diff options
| -rwxr-xr-x | wqflask/base/trait.py | 7 | ||||
| -rw-r--r-- | wqflask/utility/corr_result_helpers.py | 30 | ||||
| -rw-r--r-- | wqflask/wqflask/correlation/show_corr_results.py | 183 |
3 files changed, 44 insertions, 176 deletions
diff --git a/wqflask/base/trait.py b/wqflask/base/trait.py index 801d32c2..3429d9c1 100755 --- a/wqflask/base/trait.py +++ b/wqflask/base/trait.py @@ -286,7 +286,6 @@ class GeneralTrait(object): escape(self.dataset.name), escape(self.name)) trait_info = g.db.execute(query).fetchone() - #print("trait_info is: ", pf(trait_info)) #XZ, 05/08/2009: We also should use Geno.Id to find marker instead of just using Geno.Name # to avoid the problem of same marker name from different species. elif self.dataset.type == 'Geno': @@ -319,7 +318,6 @@ class GeneralTrait(object): #XZ: assign SQL query result to trait attributes. for i, field in enumerate(self.dataset.display_fields): - print(" mike: {} -> {} - {}".format(field, type(trait_info[i]), trait_info[i])) setattr(self, field, trait_info[i]) if self.dataset.type == 'Publish': @@ -329,9 +327,6 @@ class GeneralTrait(object): self.homologeneid = None - print("self.geneid is:", self.geneid) - print(" type:", type(self.geneid)) - print("self.dataset.group.name is:", self.dataset.group.name) if self.dataset.type == 'ProbeSet' and self.dataset.group and self.geneid: #XZ, 05/26/2010: From time to time, this query get error message because some geneid values in database are not number. #XZ: So I have to test if geneid is number before execute the query. @@ -356,7 +351,6 @@ class GeneralTrait(object): InbredSet.SpeciesId = Species.Id AND Species.TaxonomyId = Homologene.TaxonomyId """ % (escape(str(self.geneid)), escape(self.dataset.group.name)) - print("-> query is:", query) result = g.db.execute(query).fetchone() #else: # result = None @@ -388,7 +382,6 @@ class GeneralTrait(object): Geno.Name = '{}' and Geno.SpeciesId = Species.Id """.format(self.dataset.group.species, self.locus) - print("query is:", query) result = g.db.execute(query).fetchone() self.locus_chr = result[0] self.locus_mb = result[1] diff --git a/wqflask/utility/corr_result_helpers.py b/wqflask/utility/corr_result_helpers.py new file mode 100644 index 00000000..edf32449 --- /dev/null +++ b/wqflask/utility/corr_result_helpers.py @@ -0,0 +1,30 @@ +def normalize_values(a_values, b_values): + """ + Trim two lists of values to contain only the values they both share + + Given two lists of sample values, trim each list so that it contains + only the samples that contain a value in both lists. Also returns + the number of such samples. + + >>> normalize_values([2.3, None, None, 3.2, 4.1, 5], [3.4, 7.2, 1.3, None, 6.2, 4.1]) + ([2.3, 4.1, 5], [3.4, 6.2, 4.1], 3) + + """ + + min_length = min(len(a_values), len(b_values)) + a_new = [] + b_new = [] + for counter in range(min_length): + if a_values[counter] and b_values[counter]: + a_new.append(a_values[counter]) + b_new.append(b_values[counter]) + + num_overlap = len(a_new) + assert num_overlap == len(b_new), "Lengths should be the same" + + return a_new, b_new, num_overlap + + +if __name__ == '__main__': + import doctest + doctest.testmod() \ No newline at end of file diff --git a/wqflask/wqflask/correlation/show_corr_results.py b/wqflask/wqflask/correlation/show_corr_results.py index 3b1ac87d..1410ae0c 100644 --- a/wqflask/wqflask/correlation/show_corr_results.py +++ b/wqflask/wqflask/correlation/show_corr_results.py @@ -20,6 +20,9 @@ from __future__ import absolute_import, print_function, division +import sys +sys.path.append(".") + import gc import string import cPickle @@ -43,7 +46,7 @@ from utility.TDCell import TDCell from base.trait import GeneralTrait from base import data_set from base.templatePage import templatePage -from utility import webqtlUtil, helper_functions +from utility import webqtlUtil, helper_functions, corr_result_helpers from dbFunction import webqtlDatabaseFunction import utility.webqtlUtil #this is for parallel computing only. from wqflask.correlation import correlationFunction @@ -122,22 +125,24 @@ class CorrelationResults(object): self.correlation_data = {} for trait, values in