def findPlaneWithEigenVectors(coords):
centroid = average(coords)
coords -= centroid
B = matrixmultiply(transpose(coords), coords)
eigenvalues, eigenvectors = la.eigenvectors(B)
#return eigenvalues, [Vector(e) for e in eigenvectors], Vector(centroid)
return eigenvalues, [Vector([i for i in e]) for e in eigenvectors], Vector(centroid) #not sure why I had to make this change!
def __init__(self, start_vars):
trait_db_list = [trait.strip() for trait in start_vars['trait_list'].split(',')]
helper_functions.get_trait_db_obs(self, trait_db_list)
self.all_sample_list = []
self.traits = []
self.insufficient_shared_samples = False
this_group = self.trait_list[0][1].group.name #ZS: Getting initial group name before verifying all traits are in the same group in the following loop
for trait_db in self.trait_list:
if trait_db[1].group.name != this_group:
self.insufficient_shared_samples = True
break
else:
this_group = trait_db[1].group.name
this_trait = trait_db[0]
self.traits.append(this_trait)
this_sample_data = this_trait.data
for sample in this_sample_data:
if sample not in self.all_sample_list:
self.all_sample_list.append(sample)
if self.insufficient_shared_samples:
pass
else:
self.sample_data = []
for trait_db in self.trait_list:
this_trait = trait_db[0]
this_sample_data = this_trait.data
this_trait_vals = []
for sample in self.all_sample_list:
if sample in this_sample_data:
this_trait_vals.append(this_sample_data[sample].value)
else:
this_trait_vals.append('')
self.sample_data.append(this_trait_vals)
if len(this_trait_vals) < len(self.trait_list): #Shouldn't do PCA if there are more traits than observations/samples
return False
self.lowest_overlap = 8 #ZS: Variable set to the lowest overlapping samples in order to notify user, or 8, whichever is lower (since 8 is when we want to display warning)
self.corr_results = []
self.pca_corr_results = []
self.trait_data_array = []
for trait_db in self.trait_list:
this_trait = trait_db[0]
this_db = trait_db[1]
this_db_samples = this_db.group.all_samples_ordered()
this_sample_data = this_trait.data
this_trait_vals = []
for index, sample in enumerate(this_db_samples):
if (sample in this_sample_data):
sample_value = this_sample_data[sample].value
this_trait_vals.append(sample_value)
self.trait_data_array.append(this_trait_vals)
corr_result_row = []
pca_corr_result_row = []
is_spearman = False #ZS: To determine if it's above or below the diagonal
for target in self.trait_list:
target_trait = target[0]
target_db = target[1]
target_samples = target_db.group.all_samples_ordered()
target_sample_data = target_trait.data
this_trait_vals = []
target_vals = []
for index, sample in enumerate(target_samples):
if (sample in this_sample_data) and (sample in target_sample_data):
sample_value = this_sample_data[sample].value
target_sample_value = target_sample_data[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)
if num_overlap < self.lowest_overlap:
self.lowest_overlap = num_overlap
if num_overlap == 0:
corr_result_row.append([target_trait, 0, num_overlap])
pca_corr_result_row.append(0)
else:
pearson_r, pearson_p = scipy.stats.pearsonr(this_trait_vals, target_vals)
if is_spearman == False:
sample_r, sample_p = pearson_r, pearson_p
if sample_r == 1:
is_spearman = True
else:
sample_r, sample_p = scipy.stats.spearmanr(this_trait_vals, target_vals)
corr_result_row.append([target_trait, sample_r, num_overlap])
pca_corr_result_row.append(pearson_r)
self.corr_results.append(corr_result_row)
self.pca_corr_results.append(pca_corr_result_row)
corr_result_eigen = la.eigenvectors(numarray.array(self.pca_corr_results))
corr_eigen_value, corr_eigen_vectors = sortEigenVectors(corr_result_eigen)
groups = []
for sample in self.all_sample_list:
groups.append(1)
pca = self.calculate_pca(range(len(self.traits)), corr_eigen_value, corr_eigen_vectors)
self.loadings_array = self.process_loadings()
self.js_data = dict(traits = [trait.name for trait in self.traits],
groups = groups,
cols = range(len(self.traits)),
rows = range(len(self.traits)),
samples = self.all_sample_list,
sample_data = self.sample_data,)
def __init__(self,fd,InputData=None):
templatePage.__init__(self, fd)
self.dict['title'] = 'Correlation Matrix'
if not self.openMysql():
return
if not fd.genotype:
fd.readGenotype()
fd.strainlist = fd.f1list + fd.strainlist
#self.searchResult = fd.formdata.getvalue('searchResult')
self.oldSearchResult = fd.formdata.getvalue('oldSearchResult')
if self.oldSearchResult:
try:
self.searchResult = fd.formdata.getvalue('oldSearchResult')
except:
self.searchResult = fd.formdata.getvalue('searchResult')
else:
self.searchResult = fd.formdata.getvalue('searchResult')
if not self.searchResult:
heading = 'Correlation Matrix'
detail = ['You need to select at least two traits in order to generate correlation matrix.']
