def clustpipeline(datapath, mapfile=None, replicatesfile=None, normalisationfile=['1000'], outpath=None,
Ks=[n for n in range(4, 21, 4)], tightnessweight=1, stds=3.0,
OGsIncludedIfAtLeastInDatasets=1, expressionValueThreshold=-float("inf"), atleastinconditions=0,
atleastindatasets=0, absvalue=False, filteringtype='raw', filflat=True, smallestClusterSize=11,
ncores=1, optimisation=True, Q3s=2, methods=None, deterministic=False):
# Set the global objects label
if mapfile is None:
glob.set_object_label_upper('Gene')
glob.set_object_label_lower('gene')
else:
glob.set_object_label_upper('OG')
glob.set_object_label_lower('OG')
# Output: Prepare the output directory and the log file
if outpath is None:
outpathbase = os.getcwd()
#outpathbase = os.path.abspath(os.path.join(datapath, '..'))
outpathbase = '{0}/Results_{1}'.format(outpathbase, dt.datetime.now().strftime('%d_%b_%y'))
outpath = outpathbase
trial = 0
while os.path.exists(outpath):
trial += 1
outpath = '{0}_{1}'.format(outpathbase, trial)
if not os.path.exists(outpath):
os.makedirs(outpath)
glob.set_logfile(os.path.join(outpath, 'log.txt'))
glob.set_tmpfile(os.path.join(outpath, 'tmp.txt'))
# Output: Copy input files to the output
in2out_path = outpath + '/Input_files_and_params'
if not os.path.exists(in2out_path):
os.makedirs(in2out_path)
if mapfile is not None:
shutil.copy(mapfile, os.path.join(in2out_path, 'Map.txt'))
if replicatesfile is not None:
shutil.copy(replicatesfile, os.path.join(in2out_path, 'Replicates.txt'))
if normalisationfile is not None:
if len(normalisationfile) == 1 and not nu.isint(normalisationfile[0]):
shutil.copy(normalisationfile[0], os.path.join(in2out_path, 'Normalisation.txt'))
in2out_X_unproc_path = in2out_path + '/Data'
if not os.path.exists(in2out_X_unproc_path):
os.makedirs(in2out_X_unproc_path)
if os.path.isfile(datapath):
shutil.copy(datapath, in2out_X_unproc_path)
elif os.path.isdir(datapath):
for df in io.getFilesInDirectory(datapath):
shutil.copy(os.path.join(datapath, df), in2out_X_unproc_path)
else:
raise ValueError('Data path {0} does not exist. Either provide a path '.format(datapath) + \
'of a data file or a path to a directory including data file(s)')
# Output: Print initial message, and record the starting time:
initialmsg, starttime = op.generateinitialmessage()
io.log(initialmsg, addextrastick=False)
# Read data
io.log('1. Reading dataset(s)')
(X, replicates, Genes, datafiles) = io.readDatasetsFromDirectory(datapath, delimiter='\t| |, |; |,|;', skiprows=1, skipcolumns=1,
returnSkipped=True)
datafiles_noext = [os.path.splitext(d)[0] for d in datafiles]
# Read map, replicates, and normalisation files:
Map = io.readMap(mapfile)
(replicatesIDs, conditions) = io.readReplicates(replicatesfile, datapath, datafiles, replicates)
normalise = io.readNormalisation(normalisationfile, datafiles)
# Preprocessing (Mapping then top level preprocessing including summarising replicates, filtering
# low expression genes, and normalisation)
io.log('2. Data pre-processing')
(X_OGs, GDM, GDMall, OGs, MapNew, MapSpecies) \
= pp.calculateGDMandUpdateDatasets(X, Genes, Map, mapheader=True, OGsFirstColMap=True, delimGenesInMap='\\W+',
OGsIncludedIfAtLeastInDatasets=OGsIncludedIfAtLeastInDatasets)
(X_summarised_normalised, GDM, Iincluded, params, applied_norms) = \
pp.preprocess(X_OGs, GDM, normalise, replicatesIDs, flipSamples=None,
expressionValueThreshold=expressionValueThreshold, replacementVal=0.0,
atleastinconditions=atleastinconditions, atleastindatasets=atleastindatasets, absvalue=absvalue,
filteringtype=filteringtype, filterflat=filflat, params=None, datafiles=datafiles)
io.writedic('{0}/Normalisation_actual.txt'.format(outpath), applied_norms, delim='\t')
OGs = OGs[Iincluded]
if MapNew is not None:
MapNew = MapNew[Iincluded]
# Output: Save processed data
Xprocessed = op.processed_X(X_summarised_normalised, conditions, GDM, OGs, MapNew, MapSpecies) # pandas DataFrames
