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LineageProfiler.py
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LineageProfiler.py
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###LineageProfiler
#Copyright 2005-2008 J. David Gladstone Institutes, San Francisco California
#Author Nathan Salomonis - [email protected]
#Permission is hereby granted, free of charge, to any person obtaining a copy
#of this software and associated documentation files (the "Software"), to deal
#in the Software without restriction, including without limitation the rights
#to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
#copies of the Software, and to permit persons to whom the Software is furnished
#to do so, subject to the following conditions:
#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
#INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
#PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
#HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
#OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
#SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
import sys, string
from stats_scripts import statistics
import math
import os.path
import unique
import copy
import time
import export
import traceback
import warnings
#from stats_scripts import salstat_stats; reload(salstat_stats)
try:
from scipy import stats
use_scipy = True
import numpy
except Exception:
use_scipy = False ### scipy is not required but is used as a faster implementation of Fisher Exact Test when present
def filepath(filename):
fn = unique.filepath(filename)
return fn
def read_directory(sub_dir):
dir_list = unique.read_directory(sub_dir)
return dir_list
def makeUnique(item):
db1={}; list1=[]; k=0
for i in item:
try: db1[i]=[]
except TypeError: db1[tuple(i)]=[]; k=1
for i in db1:
if k==0: list1.append(i)
else: list1.append(list(i))
list1.sort()
return list1
def cleanUpLine(line):
line = string.replace(line,'\n','')
line = string.replace(line,'\c','')
data = string.replace(line,'\r','')
data = string.replace(data,'"','')
return data
def returnLargeGlobalVars():
### Prints all large global variables retained in memory (taking up space)
all = [var for var in globals() if (var[:2], var[-2:]) != ("__", "__")]
for var in all:
try:
if len(globals()[var])>1:
print var, len(globals()[var])
except Exception: null=[]
def clearObjectsFromMemory(db_to_clear):
db_keys={}
for key in db_to_clear: db_keys[key]=[]
for key in db_keys:
try: del db_to_clear[key]
except Exception:
try:
for i in key: del i ### For lists of tuples
except Exception: del key ### For plain lists
######### Below code deals is specific to this module #########
def runLineageProfiler(species,array_type,exp_input,exp_output,codingtype,compendium_platform,customMarkers=False):
"""
print species
print array_type
print export.findFilename(exp_input)
print export.findFilename(exp_output)
print codingtype
print compendium_platform
print customMarkers
"""
global exp_output_file; exp_output_file = exp_output; global targetPlatform
global tissue_specific_db; global expession_subset; global tissues; global sample_headers
global analysis_type; global coding_type; coding_type = codingtype
global tissue_to_gene; tissue_to_gene = {}; global platform; global cutoff
global customMarkerFile; global keyed_by; global compendiumPlatform
customMarkerFile = customMarkers; compendiumPlatform = compendium_platform
global correlate_by_order; correlate_by_order = 'no'
global rho_threshold; rho_threshold = -1
global correlate_to_tissue_specific; correlate_to_tissue_specific = 'no'
platform = array_type
cutoff = 0.01
global value_type
global missingValuesPresent
if 'stats.' in exp_input:
value_type = 'calls'
else:
value_type = 'expression'
tissue_specific_db={}; expession_subset=[]; sample_headers=[]; tissues=[]
