def mageckmle_main(pvargs=None, parsedargs=None, returndict=False):
'''
Main entry for MAGeCK MLE
----
Parameters:
pvargs
Arguments for parsing
returndict
If set true, will not try to run the whole prediction process, but will return after mean variance modeling
'''
# parsing arguments
if parsedargs is not None:
args = parsedargs
else:
args = mageckmle_parseargs(pvargs)
args = mageckmle_postargs(args)
# Bayes module
if hasattr(args, 'bayes') and args.bayes:
from mlemageck_bayes import mageck_bayes_main
sys.exit(0) # comment this when you think bayes module is completed
mageck_bayes_main(parsedargs=args)
sys.exit(0) #
# from mleclassdef import *
# from mledesignmat import *
# from mleem import *
# from mleinstanceio import *
# from mlemeanvar import *
import scipy
from scipy.stats import nbinom
import numpy as np
import numpy.linalg as linalg
from mageck.mleinstanceio import read_gene_from_file, write_gene_to_file, write_sgrna_to_file
from mageck.mleem import iteratenbem
from mageck.mlemeanvar import MeanVarModel
from mageck.mageckCount import normalizeCounts
from mageck.mlesgeff import read_sgrna_eff, sgrna_eff_initial_guess
from dispersion_characterization import sgrna_wide_dispersion_estimation_MAP_v2
from mageck.mlemultiprocessing import runem_multiproc, iteratenbem_permutation
# main process
maxfittinggene = args.genes_varmodeling
maxgene = np.inf
# reading sgRNA efficiency
read_sgrna_eff(args)
# reading read count table
allgenedict = read_gene_from_file(args.count_table,
includesamples=args.include_samples)
#
sgrna_eff_initial_guess(args, allgenedict)
# calculate the size factor
cttab_sel = {}
for (geneid, gk) in allgenedict.iteritems():
sgid = gk.sgrnaid
sgreadmat = gk.nb_count.getT().tolist()
for i in range(len(sgid)):
cttab_sel[sgid[i]] = sgreadmat[i]
if hasattr(args, 'norm_method'):
if args.norm_method != 'none':
size_f = normalizeCounts(cttab_sel,
method=args.norm_method,
returnfactor=True,
reversefactor=True,
controlsgfile=args.control_sgrna)
else:
size_f = None
else:
size_f = normalizeCounts(cttab_sel,
returnfactor=True,
reversefactor=True)
if size_f != None:
logging.info('size factor: ' + ','.join([str(x) for x in size_f]))
# desmat=np.matrix([[1,1,1,1],[0,0,1,0],[0,0,0,1]]).getT()
desmat = args.design_matrix
ngene = 0
for (tgid, tginst) in allgenedict.iteritems():
tginst.design_mat = desmat
meanvardict = {}
for (tgid, tginst) in allgenedict.iteritems():
#iteratenbem(tginst,debug=False,alpha_val=0.01,estimateeff=False,size_factor=size_f)
##sgrna_wide_dispersion_estimation_MAP_v2(tginst,tginst.design_mat)
ngene += 1
tginst.w_estimate = []
meanvardict[tgid] = tginst
if ngene > maxfittinggene:
break
argsdict = {
'debug': False,
'alpha_val': 0.01,
'estimateeff': False,
'size_factor': size_f
}
runem_multiproc(meanvardict, args, nproc=args.threads, argsdict=argsdict)
for (tgid, tginst) in meanvardict.iteritems():
allgenedict[tgid] = tginst
# model the mean and variance
logging.info('Modeling the mean and variance ...')
if maxfittinggene > 0:
mrm = MeanVarModel()
# old: linear model
mrm.get_mean_var_residule(allgenedict)
mrm.model_mean_var_by_lm()
# new method: generalized linear model
#mrm.model_mean_disp_by_glm(allgenedict,args.output_prefix,size_f)
else:
mrm = None
if returndict:
return (allgenedict, mrm, size_f)
# run the test again...
logging.info('Run the algorithm for the second time ...')
