Mercurial > repos > george-weingart > lefse

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/format_input.py	Tue Jul 07 13:52:29 2015 -0400
@@ -0,0 +1,453 @@
+#!/usr/bin/env python
+
+import sys,os,argparse,pickle,re,numpy
+
+
+
+
+#***************************************************************************************************************
+#*   Log of change                                                                                             *
+#*   January 16, 2014  - George Weingart - george.weingart@gmail.com                                           *
+#*                                                                                                             *
+#*   biom Support                                                                                              *
+#*   Modified the program to enable it to accept biom files as input                                           *
+#*                                                                                                             *
+#*   Added two optional input parameters:	                                                                   *
+#*   1. biom_c is the name of the biom metadata to be used as class                                            *
+#*   2. biom_s is the name of the biom metadata to be used as subclass                                         *
+#*   class and subclass are used in the same context as the original                                           *
+#*   parameters class and subclass                                                                             *
+#*   These parameters are totally optional, the default is the program                                         *
+#*   chooses as class the first metadata received from the conversion                                          *
+#*   of the biom file into a sequential (pcl) file as generated by                                             *
+#*   breadcrumbs, and similarly, the second metadata is selected as                                            *
+#*   subclass.                                                                                                 *
+#*   The syntax or logic for the original non-biom case was NOT changed.                                       *
+#*                                                                                                             *
+#*   <*******************  IMPORTANT NOTE   *************************>                                         *
+#*   The biom case requires breadcrumbs and therefore there is a                                               *
+#*      a conditional import of the breadcrumbs modules                                                        *
+#*   If the User uses a biom input and breadcrumbs is not detected,                                            *
+#*       the run is abnormally ended                                                                           *
+#*   breadcrumbs itself needs a biom environment, so if the immport                                            *
+#*       of biom in breadcrumbs fails,  the run is also abnormally
+#*       ended (Only if the input file was biom)                                                               *
+#*                                                                                                             *
+#*   USAGE EXAMPLES                                                                                            *
+#*   --------------                                                                                            *
+#*   Case #1: Using a sequential file as input (Old version - did not change                                   *
+#*  ./format_input.py hmp_aerobiosis_small.txt hmp_aerobiosis_small.in -c 1 -s 2 -u 3 -o 1000000               *
+#*   Case #2: Using a biom file as input                                                                       *
+#*  ./format_input.py hmp_aerobiosis_small.biom hmp_aerobiosis_small.in  -o 1000000                            *
+#*   Case #3: Using a biom file as input and override the class and subclass                                   *
+#*   ./format_input.py lefse.biom hmp_aerobiosis_small.in -biom_c oxygen_availability -biom_s body_site -o 1000000
+#*                                                                                                             *
+#***************************************************************************************************************
+
+def read_input_file(inp_file, CommonArea):
+
+	if inp_file.endswith('.biom'):   			#*  If the file format is biom:
+		CommonArea = biom_processing(inp_file)  #*  Process in biom format
+		return CommonArea 						#*  And return the CommonArea
+
+	with open(inp_file) as inp:
+		CommonArea['ReturnedData'] = [[v.strip() for v in line.strip().split("\t")] for line in inp.readlines()]
+		return CommonArea
+
+def transpose(data):
+	return zip(*data)
+
+def read_params(args):
+	parser = argparse.ArgumentParser(description='LEfSe formatting modules')
+	parser.add_argument('input_file', metavar='INPUT_FILE', type=str, help="the input file, feature hierarchical level can be specified with | or . and those symbols must not be present for other reasons in the input file.")