self.error(heading=heading,detail=detail)
return
if type("1") == type(self.searchResult):
self.searchResult = [self.searchResult]
if self.searchResult:
#testvals,names,dbInfos = self.getAllSearchResult(fd,self.searchResult)
if len(self.searchResult) > webqtlConfig.MAXCORR:
heading = 'Correlation Matrix'
detail = ['In order to display Correlation Matrix properly, Do not select more than %d traits for Correlation Matrix.' % webqtlConfig.MAXCORR]
self.error(heading=heading,detail=detail)
return
#XZ, 7/22/2009: this block is not necessary
#elif len(self.searchResult) > 40:
# noPCA = 1
#else:
# noPCA = 0
traitList = []
traitDataList = []
for item in self.searchResult:
thisTrait = webqtlTrait(fullname=item, cursor=self.cursor)
thisTrait.retrieveInfo()
thisTrait.retrieveData(fd.strainlist)
traitList.append(thisTrait)
traitDataList.append(thisTrait.exportData(fd.strainlist))
else:
heading = 'Correlation Matrix'
detail = [HT.Font('Error : ',color='red'),HT.Font('Error occurs while retrieving data FROM database.',color='black')]
self.error(heading=heading,detail=detail)
return
NNN = len(traitList)
if NNN == 0:
heading = "Correlation Matrix"
detail = ['No trait was selected for %s data set. No matrix generated.' % self.data.RISet]
self.error(heading=heading,detail=detail)
return
elif NNN < 2:
heading = 'Correlation Matrix'
detail = ['You need to select at least two traits in order to generate correlation matrix.']
self.error(heading=heading,detail=detail)
return
else:
corArray = [([0] * (NNN+1))[:] for i in range(NNN+1)]
pearsonArray = [([0] * (NNN))[:] for i in range(NNN)]
spearmanArray = [([0] * (NNN))[:] for i in range(NNN)]
corArray[0][0] = 'Correlation'
TD_LR = HT.TD(colspan=2,width="100%",bgColor='#eeeeee')
form = HT.Form( cgi= os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), enctype='multipart/form-data', name='showDatabase', submit=HT.Input(type='hidden'))
hddn = {'FormID':'showDatabase', 'ProbeSetID':'_','database':'_',
'CellID':'_','ProbeSetID2':'_','database2':'_','CellID2':'_',
'newNames':fd.formdata.getvalue("newNames", "_"),
'RISet':fd.RISet,'ShowStrains':'ON','ShowLine':'ON', 'rankOrder':'_',
"allstrainlist":string.join(fd.strainlist, " "), 'traitList':string.join(self.searchResult, "\t")}
if fd.incparentsf1:
hddn['incparentsf1']='ON'
for key in hddn.keys():
form.append(HT.Input(name=key, value=hddn[key], type='hidden'))
for item in self.searchResult:
form.append(HT.Input(name='oldSearchResult', value=str(item), type='hidden'))
traiturls = []
traiturls2 = []
shortNames = []
verboseNames = []
verboseNames2 = []
verboseNames3 = []
abbreviation = ''
#dbInfo.ProbeSetID = ProbeSetID
#dbInfo.CellID = CellID
for i, thisTrait in enumerate(traitList):
_url = "javascript:showDatabase2('%s','%s','%s');" % (thisTrait.db.name, thisTrait.name, thisTrait.cellid)
#_text = 'Trait%d: ' % (i+1)+str(thisTrait)
_text = 'Trait %d: ' % (i+1)+thisTrait.displayName()
if thisTrait.db.type == 'Geno':
_shortName = 'Genotype'
abbreviation = 'Genotype'
_verboseName = 'Locus %s' % (thisTrait.name)
_verboseName2 = 'Chr %s @ %s Mb' % (thisTrait.chr, '%2.3f' % thisTrait.mb)
_verboseName3 = ''
elif thisTrait.db.type == 'Publish':
if thisTrait.post_publication_abbreviation:
AbbreviationString = thisTrait.post_publication_abbreviation
else:
AbbreviationString = ''
if thisTrait.confidential:
if not webqtlUtil.hasAccessToConfidentialPhenotypeTrait(privilege=self.privilege, userName=self.userName, authorized_users=thisTrait.authorized_users):
if thisTrait.pre_publication_abbreviation:
AbbreviationString = thisTrait.pre_publication_abbreviation
else:
AbbreviationString = ''
_shortName = 'Phenotype: %s' % (AbbreviationString)
_verboseName2 = ''
_verboseName3 = ''
if thisTrait.pubmed_id:
_verboseName = 'PubMed %d: ' % thisTrait.pubmed_id
else:
_verboseName = 'Unpublished '
_verboseName += 'RecordID/%s' % (thisTrait.name)
PhenotypeString = thisTrait.post_publication_description
if thisTrait.confidential:
if not webqtlUtil.hasAccessToConfidentialPhenotypeTrait(privilege=self.privilege, userName=self.userName, authorized_users=thisTrait.authorized_users):
PhenotypeString = thisTrait.pre_publication_description
_verboseName2 = 'Phenotype: %s' % (PhenotypeString)
if thisTrait.authors:
a1 = string.split(thisTrait.authors,',')[0]
while a1[0] == '"' or a1[0] == "'" :
a1 = a1[1:]
_verboseName += ' by '
_verboseName += HT.Italic('%s, and colleagues' % (a1))
elif thisTrait.db.type == 'Temp':
abbreviation = ''
_shortName = thisTrait.name
if thisTrait.description:
_verboseName = thisTrait.description
else:
_verboseName = 'Temp'
_verboseName2 = ''
_verboseName3 = ''
else:
if thisTrait.symbol:
abbreviation = thisTrait.symbol
else:
abbreviation = ''
_shortName = 'Symbol: %s ' % thisTrait.symbol
if thisTrait.symbol:
_verboseName = thisTrait.symbol
else:
_verboseName = ''
_verboseName2 = ''
_verboseName3 = ''
if thisTrait.chr and thisTrait.mb:
_verboseName += ' on Chr %s @ %s Mb' % (thisTrait.chr,thisTrait.mb)
if thisTrait.description:
_verboseName2 = '%s' % (thisTrait.description)
if thisTrait.probe_target_description:
_verboseName3 = '%s' % (thisTrait.probe_target_description)
cururl = HT.Href(text=_text, url=_url,Class='fs12')
cururl2 = HT.Href(text='Trait%d' % (i+1),url=_url,Class='fs12')
traiturls.append(cururl)
traiturls2.append(cururl2)
shortName = HT.Div(id="shortName_" + str(i), style="display:none")
shortName.append(_shortName)
shortNames.append(shortName)
verboseName = HT.Div(id="verboseName_" + str(i), style="display:none")
verboseName.append(_verboseName)
verboseNames.append(verboseName)
verboseName2 = HT.Div(id="verboseName2_" + str(i), style="display:none")
verboseName2.append(_verboseName2)
verboseNames2.append(verboseName2)
verboseName3 = HT.Div(id="verboseName3_" + str(i), style="display:none")
verboseName3.append(_verboseName3)
verboseNames3.append(verboseName3)
corArray[i+1][0] = 'Trait%d: ' % (i+1)+str(thisTrait) + '/' + str(thisTrait) + ': ' + abbreviation + '/' + str(thisTrait) + ': ' + str(_verboseName) + ' : ' + str(_verboseName2) + ' : ' + str(_verboseName3)
corArray[0][i+1] = 'Trait%d: ' % (i+1)+str(thisTrait)
corMatrixHeading = HT.Paragraph('Correlation Matrix', Class="title")
tbl = HT.TableLite(Class="collap", border=0, cellspacing=1,
cellpadding=5, width='100%')
row1 = HT.TR(HT.TD(Class="fs14 fwb ffl b1 cw cbrb"),
HT.TD('Spearman Rank Correlation (rho)', Class="fs14 fwb ffl b1 cw cbrb", colspan= NNN+1,align="center")
)
row2 = HT.TR(
HT.TD("P e a r s o n r", rowspan= NNN+1,Class="fs14 fwb ffl b1 cw cbrb", width=10,align="center"),
HT.TD(Class="b1", width=300))
for i in range(NNN):
row2.append(HT.TD(traiturls2[i], Class="b1", align="center"))
tbl.append(row1,row2)
nOverlapTrait =9999
nnCorr = len(fd.strainlist)
for i, thisTrait in enumerate(traitList):
newrow = HT.TR()
newrow.append(HT.TD(traiturls[i], shortNames[i], verboseNames[i], verboseNames2[i],
verboseNames3[i], Class="b1"))
names1 = [thisTrait.db.name, thisTrait.name, thisTrait.cellid]
for j, thisTrait2 in enumerate(traitList):
names2 = [thisTrait2.db.name, thisTrait2.name, thisTrait2.cellid]