self.target_dataset.trait_data.iteritems(): - this_trait_values = [] - target_values = [] + this_trait_vals = [] + target_vals = [] for index, sample in enumerate(self.target_dataset.samplelist): if sample in self.sample_data: sample_value = self.sample_data[sample] target_sample_value = values[index] - this_trait_values.append(sample_value) - target_values.append(target_sample_value) + this_trait_vals.append(sample_value) + target_vals.append(target_sample_value) + + this_trait_vals, target_vals, num_overlap = corr_result_helpers.normalize_values( + this_trait_vals, target_vals) - this_trait_values, target_values, num_overlap = normalize_values(this_trait_values, - target_values) + print("num_overlap:", num_overlap) if self.corr_method == 'pearson': - sample_r, sample_p = scipy.stats.pearsonr(this_trait_values, target_values) + sample_r, sample_p = scipy.stats.pearsonr(this_trait_vals, target_vals) else: - sample_r, sample_p = scipy.stats.spearmanr(this_trait_values, target_values) + sample_r, sample_p = scipy.stats.spearmanr(this_trait_vals, target_vals) self.correlation_data[trait] = [sample_r, sample_p, num_overlap] @@ -940,163 +945,3 @@ class CorrelationResults(object): return traitList - - def createExcelFileWithTitleAndFooter(self, workbook=None, identification=None, db=None, returnNumber=None): - - worksheet = workbook.add_worksheet() - - titleStyle = workbook.add_format(align = 'left', bold = 0, size=14, border = 1, border_color="gray") - - ##Write title Info - # Modified by Hongqiang Li - worksheet.write([1, 0], "Citations: Please see %s/reference.html" % webqtlConfig.PORTADDR, titleStyle) - worksheet.write([1, 0], "Citations: Please see %s/reference.html" % webqtlConfig.PORTADDR, titleStyle) - worksheet.write([2, 0], "Trait : %s" % identification, titleStyle) - worksheet.write([3, 0], "Database : %s" % db.fullname, titleStyle) - worksheet.write([4, 0], "Date : %s" % time.strftime("%B %d, %Y", time.gmtime()), titleStyle) - worksheet.write([5, 0], "Time : %s GMT" % time.strftime("%H:%M ", time.gmtime()), titleStyle) - worksheet.write([6, 0], "Status of data ownership: Possibly unpublished data; please see %s/statusandContact.html for details on sources, ownership, and usage of these data." % webqtlConfig.PORTADDR, titleStyle) - #Write footer info - worksheet.write([9 + returnNumber, 0], "Funding for The GeneNetwork: NIAAA (U01AA13499, U24AA13513), NIDA, NIMH, and NIAAA (P20-DA21131), NCI MMHCC (U01CA105417), and NCRR (U01NR 105417)", titleStyle) - worksheet.write([10 + returnNumber, 0], "PLEASE RETAIN DATA SOURCE INFORMATION WHENEVER POSSIBLE", titleStyle) - - return worksheet - - - def getTableHeaderForGeno(self, method=None, worksheet=None, newrow=None, headingStyle=None): - - tblobj_header = [] - - if method in ["1","3","4"]: - tblobj_header = [[THCell(HT.TD(' ', Class="fs13 fwb ffl b1 cw cbrb"), sort=0), - THCell(HT.TD('Record', HT.BR(), 'ID', HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text='Record ID', idx=1), - THCell(HT.TD('Location', HT.BR(), 'Chr and Mb', HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text='Location (Chr and Mb)', idx=2), - THCell(HT.TD(HT.Href( - text = HT.Span('Sample',HT.BR(), 'r', HT.Sup(' ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"), - target = '_blank', - url = "/correlationAnnotation.html#genetic_r"), - Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Sample r", idx=3), - THCell(HT.TD('N',HT.BR(),'Cases',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="N Cases", idx=4), - THCell(HT.TD(HT.Href( - text = HT.Span('Sample',HT.BR(), 'p(r)', HT.Sup(' ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"), - target = '_blank', - url = "/correlationAnnotation.html#genetic_p_r"), - Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Sample p(r)", idx=5)]] - - for ncol, item in enumerate(['Record ID', 'Location (Chr, Mb)', 'Sample r', 'N Cases', 'Sample p(r)']): - worksheet.write([newrow, ncol], item, headingStyle) - worksheet.set_column([ncol, ncol], 2*len(item)) - else: - tblobj_header = [[THCell(HT.TD(' ', Class="fs13 fwb ffl b1 cw cbrb"), sort=0), - THCell(HT.TD('Record', HT.BR(), 'ID', HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text='Record