if len(array_type)==2:
### When a user-supplied expression is provided (no ExpressionOutput files provided - importGeneIDTranslations)
vendor, array_type = array_type
platform = array_type
else: vendor = 'Not needed'
if 'other:' in vendor:
vendor = string.replace(vendor,'other:','')
array_type = "3'array"
if 'RawSplice' in exp_input or 'FullDatasets' in exp_input or coding_type == 'AltExon':
analysis_type = 'AltExon'
if platform != compendium_platform: ### If the input IDs are not Affymetrix Exon 1.0 ST probesets, then translate to the appropriate system
translate_to_genearray = 'no'
targetPlatform = compendium_platform
translation_db = importExonIDTranslations(array_type,species,translate_to_genearray)
keyed_by = 'translation'
else: translation_db=[]; keyed_by = 'primaryID'; targetPlatform = compendium_platform
elif array_type == "3'array" or array_type == 'AltMouse':
### Get arrayID to Ensembl associations
if vendor != 'Not needed':
### When no ExpressionOutput files provided (user supplied matrix)
translation_db = importVendorToEnsemblTranslations(species,vendor,exp_input)
else:
try: translation_db = importGeneIDTranslations(exp_output)
except: translation_db = importVendorToEnsemblTranslations(species,'Symbol',exp_input)
keyed_by = 'translation'
targetPlatform = compendium_platform
analysis_type = 'geneLevel'
else:
translation_db=[]; keyed_by = 'primaryID'; targetPlatform = compendium_platform; analysis_type = 'geneLevel'
if compendium_platform == "3'array" and array_type != "3'array":
keyed_by = 'ensembl' ### ensembl is not indicated anywhere but avoides key by primaryID and translation -> works for RNASeq
targetPlatform = compendium_platform ### Overides above
""" Determine if a PSI file with missing values """
if vendor == 'PSI':
missingValuesPresent = True
else:
missingValuesPresent = importTissueSpecificProfiles(species,checkForMissingValues=True)
try: importTissueSpecificProfiles(species)
except Exception:
try:
try:
targetPlatform = 'exon'
importTissueSpecificProfiles(species)
except Exception:
try:
targetPlatform = 'gene'
importTissueSpecificProfiles(species)
except Exception:
targetPlatform = "3'array"
importTissueSpecificProfiles(species)
except Exception,e:
print traceback.format_exc()
print 'No compatible compendiums present...'
forceTissueSpecificProfileError
try: importGeneExpressionValues(exp_input,tissue_specific_db,translation_db,species=species)
except:
print "Changing platform to 3'array"
array_type = "3'array"
exp_input = string.replace(exp_input,'-steady-state.txt','.txt')
importGeneExpressionValues(exp_input,tissue_specific_db,translation_db,species=species)
### If the incorrect gene system was indicated re-run with generic parameters
if len(expession_subset)==0 and (array_type == "3'array" or array_type == 'AltMouse' or array_type == 'Other'):
translation_db=[]; keyed_by = 'primaryID'; targetPlatform = compendium_platform; analysis_type = 'geneLevel'
tissue_specific_db={}
try: importTissueSpecificProfiles(species)
except Exception:
try: targetPlatform = 'exon'; importTissueSpecificProfiles(species)
except Exception:
try: targetPlatform = 'gene'; importTissueSpecificProfiles(species)
except Exception: targetPlatform = "3'array"; importTissueSpecificProfiles(species)
importGeneExpressionValues(exp_input,tissue_specific_db,translation_db,species=species)
zscore_output_dir = analyzeTissueSpecificExpressionPatterns(expInput=exp_input)
return zscore_output_dir
def importVendorToEnsemblTranslations(species,vendor,exp_input):
translation_db={}
"""
### Faster method but possibly not as good
uid_db = simpleUIDImport(exp_input)
import gene_associations
### Use the same annotation method that is used to create the ExpressionOutput annotations
array_to_ens = gene_associations.filterGeneToUID(species,'Ensembl',vendor,associated_IDs)