if j < i:
corr,nOverlap = webqtlUtil.calCorrelation(traitDataList[i],traitDataList[j],nnCorr)
rank = fd.formdata.getvalue("rankOrder", "0")
if nOverlap < nOverlapTrait:
nOverlapTrait = nOverlap
if corr > 0.7:
fontcolor="red"
elif corr > 0.5:
fontcolor="#FF6600"
elif corr < -0.7:
fontcolor="blue"
elif corr < -0.5:
fontcolor="#009900"
else:
fontcolor ="#000000"
pearsonArray[i][j] = corr
pearsonArray[j][i] = corr
if corr!= 0.0:
corArray[i+1][j+1] = '%2.3f/%d' % (corr,nOverlap)
thisurl = HT.Href(text=HT.Font('%2.3f'% corr,HT.BR(),'%d' % nOverlap ,color=fontcolor, Class="fs11 fwn"),url = "javascript:showCorrelationPlot2(db='%s',ProbeSetID='%s',CellID='%s',db2='%s',ProbeSetID2='%s',CellID2='%s',rank='%s')" % (names1[0], names1[1], names1[2], names2[0], names2[1], names2[2], rank))
else:
corArray[i+1][j+1] = '---/%d' % nOverlap
thisurl = HT.Font('---',HT.BR(), '%d' % nOverlap)
newrow.append(HT.TD(thisurl,Class="b1",NOWRAP="ON",align="middle"))
elif j == i:
corr,nOverlap = webqtlUtil.calCorrelation(traitDataList[i],traitDataList[j],nnCorr)
pearsonArray[i][j] = 1.0
spearmanArray[i][j] = 1.0
corArray[i+1][j+1] = '%2.3f/%d' % (corr,nOverlap)
nOverlap = webqtlUtil.calCorrelation(traitDataList[i],traitDataList[j],nnCorr)[1]
newrow.append(HT.TD(HT.Href(text=HT.Font(HT.Italic("n"),HT.BR(),str(nOverlap),Class="fs11 fwn b1",align="center", color="000000"), url="javascript:showDatabase2('%s','%s','%s')" % (thisTrait.db.name, thisTrait.name, thisTrait.cellid)), bgColor='#cccccc', align="center", Class="b1", NOWRAP="ON"))
else:
corr,nOverlap = webqtlUtil.calCorrelationRank(traitDataList[i],traitDataList[j],nnCorr)
rank = fd.formdata.getvalue("rankOrder", "1")
if corr > 0.7:
fontcolor="red"
elif corr > 0.5:
fontcolor="#FF6600"
elif corr < -0.7:
fontcolor="blue"
elif corr < -0.5:
fontcolor="#009900"
else:
fontcolor ="#000000"
spearmanArray[i][j] = corr
spearmanArray[j][i] = corr
if corr!= 0.0:
corArray[i+1][j+1] = '%2.3f/%d' % (corr,nOverlap)
thisurl = HT.Href(text=HT.Font('%2.3f'% corr,HT.BR(),'%d' % nOverlap ,color=fontcolor, Class="fs11 fwn"),url = "javascript:showCorrelationPlot2(db='%s',ProbeSetID='%s',CellID='%s',db2='%s',ProbeSetID2='%s',CellID2='%s',rank='%s')" % (names1[0], names1[1], names1[2], names2[0], names2[1], names2[2], rank))
else:
corArray[i+1][j+1] = '---/%d' % nOverlap
thisurl = HT.Span('---',HT.BR(), '%d' % nOverlap, Class="fs11 fwn")
newrow.append(HT.TD(thisurl,Class="b1", NOWRAP="ON",align="middle"))
tbl.append(newrow)
info = HT.Blockquote('Lower left cells list Pearson product-moment correlations; upper right cells list Spearman rank order correlations. Each cell also contains the n of cases. Values higher than 0.7 are displayed in ',HT.Font('red', color='red'),'; those between 0.5 and 0.7 in ',HT.Font('orange', color='#FF6600'),'; Values lower than -0.7 are in ',HT.Font('blue', color='blue'),'; between -0.5 and -0.7 in ',HT.Font('green', color='#009900'),'. Select any cell to generate a scatter plot. Select trait labels for more information.', Class="fs13 fwn")
exportbutton = HT.Input(type='button', name='export', value='Export', onClick="exportText(allCorrelations);",Class="button")
shortButton = HT.Input(type='button' ,name='dispShort',value=' Short Labels ', onClick="displayShortName();",Class="button")
verboseButton = HT.Input(type='button' ,name='dispVerbose',value=' Long Labels ', onClick="displayVerboseName();", Class="button")
form.append(HT.Blockquote(tbl,HT.P(),shortButton,verboseButton,exportbutton))
TD_LR.append(corMatrixHeading,info,form,HT.P())
#if noPCA:
# TD_LR.append(HT.Blockquote('No PCA is computed if more than 32 traits are selected.'))
#print corArray
exportScript = """
<SCRIPT language=JavaScript>
var allCorrelations = %s;
</SCRIPT>
"""
exportScript = exportScript % str(corArray)
self.dict['js1'] = exportScript+'<SCRIPT SRC="/javascript/correlationMatrix.js"></SCRIPT><BR>'
self.dict['body'] = str(TD_LR)
#don't calculate PCA while number exceed 32
#if noPCA:
# return
#XZ, 7/22/2009: deal with PCA stuff
#Only for Array Data
if NNN > 2:
traitname = map(lambda X:str(X.name), traitList)
#generate eigenvalues
# import sys
sys.argv=[" "]
# import numarray
# import numarray.linear_algebra as la
#spearmanEigen = eigenvectors(array(spearmanArray))
pearsonEigen = la.eigenvectors(numarray.array(pearsonArray))
#spearmanEigenValue,spearmanEigenVectors = self.sortEigenVectors(spearmanEigen)
pearsonEigenValue,pearsonEigenVectors = self.sortEigenVectors(pearsonEigen)
"""
for i in range(len(pearsonEigenValue)):
if type(pearsonEigenValue[i]).__name__ == 'complex':
pearsonEigenValue[i] = pearsonEigenValue[i].real
for i in range(len(pearsonEigenVectors)):
for j in range(len(pearsonEigenVectors[i])):
if type(pearsonEigenVectors[i][j]).__name__ == 'complex':
pearsonEigenVectors[i][j] = pearsonEigenVectors[i][j].real
if type(pearsonEigenVectors[i][j]).__name__ == 'complex':
pearsonEigenVectors[i][j] = pearsonEigenVectors[i][j].real
"""
#if type(pearsonEigenValue[0]).__name__ == 'complex':
if False:
pass
else:
traitHeading = HT.Paragraph('PCA Traits',align='left', Class="title")
tbl2 = self.calcPCATraits(traitDataList=traitDataList, nnCorr=nnCorr, NNN=NNN, pearsonEigenValue=pearsonEigenValue,
pearsonEigenVectors=pearsonEigenVectors, form=form, fd=fd)
#Buttons on search page
#mintmap = HT.Input(type='button' ,name='mintmap',value='Multiple Mapping', onClick="databaseFunc(this.form,'showIntMap');",Class="button")
addselect = HT.Input(type='button' ,name='addselect',value='Add to Collection', onClick="addRmvSelection('%s', this.form, 'addToSelection');" % fd.RISet,Class="button")
selectall = HT.Input(type='button' ,name='selectall',value='Select All', onClick="checkAll(this.form);",Class="button")
reset = HT.Input(type='reset',name='',value='Select None',Class="button")
updateNames = HT.Input(type='button', name='updateNames',value='Update Trait Names', onClick="editPCAName(this.form);", Class="button")
chrMenu = HT.Input(type='hidden',name='chromosomes',value='all')
"""
#need to be refined
if fd.genotype.Mbmap:
scaleMenu = HT.Select(name='scale')
scaleMenu.append(tuple(["Genetic Map",'morgan']))
scaleMenu.append(tuple(["Physical Map",'physic']))
else:
scaleMenu = ""
"""
tbl2.append(HT.TR(HT.TD(HT.P(),chrMenu,updateNames,selectall,reset,addselect,colspan=3)))
form.append(HT.P(),traitHeading,HT.Blockquote(tbl2))
plotHeading1 = HT.Paragraph('Scree Plot', Class="title")
TD_LR.append(plotHeading1)
img1 = self.screePlot(NNN=NNN, pearsonEigenValue=pearsonEigenValue)
TD_LR.append(HT.Blockquote(img1))
plotHeading2 = HT.Paragraph('Factor Loadings Plot', Class="title")
TD_LR.append(plotHeading2)
img2 = self.factorLoadingsPlot(pearsonEigenVectors=pearsonEigenVectors, traitList=traitList)
TD_LR.append(HT.Blockquote(img2))
self.dict['body'] = str(TD_LR)
def __init__(self, start_vars):
trait_db_list = [
trait.strip() for trait in start_vars['trait_list'].split(',')
]
helper_functions.get_trait_db_obs(self, trait_db_list)
self.all_sample_list = []
self.traits = []
self.insufficient_shared_samples = False
