def sortbycentermass(expdat,field=False,numeric=True,uselog=True):
"""
sort bacteria in the experiment according to a 1d gradient by calculating the center of mass
input:
expdat
field : string
the name of the field to sort by or False to skip sorting
numeric : bool
True if the sort field is numeric (ignored if no sort field)
uselog : bool
True to log transform the data before mass center calculation
output:
newexp - the experiment with sorted bacteria
"""
params=locals()
if field:
newexp=hs.sortsamples(expdat,field,numeric=numeric)
else:
newexp=hs.copyexp(expdat)
dat=newexp.data
if uselog:
dat[dat<1]=1
dat=np.log2(dat)
cm=[]
multpos=np.arange(len(newexp.samples))
for cseqind in range(len(newexp.seqs)):
cm.append(np.dot(dat[cseqind,:],multpos)/np.sum(dat[cseqind,:]))
sv,si=hs.isort(cm)
newexp=hs.reorderbacteria(expdat,si)
newexp.filters.append("sort by center of mass field=%s, uselog=%s" % (field,uselog))
hs.addcommand(newexp,"sortbycentermass",params=params,replaceparams={'expdat':expdat})
return newexp
def savetsvtable(expdat,filename,logtransform=True):
"""
save an experiment as a tab separated table, with columns for samples and rows for bacteria
for jose clemente long babies paper
input:
expdat
filename - name of the output tsv file
minreads - save only bacteria with >=minreads reads
logtransform - True to save the log2 of the reads, False to save the reads
"""
ldat=hs.copyexp(expdat.data)
if logtransform:
ldat[np.where(ldat<1)]=1
ldat=np.log2(ldat)
of=open(filename,'w')
of.write("Taxonomy\tSequence")
for csamp in expdat.samples:
of.write("\t%s" % csamp)
of.write("\n")
for idx,cseq in enumerate(expdat.seqs):
of.write("%s\t%s" % (expdat.tax[idx],cseq))
for cval in ldat[idx,:]:
of.write("\t%f" % cval)
of.write("\n")
of.close()
def normalizeprctile(expdat,percent=80):
"""
normalize reads per experiment so percentile (rather than mean) will be normalized
used to reduce effect of outliers (compositionality correction)
note normalization is done on the same set of bacteria for all samples
input:
expdat : Experiment
percent : float
the percentile to normalize (0-100)
output:
newexp : Experiment
the new normalized experiment
"""
params=locals()
# select the bacteria to use - don't want to include very low freq. bacteria
newexp=hs.filterminreads(expdat,1*len(expdat.samples))
percvals=np.percentile(newexp.data,percent,axis=0)
# plt.figure()
# plt.plot(percvals)
percvals=percvals/np.mean(percvals)
newexp=hs.copyexp(expdat)
for idx,samp in enumerate(expdat.samples):
newexp.data[:,idx]=newexp.data[:,idx]*percvals[idx]
newexp.filters.append("normalize percentile %f" % percent)
hs.addcommand(newexp,"normalizeprctile",params=params,replaceparams={'expdat':expdat})
return newexp
def convertdatefield(expdat,field,newfield,timeformat='%m/%d/%y %H:%M'):
"""
convert a field containing date/time to a numeric (seocds since epoch) field (create a new field for that)
input:
expdat : Experiment
the experiment to add the field to
field : string
name of the field containing the date/time format
newfield : string
name of the new field (with seconds since epoch)
timeformat : string
format of the date/time field (based on time format)
output:
newexp : Experiment
the experiment with the added time since epoch field
"""
params=locals()
newexp=hs.copyexp(expdat)
newexp.fields.append(newfield)
for csamp in newexp.samples:
newexp.smap[csamp][newfield]=time.mktime(time.strptime(newexp.smap[csamp][field],timeformat))
newexp.filters.append('add time field %s (based on field %s)' % (newfield,field))
hs.addcommand(newexp,"convertdatefield",params=params,replaceparams={'expdat':expdat})
return(newexp)
def reloadmap(expdat,mapfilename):
"""