+	parser.add_argument('output_file', metavar='OUTPUT_FILE', type=str,
+		help="the output file containing the data for LEfSe")
+	parser.add_argument('--output_table', type=str, required=False, default="",
+		help="the formatted table in txt format")
+	parser.add_argument('-f',dest="feats_dir", choices=["c","r"], type=str, default="r",
+		help="set whether the features are on rows (default) or on columns")
+	parser.add_argument('-c',dest="class", metavar="[1..n_feats]", type=int, default=1,
+		help="set which feature use as class (default 1)")
+	parser.add_argument('-s',dest="subclass", metavar="[1..n_feats]", type=int, default=None,
+		help="set which feature use as subclass (default -1 meaning no subclass)")
+	parser.add_argument('-o',dest="norm_v", metavar="float", type=float, default=-1.0,
+		help="set the normalization value (default -1.0 meaning no normalization)")
+	parser.add_argument('-u',dest="subject", metavar="[1..n_feats]", type=int, default=None,
+		help="set which feature use as subject (default -1 meaning no subject)")
+	parser.add_argument('-m',dest="missing_p", choices=["f","s"], type=str, default="d",
+		help="set the policy to adopt with missin values: f removes the features with missing values, s removes samples with missing values (default f)")
+	parser.add_argument('-n',dest="subcl_min_card", metavar="int", type=int, default=10,
+		help="set the minimum cardinality of each subclass (subclasses with low cardinalities will be grouped together, if the cardinality is still low, no pairwise comparison will be performed with them)")
+
+	parser.add_argument('-biom_c',dest="biom_class", type=str,
+		help="For biom input files: Set which feature use as class  ")
+	parser.add_argument('-biom_s',dest="biom_subclass", type=str,
+		help="For biom input files: set which feature use as subclass   ")
+
+	args = parser.parse_args()
+
+	return vars(args)
+
+def remove_missing(data,roc):
+	if roc == "c": data = transpose(data)
+	max_len = max([len(r) for r in data])
+	to_rem = []
+	for i,r in enumerate(data):
+		if len([v for v in r if not( v == "" or v.isspace())]) < max_len: to_rem.append(i)
+	if len(to_rem):
+		for i in to_rem.reverse():
+			data.pop(i)
+	if roc == "c": return transpose(data)
+	return data
+
+
+def sort_by_cl(data,n,c,s,u):
+	def sort_lines1(a,b):
+        	return int(a[c] > b[c])*2-1
+	def sort_lines2u(a,b):
+        	if a[c] != b[c]: return int(a[c] > b[c])*2-1
+		return int(a[u] > b[u])*2-1
+	def sort_lines2s(a,b):
+        	if a[c] != b[c]: return int(a[c] > b[c])*2-1
+		return int(a[s] > b[s])*2-1
+	def sort_lines3(a,b):
+        	if a[c] != b[c]: return int(a[c] > b[c])*2-1
+		if a[s] != b[s]: return int(a[s] > b[s])*2-1
+		return int(a[u] > b[u])*2-1
+        if n == 3: data.sort(sort_lines3)
+        if n == 2:
+	  if s is None:
+	    data.sort(sort_lines2u)
+	  else:
+	    data.sort(sort_lines2s)
+        if n == 1: data.sort(sort_lines1)
+	return data
+
+def group_small_subclasses(cls,min_subcl):
+	last = ""
+	n = 0
+	repl = []
+	dd = [list(cls['class']),list(cls['subclass'])]
+	for d in dd:
+		if d[1] != last:
+			if n < min_subcl and last != "":
+				repl.append(d[1])
+			last = d[1]
+		n = 1
+	for i,d in enumerate(dd):
+		if d[1] in repl: dd[i][1] = "other"
+		dd[i][1] = str(dd[i][0])+"_"+str(dd[i][1])
+	cls['class'] = dd[0]
+	cls['subclass'] = dd[1]
+	return cls
+
+def get_class_slices(data):
+        previous_class = data[0][0]
+        previous_subclass = data[0][1]
+        subclass_slices = []
+        class_slices = []
+        last_cl = 0
+        last_subcl = 0
+        class_hierarchy = []
+        subcls = []
+        for i,d in enumerate(data):
+                if d[1] != previous_subclass:
+                        subclass_slices.append((previous_subclass,(last_subcl,i)))
+                        last_subcl = i
+                        subcls.append(previous_subclass)
+                if d[0] != previous_class:
+                        class_slices.append((previous_class,(last_cl,i)))
+                        class_hierarchy.append((previous_class,subcls))
+                        subcls = []
+                        last_cl = i
+                previous_subclass = d[1]
+                previous_class = d[0]
+        subclass_slices.append((previous_subclass,(last_subcl,i+1)))
+        subcls.append(previous_subclass)
+        class_slices.append((previous_class,(last_cl,i+1)))
+        class_hierarchy.append((previous_class,subcls))
+        return dict(class_slices), dict(subclass_slices), dict(class_hierarchy)
+
+def numerical_values(feats,norm):
+	mm = []
+	for k,v in feats.items():
+		feats[k] = [float(val) for val in v]
+	if norm < 0.0: return feats
+	tr = zip(*(feats.values()))
+	mul = []
+	fk = feats.keys()
+	hie = True if sum([k.count(".") for k in fk]) > len(fk) else False
+	for i in range(len(feats.values()[0])):
+		if hie: mul.append(sum([t for j,t in enumerate(tr[i]) if fk[j].count(".") < 1 ]))
+		else: mul.append(sum(tr[i]))
+	if hie and sum(mul) == 0:
+		mul = []
+		for i in range(len(feats.values()[0])):
+			mul.append(sum(tr[i]))
+	for i,m in enumerate(mul):
+		if m == 0: mul[i] = 0.0
+		else: mul[i] = float(norm) / m
+	for k,v in feats.items():
+		feats[k] = [val*mul[i] for i,val in enumerate(v)]
+		if numpy.mean(feats[k]) and (numpy.std(feats[k])/numpy.mean(feats[k])) < 1e-10:
+			feats[k] = [ float(round(kv*1e6)/1e6) for kv in feats[k]]
+	return feats
+
+def add_missing_levels2(ff):
+
+	if sum( [f.count(".") for f in ff] ) < 1: return ff
+
+	dn = {}
+
+	added = True
+	while added:
+		added = False
+		for f in ff:
+			lev = f.count(".")
