def output_gm(prefix, gm_res):
header = '\t'.join(
['# Scaffold', 'cDNA IDs', 'Status', 'Linkage group(s)'])
full_out = '-'.join([prefix, 'full-genetic-map-results-table.tsv'])
with open(full_out, 'w') as outfile:
print >> outfile, util.report_cmd()
print >> outfile, header
for status, result in gm_res.items(
): ### what do I do with status here? Or is it just discarded because there should only be complete, single cDNAs in gm_res
for res in result:
for t in util.LG_table_formatter(res):
print >> outfile, t
def output_cDNA(prefix, cDNA_results, gm=''):
# write out cDNA:scaffold mappings
full_out = '-'.join([prefix, 'full-cDNA-results-table.tsv'])
with open(full_out, 'w') as outfile:
if len(gm) > 0:
header = '\t'.join(
['# cDNA ID', 'Status', 'Scaffold', 'Linkage group'])
print >> outfile, util.report_cmd()
print >> outfile, header
for status, result in cDNA_results.items():
for res in result:
for t in util.table_formatter_wGM(res, gm):
print >> outfile, t
else:
header = '\t'.join(['# cDNA ID', 'Status', 'Scaffold'])
print >> outfile, util.report_cmd()
print >> outfile, header
for status, result in cDNA_results.items():
for res in result:
for t in util.table_formatter(res):
print >> outfile, t
def report_cDNA(prefix, cDNA_results, TOT):
"""calc percentages and report cDNA results"""
num_complete = len(cDNA_results['Complete'])
num_duplicated = len(cDNA_results['Duplicated'])
num_partial = len(cDNA_results['Partial'])
num_fragmented = len(cDNA_results['Fragmented'])
num_poor = len(cDNA_results['Poorly mapped'])
num_missing = len(cDNA_results['Missing'])
rate_complete = float(num_complete) / float(TOT)
rate_duplicated = float(num_duplicated) / float(TOT)
rate_partial = float(num_partial) / float(TOT)
rate_fragmented = float(num_fragmented) / float(TOT)
rate_poor = float(num_poor) / float(TOT)
rate_missing = float(num_missing) / float(TOT)
pct_complete = round(100.0 * rate_complete, 2)
pct_duplicated = round(100.0 * rate_duplicated, 2)
pct_partial = round(100.0 * rate_partial, 2)
pct_fragmented = round(100 * rate_fragmented, 2)
pct_poor = round(100 * rate_poor, 2)
pct_missing = round(100 * rate_missing, 2)
sum_complete = num_complete + num_duplicated
pct_sum_complete = pct_complete + pct_duplicated
# report if the right number of sequences have a result
num_counted = sum([
num_complete, num_duplicated, num_fragmented, num_partial, num_poor,
num_missing
])
rate_counted = float(num_counted) / float(TOT)
pct_counted = round(100 * rate_counted, 2)
# write to tsv
tsvout = '-'.join([prefix, 'results.tsv'])
header_txt = [
'', 'Complete', 'Complete, single copy', 'Complete, multiple copies',
'Fragmented', 'Partial', 'Poorly Mapped', 'Missing',
'Total cDNAs searched'
]
with open(tsvout, 'w') as outfile:
header = '\t'.join(header_txt)
nums = '\t'.join([
str(x) for x in [
'Number', sum_complete, num_complete, num_duplicated,
num_fragmented, num_partial, num_poor, num_missing, num_counted
]
])
pcts = '\t'.join([
str(x) for x in [
'Percent', pct_sum_complete, pct_complete, pct_duplicated,
pct_fragmented, pct_partial, pct_poor, pct_missing, pct_counted
]
])
print >> outfile, util.report_cmd()
print >> outfile, header
print >> outfile, nums
print >> outfile, pcts
jiraout = '-'.join([prefix, 'results.jira'])
with open(jiraout, 'w') as outfile:
header_txt.extend(
['']) # add final empty string for jira table separator
header = '||'.join(header_txt)
nums = [
sum_complete, num_complete, num_duplicated, num_fragmented,
num_partial, num_poor, num_missing, num_counted
]
pcts = [
pct_sum_complete, pct_complete, pct_duplicated, pct_fragmented,
pct_partial, pct_poor, pct_missing, pct_counted
]
res = '|' + '|'.join([util.jira_formatter(x)
for x in zip(nums, pcts)]) + '|'
print >> outfile, util.report_cmd()
print >> outfile, header
print >> outfile, res
# print to STDOUT
print '\n=== GNAVIGATOR cDNA RESULTS ==='