X_proc_path = outpath + '/Processed_Data'
if not os.path.exists(X_proc_path):
os.makedirs(X_proc_path)
for l in range(len(datafiles)):
pd.DataFrame.to_csv(Xprocessed[l], '{0}/{1}_processed.tsv'.format(X_proc_path, datafiles[l]), sep='\t', encoding='utf-8', index=None, columns=None, header=False)
#np.savetxt('{0}/{1}_processed.tsv'.format(X_proc_path, datafiles[l]), Xprocessed[l], fmt='%s', delimiter='\t')
# UNCLES and M-N plots
io.log('3. Seed clusters production (the Bi-CoPaM method)')
ures = unc.uncles(X_summarised_normalised, type='A', GDM=GDM, Ks=Ks, params=params, methods=methods,
Xnames=datafiles_noext, ncores=ncores, deterministic=deterministic)
io.log('4. Cluster evaluation and selection (the M-N scatter plots technique)')
mnres = mn.mnplotsgreedy(X_summarised_normalised, ures.B, GDM=GDM, tightnessweight=tightnessweight,
params=ures.params, smallestClusterSize=smallestClusterSize, Xnames=datafiles_noext,
ncores=ncores)
# Post-processing
ppmethod = 'tukey_sqrtSCG'
if optimisation:
io.log('5. Cluster optimisation and completion')
if len(mnres.I) > 0 and sum(mnres.I) > 0: # Otherwise, there are no clusters, so nothing to be corrected
try:
if ppmethod == 'weighted_outliers':
B_corrected = ecorr.correcterrors_weighted_outliers(mnres.B, X_summarised_normalised, GDM,
mnres.allDists[mnres.I], stds, smallestClusterSize)
elif ppmethod == 'tukey_sqrtSCG':
B_corrected = ecorr.optimise_tukey_sqrtSCG(mnres.B, X_summarised_normalised, GDM,
mnres.allDists[mnres.I], smallestClusterSize,
tails=1, Q3s=Q3s)
else:
raise ValueError('Invalid post processing method (ppmethod): {0}.'.format(ppmethod))
B_corrected = ecorr.reorderClusters(B_corrected, X_summarised_normalised, GDM)
except:
io.logerror(sys.exc_info())
io.log('\n* Failed to perform cluster optimisation and completion!\n'
'* Skipped cluster optimisation and completion!\n')
B_corrected = mnres.B
else:
B_corrected = mnres.B
else:
io.log('5. Skipping cluster optimisation and completion')
B_corrected = mnres.B
# Output: Write input parameters:
io.log('6. Saving results in\n{0}'.format(outpath))
inputparams = op.params(mnres.params, Q3s, OGsIncludedIfAtLeastInDatasets,
expressionValueThreshold, atleastinconditions, atleastindatasets,
deterministic, ures.params['methods'], MapNew)
io.writedic('{0}/input_params.tsv'.format(in2out_path), inputparams, delim='\t')
# Output: Generating and saving clusters
res_og = op.clusters_genes_OGs(B_corrected, OGs, MapNew, MapSpecies, '; ') # pandas DataFrame
if mapfile is None:
pd.DataFrame.to_csv(res_og, '{0}/Clusters_Objects.tsv'.format(outpath), sep='\t',
encoding='utf-8', index=None, columns=None, header=False)
#np.savetxt('{0}/Clusters_Objects.tsv'.format(outpath), res_og, fmt='%s', delimiter='\t')
else:
pd.DataFrame.to_csv(res_og, '{0}/Clusters_OGs.tsv'.format(outpath), sep='\t',
encoding='utf-8', index=None, columns=None, header=False)
#np.savetxt('{0}/Clusters_OGs.tsv'.format(outpath), res_og, fmt='%s', delimiter='\t')
res_sp = op.clusters_genes_Species(B_corrected, OGs, MapNew, MapSpecies) # pandas DataFrame
for sp in range(len(res_sp)):
pd.DataFrame.to_csv(res_sp[sp], '{0}/Clusters_{1}.tsv'.format(outpath, MapSpecies[sp]), sep='\t',
encoding='utf-8', index=None, columns=None, header=False)
#np.savetxt('{0}/Clusters_{1}.tsv'.format(outpath, MapSpecies[sp]), res_sp[sp], fmt='%s', delimiter='\t')
# Output: Save figures to a PDF
try:
if np.shape(B_corrected)[1] > 0:
clusts_fig_file_name = '{0}/Clusters_profiles.pdf'.format(outpath)
graph.plotclusters(X_summarised_normalised, B_corrected, datafiles_noext, conditions, clusts_fig_file_name,
GDM=GDM, Cs='all', setPageToDefault=True, printToPDF=True, showPlots=False)
except:
io.log('Error: could not save clusters plots in a PDF file.\n'
'Resuming producing the other results files ...')