+			if lev == 0: continue
+			if lev not in dn: dn[lev] = [f]
+			else: dn[lev].append(f)
+		for fn in sorted(dn,reverse=True):
+			for f in dn[fn]:
+				fc = ".".join(f.split('.')[:-1])
+				if fc not in ff:
+					ab_all = [ff[fg] for fg in ff if (fg.count(".") == 0 and fg == fc) or (fg.count(".") > 0 and fc == ".".join(fg.split('.')[:-1]))]
+					ab =[]
+					for l in [f for f in zip(*ab_all)]:
+						ab.append(sum([float(ll) for ll in l]))
+					ff[fc] = ab
+					added = True
+			if added:
+				break
+
+	return ff
+
+
+def add_missing_levels(ff):
+	if sum( [f.count(".") for f in ff] ) < 1: return ff
+
+	clades2leaves = {}
+	for f in ff:
+		fs = f.split(".")
+		if len(fs) < 2:
+			continue
+		for l in range(len(fs)):
+			n = ".".join( fs[:l] )
+			if n in clades2leaves:
+				clades2leaves[n].append( f )
+			else:
+				clades2leaves[n] = [f]
+	for k,v in clades2leaves.items():
+		if k and k not in ff:
+			ff[k] = [sum(a) for a in zip(*[[float(fn) for fn in ff[vv]] for vv in v])]
+	return ff
+
+
+
+def modify_feature_names(fn):
+	ret = fn
+
+	for v in [' ',r'\$',r'\@',r'#',r'%',r'\^',r'\&',r'\*',r'\"',r'\'']:
+                ret = [re.sub(v,"",f) for f in ret]
+        for v in ["/",r'\(',r'\)',r'-',r'\+',r'=',r'{',r'}',r'\[',r'\]',
+		r',',r'\.',r';',r':',r'\?',r'\<',r'\>',r'\.',r'\,']:
+                ret = [re.sub(v,"_",f) for f in ret]
+        for v in ["\|"]:
+                ret = [re.sub(v,".",f) for f in ret]
+
+	ret2 = []
+	for r in ret:
+		if r[0] in ['0','1','2','3','4','5','6','7','8','9']:
+			ret2.append("f_"+r)
+		else: ret2.append(r)
+
+	return ret2
+
+
+def rename_same_subcl(cl,subcl):
+	toc = []
+	for sc in set(subcl):
+		if len(set([cl[i] for i in range(len(subcl)) if sc == subcl[i]])) > 1:
+			toc.append(sc)
+	new_subcl = []
+	for i,sc in enumerate(subcl):
+		if sc in toc: new_subcl.append(cl[i]+"_"+sc)
+		else: new_subcl.append(sc)
+	return new_subcl
+
+
+#*************************************************************************************
+#*  Modifications by George Weingart,  Jan 15, 2014                                  *
+#*  If the input file is biom:                                                       *
+#*  a. Load an AbundanceTable (Using breadcrumbs)                                    *
+#*  b. Create a sequential file from the AbundanceTable (de-facto - pcl)             *
+#*  c. Use that file as input to the rest of the program                             *
+#*  d. Calculate the c,s,and u parameters, either from the values the User entered   *
+#*     from the meta data values in the biom file or set up defaults                 *
+#*  <<<-------------  I M P O R T A N T     N O T E ------------------->>            *
+#*  breadcrumbs src directory must be included in the PYTHONPATH                     *
+#*  <<<-------------  I M P O R T A N T     N O T E ------------------->>            *
+#*************************************************************************************
+def biom_processing(inp_file):
+	CommonArea = dict()			#* Set up a dictionary to return
+	CommonArea['abndData']   = AbundanceTable.funcMakeFromFile(inp_file,	#* Create AbundanceTable from input biom file
+		cDelimiter = None,
+		sMetadataID = None,
+		sLastMetadataRow = None,
+		sLastMetadata = None,
+		strFormat = None)
+
+	#****************************************************************
+	#*  Building the data element here                              *
+	#****************************************************************
+	ResolvedData = list()		#This is the Resolved data that will be returned