if hasattr(args, 'threads') and args.threads > 1:
# multipel threads
argsdict = {
'debug': False,
'estimateeff': True,
'meanvarmodel': mrm,
'restart': False,
'removeoutliers': args.remove_outliers,
'size_factor': size_f,
'updateeff': args.update_efficiency,
'logem': False
}
runem_multiproc(allgenedict,
args,
nproc=args.threads,
argsdict=argsdict)
else:
# only 1 thread
ngene = 0
for (tgid, tginst) in allgenedict.iteritems():
#try:
if ngene % 1000 == 1 or args.debug:
logging.info('Calculating ' + tgid + ' (' + str(ngene) +
') ... ')
if hasattr(
args, 'debug_gene'
) and args.debug_gene != None and tginst.prefix != args.debug_gene:
continue
iteratenbem(tginst,
debug=False,
estimateeff=True,
meanvarmodel=mrm,
restart=False,
removeoutliers=args.remove_outliers,
size_factor=size_f,
updateeff=args.update_efficiency)
# Tracer()()
ngene += 1
if ngene > maxgene:
break
#except:
# logging.error('Error occurs while calculating beta values of gene '+tgid+'.')
# sys.exit(-1)
# set up the w vector
for (tgid, tginst) in allgenedict.iteritems():
if len(tginst.w_estimate) == 0:
tginst.w_estimate = np.ones(len(tginst.sgrnaid))
#Tracer()()
# permutation
iteratenbem_permutation(allgenedict,
args,
nround=args.permutation_round,
removeoutliers=args.remove_outliers,
size_factor=size_f)
# correct for FDR
from mageck.mleclassdef import gene_fdr_correction
gene_fdr_correction(allgenedict, args.adjust_method)
# write to file
genefile = args.output_prefix + '.gene_summary.txt'
sgrnafile = args.output_prefix + '.sgrna_summary.txt'
logging.info('Writing gene results to ' + genefile)
logging.info('Writing sgRNA results to ' + sgrnafile)
write_gene_to_file(allgenedict, genefile, betalabels=args.beta_labels)
write_sgrna_to_file(allgenedict, sgrnafile)
return (allgenedict, mrm)
def mageckmle_main(pvargs=None,parsedargs=None,returndict=False):
'''
Main entry for MAGeCK MLE
----
Parameters:
pvargs
Arguments for parsing
returndict
If set true, will not try to run the whole prediction process, but will return after mean variance modeling
'''
# parsing arguments
if parsedargs is not None:
args=parsedargs
else:
args=mageckmle_parseargs(pvargs)
args=mageckmle_postargs(args)
# Bayes module
if hasattr(args,'bayes') and args.bayes:
from mlemageck_bayes import mageck_bayes_main
sys.exit(0) # comment this when you think bayes module is completed
mageck_bayes_main(parsedargs=args)
sys.exit(0) #
# from mleclassdef import *
# from mledesignmat import *
# from mleem import *
# from mleinstanceio import *
# from mlemeanvar import *
import scipy
from scipy.stats import nbinom
import numpy as np
import numpy.linalg as linalg
from mageck.mleinstanceio import read_gene_from_file,write_gene_to_file,write_sgrna_to_file
from mageck.mleem import iteratenbem
from mageck.mlemeanvar import MeanVarModel
from mageck.mageckCount import normalizeCounts
from mageck.mlesgeff import read_sgrna_eff,sgrna_eff_initial_guess