reload the mapping file for a loaded experiment
input:
expdat : Experiment
mapfilename : string
Name of the mapping file to reload
output:
newexp : Experiment
like expdat but with fields from new map file
"""
params=locals()
newexp=hs.copyexp(expdat)
mapsamples,smap,fields,mapmd5=loadmap(mapfilename)
newexp.smap=smap
newexp.fields=fields
newexp.mapmd5=mapmd5
for csamp in newexp.samples:
if csamp not in mapsamples:
hs.Debug(7,'Sample %s not in new map!' % csamp)
newexp.filters.append('reload map %s' % mapfilename)
hs.addcommand(newexp,"reloadmapfile",params=params,replaceparams={'expdat':expdat})
return newexp
def renamesamples(expdat,addstr,addbefore=True):
"""
rename all the samples in expdat by adding addbefore before or after the name of each sample
input:
expdat : Experiment
the experiment to change the sample names in
addstr : str
the string to add to each sampleid
addbefore : bool (optional)
True (default) to add addstr before each sampleid
False to add addstr after each sampleid
output:
newexp : Experiment
with new sample names
"""
newexp=hs.copyexp(expdat)
newids=[]
newmap={}
for csamp in newexp.samples:
if addbefore:
cnewid=addstr+csamp
else:
cnewid=csamp+addstr
newids.append(cnewid)
newmap[cnewid]={}
for ckey,cval in newexp.smap[csamp].items():
newmap[cnewid][ckey]=cval
newexp.samples=newids
newexp.smap=newmap
return newexp
def toorigreads(expdat,inplace=False):
"""
convert the number of reads to absolute using the origreads field
input:
expdat
inplace - True to replace current exp, false to create a new one
output:
newexp - each sample has origreads reads (instead of 10k)
"""
params=locals()
if inplace:
newexp=expdat
else:
newexp=hs.copyexp(expdat)
for idx,csamp in enumerate(newexp.samples):
totreads=np.sum(newexp.data[:,idx])
origreads=newexp.origreads[idx]
if totreads==0:
continue
ratio=float(origreads)/totreads
newexp.data[:,idx]=newexp.data[:,idx]*ratio
newexp.filters.append("changed reads to origread value")
hs.addcommand(newexp,"toorigreads",params=params,replaceparams={'expdat':expdat})
return newexp
def toorigreads(expdat,inplace=False):
"""
convert the number of reads to absolute using the origreads field
input:
expdat
inplace - True to replace current exp, false to create a new one
output:
newexp - each sample has origreads reads (instead of 10k)
"""
params=locals()
if inplace:
newexp=expdat
else:
newexp=hs.copyexp(expdat)
newexp.data=hs.multvec(newexp.data,newexp.scalingfactor)
newexp.data=np.round(newexp.data)
newexp.data=newexp.data.astype(int)
newexp.scalingfactor=1
newexp.filters.append("changed reads to origread value")
hs.addcommand(newexp,"toorigreads",params=params,replaceparams={'expdat':expdat})
return newexp
def fieldtobact(expdat,field,bactname='',meanreads=1000,cutoff=0):
"""
convert values in a map file field to a new bacteria (to facilitate numeric analysis)
input:
expdat : Experiment
field : string
name of the field to convert
bactname : string
name of the new bacteria (empty to have similar to field name)
meanreads : int
the mean number of reads for the new field bacteria
cutoff : int
the minimal value of the field per sample (otherwise replace with meanreads)
output:
newexp : Experiment
with added bacteria with the field vals as reads
"""
params=locals()
if len(bactname)==0:
bactname=field
fv=hs.getfieldvals(expdat,field)
vals=np.array(hs.tofloat(fv))
okpos=np.where(vals>=cutoff)[0]
badpos=np.where(vals<cutoff)[0]
scalefactor=np.mean(vals[okpos])
vals[okpos]=(vals[okpos]/scalefactor)*meanreads
vals[badpos]=meanreads
newexp=hs.copyexp(expdat)
hs.insertbacteria(newexp,vals,bactname,bactname,logit=False)
newexp.filters.append('add bacteria from map field %s' % field)