+	IDMetadataName  = CommonArea['abndData'].funcGetIDMetadataName()   #* ID Metadataname
+	IDMetadata = [CommonArea['abndData'].funcGetIDMetadataName()]  #* The first Row
+	for IDMetadataEntry in CommonArea['abndData'].funcGetMetadataCopy()[IDMetadataName]:  #* Loop on all the metadata values
+		IDMetadata.append(IDMetadataEntry)
+	ResolvedData.append(IDMetadata)					#Add the IDMetadata with all its values to the resolved area
+	for key, value in  CommonArea['abndData'].funcGetMetadataCopy().iteritems():
+		if  key  != IDMetadataName:
+			MetadataEntry = list()		#*  Set it up
+			MetadataEntry.append(key)	#*	And post it to the area
+			for x in value:
+				MetadataEntry.append(x)		#* Append the metadata value name
+			ResolvedData.append(MetadataEntry)
+	for AbundanceDataEntry in    CommonArea['abndData'].funcGetAbundanceCopy(): 		#* The Abundance Data
+		lstAbundanceDataEntry = list(AbundanceDataEntry)	#Convert tuple to list
+		ResolvedData.append(lstAbundanceDataEntry)			#Append the list to the metadata list
+	CommonArea['ReturnedData'] = 	ResolvedData			#Post the results
+	return CommonArea
+
+
+#*******************************************************************************
+#*    Check the params and override in the case of biom                        *
+#*******************************************************************************
+def  check_params_for_biom_case(params, CommonArea):
+	CommonArea['MetadataNames'] = list()			#Metadata  names
+	params['original_class'] = params['class']			#Save the original class
+	params['original_subclass'] = params['subclass']	#Save the original subclass
+	params['original_subject'] = params['subject']	#Save the original subclass
+
+
+	TotalMetadataEntriesAndIDInBiomFile = len(CommonArea['abndData'].funcGetMetadataCopy())  # The number of metadata entries
+	for i in range(0,TotalMetadataEntriesAndIDInBiomFile): 	#* Populate the meta data names table
+		CommonArea['MetadataNames'].append(CommonArea['ReturnedData'][i][0])	#Add the metadata name
+
+
+	#****************************************************
+	#* Setting the params here                          *
+	#****************************************************
+
+	if TotalMetadataEntriesAndIDInBiomFile > 0:		#If there is at least one entry - has to be the subject
+		params['subject'] =  1
+	if TotalMetadataEntriesAndIDInBiomFile == 2:		#If there are 2 - The first is the subject and the second has to be the metadata, and that is the class
+		params['class'] =  2
+	if TotalMetadataEntriesAndIDInBiomFile == 3:		#If there are 3:  Set up default that the second entry is the class and the third is the subclass
+		params['class'] =  2
+		params['subclass'] =  3
+		FlagError = False								#Set up error flag
+
+		if not params['biom_class'] is None and not params['biom_subclass'] is None:				#Check if the User passed a valid class and subclass
+			if  params['biom_class'] in CommonArea['MetadataNames']:
+				params['class'] =  CommonArea['MetadataNames'].index(params['biom_class']) +1	#* Set up the index for that metadata
+			else:
+				FlagError = True
+			if  params['biom_subclass'] in  CommonArea['MetadataNames']:
+				params['subclass'] =  CommonArea['MetadataNames'].index(params['biom_subclass']) +1 #* Set up the index for that metadata
+			else:
+				FlagError = True
+		if FlagError == True:		#* If the User passed an invalid class
+			print "**Invalid biom class or subclass passed - Using defaults: First metadata=class, Second Metadata=subclass\n"
+			params['class'] =  2
+			params['subclass'] =  3
+	return params
+
+
+
