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authorPjotr Prins2016-10-09 11:27:51 +0000
committerPjotr Prins2016-10-09 11:27:51 +0000
commit8d1f6f1654965dde737003595b1b4cc0b2c77a8f (patch)
tree34eda51b3e03c4647c2d205d16b13a6e0f52340a
parent03ba5bf1edcf4f7adec15b51d0d8e22274fd4a07 (diff)
downloadgenenetwork2-8d1f6f1654965dde737003595b1b4cc0b2c77a8f.tar.gz
Revert on Zach tabs and trailing spaces
-rw-r--r--wqflask/wqflask/marker_regression/marker_regression.py16
-rw-r--r--wqflask/wqflask/marker_regression/qtlreaper_mapping.py186
2 files changed, 101 insertions, 101 deletions
diff --git a/wqflask/wqflask/marker_regression/marker_regression.py b/wqflask/wqflask/marker_regression/marker_regression.py
index f340e309..200f2207 100644
--- a/wqflask/wqflask/marker_regression/marker_regression.py
+++ b/wqflask/wqflask/marker_regression/marker_regression.py
@@ -201,14 +201,14 @@ class MarkerRegression(object):
self.control_marker = start_vars['control_marker']
self.do_control = start_vars['do_control']
logger.info("Running qtlreaper")
- results, self.json_data, self.perm_output, self.suggestive, self.significant, self.bootstrap_results = qtlreaper_mapping.gen_reaper_results(self.this_trait,
- self.dataset,
- self.samples,
- self.json_data,
- self.num_perm,
- self.bootCheck,
- self.num_bootstrap,
- self.do_control,
+ results, self.json_data, self.perm_output, self.suggestive, self.significant, self.bootstrap_results = qtlreaper_mapping.gen_reaper_results(self.this_trait,
+ self.dataset,
+ self.samples,
+ self.json_data,
+ self.num_perm,
+ self.bootCheck,
+ self.num_bootstrap,
+ self.do_control,
self.control_marker,
self.manhattan_plot)
elif self.mapping_method == "plink":
diff --git a/wqflask/wqflask/marker_regression/qtlreaper_mapping.py b/wqflask/wqflask/marker_regression/qtlreaper_mapping.py
index 568991f7..b072584c 100644
--- a/wqflask/wqflask/marker_regression/qtlreaper_mapping.py
+++ b/wqflask/wqflask/marker_regression/qtlreaper_mapping.py
@@ -1,93 +1,93 @@
-def gen_reaper_results(this_trait, dataset, samples_before, json_data, num_perm, bootCheck, num_bootstrap, do_control, control_marker, manhattan_plot):
- genotype = dataset.group.read_genotype_file()
-
- if manhattan_plot != True:
- genotype = genotype.addinterval()
-
- samples, values, variances, sample_aliases = this_trait.export_informative()
-
- trimmed_samples = []
- trimmed_values = []
- for i in range(0, len(samples)):
- if this_trait.data[samples[i]].name in samples_before:
- trimmed_samples.append(samples[i])
- trimmed_values.append(values[i])
-
- perm_output = []
- bootstrap_results = []
-
- if num_perm < 100:
- suggestive = 0
- significant = 0
- else:
- perm_output = genotype.permutation(strains = trimmed_samples, trait = trimmed_values, nperm=num_perm)
- suggestive = perm_output[int(num_perm*0.37-1)]
- significant = perm_output[int(num_perm*0.95-1)]
- highly_significant = perm_output[int(num_perm*0.99-1)]
-
- json_data['suggestive'] = suggestive
- json_data['significant'] = significant
-
- if control_marker != "" and do_control == "true":
- reaper_results = genotype.regression(strains = trimmed_samples,
- trait = trimmed_values,
- control = str(control_marker))
- if bootCheck:
- control_geno = []
- control_geno2 = []
- _FIND = 0
- for _chr in genotype:
- for _locus in _chr:
- if _locus.name == control_marker:
- control_geno2 = _locus.genotype
- _FIND = 1
- break
- if _FIND:
- break
- if control_geno2:
- _prgy = list(genotype.prgy)
- for _strain in trimmed_samples:
- _idx = _prgy.index(_strain)
- control_geno.append(control_geno2[_idx])
-
- bootstrap_results = genotype.bootstrap(strains = trimmed_samples,
- trait = trimmed_values,
- control = control_geno,
- nboot = num_bootstrap)
- else:
- reaper_results = genotype.regression(strains = trimmed_samples,
- trait = trimmed_values)
-
- if bootCheck:
- bootstrap_results = genotype.bootstrap(strains = trimmed_samples,
- trait = trimmed_values,
- nboot = num_bootstrap)
-
- json_data['chr'] = []
- json_data['pos'] = []
- json_data['lod.hk'] = []
- json_data['markernames'] = []
- #if self.additive:
- # self.json_data['additive'] = []