from dispersion_characterization import sgrna_wide_dispersion_estimation_MAP_v2
from mageck.mlemultiprocessing import runem_multiproc,iteratenbem_permutation
# main process
maxfittinggene=args.genes_varmodeling
maxgene=np.inf
# reading sgRNA efficiency
read_sgrna_eff(args)
# reading read count table
allgenedict=read_gene_from_file(args.count_table,includesamples=args.include_samples)
#
sgrna_eff_initial_guess(args,allgenedict)
# calculate the size factor
cttab_sel={}
for (geneid,gk) in allgenedict.iteritems():
sgid=gk.sgrnaid
sgreadmat=gk.nb_count.getT().tolist()
for i in range(len(sgid)):
cttab_sel[sgid[i]]=sgreadmat[i]
if hasattr(args,'norm_method'):
if args.norm_method!='none':
size_f=normalizeCounts(cttab_sel,method=args.norm_method,returnfactor=True,reversefactor=True,controlsgfile=args.control_sgrna)
else:
size_f=None
else:
size_f=normalizeCounts(cttab_sel,returnfactor=True,reversefactor=True)
if size_f !=None:
logging.info('size factor: '+','.join([str(x) for x in size_f]))
# desmat=np.matrix([[1,1,1,1],[0,0,1,0],[0,0,0,1]]).getT()
desmat=args.design_matrix
ngene=0
for (tgid,tginst) in allgenedict.iteritems():
tginst.design_mat=desmat
meanvardict={}
for (tgid,tginst) in allgenedict.iteritems():
#iteratenbem(tginst,debug=False,alpha_val=0.01,estimateeff=False,size_factor=size_f)
##sgrna_wide_dispersion_estimation_MAP_v2(tginst,tginst.design_mat)
ngene+=1
tginst.w_estimate=[]
meanvardict[tgid]=tginst
if ngene>maxfittinggene:
break
argsdict={'debug':False, 'alpha_val':0.01, 'estimateeff':False,'size_factor':size_f}
runem_multiproc(meanvardict,args,nproc=args.threads,argsdict=argsdict)
for (tgid,tginst) in meanvardict.iteritems():
allgenedict[tgid]=tginst
# model the mean and variance
logging.info('Modeling the mean and variance ...')
if maxfittinggene>0:
mrm=MeanVarModel()
# old: linear model
mrm.get_mean_var_residule(allgenedict)
mrm.model_mean_var_by_lm()
# new method: generalized linear model
#mrm.model_mean_disp_by_glm(allgenedict,args.output_prefix,size_f)
else:
mrm=None
if returndict:
return (allgenedict,mrm,size_f)
# run the test again...
logging.info('Run the algorithm for the second time ...')
if hasattr(args,'threads') and args.threads>1:
# multipel threads
argsdict={'debug':False,'estimateeff':True,'meanvarmodel':mrm,'restart':False,'removeoutliers':args.remove_outliers,'size_factor':size_f,'updateeff':args.update_efficiency,'logem':False}
runem_multiproc(allgenedict,args,nproc=args.threads,argsdict=argsdict)
else:
# only 1 thread
ngene=0
for (tgid,tginst) in allgenedict.iteritems():
#try:
if ngene % 1000 ==1 or args.debug:
logging.info('Calculating '+tgid+' ('+str(ngene)+') ... ')
if hasattr(args,'debug_gene') and args.debug_gene!=None and tginst.prefix != args.debug_gene:
continue
iteratenbem(tginst,debug=False,estimateeff=True,meanvarmodel=mrm,restart=False,removeoutliers=args.remove_outliers,size_factor=size_f,updateeff=args.update_efficiency)
# Tracer()()
ngene+=1
if ngene>maxgene:
break
#except:
# logging.error('Error occurs while calculating beta values of gene '+tgid+'.')
# sys.exit(-1)
# set up the w vector
for (tgid,tginst) in allgenedict.iteritems():
if len(tginst.w_estimate)==0:
tginst.w_estimate=np.ones(len(tginst.sgrnaid))
#Tracer()()
# permutation
iteratenbem_permutation(allgenedict,args,nround=args.permutation_round,removeoutliers=args.remove_outliers,size_factor=size_f)
# correct for FDR
from mageck.mleclassdef import gene_fdr_correction;
gene_fdr_correction(allgenedict,args.adjust_method);
# write to file
genefile=args.output_prefix+'.gene_summary.txt'
sgrnafile=args.output_prefix+'.sgrna_summary.txt'
logging.info('Writing gene results to '+genefile)
logging.info('Writing sgRNA results to '+sgrnafile)
write_gene_to_file(allgenedict,genefile,betalabels=args.beta_labels)
write_sgrna_to_file(allgenedict,sgrnafile)
return (allgenedict,mrm)
def mageckmle_main(pvargs=None, parsedargs=None, returndict=False):
'''
Main entry for MAGeCK MLE
----
Parameters:
pvargs
Arguments for parsing
returndict
If set true, will not try to run the whole prediction process, but will return after mean variance modeling
'''
# parsing arguments
if parsedargs is not None:
args = parsedargs
else:
args = mageckmle_parseargs(pvargs)
args = mageckmle_postargs(args)
# Bayes module
if hasattr(args, 'bayes') and args.bayes:
from mlemageck_bayes import mageck_bayes_main
sys.exit(0) # comment this when you think bayes module is completed
mageck_bayes_main(parsedargs=args)
sys.exit(0) #
# from mleclassdef import *
# from mledesignmat import *
# from mleem import *
# from mleinstanceio import *
# from mlemeanvar import *
import scipy
from scipy.stats import nbinom
import numpy as np
import numpy.linalg as linalg
from mageck.mleinstanceio import read_gene_from_file, write_gene_to_file, write_sgrna_to_file
from mageck.mleem import iteratenbem
from mageck.mlemeanvar import MeanVarModel
from mageck.mageckCount import normalizeCounts
from mageck.mlesgeff import read_sgrna_eff, sgrna_eff_initial_guess
from mageck.dispersion_characterization import sgrna_wide_dispersion_estimation_MAP_v2
from mageck.mlemultiprocessing import runem_multiproc, iteratenbem_permutation, iteratenbem_permutation_by_nsg
from mageck.cnv_normalization import read_CNVdata, match_sgrnaCN, betascore_piecewisenorm, betascore_piecewisenorm
from mageck.cnv_estimation import mageckmleCNVestimation
# main process
maxfittinggene = args.genes_varmodeling
maxgene = np.inf
# reading sgRNA efficiency
read_sgrna_eff(args)
# reading read count table
allgenedict = read_gene_from_file(args.count_table,
includesamples=args.include_samples)
#
sgrna_eff_initial_guess(args, allgenedict)
#
#
#
# calculate the size factor
cttab_sel = {}
for (geneid, gk) in allgenedict.items():
sgid = gk.sgrnaid
sgreadmat = gk.nb_count.getT().tolist()
for i in range(len(sgid)):
cttab_sel[sgid[i]] = sgreadmat[i]
if hasattr(args, 'norm_method'):
if args.norm_method != 'none':
size_f = normalizeCounts(cttab_sel,
method=args.norm_method,
returnfactor=True,
reversefactor=True,
controlsgfile=args.control_sgrna)
else:
size_f = None
else:
size_f = normalizeCounts(cttab_sel,
returnfactor=True,
reversefactor=True)
if size_f != None:
logging.info('size factor: ' + ','.join([str(x) for x in size_f]))
# desmat=np.matrix([[1,1,1,1],[0,0,1,0],[0,0,0,1]]).getT()
desmat = args.design_matrix
ngene = 0
for (tgid, tginst) in allgenedict.items():
tginst.design_mat = desmat
#
#
#
#
# perform copy number estimation if option selected
if args.cnv_est is not None:
logging.info('Performing copy number estimation...')
# organize sgRNA-gene pairing into dictionary
sgrna2genelist = {
sgrna: gene
for gene in allgenedict for sgrna in allgenedict[gene].sgrnaid
}
# estimate CNV and write results to file
mageckmleCNVestimation(args.cnv_est, cttab_sel, desmat, sgrna2genelist,
args.beta_labels[1:], args.output_prefix)
#
#
#
#
# run the EM for a few genes to perform gene fitting process
meanvardict = {}
for (tgid, tginst) in allgenedict.items():
#iteratenbem(tginst,debug=False,alpha_val=0.01,estimateeff=False,size_factor=size_f)
##sgrna_wide_dispersion_estimation_MAP_v2(tginst,tginst.design_mat)
ngene += 1
tginst.w_estimate = []
meanvardict[tgid] = tginst
if ngene > maxfittinggene:
break
argsdict = {
'debug': False,
'alpha_val': 0.01,
'estimateeff': False,
'size_factor': size_f
}
runem_multiproc(meanvardict, args, nproc=args.threads, argsdict=argsdict)
for (tgid, tginst) in meanvardict.items():
allgenedict[tgid] = tginst
#
#
#
#
# model the mean and variance
logging.info('Modeling the mean and variance ...')