+if  __name__ == '__main__':
+	CommonArea = dict()			#Build a Common Area to pass variables in the biom case
+	params = read_params(sys.argv)
+
+	#*************************************************************
+	#* Conditionally import breadcrumbs if file is a biom file   *
+	#* If it is and no breadcrumbs found - abnormally exit       *
+	#*************************************************************
+	if  params['input_file'].endswith('.biom'):
+		try:
+			from lefsebiom.ConstantsBreadCrumbs import *
+			from lefsebiom.AbundanceTable import *
+		except ImportError:
+			sys.stderr.write("************************************************************************************************************ \n")
+			sys.stderr.write("* Error:   Breadcrumbs libraries not detected - required to process biom files - run abnormally terminated * \n")
+			sys.stderr.write("************************************************************************************************************ \n")
+			exit(1)
+
+
+	if type(params['subclass']) is int and int(params['subclass']) < 1:
+		params['subclass'] = None
+	if type(params['subject']) is int and int(params['subject']) < 1:
+		params['subject'] = None
+
+
+	CommonArea = read_input_file(sys.argv[1], CommonArea)		#Pass The CommonArea to the Read
+	data = CommonArea['ReturnedData']					#Select the data
+
+	if sys.argv[1].endswith('biom'):	#*	Check if biom:
+		params = check_params_for_biom_case(params, CommonArea)	#Check the params for the biom case
+
+	if params['feats_dir'] == "c":
+		data = transpose(data)
+
+	ncl = 1
+	if not params['subclass'] is None: ncl += 1
+	if not params['subject'] is None: ncl += 1
+
+	first_line = zip(*data)[0]
+
+	first_line = modify_feature_names(list(first_line))
+
+	data = zip(	first_line,
+			*sort_by_cl(zip(*data)[1:],
+			  ncl,
+			  params['class']-1,
+			  params['subclass']-1 if not params['subclass'] is None else None,
+			  params['subject']-1 if not params['subject'] is None else None))
+#	data.insert(0,first_line)
+#	data = remove_missing(data,params['missing_p'])
+	cls = {}
+
+	cls_i = [('class',params['class']-1)]
+	if params['subclass'] > 0: cls_i.append(('subclass',params['subclass']-1))
+	if params['subject'] > 0: cls_i.append(('subject',params['subject']-1))
+	cls_i.sort(lambda x, y: -cmp(x[1],y[1]))
+	for v in cls_i: cls[v[0]] = data.pop(v[1])[1:]
+	if not params['subclass'] > 0: cls['subclass'] = [str(cl)+"_subcl" for cl in cls['class']]
+
+	cls['subclass'] = rename_same_subcl(cls['class'],cls['subclass'])
+#	if 'subclass' in cls.keys(): cls = group_small_subclasses(cls,params['subcl_min_card'])
+	class_sl,subclass_sl,class_hierarchy = get_class_slices(zip(*cls.values()))
+
+	feats = dict([(d[0],d[1:]) for d in data])
+
+	feats = add_missing_levels(feats)
+
+	feats = numerical_values(feats,params['norm_v'])
+	out = {}
+	out['feats'] = feats
+	out['norm'] = params['norm_v']
+	out['cls'] = cls
+	out['class_sl'] = class_sl
+	out['subclass_sl'] = subclass_sl
+	out['class_hierarchy'] = class_hierarchy
+
+	if params['output_table']:
+		with open( params['output_table'], "w") as outf:
+			if 'class' in cls: outf.write( "\t".join(list(["class"])+list(cls['class'])) + "\n" )
+			if 'subclass' in cls: outf.write( "\t".join(list(["subclass"])+list(cls['subclass'])) + "\n" )
+			if 'subject' in cls: outf.write( "\t".join(list(["subject"])+list(cls['subject']))  + "\n" )
+			for k,v in out['feats'].items(): outf.write( "\t".join([k]+[str(vv) for vv in v]) + "\n" )
+
+	with open(params['output_file'], 'wb') as back_file:
+		pickle.dump(out,back_file)
+