print '%s (%s%%) complete sequences' % (sum_complete, pct_sum_complete)
print ' %s (%s%%) complete, single copy sequences' % (num_complete,
pct_complete)
print ' %s (%s%%) complete, multiple copy sequences' % (num_duplicated,
pct_duplicated)
print '%s (%s%%) fragmented sequences' % (num_fragmented, pct_fragmented)
print '%s (%s%%) partial sequences' % (num_partial, pct_partial)
print '%s (%s%%) poorly mapped sequences' % (num_poor, pct_poor)
print '%s (%s%%) missing sequences' % (num_missing, pct_missing)
print '%s (%s%%) sequences were evaluated' % (num_counted, pct_counted)
print util.report_time()
def report_gm_cDNA(gm_results, uniqMapDat_select, prefix):
"""Report genetic map results as a proportion of eligible cDNAs"""
# input cDNA_results is dict with keys == cDNA status
# see src.classify.check_aln
gm_cdna_res = {}
statuses = {
'Solo': 0,
'Same LG, expected order': 0,
'Same LG, unexpected order': 0,
'Different LG': 0,
'Same LG, order undetermined': 0
}
tot = 0
for res in gm_results.items():
for pres in res[1]:
scaf, cdnaS, stat, lg = pres
cdnaL = cdnaS.split(';')
for cdna in cdnaL:
gm_cdna_res[cdna] = [scaf, stat]
statuses[stat] += 1
tot += 1
miss = 'Solo'
for rec in uniqMapDat_select.iterrows():
cdna = rec[1][9]
scaf = rec[1][13]
if cdna not in gm_cdna_res:
gm_cdna_res[cdna] = [scaf, miss]
statuses[miss] += 1
tot += 1
num_solo = statuses['Solo']
num_good = statuses['Same LG, expected order']
num_wo = statuses['Same LG, unexpected order']
num_diff = statuses['Different LG']
num_undet = statuses['Same LG, order undetermined']
rate_solo = float(statuses['Solo']) / float(tot)
rate_good = float(statuses['Same LG, expected order']) / float(tot)
rate_wo = float(statuses['Same LG, unexpected order']) / float(tot)
rate_diff = float(statuses['Different LG']) / float(tot)
rate_undet = float(statuses['Same LG, order undetermined']) / float(tot)
pct_solo = round(100.0 * rate_solo, 2)
pct_good = round(100.0 * rate_good, 2)
pct_wo = round(100.0 * rate_wo, 2)
pct_diff = round(100.0 * rate_diff, 2)
pct_undet = round(100.0 * rate_undet, 2)
# write to tsv
tsvout = '-'.join([prefix, 'genetic-map-cDNA-results.tsv'])
with open(tsvout, 'w') as outfile:
header_txt = [
'', 'Solo (unevaluated)', 'Same LG, expected order',
'Same LG, unexpected order', 'Different LG',
'Same LG, order undetermined'
]
header = '\t'.join(header_txt)
nums = '\t'.join([
str(x) for x in
['Number', num_solo, num_good, num_wo, num_diff, num_undet]
])
pcts = '\t'.join([
str(x) for x in
['Percent', pct_solo, pct_good, pct_wo, pct_diff, pct_undet]
])
print >> outfile, util.report_cmd()
print >> outfile, header
print >> outfile, nums
print >> outfile, pcts
jiraout = '-'.join([prefix, 'genetic-map-cDNA-results.jira'])
with open(jiraout, 'w') as outfile:
header_txt.extend(
['']) # add final empty string for jira table separator
header = '||'.join(header_txt)
nums = [num_solo, num_good, num_wo, num_diff, num_undet]
pcts = [pct_solo, pct_good, pct_wo, pct_diff, pct_undet]
res = '|' + '|'.join([util.jira_formatter(x)
for x in zip(nums, pcts)]) + '|'
print >> outfile, util.report_cmd()
print >> outfile, header
print >> outfile, res
print '\n=== GNAVIGATOR GENETIC MAP RESULTS per COMPLETE cDNA ==='
print ' '.join([
'%s (%s%%) were found alone on a scaffold and were not evaluated',
'further.'
]) % (num_solo, pct_solo)
print ' '.join([
'%s (%s%%) were from the same linkage group and were found on scaffolds in',
'the expected order'
]) % (num_good, pct_good)
print ' '.join([
'%s (%s%%) were from the same linkage group but were found on scaffolds in an',
'unexpected order.'
]) % (num_wo, pct_wo)
print ' '.join([
'%s (%s%%) were from different linkage groups but were found on the same',
'scaffold.'
]) % (num_diff, pct_diff)
print ' '.join([
'%s (%s%%) were from the same linkage group but their order could not be',
'determined.'