ID', idx=1), - THCell(HT.TD('Location', HT.BR(), 'Chr and Mb', HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text='Location (Chr and Mb)', idx=2), - THCell(HT.TD(HT.Href( - text = HT.Span('Sample',HT.BR(), 'rho', HT.Sup(' ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"), - target = '_blank', - url = "/correlationAnnotation.html#genetic_rho"), - Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Sample rho", idx=3), - THCell(HT.TD('N',HT.BR(),'Cases',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="N Cases", idx=4), - THCell(HT.TD(HT.Href( - text = HT.Span('Sample',HT.BR(), 'p(rho)', HT.Sup(' ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"), - target = '_blank', - url = "/correlationAnnotation.html#genetic_p_rho"), - Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Sample p(rho)", idx=5)]] - - for ncol, item in enumerate(['Record ID', 'Location (Chr, Mb)', 'Sample rho', 'N Cases', 'Sample p(rho)']): - worksheet.write([newrow, ncol], item, headingStyle) - worksheet.set_column([ncol, ncol], 2*len(item)) - - - return tblobj_header, worksheet - - - def getTableBodyForGeno(self, traitList, formName=None, worksheet=None, newrow=None, corrScript=None): - - tblobj_body = [] - - for thisTrait in traitList: - tr = [] - - trId = str(thisTrait) - - corrScript.append('corrArray["%s"] = {corr:%1.4f};' % (trId, thisTrait.corr)) - - tr.append(TDCell(HT.TD(HT.Input(type="checkbox", Class="checkbox", name="searchResult",value=trId, onClick="highlight(this)"), nowrap="on", Class="fs12 fwn ffl b1 c222"), text=trId)) - - tr.append(TDCell(HT.TD(HT.Href(text=thisTrait.name,url="javascript:showTrait('%s', '%s')" % (formName, thisTrait.name), Class="fs12 fwn ffl"),align="left", Class="fs12 fwn ffl b1 c222"), text=thisTrait.name, val=thisTrait.name.upper())) - - #XZ: trait_location_value is used for sorting - trait_location_repr = '--' - trait_location_value = 1000000 - - if thisTrait.chr and thisTrait.mb: - try: - trait_location_value = int(thisTrait.chr)*1000 + thisTrait.mb - except: - if thisTrait.chr.upper() == 'X': - trait_location_value = 20*1000 + thisTrait.mb - else: - trait_location_value = ord(str(thisTrait.chr).upper()[0])*1000 + thisTrait.mb - - trait_location_repr = 'Chr%s: %.6f' % (thisTrait.chr, float(thisTrait.mb) ) - - tr.append(TDCell(HT.TD(trait_location_repr, Class="fs12 fwn b1 c222", nowrap="on"), trait_location_repr, trait_location_value)) - - - repr='%3.3f' % thisTrait.corr - tr.append(TDCell(HT.TD(HT.Href(text=repr, url="javascript:showCorrPlot('%s', '%s')" % (formName, thisTrait.name), Class="fs12 fwn ffl"), Class="fs12 fwn ffl b1 c222", nowrap='ON', align='right'),repr,abs(thisTrait.corr))) - - repr = '%d' % thisTrait.nOverlap - tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222",align='right'),repr,thisTrait.nOverlap)) - - repr = webqtlUtil.SciFloat(thisTrait.corrPValue) - tr.append(TDCell(HT.TD(repr,nowrap='ON', Class="fs12 fwn ffl b1 c222", align='right'),repr,thisTrait.corrPValue)) - - tblobj_body.append(tr) - - for ncol, item in enumerate([thisTrait.name, trait_location_repr, thisTrait.corr, thisTrait.nOverlap, thisTrait.corrPValue]): - worksheet.write([newrow, ncol], item) - newrow += 1 - - return tblobj_body, worksheet, corrScript - -def normalize_values(values_1, values_2): - N = min(len(values_1), len(values_2)) - X = [] - Y = [] - for i in range(N): - if values_1[i]!= None and values_2[i]!= None: - X.append(values_1[i]) - Y.append(values_2[i]) - num_overlap = len(X) - - return (X, Y, num_overlap) - - -def cal_correlation(values_1, values_2): - N = min(len(values_1), len(values_2)) - X = [] - Y = [] - for i in range(N): - if values_1[i]!= None and values_2[i]!= None: - X.append(values_1[i]) - Y.append(values_2[i]) - NN = len(X) - if NN <6: - return (0.0,NN) - sx = reduce(lambda x,y:x+y,X,0.0) - sy = reduce(lambda x,y:x+y,Y,0.0) - x_mean = sx/NN - y_mean = sy/NN - xyd = 0.0 - sxd = 0.0 - syd = 0.0 - for i in range(NN): - xyd += (X[i] - x_mean)*(Y[i] - y_mean) - sxd += (X[i] - x_mean)*(X[i] - x_mean) - syd += (Y[i] - y_mean)*(Y[i] - y_mean) - try: - corr = xyd/(sqrt(sxd)*sqrt(syd)) - except: - corr = 0 - return (corr, NN) |