if maxfittinggene > 0:
mrm = MeanVarModel()
# old: linear model
mrm.get_mean_var_residule(allgenedict)
mrm.model_mean_var_by_lm()
# new method: generalized linear model
#mrm.model_mean_disp_by_glm(allgenedict,args.output_prefix,size_f)
else:
mrm = None
if returndict:
return (allgenedict, mrm, size_f)
# run the test again...
logging.info('Run the algorithm for the second time ...')
if hasattr(args, 'threads') and args.threads > 0:
# multipel threads
argsdict = {
'debug': False,
'estimateeff': True,
'meanvarmodel': mrm,
'restart': False,
'removeoutliers': args.remove_outliers,
'size_factor': size_f,
'updateeff': args.update_efficiency,
'logem': False
}
runem_multiproc(allgenedict,
args,
nproc=args.threads,
argsdict=argsdict)
else:
# only 1 thread
# the following codes should be merged to the above code section
ngene = 0
for (tgid, tginst) in allgenedict.items():
#try:
if ngene % 1000 == 1 or args.debug:
logging.info('Calculating ' + tgid + ' (' + str(ngene) +
') ... ')
if hasattr(
args, 'debug_gene'
) and args.debug_gene != None and tginst.prefix != args.debug_gene:
continue
iteratenbem(tginst,
debug=False,
estimateeff=True,
meanvarmodel=mrm,
restart=False,
removeoutliers=args.remove_outliers,
size_factor=size_f,
updateeff=args.update_efficiency)
# Tracer()()
ngene += 1
if ngene > maxgene:
break
#except:
# logging.error('Error occurs while calculating beta values of gene '+tgid+'.')
# sys.exit(-1)
# set up the w vector
for (tgid, tginst) in allgenedict.items():
if len(tginst.w_estimate) == 0:
tginst.w_estimate = np.ones(len(tginst.sgrnaid))
#Tracer()()
# permutation, either by group or together
if args.no_permutation_by_group:
iteratenbem_permutation(allgenedict,
args,
nround=args.permutation_round,
removeoutliers=args.remove_outliers,
size_factor=size_f)
else:
iteratenbem_permutation_by_nsg(allgenedict, args, size_f=size_f)
# correct for FDR
from mageck.mleclassdef import gene_fdr_correction
gene_fdr_correction(allgenedict, args.adjust_method)
# correct for CNV
if args.cnv_norm is not None or args.cnv_est is not None:
if args.cnv_norm is not None: # get copy number data from external copy number dataset
logging.info('Performing copy number normalization.')
(CN_arr, CN_celldict,
CN_genedict) = read_CNVdata(args.cnv_norm, args.beta_labels[1:])
genes2correct = False # do not select only subset of genes to correct (i.e. correct all genes)
elif args.cnv_est is not None: # get copy number data from copy number estimates calculated earlier
logging.info(
'Performing copy number normalization using copy number estimates.'
)
(CN_arr, CN_celldict, CN_genedict) = read_CNVdata(
str(args.output_prefix) + 'CNVestimates.txt',
args.beta_labels[1:])
genes2correct = highestCNVgenes(CN_arr, CN_genedict, percentile=98)
for i in range(len(args.beta_labels[1:])):
if args.beta_labels[1:][i] not in CN_celldict:
logging.warning(
args.beta_labels[1:][i] +
' is not represented in the inputted copy number variation data.'
)
else:
logging.info('Normalizing by copy number with ' +
args.beta_labels[1:][i] +
' as the reference cell line.')
betascore_piecewisenorm(allgenedict,
args.beta_labels,
CN_arr,
CN_celldict,
CN_genedict,
selectGenes=genes2correct)
# write to file
genefile = args.output_prefix + '.gene_summary.txt'
sgrnafile = args.output_prefix + '.sgrna_summary.txt'
logging.info('Writing gene results to ' + genefile)
logging.info('Writing sgRNA results to ' + sgrnafile)
write_gene_to_file(allgenedict,
genefile,
args,
betalabels=args.beta_labels)
write_sgrna_to_file(allgenedict, sgrnafile)
return (allgenedict, mrm)