for arrayid in array_to_ens:
ensembl_list = array_to_ens[arrayid]
try: translation_db[arrayid] = ensembl_list[0] ### This first Ensembl is ranked as the most likely valid based on various metrics in getArrayAnnotationsFromGOElite
except Exception: None
"""
translation_db={}
from import_scripts import BuildAffymetrixAssociations
### Use the same annotation method that is used to create the ExpressionOutput annotations
use_go = 'yes'
conventional_array_db={}
conventional_array_db = BuildAffymetrixAssociations.getUIDAnnotationsFromGOElite(conventional_array_db,species,vendor,use_go)
for arrayid in conventional_array_db:
ca = conventional_array_db[arrayid]
ens = ca.Ensembl()
try: translation_db[arrayid] = ens[0] ### This first Ensembl is ranked as the most likely valid based on various metrics in getArrayAnnotationsFromGOElite
except Exception: None
return translation_db
def importTissueSpecificProfiles(species,checkForMissingValues=False):
if analysis_type == 'AltExon':
filename = 'AltDatabase/ensembl/'+species+'/'+species+'_'+targetPlatform +'_tissue-specific_AltExon_protein_coding.txt'
else:
filename = 'AltDatabase/ensembl/'+species+'/'+species+'_'+targetPlatform +'_tissue-specific_'+coding_type+'.txt'
if customMarkerFile != False and customMarkerFile != None:
if len(customMarkerFile)>0:
filename = customMarkerFile
#filename = 'AltDatabase/ensembl/'+species+'/random.txt'
#print 'Target platform used for analysis:',species, targetPlatform, coding_type
if value_type == 'calls':
filename = string.replace(filename,'.txt','_stats.txt')
fn=filepath(filename); x=0
tissue_index = 1
tissues_added={}
missing_values_present = False
for line in open(fn,'rU').xreadlines():
data = cleanUpLine(line)
t = string.split(data,'\t')
if x==0:
print 'Importing the tissue compedium database:',export.findFilename(filename)
headers = t; x=1; index=0
for i in headers:
if 'UID' == i: ens_index = index; uid_index = index
if analysis_type == 'AltExon': ens_index = ens_index ### Assigned above when analyzing probesets
elif 'Ensembl' in i: ens_index = index
if 'marker-in' in i: tissue_index = index+1; marker_in = index
index+=1
try:
for i in t[tissue_index:]: tissues.append(i)
except Exception:
for i in t[1:]: tissues.append(i)
if keyed_by == 'primaryID':
try: ens_index = uid_index
except Exception: None
else:
try:
gene = t[0]
try: gene = string.split(gene,'|')[0] ### Only consider the first listed gene - this gene is the best option based on ExpressionBuilder rankings
except Exception: pass
tissue_exp = map(float, t[1:])
tissue_specific_db[gene]=x,tissue_exp ### Use this to only grab relevant gene expression profiles from the input dataset
except Exception:
try: gene = string.split(gene,'|')[0] ### Only consider the first listed gene - this gene is the best option based on ExpressionBuilder rankings
except Exception: pass
#if 'Pluripotent Stem Cells' in t[marker_in] or 'Heart' in t[marker_in]:
#if t[marker_in] not in tissues_added: ### Only add the first instance of a gene for that tissue - used more for testing to quickly run the analysis
tissue_exp = t[tissue_index:]
if '' in tissue_exp:
missing_values_present = True
### If missing values present (PSI values)
tissue_exp = ['0.000101' if i=='' else i for i in tissue_exp]
tissue_exp = map(float,tissue_exp)
if value_type == 'calls':
tissue_exp = produceDetectionCalls(tissue_exp,platform) ### 0 or 1 calls
tissue_specific_db[gene]=x,tissue_exp ### Use this to only grab relevant gene expression profiles from the input dataset
try: tissues_added[t[marker_in]]=[] ### Not needed currently
except Exception: pass
x+=1
print len(tissue_specific_db), 'genes in the tissue compendium database'
if correlate_to_tissue_specific == 'yes':
try: importTissueCorrelations(filename)
except Exception:
null=[]