# Output: Generating and writing eigengenes
try:
if np.shape(B_corrected)[1] > 0:
if len(X_summarised_normalised) == 1:
eigengene_matrix = eig.eigengenes_dataframe(X_summarised_normalised, B_corrected, conditions)
eigengene_matrix.to_csv('{0}/Eigengenes.tsv'.format(outpath), sep='\t',
encoding='utf-8')
else:
io.log('Eigengene computation is currently not supported for multiple datasets.')
except:
io.log('Error: could not save eigengenes into a file.\n'
'Resuming producing the other results files ...')
# Output: Prepare message to standard output and the summary then save the summary to a file and print the message
summarymsg, endtime, timeconsumedtxt = \
op.generateoutputsummaryparag(X, X_summarised_normalised, MapNew, GDMall, GDM,
ures, mnres, B_corrected, starttime)
summary = op.summarise_results(X, X_summarised_normalised, MapNew, GDMall, GDM,
ures, mnres, B_corrected, starttime, endtime, timeconsumedtxt)
io.writedic(outpath + '/Summary.tsv', summary, delim='\t')
io.log(summarymsg, addextrastick=False)
io.deletetmpfile()
def runclust(X, Map=None, replicatesIDs=None, normalise=1000,
Ks=[n for n in range(4, 21, 4)], tightnessweight=1, stds=3.0,
OGsIncludedIfAtLeastInDatasets=1, expressionValueThreshold=-float("inf"), atleastinconditions=0,
atleastindatasets=0, absvalue=False, filteringtype='raw', filflat=True, smallestClusterSize=11,
ncores=1, optimisation=True, Q3s=2, methods=None, deterministic=False, showPlots=True,
printToConsole=True):
# Set the global objects label
glob.set_print_to_log_file(False)
glob.set_print_to_console(printToConsole)
if Map is None:
glob.set_object_label_upper('Gene')
glob.set_object_label_lower('gene')
else:
glob.set_object_label_upper('OG')
glob.set_object_label_lower('OG')
glob.set_tmpfile('clust_tmp.txt')
# Output: Print initial message, and record the starting time:
initialmsg, starttime = op.generateinitialmessage()
io.log(initialmsg, addextrastick=False)
# Consider X as a list of arrays or of data frames. Otherwise, make it as such first
# If the user entered a single dataset as an input (not as a list of arrays), save this fact in a flag, ...
# so the result is returned as a single output
input_is_one_dataset = False
if isinstance(X, pd.DataFrame):
input_is_one_dataset = True
X = [X]
elif isinstance(X, np.ndarray) and ds.maxDepthOfArray(X) == 2:
input_is_one_dataset = True
X = [X]
# Format data (X: list of arrays, Genes: list of arrays of strings, replicates: list of arrays of strings)
L = len(X) # Number of datasets
replicates = [None] * L
Genes = [None] * L
io.log('1. Reading dataset(s)')
for l in range(L):
if type(X[l]) == pd.DataFrame:
Genes[l] = np.array(X[l].index, dtype=str, ndmin=2).transpose()
Genes[l] = np.array(Genes[l], dtype=object)
replicates[l] = np.array(X[l].columns, dtype=str)
X[l] = X[l].values
else:
X[l] = np.array(X[l])
ngenes_digits = int(math.ceil(math.log10(X[l].shape[0])))
nreps_digits = int(math.ceil(math.log10(X[l].shape[1])))
Genes[l] = np.array([['{0}'.format(str(g).zfill(ngenes_digits))] for g in range(X[l].shape[0])])
Genes[l] = np.array(Genes[l], dtype=object)
replicates[l] = np.array(['X{0}'.format(str(r).zfill(nreps_digits)) for r in range(X[l].shape[1])])
ndatasets_digits = int(math.ceil(math.log10(L)))
datafiles = np.array(['D{0}'.format(str(r).zfill(ndatasets_digits)) for r in range(L)])
datafiles_noext = datafiles
# Sort out conditions based on replicates structure if given
if replicatesIDs is None:
conditions = replicates
else:
valresult = val.validate_replicatesIDs(replicatesIDs, X)
if valresult[0]:
conditions = [None] * L
for l in range(L):
if replicatesIDs[l] is None:
conditions[l] = np.array(replicates[l])
else:
uniq_reps, cond_indices = np.unique(replicatesIDs[l], return_index=True)
if -1 in uniq_reps:
cond_indices = cond_indices[1:]
conditions[l] = replicates[l][cond_indices]
else:
io.log(valresult[1])
io.log("Terminating ...")
raise Exception("Terminated by an invalid input argument.")