-
- #Need to convert the QTL objects that qtl reaper returns into a json serializable dictionary
- qtl_results = []
- for qtl in reaper_results:
- reaper_locus = qtl.locus
- #ZS: Convert chr to int
- converted_chr = reaper_locus.chr
- if reaper_locus.chr != "X" and reaper_locus.chr != "X/Y":
- converted_chr = int(reaper_locus.chr)
- json_data['chr'].append(converted_chr)
- json_data['pos'].append(reaper_locus.Mb)
- json_data['lod.hk'].append(qtl.lrs)
- json_data['markernames'].append(reaper_locus.name)
- #if self.additive:
- # self.json_data['additive'].append(qtl.additive)
- locus = {"name":reaper_locus.name, "chr":reaper_locus.chr, "cM":reaper_locus.cM, "Mb":reaper_locus.Mb}
- qtl = {"lrs_value": qtl.lrs, "chr":converted_chr, "Mb":reaper_locus.Mb,
- "cM":reaper_locus.cM, "name":reaper_locus.name, "additive":qtl.additive, "dominance":qtl.dominance}
- qtl_results.append(qtl)
-
-
- return qtl_results, json_data, perm_output, suggestive, significant, bootstrap_results
+def gen_reaper_results(this_trait, dataset, samples_before, json_data, num_perm, bootCheck, num_bootstrap, do_control, control_marker, manhattan_plot):
+ genotype = dataset.group.read_genotype_file()
+
+ if manhattan_plot != True:
+ genotype = genotype.addinterval()
+
+ samples, values, variances, sample_aliases = this_trait.export_informative()
+
+ trimmed_samples = []
+ trimmed_values = []
+ for i in range(0, len(samples)):
+ if this_trait.data[samples[i]].name in samples_before:
+ trimmed_samples.append(samples[i])
+ trimmed_values.append(values[i])
+
+ perm_output = []
+ bootstrap_results = []
+
+ if num_perm < 100:
+ suggestive = 0
+ significant = 0
+ else:
+ perm_output = genotype.permutation(strains = trimmed_samples, trait = trimmed_values, nperm=num_perm)
+ suggestive = perm_output[int(num_perm*0.37-1)]
+ significant = perm_output[int(num_perm*0.95-1)]
+ highly_significant = perm_output[int(num_perm*0.99-1)]
+
+ json_data['suggestive'] = suggestive
+ json_data['significant'] = significant
+
+ if control_marker != "" and do_control == "true":
+ reaper_results = genotype.regression(strains = trimmed_samples,
+ trait = trimmed_values,
+ control = str(control_marker))
+ if bootCheck:
+ control_geno = []
+ control_geno2 = []
+ _FIND = 0
+ for _chr in genotype:
+ for _locus in _chr:
+ if _locus.name == control_marker:
+ control_geno2 = _locus.genotype
+ _FIND = 1
+ break
+ if _FIND:
+ break
+ if control_geno2:
+ _prgy = list(genotype.prgy)
+ for _strain in trimmed_samples:
+ _idx = _prgy.index(_strain)
+ control_geno.append(control_geno2[_idx])
+
+ bootstrap_results = genotype.bootstrap(strains = trimmed_samples,
+ trait = trimmed_values,
+ control = control_geno,
+ nboot = num_bootstrap)
+ else:
+ reaper_results = genotype.regression(strains = trimmed_samples,
+ trait = trimmed_values)
+
+ if bootCheck:
+ bootstrap_results = genotype.bootstrap(strains = trimmed_samples,
+ trait = trimmed_values,
+ nboot = num_bootstrap)
+
+ json_data['chr'] = []
+ json_data['pos'] = []
+ json_data['lod.hk'] = []
+ json_data['markernames'] = []
+ #if self.additive:
+ # self.json_data['additive'] = []
+
+ #Need to convert the QTL objects that qtl reaper returns into a json serializable dictionary
+ qtl_results = []
+ for qtl in reaper_results:
+ reaper_locus = qtl.locus
+ #ZS: Convert chr to int
+ converted_chr = reaper_locus.chr
+ if reaper_locus.chr != "X" and reaper_locus.chr != "X/Y":
+ converted_chr = int(reaper_locus.chr)
+ json_data['chr'].append(converted_chr)
+ json_data['pos'].append(reaper_locus.Mb)
+ json_data['lod.hk'].append(qtl.lrs)
+ json_data['markernames'].append(reaper_locus.name)
+ #if self.additive:
+ # self.json_data['additive'].append(qtl.additive)
+ locus = {"name":reaper_locus.name, "chr":reaper_locus.chr, "cM":reaper_locus.cM, "Mb":reaper_locus.Mb}
+ qtl = {"lrs_value": qtl.lrs, "chr":converted_chr, "Mb":reaper_locus.Mb,
+ "cM":reaper_locus.cM, "name":reaper_locus.name, "additive":qtl.additive, "dominance":qtl.dominance}
+ qtl_results.append(qtl)
+
+
+ return qtl_results, json_data, perm_output, suggestive, significant, bootstrap_results