#print '\nNo tissue-specific correlations file present. Skipping analysis.'; kill
if checkForMissingValues:
return missing_values_present
def importTissueCorrelations(filename):
filename = string.replace(filename,'specific','specific_correlations')
fn=filepath(filename); x=0
for line in open(fn,'rU').xreadlines():
data = cleanUpLine(line)
if x==0: x=1 ### Ignore header line
else:
uid,symbol,rho,tissue = string.split(data,'\t')
if float(rho)>rho_threshold: ### Variable used for testing different thresholds internally
try: tissue_to_gene[tissue].append(uid)
except Exception: tissue_to_gene[tissue] = [uid]
def simpleUIDImport(filename):
"""Import the UIDs in the gene expression file"""
uid_db={}
fn=filepath(filename)
for line in open(fn,'rU').xreadlines():
data = cleanUpLine(line)
uid_db[string.split(data,'\t')[0]]=[]
return uid_db
def importGeneExpressionValues(filename,tissue_specific_db,translation_db,useLog=False,previouslyRun=False,species=None):
### Import gene-level expression raw values
fn=filepath(filename); x=0; genes_added={}; gene_expression_db={}
dataset_name = export.findFilename(filename)
max_val=0
print 'importing:',dataset_name
try:
import gene_associations, OBO_import
gene_to_symbol = gene_associations.getGeneToUid(species,('hide','Ensembl-Symbol'))
symbol_to_gene = OBO_import.swapKeyValues(gene_to_symbol)
except Exception: symbol_to_gene={}
for line in open(fn,'rU').xreadlines():
data = cleanUpLine(line)
t = string.split(data,'\t')
if x==0:
if '#' not in data:
for i in t[1:]: sample_headers.append(i)
x=1
else:
gene = t[0]
try: gene = string.split(t[0],'|')[0]
except Exception: pass
#if '-' not in gene and ':E' in gene: print gene;sys.exit()
if analysis_type == 'AltExon':
try: ens_gene,exon = string.split(gene,'-')[:2]
except Exception: exon = gene
gene = exon
if keyed_by == 'translation': ### alternative value is 'primaryID'
"""if gene == 'ENSMUSG00000025915-E19.3':
for i in translation_db: print [i], len(translation_db); break
print gene, [translation_db[gene]];sys.exit()"""
try: gene = translation_db[gene] ### Ensembl annotations
except Exception: pass
try: gene = symbol_to_gene[gene][0] ### If RNASeq is the selected platform and Symbol is the uid
except Exception: pass
if gene in tissue_specific_db:
index,tissue_exp=tissue_specific_db[gene]
try: genes_added[gene]+=1
except Exception: genes_added[gene]=1
proceed=True
try:
exp_vals = t[1:]
if '' in exp_vals:
### If missing values present (PSI values)
exp_vals = ['0.000101' if i=='' else i for i in exp_vals]
useLog = False
exp_vals = map(float, exp_vals)
if platform == 'RNASeq':
if max(exp_vals)>max_val: max_val = max(exp_vals)
#if max(exp_vals)<3: proceed=False
if useLog==False:
exp_vals = map(lambda x: math.log(x+1,2),exp_vals)
if value_type == 'calls': ### Hence, this is a DABG or RNA-Seq expression
exp_vals = produceDetectionCalls(exp_vals,targetPlatform) ### 0 or 1 calls
if proceed:
gene_expression_db[gene] = [index,exp_vals]
except Exception:
print 'Non-numeric values detected:'
x = 5
print t[:x]
while x < t:
t[x:x+5]
x+=5
print 'Formatting error encountered in:',dataset_name; forceError
"""else:
for gene in tissue_specific_db:
if 'Ndufa9:ENSMUSG00000000399:I2.1-E3.1' in gene:
print gene, 'dog';sys.exit()
print gene;kill"""
print len(gene_expression_db), 'matching genes in the dataset and tissue compendium database'
for gene in genes_added:
if genes_added[gene]>1:
del gene_expression_db[gene] ### delete entries that are present in the input set multiple times (not trustworthy)
else: expession_subset.append(gene_expression_db[gene]) ### These contain the rank order and expression
#print len(expession_subset);sys.exit()
expession_subset.sort() ### This order now matches that of
gene_expression_db=[]
if max_val<20 and platform == 'RNASeq' and previouslyRun==False: ### Only allow to happen once