# Validate normalisation
valresult = val.validate_normalisation(normalise, X)
if not valresult[0]:
io.log(valresult[1])
io.log("Terminating ...")
raise Exception("Terminated by an invalid input argument.")
# Preprocessing (Mapping then top level preprocessing including summarising replicates, filtering
# low expression genes, and normalisation)
io.log('2. Data pre-processing')
(X_OGs, GDM, GDMall, OGs, MapNew, MapSpecies) \
= pp.calculateGDMandUpdateDatasets(X, Genes, Map, mapheader=True, OGsFirstColMap=True, delimGenesInMap='\\W+',
OGsIncludedIfAtLeastInDatasets=OGsIncludedIfAtLeastInDatasets)
(X_summarised_normalised, GDM, Iincluded, params, applied_norms) = \
pp.preprocess(X_OGs, GDM, normalise, replicatesIDs, flipSamples=None,
expressionValueThreshold=expressionValueThreshold, replacementVal=0.0,
atleastinconditions=atleastinconditions, atleastindatasets=atleastindatasets, absvalue=absvalue,
filteringtype=filteringtype, filterflat=filflat, params=None, datafiles=datafiles)
OGs = OGs[Iincluded]
if MapNew is not None:
MapNew = MapNew[Iincluded]
# UNCLES and M-N plots
io.log('3. Seed clusters production (the Bi-CoPaM method)')
ures = unc.uncles(X_summarised_normalised, type='A', GDM=GDM, Ks=Ks, params=params, methods=methods,
Xnames=datafiles_noext, ncores=ncores, deterministic=deterministic)
io.log('4. Cluster evaluation and selection (the M-N scatter plots technique)')
mnres = mn.mnplotsgreedy(X_summarised_normalised, ures.B, GDM=GDM, tightnessweight=tightnessweight,
params=ures.params, smallestClusterSize=smallestClusterSize, Xnames=datafiles_noext,
ncores=ncores)
# Post-processing
ppmethod = 'tukey_sqrtSCG'
if optimisation:
io.log('5. Cluster optimisation and completion')
if len(mnres.I) > 0 and sum(mnres.I) > 0: # Otherwise, there are no clusters, so nothing to be corrected
try:
if ppmethod == 'weighted_outliers':
B_corrected = ecorr.correcterrors_weighted_outliers(mnres.B, X_summarised_normalised, GDM,
mnres.allDists[mnres.I], stds, smallestClusterSize)
elif ppmethod == 'tukey_sqrtSCG':
B_corrected = ecorr.optimise_tukey_sqrtSCG(mnres.B, X_summarised_normalised, GDM,
mnres.allDists[mnres.I], smallestClusterSize,
tails=1, Q3s=Q3s)
else:
raise ValueError('Invalid post processing method (ppmethod): {0}.'.format(ppmethod))
B_corrected = ecorr.reorderClusters(B_corrected, X_summarised_normalised, GDM)
except:
io.logerror(sys.exc_info())
io.log('\n* Failed to perform cluster optimisation and completion!\n'
'* Skipped cluster optimisation and completion!\n')
B_corrected = mnres.B
else:
B_corrected = mnres.B
else:
io.log('5. Skipping cluster optimisation and completion')
B_corrected = mnres.B
# Output: Preparing output parameters as DataFrames
if Map is None:
Bout = op.clusters_B_as_dataframes(B_corrected, OGs, None)
else:
Bout, B_species = op.clusters_B_as_dataframes(B_corrected, OGs, MapNew)
Xout = op.processed_X_as_dataframes(X_summarised_normalised, OGs, conditions)
if input_is_one_dataset:
Xout = Xout[0]
# Output: Plot figures
if showPlots:
try:
if np.shape(B_corrected)[1] > 0:
graph.plotclusters(X_summarised_normalised, B_corrected, datafiles_noext, conditions, GDM=GDM,
Cs='all', setPageToDefault=True, showPlots=showPlots, printToPDF=False)
except:
io.log('Error: could not generate clusters'' plots. Resuming the rest of steps ...')
# Output: Prepare message to standard output and the summary then save the summary to a file and print the message
summarymsg, endtime, timeconsumedtxt = \
op.generateoutputsummaryparag(X, X_summarised_normalised, MapNew, GDMall, GDM,
ures, mnres, B_corrected, starttime)
io.log(summarymsg, addextrastick=False)
io.deletetmpfile()
return Bout, Xout, GDM
