diff options
Diffstat (limited to 'wqflask')
| -rwxr-xr-x | wqflask/base/data_set.py | 20 | ||||
| -rw-r--r-- | wqflask/maintenance/quick_search_table.py | 8 | ||||
| -rw-r--r-- | wqflask/wqflask/interval_mapping/interval_mapping.py | 312 | ||||
| -rw-r--r-- | wqflask/wqflask/my_pylmm/data/geno_to_ped.py | 22 |
4 files changed, 350 insertions, 12 deletions
diff --git a/wqflask/base/data_set.py b/wqflask/base/data_set.py index beb62bd7..9fa7beb3 100755 --- a/wqflask/base/data_set.py +++ b/wqflask/base/data_set.py @@ -168,13 +168,13 @@ class Markers(object): for marker, p_value in itertools.izip(self.markers, p_values): marker['p_value'] = p_value - if marker['p_value'] == 0: - marker['lod_score'] = 0 - marker['lrs_value'] = 0 - else: - marker['lod_score'] = -math.log10(marker['p_value']) - #Using -log(p) for the LRS; need to ask Rob how he wants to get LRS from p-values - marker['lrs_value'] = -math.log10(marker['p_value']) * 4.61 + if math.isnan(marker['p_value']): + print("p_value is:", marker['p_value']) + marker['lod_score'] = -math.log10(marker['p_value']) + #Using -log(p) for the LRS; need to ask Rob how he wants to get LRS from p-values + marker['lrs_value'] = -math.log10(marker['p_value']) * 4.61 + + class HumanMarkers(Markers): @@ -189,6 +189,8 @@ class HumanMarkers(Markers): marker['name'] = splat[1] marker['Mb'] = float(splat[3]) / 1000000 self.markers.append(marker) + + #print("markers is: ", pf(self.markers)) def add_pvalues(self, p_values): @@ -315,10 +317,10 @@ class DatasetGroup(object): #determine default genotype object if self.incparentsf1 and genotype_1.type != "intercross": - genotype = genotype_2 + self.genotype = genotype_2 else: self.incparentsf1 = 0 - genotype = genotype_1 + self.genotype = genotype_1 self.samplelist = list(genotype.prgy) diff --git a/wqflask/maintenance/quick_search_table.py b/wqflask/maintenance/quick_search_table.py index eef61857..f0075df0 100644 --- a/wqflask/maintenance/quick_search_table.py +++ b/wqflask/maintenance/quick_search_table.py @@ -13,8 +13,10 @@ each trait, its dataset, and several columns determined by its trait type (pheno from __future__ import absolute_import, division, print_function -#import sys -#sys.path.append("../../..") + # We do this here so we can use zach_settings +# Not to avoid other absoulte_imports +import sys +sys.path.append("../../..") import simplejson as json @@ -504,4 +506,4 @@ def main(): ProbeSetXRef.run() if __name__ == "__main__": - main()
\ No newline at end of file + main() diff --git a/wqflask/wqflask/interval_mapping/interval_mapping.py b/wqflask/wqflask/interval_mapping/interval_mapping.py new file mode 100644 index 00000000..5d660224 --- /dev/null +++ b/wqflask/wqflask/interval_mapping/interval_mapping.py @@ -0,0 +1,312 @@ +from __future__ import absolute_import, print_function, division + +from base.trait import GeneralTrait +from base import data_set #import create_dataset + +from pprint import pformat as pf + +import string +import sys +import os +import collections + +import numpy as np +from scipy import linalg +import rpy2.robjects + +import simplejson as json + +#from redis import Redis + + +from base.trait import GeneralTrait +from base import data_set +from base import species +from base import webqtlConfig +from wqflask.my_pylmm.data import prep_data +from wqflask.my_pylmm.pyLMM import lmm +from wqflask.my_pylmm.pyLMM import input +from utility import helper_functions +from utility import Plot, Bunch +from utility import temp_data + +from utility.benchmark import Bench + + +class IntervalMapping(object): + + def __init__(self, start_vars, temp_uuid): + + #Currently only getting trait data for one trait, but will need + #to change this to accept multiple traits once the collection page is implemented + helper_functions.get_species_dataset_trait(self, start_vars) + + tempdata = temp_data.TempData(temp_uuid) + + self.samples = [] # Want only ones with values + self.vals = [] + + for sample in self.dataset.group.samplelist: + value = start_vars['value:' + sample] + self.samples.append(str(sample)) + self.vals.append(value) + + self.set_options(start_vars) + + self.gen_qtl_results(tempdata) + + #Get chromosome lengths for drawing the interval map plot + chromosome_mb_lengths = {} + for key in self.species.chromosomes.chromosomes.keys(): + chromosome_mb_lengths[key] = self.species.chromosomes.chromosomes[key].mb_length + + self.js_data = dict( + lrs_lod = self.lrs_lod, + chromosomes = chromosome_mb_lengths, + qtl_results = self.qtl_results, + ) + + def set_options(self, start_vars): + """Sets various options (physical/genetic mapping, # permutations, which chromosome""" + + self.plot_scale = start_vars['scale'] + #if self.plotScale == 'physic' and not fd.genotype.Mbmap: + # self.plotScale = 'morgan' + self.num_permutations = start_vars['num_permutations'] + self.do_bootstrap = start_vars['do_bootstrap'] + self.control_locus = start_vars['control_locus'] + self.selected_chr = start_vars['selected_chr'] + self.weighted_regression = start_vars['weighted'] + self.lrs_lod = start_vars['lrs_lod'] + + + def gen_qtl_results(self, tempdata): + """Generates qtl results for plotting interval map""" + + self.dataset.group.get_markers() + self.dataset.read_genotype_file() + + samples, values, variances = self.trait.export_informative() + if self.control_locus: + if self.weighted_regression: + qtl_result = self.dataset.genotype.regression(strains = samples, + trait = values, + variance = variances, + control = self.control_locus) + else: + qtl_result = self.dataset.genotype.regression(strains = samples, + trait = values, + control = self.control_locus) + else: + if self.weighted_regression: + qtl_result = self.dataset.genotype.regression(strains = samples, + trait = values, + variance = variances) + else: + qtl_result = self.dataset.genotype.regression(strains = samples, + trait = values) + + + pheno_vector = np.array([val == "x" and np.nan or float(val) for val in self.vals]) + + #if self.dataset.group.species == "human": + # p_values, t_stats = self.gen_human_results(pheno_vector, tempdata) + #else: + genotype_data = [marker['genotypes'] for marker in self.dataset.group.markers.markers] + + no_val_samples = self.identify_empty_samples() + trimmed_genotype_data = self.trim_genotypes(genotype_data, no_val_samples) + + genotype_matrix = np.array(trimmed_genotype_data).T + + t_stats, p_values = lmm.run( + pheno_vector, + genotype_matrix, + restricted_max_likelihood=True, + refit=False, + temp_data=tempdata + ) + + self.dataset.group.markers.add_pvalues(p_values) + + self.qtl_results = self.dataset.group.markers.markers + + #def gen_qtl_results_2(self, tempdata): + # """Generates qtl results for plotting interval map""" + # + # self.dataset.group.get_markers() + # self.dataset.read_genotype_file() + # + # pheno_vector = np.array([val == "x" and np.nan or float(val) for val in self.vals]) + # + # #if self.dataset.group.species == "human": + # # p_values, t_stats = self.gen_human_results(pheno_vector, tempdata) + # #else: + # genotype_data = [marker['genotypes'] for marker in self.dataset.group.markers.markers] + # + # no_val_samples = self.identify_empty_samples() + # trimmed_genotype_data = self.trim_genotypes(genotype_data, no_val_samples) + # + # genotype_matrix = np.array(trimmed_genotype_data).T + # + # t_stats, p_values = lmm.run( + # pheno_vector, + # genotype_matrix, + # restricted_max_likelihood=True, + # refit=False, + # temp_data=tempdata + # ) + # + # self.dataset.group.markers.add_pvalues(p_values) + # + # self.qtl_results = self.dataset.group.markers.markers + + + def identify_empty_samples(self): + no_val_samples = [] + for sample_count, val in enumerate(self.vals): + if val == "x": + no_val_samples.append(sample_count) + return no_val_samples + + + def trim_genotypes(self, genotype_data, no_value_samples): + trimmed_genotype_data = [] + for marker in genotype_data: + new_genotypes = [] + for item_count, genotype in enumerate(marker): + if item_count in no_value_samples: + continue + try: + genotype = float(genotype) + except ValueError: + genotype = np.nan + pass + new_genotypes.append(genotype) + trimmed_genotype_data.append(new_genotypes) + return trimmed_genotype_data + + #def get_qtl_results(self): + # """Gets the LOD (or LRS) score at each marker in order do the qtl mapping""" + # + # + # + # #self.genotype = self.genotype.addinterval() + # #resultSlice = [] + # #controlGeno = [] + # + # #if self.multipleInterval: + # # self.suggestive = 0 + # # self.significance = 0 + # # if self.selectedChr > -1: + # # self.genotype.chromosome = [self.genotype[self.selectedChr]] + # #else: + # #single interval mapping + # #try: + # # self.suggestive = float(fd.formdata.getvalue('permSuggestive')) + # # self.significance = float(fd.formdata.getvalue('permSignificance')) + # #except: + # # self.suggestive = None + # # self.significance = None + # + # #NOT DOING MULTIPLE TRAITS YET, BUT WILL NEED TO LATER + # #_strains, _vals, _vars = self.traitList[0].exportInformative(weightedRegression) + # + # #if webqtlUtil.ListNotNull(_vars): + # # pass + # #else: + # # weightedRegression = 0 + # # _strains, _vals, _vars = self.traitList[0].exportInformative() + # + # ##locate genotype of control Locus + # #if self.controlLocus: + # # controlGeno2 = [] + # # _FIND = 0 + # # for _chr in self.genotype: + # # for _locus in _chr: + # # if _locus.name == self.controlLocus: + # # controlGeno2 = _locus.genotype + # # _FIND = 1 + # # break + # # if _FIND: + # # break + # # if controlGeno2: + # # _prgy = list(self.genotype.prgy) + # # for _strain in _strains: + # # _idx = _prgy.index(_strain) + # # controlGeno.append(controlGeno2[_idx]) + # # else: + # # return "The control marker you selected is not in the genofile." + # # + # # + # # if self.significance and self.suggestive: + # # pass + # # else: + # # if self.permChecked: + # # if weightedRegression: + # # self.LRSArray = self.genotype.permutation(strains = _strains, trait = _vals, + # # variance = _vars, nperm=fd.nperm) + # # else: + # # self.LRSArray = self.genotype.permutation(strains = _strains, trait = _vals, + # # nperm=fd.nperm) + # # self.suggestive = self.LRSArray[int(fd.nperm*0.37-1)] + # # self.significance = self.LRSArray[int(fd.nperm*0.95-1)] + # # + # # else: + # # self.suggestive = 9.2 + # # self.significance = 16.1 + # # + # # #calculating bootstrap + # # #from now on, genotype could only contain a single chromosome + # # #permutation need to be performed genome wide, this is not the case for bootstrap + # # + # # #due to the design of qtlreaper, composite regression need to be performed genome wide + # # if not self.controlLocus and self.selectedChr > -1: + # # self.genotype.chromosome = [self.genotype[self.selectedChr]] + # # elif self.selectedChr > -1: #self.controlLocus and self.selectedChr > -1 + # # lociPerChr = map(len, self.genotype) + # # resultSlice = reduce(lambda X, Y: X+Y, lociPerChr[:self.selectedChr], 0) + # # resultSlice = [resultSlice,resultSlice+lociPerChr[self.selectedChr]] + # # else: + # # pass + # + # #calculate QTL for each trait + # self.qtl_results = [] + # + # #for thisTrait in self.traitList: + # _strains, _vals, _vars = thisTrait.exportInformative(weightedRegression) + # if self.controlLocus: + # if weightedRegression: + # qtlresult = self.genotype.regression(strains = _strains, trait = _vals, + # variance = _vars, control = self.controlLocus) + # else: + # qtlresult = self.genotype.regression(strains = _strains, trait = _vals, + # control = self.controlLocus) + # if resultSlice: + # qtlresult = qtlresult[resultSlice[0]:resultSlice[1]] + # else: + # if weightedRegression: + # qtlresult = self.genotype.regression(strains = _strains, trait = _vals, + # variance = _vars) + # else: + # qtlresult = self.genotype.regression(strains = _strains, trait = _vals) + # + # self.qtlresults.append(qtlresult) + # + # if not self.multipleInterval: + # if self.controlLocus and self.selectedChr > -1: + # self.genotype.chromosome = [self.genotype[self.selectedChr]] + # + # if self.bootChecked: + # if controlGeno: + # self.bootResult = self.genotype.bootstrap(strains = _strains, trait = _vals, + # control = controlGeno, nboot=fd.nboot) + # elif weightedRegression: + # self.bootResult = self.genotype.bootstrap(strains = _strains, trait = _vals, + # variance = _vars, nboot=fd.nboot) + # else: + # self.bootResult = self.genotype.bootstrap(strains = _strains, trait = _vals, + # nboot=fd.nboot) + # else: + # self.bootResult = [] + diff --git a/wqflask/wqflask/my_pylmm/data/geno_to_ped.py b/wqflask/wqflask/my_pylmm/data/geno_to_ped.py new file mode 100644 index 00000000..9091ad9a --- /dev/null +++ b/wqflask/wqflask/my_pylmm/data/geno_to_ped.py @@ -0,0 +1,22 @@ +from __future__ import absolute_import, division, print_function + +import csv + +class ConvertToPed(object): + + def __init__(self, input_file, output_file): + self.input_file = input_file + self.output_file = output_file + + def convert(self): + + self.haplotype_notation = { + '@mat': "1", + '@pat': "0", + '@het': "0.5", + '@unk': "NA" + } + + with open(self.output_file, "w") as self.output_fh: + self.process_csv() +
\ No newline at end of file |