
opts.add_argument('--embryonotch', action='store_true')
opts.add_argument('--mincoord', type=int)
opts.add_argument('--maxcoord', type=int)
opts.add_argument('--saygenes', action='store_true')
args = opts.parse_args()
# load expression data
expr_alleles = dr_tools.loadexpr([args.rpkmf_alleles], counts=True)
samples_alleles = sorted(e for e in expr_alleles if e not in ('IDs', 'symbols') and (args.filter is None or any(part in e for part in args.filter)))
for p in dr_tools.splitlines(args.rpkmf_alleles):
if p[0] == '#samples': samples = p[1:]; break
samples_alleles = [e for e in samples if (args.filter is None or any(part in e for part in args.filter))]
# sort the genes by position
# only include transcripts which are the first ID in the entry of the rpkm file
if args.allowedgenes is None and args.disallowedgenes is None:
allowed_IDs = set(IDs.split('+')[0] for IDs in expr_alleles['IDs'])
else:
if args.allowedgenes:
allowed_set = set(dr_tools.loadlist(args.allowedgenes))
if args.disallowedgenes:
disallowed_set = set(dr_tools.loadlist(args.disallowedgenes))
if '__main__' == __name__:
opts = argparse.ArgumentParser()
opts.add_argument('-i1', '--inf1', required=True) # e.g. ooref15...
opts.add_argument('-F1', default=0.02, type=float)
opts.add_argument('-i2', '--inf2') # e.g. ooref13...
opts.add_argument('-F2', type=float, default=0)
opts.add_argument('-o', '--outf', default='/dev/stdout')
opts.add_argument('--addminreads', type=int, default=0)
opts.add_argument('--round',
choices=['0.5up', 'ceil', 'floor'],
default='ceil')
opts.add_argument('--minreadsboth', type=int, default=0)
args = opts.parse_args()
expr1 = dr_tools.loadexpr([args.inf1], counts=True)
if args.inf2 is not None:
expr2 = dr_tools.loadexpr([args.inf2], counts=True)
for i, p in enumerate(dr_tools.splitlines(args.inf1)):
samples = p[1:]
break
gene_counts_out = defaultdict(list)
for s1, s2 in zip(samples[::2], samples[1::2]):
if s1.rsplit('_', 1)[0] != s2.rsplit('_', 1)[0]: raise Exception
for gene_i, symbol in enumerate(expr1['symbols']):
# remove a fraction F of the paternal chromosome's expression from the maternal chromosome's, and vice versa
if expr1[s1][gene_i] + expr1[s2][gene_i] < args.minreadsboth:
s1e = 0
roundfunc = {'ceil':math.ceil, '0.5up':round, 'floor':math.floor}[rounding]
return max(0, expr_s1 - roundfunc(F*expr_s2+addminreads))
if '__main__' == __name__:
opts = argparse.ArgumentParser()
opts.add_argument('-i1', '--inf1', required=True) # e.g. ooref15...
opts.add_argument('-F1', default=0.02, type=float)
opts.add_argument('-i2', '--inf2') # e.g. ooref13...
opts.add_argument('-F2', type=float, default=0)
opts.add_argument('-o', '--outf', default='/dev/stdout')
opts.add_argument('--addminreads', type=int, default=0)
opts.add_argument('--round', choices=['0.5up', 'ceil', 'floor'], default='ceil')
opts.add_argument('--minreadsboth', type=int, default=0)
args = opts.parse_args()
expr1 = dr_tools.loadexpr([args.inf1], counts=True)
if args.inf2 is not None: expr2 = dr_tools.loadexpr([args.inf2], counts=True)
for i, p in enumerate(dr_tools.splitlines(args.inf1)):
samples = p[1:]
break
gene_counts_out = defaultdict(list)
for s1, s2 in zip(samples[::2], samples[1::2]):
if s1.rsplit('_',1)[0] != s2.rsplit('_',1)[0]: raise Exception
for gene_i, symbol in enumerate(expr1['symbols']):
# remove a fraction F of the paternal chromosome's expression from the maternal chromosome's, and vice versa
if expr1[s1][gene_i] + expr1[s2][gene_i] < args.minreadsboth:
s1e = 0
parser.add_argument('-n2', '--end_n', default=15, type=int)
parser.add_argument('-o', '--figure', default='pool_n.pdf')
parser.add_argument('--nonrandom_n1', action='store_true')
o = parser.parse_args()
if o.random_seed is not None: random.seed(o.random_seed)
allowedgenes = set(dr_tools.loadlist(o.allowedgenes)) if o.allowedgenes else None
if o.disallowedgenes:
disallowedgenes = set()
for filename in o.disallowedgenes:
disallowedgenes.update(set(dr_tools.loadlist(filename)))
else:
disallowedgenes = None
expra = dr_tools.loadexpr(o.allelehits, True)
c57fraction = 0.5
for clonal_group in dr_tools.loadlist(o.clonal_groups):
if not o.clonal_group in clonal_group: continue
samples = [s.rsplit('_',1)[0] for s in expra.samples[::2]]
samples = [s for s in samples if any(s.startswith(clonal_group_start) or s.startswith('pool.'+clonal_group_start) for clonal_group_start in clonal_group.split('\t'))]
xarr_n = []
yarr_mono = []
xarr_ctrl_n = []
yarr_ctrl = []
xarr_n_line = []
yarr_mono_line = []
yarr_ctrl_line = []
for n in range(o.start_n, o.end_n+1):