]) % (num_undet, pct_undet)
print util.report_time()
# return statuses so certain stats can be used elsewhere
return statuses
def report_gm(uniqDatMap_select, gm_results, gm_cdna_statuses, prefix):
"""report summary of genetic map results"""
num_scaff_toCheck = len(uniqDatMap_select.tname.unique())
num_solo = gm_cdna_statuses['Solo']
#tot = num_scaff_toCheck + gm_cdna_statuses['Solo']
num_goodLG = len(gm_results['goodLG'])
num_WO_LG = len(gm_results['WO_LG'])
num_diffLG = len(gm_results['diffLG'])
num_undet = len(gm_results['undet'])
num_scaff_checked = num_goodLG + num_WO_LG + num_diffLG + num_undet + num_solo
num_2plus_scaff = num_goodLG + num_WO_LG + num_diffLG + num_undet
tot = num_scaff_checked # for legacy reasons
if num_scaff_toCheck == num_scaff_checked:
rate_solo = float(num_solo) / float(tot)
rate_LGscaff = float(num_2plus_scaff) / float(tot)
rate_goodLG = float(num_goodLG) / float(tot)
rate_WO_LG = float(num_WO_LG) / float(tot)
rate_diffLG = float(num_diffLG) / float(tot)
rate_undet = float(num_undet) / float(tot)
pct_solo = round(100.0 * rate_solo, 2)
pct_LGscaff = round(100.0 * rate_LGscaff, 2)
pct_goodLG = round(100.0 * rate_goodLG, 2)
pct_WO_LG = round(100.0 * rate_WO_LG, 2)
pct_diffLG = round(100.0 * rate_diffLG, 2)
pct_undet = round(100.0 * rate_undet, 2)
else:
print ''.join('Not all scaffolds to be checked against genetic map'
' were successfully checked.')
print 'Maybe something is wrong with the input data?'
print util.report_time()
sys.exit(2)
# write to tsv
tsvout = '-'.join([prefix, 'genetic-map-scaffold-results.tsv'])
with open(tsvout, 'w') as outfile:
header_txt = [
'', '1 complete cDNA', '2+ complete cDNAs',
'Same LG, expected order', 'Same LG, unexpected order',
'Different LG', 'Same LG, undetermined order'
]
header = '\t'.join(header_txt)
nums = '\t'.join([
str(x) for x in [
'Number', num_solo, num_2plus_scaff, num_goodLG, num_WO_LG,
num_diffLG, num_undet
]
])
pcts = '\t'.join([
str(x) for x in [
'Percent', pct_solo, pct_LGscaff, pct_goodLG, pct_WO_LG,
pct_diffLG, pct_undet
]
])
print >> outfile, util.report_cmd()
print >> outfile, header
print >> outfile, nums
print >> outfile, pcts
jiraout = '-'.join([prefix, 'genetic-map-scaffold-results.jira'])
with open(jiraout, 'w') as outfile:
header_txt.extend(
['']) # add final empty string for jira table separator
header = '||'.join(header_txt)
nums = [
num_solo, num_2plus_scaff, num_goodLG, num_WO_LG, num_diffLG,
num_undet
]
pcts = [
pct_solo, pct_LGscaff, pct_goodLG, pct_WO_LG, pct_diffLG, pct_undet
]
res = '|' + '|'.join([util.jira_formatter(x)
for x in zip(nums, pcts)]) + '|'
print >> outfile, util.report_cmd()
print >> outfile, header
print >> outfile, res
# print to STDOUT
print '\n=== GNAVIGATOR GENETIC MAP RESULTS per SCAFFOLD with 1+ COMPLETE cDNA ==='
print ' '.join([
'%s (%s%%) had exactly 1 complete cDNA from the genetic map',
'aligned to them.'
]) % (num_solo, pct_solo)
print ' '.join([
'%s (%s%%) had 2+ complete cDNAs from the genetic map',
'aligned to them.'
]) % (num_2plus_scaff, pct_LGscaff)
print ' '.join([
' %s (%s%%) were from the same linkage group and in the',
'expected order.'
]) % (num_goodLG, pct_goodLG)
print ' '.join([
' %s (%s%%) were from the same linkage group, but NOT in',
'the expected order.'
]) % (num_WO_LG, pct_WO_LG)
print ' %s (%s%%) were from different linkage groups.' % (num_diffLG,
pct_diffLG)
print ' '.join([
' %s (%s%%) were from the same linkage group but their order',
'could not be determined.'
]) % (num_undet, pct_undet)
print util.report_time()