importGeneExpressionValues(filename,tissue_specific_db,translation_db,useLog=True,previouslyRun=True,species=species)
def produceDetectionCalls(values,Platform):
# Platform can be the compendium platform (targetPlatform) or analyzed data platform (platform or array_type)
new=[]
for value in values:
if Platform == 'RNASeq':
if value>1:
new.append(1) ### expressed
else:
new.append(0)
else:
if value<cutoff: new.append(1)
else: new.append(0)
return new
def importGeneIDTranslations(filename):
### Import ExpressionOutput/DATASET file to obtain Ensembl associations (typically for Affymetrix 3' arrays)
fn=filepath(filename); x=0; translation_db={}
for line in open(fn,'rU').xreadlines():
data = cleanUpLine(line)
t = string.split(data,'\t')
if x==0:
headers = t; x=1; index=0
for i in headers:
if 'Ensembl' in i: ens_index = index; break
index+=1
else:
uid = t[0]
ens_geneids = t[ens_index]
ens_geneid = string.split(ens_geneids,'|')[0] ### In v.2.0.5, the first ID is the best protein coding candidate
if len(ens_geneid)>0:
translation_db[uid] = ens_geneid
return translation_db
def remoteImportExonIDTranslations(array_type,species,translate_to_genearray,targetplatform):
global targetPlatform; targetPlatform = targetplatform
translation_db = importExonIDTranslations(array_type,species,translate_to_genearray)
return translation_db
def importExonIDTranslations(array_type,species,translate_to_genearray):
gene_translation_db={}; gene_translation_db2={}
if targetPlatform == 'gene' and translate_to_genearray == 'no':
### Get gene array to exon array probeset associations
gene_translation_db = importExonIDTranslations('gene',species,'yes')
for geneid in gene_translation_db:
exonid = gene_translation_db[geneid]
gene_translation_db2[exonid] = geneid
#print exonid, geneid
translation_db = gene_translation_db2
else:
filename = 'AltDatabase/'+species+'/'+array_type+'/'+species+'_'+array_type+'-exon_probesets.txt'
### Import exon array to target platform translations (built for DomainGraph visualization)
fn=filepath(filename); x=0; translation_db={}
print 'Importing the translation file',export.findFilename(fn)
for line in open(fn,'rU').xreadlines():
data = cleanUpLine(line)
t = string.split(data,'\t')
if x==0: x=1
else:
platform_id,exon_id = t
if targetPlatform == 'gene' and translate_to_genearray == 'no':
try:
translation_db[platform_id] = gene_translation_db[exon_id] ### return RNA-Seq to gene array probeset ID
#print platform_id, exon_id, gene_translation_db[exon_id];sys.exit()
except Exception: null=[]
else:
translation_db[platform_id] = exon_id
del gene_translation_db; del gene_translation_db2
return translation_db
def analyzeTissueSpecificExpressionPatterns(expInput=None):
tissue_specific_sorted = []; genes_present={}; tissue_exp_db={}; gene_order_db={}; gene_order=[]
for (index,vals) in expession_subset: genes_present[index]=[]
for gene in tissue_specific_db:
tissue_specific_sorted.append(tissue_specific_db[gene])
# tissue_specific_db[gene][1]
#print tissue_specific_db[gene][1].count(0.000101);sys.exit()
gene_order_db[tissue_specific_db[gene][0]] = gene ### index order (this index was created before filtering)
tissue_specific_sorted.sort()
new_index=0
for (index,tissue_exp) in tissue_specific_sorted:
try:
null=genes_present[index]
i=0
gene_order.append([new_index,gene_order_db[index]]); new_index+=1
for f in tissue_exp:
### The order of the tissue specific expression profiles is based on the import gene order
try: tissue_exp_db[tissues[i]].append(f)
except Exception: tissue_exp_db[tissues[i]] = [f]
i+=1
except Exception:
#print gene;sys.exit()
null=[] ### Gene is not present in the input dataset
### Organize sample expression, with the same gene order as the tissue expression set
sample_exp_db={}
for (index,exp_vals) in expession_subset:
i=0
for f in exp_vals:
### The order of the tissue specific expression profiles is based on the import gene order
try: sample_exp_db[sample_headers[i]].append(f)
except Exception: sample_exp_db[sample_headers[i]] = [f]
i+=1
if correlate_by_order == 'yes':
### Rather than correlate to the absolute expression order, correlate to the order of expression (lowest to highest)
sample_exp_db = replaceExpressionWithOrder(sample_exp_db)
tissue_exp_db = replaceExpressionWithOrder(tissue_exp_db)
global tissue_comparison_scores; tissue_comparison_scores={}
if correlate_to_tissue_specific == 'yes':
### Create a gene_index that reflects the current position of each gene
gene_index={}
for (i,gene) in gene_order: gene_index[gene] = i
### Create a tissue to gene-index from the gene_index
tissue_to_index={}
for tissue in tissue_to_gene:
for gene in tissue_to_gene[tissue]:
if gene in gene_index: ### Some are not in both tissue and sample datasets
index = gene_index[gene] ### Store by index, since the tissue and expression lists are sorted by index
try: tissue_to_index[tissue].append(index)
except Exception: tissue_to_index[tissue] = [index]
tissue_to_index[tissue].sort()
sample_exp_db,tissue_exp_db = returnTissueSpecificExpressionProfiles(sample_exp_db,tissue_exp_db,tissue_to_index)
PearsonCorrelationAnalysis(sample_exp_db,tissue_exp_db)
sample_exp_db=[]; tissue_exp_db=[]
zscore_output_dir = exportCorrelationResults(expInput)
return zscore_output_dir
def returnTissueSpecificExpressionProfiles(sample_exp_db,tissue_exp_db,tissue_to_index):
tissue_exp_db_abreviated={}
sample_exp_db_abreviated={} ### This db is designed differently than the non-tissue specific (keyed by known tissues)
### Build the tissue specific expression profiles
for tissue in tissue_exp_db:
tissue_exp_db_abreviated[tissue] = []
for index in tissue_to_index[tissue]:
tissue_exp_db_abreviated[tissue].append(tissue_exp_db[tissue][index]) ### populate with just marker expression profiles
### Build the sample specific expression profiles
for sample in sample_exp_db:
sample_tissue_exp_db={}
sample_exp_db[sample]
for tissue in tissue_to_index:
sample_tissue_exp_db[tissue] = []
for index in tissue_to_index[tissue]:
sample_tissue_exp_db[tissue].append(sample_exp_db[sample][index])
sample_exp_db_abreviated[sample] = sample_tissue_exp_db
return sample_exp_db_abreviated, tissue_exp_db_abreviated
def replaceExpressionWithOrder(sample_exp_db):
for sample in sample_exp_db:
sample_exp_sorted=[]; i=0
for exp_val in sample_exp_db[sample]: sample_exp_sorted.append([exp_val,i]); i+=1
sample_exp_sorted.sort(); sample_exp_resort = []; order = 0
for (exp_val,i) in sample_exp_sorted: sample_exp_resort.append([i,order]); order+=1
sample_exp_resort.sort(); sample_exp_sorted=[] ### Order lowest expression to highest
for (i,o) in sample_exp_resort: sample_exp_sorted.append(o) ### The expression order replaces the expression, in the original order
sample_exp_db[sample] = sample_exp_sorted ### Replace exp with order
return sample_exp_db
def PearsonCorrelationAnalysis(sample_exp_db,tissue_exp_db):
print "Beginning LineageProfiler analysis"; k=0
original_increment = int(len(tissue_exp_db)/15.00); increment = original_increment
p = 1 ### Default value if not calculated
for tissue in tissue_exp_db:
#print k,"of",len(tissue_exp_db),"classifier tissue/cell-types"
if k == increment: increment+=original_increment; print '*',
k+=1
tissue_expression_list = tissue_exp_db[tissue]
for sample in sample_exp_db:
if correlate_to_tissue_specific == 'yes':
### Keyed by tissue specific sample profiles
sample_expression_list = sample_exp_db[sample][tissue] ### dictionary as the value for sample_exp_db[sample]
#print tissue, sample_expression_list
#print tissue_expression_list; sys.exit()
else: sample_expression_list = sample_exp_db[sample]
try:
### p-value is likely useful to report (not supreemly accurate but likely sufficient)
if missingValuesPresent:
### For PSI values
tissue_expression_list = numpy.ma.masked_values(tissue_expression_list,0.000101)
#tissue_expression_list = numpy.ma.array([numpy.nan if i==0.000101 else i for i in tissue_expression_list])
sample_expression_list = numpy.ma.masked_values(sample_expression_list,0.000101)
#tissue_expression_list = numpy.ma.array([numpy.nan if i==0.000101 else i for i in tissue_expression_list])
updated_tissue_expression_list=[]
updated_sample_expression_list=[]
i=0
coefr=numpy.ma.corrcoef(tissue_expression_list,sample_expression_list)
rho = coefr[0][1]
"""
if sample == 'Cmp.21':
#print rho
#print tissue_expression_list[:10]
#print string.join(map(str,tissue_expression_list[:20]),'\t')
#print sample_expression_list[:10]
#print string.join(map(str,sample_expression_list[:20]),'\t')
#coefr=numpy.ma.corrcoef(numpy.array(tissue_expression_list[:10]),numpy.array(sample_expression_list[:10]))
print tissue, sample, rho, len(tissue_expression_list), len(sample_expression_list)
"""
else:
with warnings.catch_warnings():
warnings.filterwarnings("ignore",category=RuntimeWarning) ### hides import warnings
rho,p = stats.pearsonr(tissue_expression_list,sample_expression_list)
except Exception:
#print traceback.format_exc(); sys.exit()
### simple pure python implementation - no scipy required (not as fast though and no p-value)
rho = pearson(tissue_expression_list,sample_expression_list)
#tst = salstat_stats.TwoSampleTests(tissue_expression_list,sample_expression_list)
#pp,pr = tst.PearsonsCorrelation()
#sp,sr = tst.SpearmansCorrelation()
#print tissue, sample
#if rho>.5: print [rho, pr, sr],[pp,sp];sys.exit()
#if rho<.5: print [rho, pr, sr],[pp,sp];sys.exit()
try: tissue_comparison_scores[tissue].append([rho,p,sample])
except Exception: tissue_comparison_scores[tissue] = [[rho,p,sample]]
sample_exp_db=[]; tissue_exp_db=[]
print 'Correlation analysis finished'
def pearson(array1,array2):
item = 0; sum_a = 0; sum_b = 0; sum_c = 0
while item < len(array1):
a = (array1[item] - avg(array1))*(array2[item] - avg(array2))
b = math.pow((array1[item] - avg(array1)),2)
c = math.pow((array2[item] - avg(array2)),2)
sum_a = sum_a + a
sum_b = sum_b + b
sum_c = sum_c + c
item = item + 1
r = sum_a/math.sqrt(sum_b*sum_c)
return r
def avg(array):
return sum(array)/len(array)
def adjustPValues():
""" Can be applied to calculate an FDR p-value on the p-value reported by scipy.
Currently this method is not employed since the p-values are not sufficiently
stringent or appropriate for this type of analysis """
all_sample_data={}
for tissue in tissue_comparison_scores:
for (r,p,sample) in tissue_comparison_scores[tissue]:
all_sample_data[sample] = db = {} ### populate this dictionary and create sub-dictionaries
break
for tissue in tissue_comparison_scores:
for (r,p,sample) in tissue_comparison_scores[tissue]:
gs = statistics.GroupStats('','',p)
all_sample_data[sample][tissue] = gs
for sample in all_sample_data:
statistics.adjustPermuteStats(all_sample_data[sample])
for tissue in tissue_comparison_scores:
scores = []
for (r,p,sample) in tissue_comparison_scores[tissue]:
p = all_sample_data[sample][tissue].AdjP()
scores.append([r,p,sample])
tissue_comparison_scores[tissue] = scores
def replacePearsonPvalueWithZscore():
all_sample_data={}
for tissue in tissue_comparison_scores:
for (r,p,sample) in tissue_comparison_scores[tissue]:
all_sample_data[sample] = [] ### populate this dictionary and create sub-dictionaries
break
for tissue in tissue_comparison_scores:
for (r,p,sample) in tissue_comparison_scores[tissue]:
all_sample_data[sample].append(r)
sample_stats={}
all_dataset_rho_values=[]
### Get average and standard deviation for all sample rho's
for sample in all_sample_data:
all_dataset_rho_values+=all_sample_data[sample]
avg=statistics.avg(all_sample_data[sample])
stdev=statistics.stdev(all_sample_data[sample])
sample_stats[sample]=avg,stdev
global_rho_avg = statistics.avg(all_dataset_rho_values)
global_rho_stdev = statistics.stdev(all_dataset_rho_values)
### Replace the p-value for each rho
for tissue in tissue_comparison_scores:
scores = []
for (r,p,sample) in tissue_comparison_scores[tissue]:
#u,s=sample_stats[sample]
#z = (r-u)/s
z = (r-global_rho_avg)/global_rho_stdev ### Instead of doing this for the sample background, do it relative to all analyzed samples
scores.append([r,z,sample])
tissue_comparison_scores[tissue] = scores
def exportCorrelationResults(exp_input):
input_file = export.findFilename(exp_input)
if '.txt' in exp_output_file:
corr_output_file = string.replace(exp_output_file,'DATASET','LineageCorrelations')
else: ### Occurs when processing a non-standard AltAnalyze file
corr_output_file = exp_output_file+'/'+input_file
corr_output_file = string.replace(corr_output_file,'.txt','-'+coding_type+'-'+compendiumPlatform+'.txt')
if analysis_type == 'AltExon':
corr_output_file = string.replace(corr_output_file,coding_type,'AltExon')
filename = export.findFilename(corr_output_file)
score_data = export.ExportFile(corr_output_file)
if use_scipy:
zscore_output_dir = string.replace(corr_output_file,'.txt','-zscores.txt')
probability_data = export.ExportFile(zscore_output_dir)
#adjustPValues()
replacePearsonPvalueWithZscore()
### Make title row
headers=['Sample_name']
for tissue in tissue_comparison_scores:
for (r,p,sample) in tissue_comparison_scores[tissue]: headers.append(sample)
break
title_row = string.join(headers,'\t')+'\n'
score_data.write(title_row)
if use_scipy:
probability_data.write(title_row)
### Export correlation data
tissue_scores = {}; tissue_probabilities={}; tissue_score_list = [] ### store and rank tissues according to max(score)
for tissue in tissue_comparison_scores:
scores=[]
probabilities=[]
for (r,p,sample) in tissue_comparison_scores[tissue]:
scores.append(r)
probabilities.append(p)
tissue_score_list.append((max(scores),tissue))
tissue_scores[tissue] = string.join(map(str,[tissue]+scores),'\t')+'\n' ### export line
if use_scipy:
tissue_probabilities[tissue] = string.join(map(str,[tissue]+probabilities),'\t')+'\n'
tissue_score_list.sort()
tissue_score_list.reverse()
for (score,tissue) in tissue_score_list:
score_data.write(tissue_scores[tissue])
if use_scipy:
probability_data.write(tissue_probabilities[tissue])
score_data.close()
if use_scipy:
probability_data.close()
print filename,'exported...'
return zscore_output_dir
def visualizeLineageZscores(zscore_output_dir,grouped_lineage_zscore_dir,graphic_links):
from visualization_scripts import clustering
### Perform hierarchical clustering on the LineageProfiler Zscores
graphic_links = clustering.runHCOnly(zscore_output_dir,graphic_links)
return graphic_links
if __name__ == '__main__':
species = 'Hs'
array_type = "3'array"
vendor = 'Affymetrix'
vendor = 'other:Symbol'
vendor = 'other:Ensembl'
#vendor = 'RNASeq'
array_type = "exon"
#array_type = "3'array"
#array_type = "RNASeq"
compendium_platform = "3'array"
compendium_platform = "exon"
#compendium_platform = "gene"
#array_type = "junction"
codingtype = 'ncRNA'
codingtype = 'protein_coding'
#codingtype = 'AltExon'
array_type = vendor, array_type
exp_input = "/Users/saljh8/Documents/1-conferences/GE/LineageMarkerAnalysis/Synapse-ICGS-EB-Ensembl.txt"
exp_output = "/Users/saljh8/Documents/1-conferences/GE/LineageMarkerAnalysis/temp.txt"
#customMarkers = "/Users/nsalomonis/Desktop/dataAnalysis/qPCR/PAM50/AltAnalyze/ExpressionOutput/MarkerFinder/AVERAGE-training.txt"
customMarkers = False
runLineageProfiler(species,array_type,exp_input,exp_output,codingtype,compendium_platform,customMarkers)