this_group = self.trait_list[0][
1].group.name #ZS: Getting initial group name before verifying all traits are in the same group in the following loop
for trait_db in self.trait_list:
if trait_db[1].group.name != this_group:
self.insufficient_shared_samples = True
break
else:
this_group = trait_db[1].group.name
this_trait = trait_db[0]
self.traits.append(this_trait)
this_sample_data = this_trait.data
for sample in this_sample_data:
if sample not in self.all_sample_list:
self.all_sample_list.append(sample)
if self.insufficient_shared_samples:
pass
else:
self.sample_data = []
for trait_db in self.trait_list:
this_trait = trait_db[0]
this_sample_data = this_trait.data
this_trait_vals = []
for sample in self.all_sample_list:
if sample in this_sample_data:
this_trait_vals.append(this_sample_data[sample].value)
else:
this_trait_vals.append('')
self.sample_data.append(this_trait_vals)
if len(this_trait_vals) < len(
self.trait_list
): #Shouldn't do PCA if there are more traits than observations/samples
return False
self.lowest_overlap = 8 #ZS: Variable set to the lowest overlapping samples in order to notify user, or 8, whichever is lower (since 8 is when we want to display warning)
self.corr_results = []
self.pca_corr_results = []
self.trait_data_array = []
for trait_db in self.trait_list:
this_trait = trait_db[0]
this_db = trait_db[1]
this_db_samples = this_db.group.all_samples_ordered()
this_sample_data = this_trait.data
this_trait_vals = []
for index, sample in enumerate(this_db_samples):
if (sample in this_sample_data):
sample_value = this_sample_data[sample].value
this_trait_vals.append(sample_value)
self.trait_data_array.append(this_trait_vals)
corr_result_row = []
pca_corr_result_row = []
is_spearman = False #ZS: To determine if it's above or below the diagonal
for target in self.trait_list:
target_trait = target[0]
target_db = target[1]
target_samples = target_db.group.all_samples_ordered()
target_sample_data = target_trait.data
this_trait_vals = []
target_vals = []
for index, sample in enumerate(target_samples):
if (sample in this_sample_data) and (
sample in target_sample_data):
sample_value = this_sample_data[sample].value
target_sample_value = target_sample_data[
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)
if num_overlap < self.lowest_overlap:
self.lowest_overlap = num_overlap
if num_overlap == 0:
corr_result_row.append([target_trait, 0, num_overlap])
pca_corr_result_row.append(0)
else:
pearson_r, pearson_p = scipy.stats.pearsonr(
this_trait_vals, target_vals)
if is_spearman == False:
sample_r, sample_p = pearson_r, pearson_p
if sample_r == 1:
is_spearman = True
else:
sample_r, sample_p = scipy.stats.spearmanr(
this_trait_vals, target_vals)
corr_result_row.append(
[target_trait, sample_r, num_overlap])
pca_corr_result_row.append(pearson_r)
self.corr_results.append(corr_result_row)
self.pca_corr_results.append(pca_corr_result_row)
corr_result_eigen = la.eigenvectors(
numarray.array(self.pca_corr_results))
corr_eigen_value, corr_eigen_vectors = sortEigenVectors(
corr_result_eigen)
groups = []
for sample in self.all_sample_list:
groups.append(1)
pca = self.calculate_pca(range(len(self.traits)), corr_eigen_value,
corr_eigen_vectors)
self.loadings_array = self.process_loadings()
self.js_data = dict(
traits=[trait.name for trait in self.traits],
groups=groups,
cols=range(len(self.traits)),
rows=range(len(self.traits)),
samples=self.all_sample_list,
sample_data=self.sample_data,
)