hs.addcommand(newexp,"fieldtobact",params=params,replaceparams={'expdat':expdat})
return(newexp)
def addmapfield(expdat,fieldname,defaultval='NA',inplace=False): """ add a new field to the mapping file input: expdat : Experiment fieldname : str name of the new field defaultval : str the value for all samples inplace : bool True to overwrite current experiment, False (default) to copy output: newexp : Experiment with the new field added """ if inplace: newexp=expdat else: newexp=hs.copyexp(expdat) if fieldname in newexp.fields: hs.Debug(8,'field %s already exists') return newexp newexp.fields.append(fieldname) for csamp in newexp.samples: newexp.smap[csamp][fieldname]=defaultval return newexp
def normalizereads(expdat,numreads=10000,fixorig=False,inplace=False):
"""
normalize the number of reads per sample (default to 10k)
input:
expdat
numreads - the number of reads to normalize to
fixorig - True to fix origreads with the same ratio, False to keep as before
inplace - true to replace orig experiment, false to create a new experiment
output:
newexp - the normalized experiment
"""
params=locals()
if inplace:
newexp=expdat
else:
newexp=hs.copyexp(expdat)
for idx,csamp in enumerate(newexp.samples):
totreads=np.sum(newexp.data[:,idx])
if totreads==0:
continue
ratio=float(numreads)/totreads
newexp.data[:,idx]=newexp.data[:,idx]*ratio
if fixorig:
hs.Debug(2,'fixing original frequencies')
newexp.origreads[idx]=float(newexp.origreads[idx])/ratio
newexp.filters.append("renormalized reads to sum %d" % numreads)
hs.addcommand(newexp,"normalizereads",params=params,replaceparams={'expdat':expdat})
return newexp
def normalizebyseqs(expdat,seqs,exclude=False,fixorig=True):
"""
normalize experiment by making the sum of frequencies in seqs constant in each sample
input:
expdat
seqs - the sequences to use as the normalization factor (sum of the sequences)
exclude - true to use all sequences except in seqs as the normalization factor, False to use seqs
fixorig - True to modify the origreads field, false to leave it as it was
"""
params=locals()
newexp=hs.copyexp(expdat)
spos=[]
for cseq in seqs:
spos.append(expdat.seqdict[cseq])
if exclude:
spos=np.setdiff1d(np.arange(len(expdat.seqs)),spos)
ssum=np.sum(expdat.data[spos,:],axis=0)+0.0
ssum[ssum==0]=1
frat=ssum/np.mean(ssum)
for idx in range(len(expdat.samples)):
newexp.data[:,idx]=newexp.data[:,idx]/frat[idx]
if fixorig:
newexp.origreads[idx]=newexp.origreads[idx]/frat[idx]
newexp.scalingfactor[idx]=newexp.scalingfactor[idx]*frat
filt='Normalize By Seqs '
if len(spos)==1:
filt+=newexp.tax[spos[0]]
else:
filt+=str(len(spos))
if exclude:
filt+=' Exclude'
newexp.filters.append(filt)
hs.addcommand(newexp,"normalizebyseqs",params=params,replaceparams={'expdat':expdat})
return newexp
def addsubtrees(expdat,tree,inplace=False):
"""
add otus for all subtrees with the frequency being the sum of all bacteria in the subtree
input:
expdat - the experiment
tree - the tree for the experiment
inplace - if true, replace current experiment
output:
newexp - the new experiment with twice-1 number of otus
"""
params=locals()
# if not expdat.tree:
# hs.Debug(8,"No tree loaded for experiment")
# return False
if inplace:
newexp=expdat
else:
newexp=hs.copyexp(expdat)
subtrees=tree.subsets()
for csubtree in subtrees:
newname=""
newtax=""
numuse=0
newfreq=np.zeros([1,len(newexp.samples)])
for cbact in csubtree:
if cbact not in newexp.seqdict:
hs.Debug(4,'sequence not in seqdict',cbact)
continue
numuse+=1
cpos=newexp.seqdict[cbact]
newfreq+=newexp.data[cpos,:]
newname+='%d,' % cpos
if newtax=='':
newtax=newexp.tax[cpos]
else:
newtax=hs.common_start(newtax,newexp.tax[cpos])
# add only if we have 2 bacteria or more
if numuse>1:
if newname not in newexp.seqdict:
newexp,newpos=insertbacteria(newexp,freqs=newfreq,seq=newname,tax=newtax,logit=False)
newexp.filters.append("Add subtrees")
hs.addcommand(newexp,"addsubtrees",params=params,replaceparams={'expdat':expdat})
return(newexp)
def filtermapfields(expdat,fields=['#SampleID'],keep=True,inplace=False):
"""
filter fields from the experiment mapping data
input:
expdat : Experiment
fields : list of str
the list of the fields to keep/remove
keep : bool (optional)
True (default) to keep only the fields specified
False to remove the fields specified
inplace : bool (optional)
False (default) to create new experiment
True to replace in current experiment
output:
newexp : Experiment
with only the fields requested
"""
params=locals()
newsmap={}
newfields=set(expdat.fields)
if keep:
newfields=newfields.intersection(set(fields))
else:
newfields=newfields.difference(set(fields))
newfields.add('#SampleID')
for csamp in expdat.samples:
newsmap[csamp]={}
for cfield in newfields:
newsmap[csamp][cfield]=expdat.smap[csamp][cfield]
if inplace:
newexp=expdat
else:
newexp=hs.copyexp(expdat)
newexp.fields=list(newfields)
newexp.smap=newsmap
expdat.filters.append('filter map fields %s (keep=%s)' % (fields,keep))
hs.addcommand(expdat,"filtermapfields",params=params,replaceparams={'expdat':expdat})
return newexp
def sortbysign(expdat,field=False,value='',exclude=False,exact=True,maxfval=0.2):
"""
sort bacteria in the experiment based on the number of positive/negative samples
(ignoring nans)
input:
expdat : Experiment
field,value,exclude,exact : name of field and value of field in order to sort based only on these samples
or field=False for all samples (default)
maxfval - the maximal f-value
output:
newexp : Experiment
sorted by difference between positive/negative
"""
params=locals()
if field:
texp=hs.filtersamples(expdat,field,value,exact=exact,exclude=exclude)
else:
texp=hs.copyexp(expdat)
texp.data=np.sign(texp.data)
numpos=np.nansum(texp.data>0,axis=1)
numneg=np.nansum(texp.data<0,axis=1)
pval=np.ones(len(numpos))
for cpos in range(len(pval)):
if numpos[cpos]==0 and numneg[cpos]==0:
continue
pval1=stats.binom.cdf(numpos[cpos],numpos[cpos]+numneg[cpos],0.5)
pval2=stats.binom.cdf(numneg[cpos],numpos[cpos]+numneg[cpos],0.5)
pval[cpos]=np.min([pval1,pval2])
signs=np.nanmean(texp.data,axis=1)
fval=hs.fdr(pval)
keep=np.where(np.array(fval)<=maxfval)[0]
newexp=hs.reorderbacteria(expdat,keep)
signs=signs[keep]
si=np.argsort(signs)
newexp=hs.reorderbacteria(newexp,si)
newexp.filters.append("sort by sign field %s max-f-val %f" % (field,maxfval))
hs.addcommand(newexp,"sortbysign",params=params,replaceparams={'expdat':expdat})
return newexp
def zerobacteria(expdat,inplace=False):
"""
zero all the bacteria in an experiment (can then add insertbacteria)
input:
expdat : Experiment
inplace : bool
True to do inplace, False to make new copy
output:
newexp : Experiment
all bacteria have been removed
"""
if inplace:
newexp=expdat
else:
newexp=hs.copyexp(expdat)
newexp.data=np.zeros([0,len(newexp.samples)])
newexp.seqs=[]
newexp.tax=[]
newexp.seqdict={}
newexp.sids=[]
return newexp
def taxtoseq(expdat,fixtax=False):
"""
put the taxonomy into the sequence field
input:
expdat : Experiment
fixtax: bool (optional)
False (default) to just copy, True to remove the k__ etc.
output:
newexp : Experiment
with seqs=taxonomies
"""
newexp=hs.copyexp(expdat)
newexp.seqs=newexp.tax
if fixtax:
newtax=[]
for ctax in newexp.tax:
cstr=''
cctax=ctax.split(';')
for clevel in range(7):
if len(cctax)>clevel:
cstr+=cctax[clevel][3:]
cstr+=';'
newtax.append(cstr)
newexp.seqs=newtax
newexp.seqdict={}
newseqs=[]
for idx,cseq in enumerate(newexp.seqs):
if cseq in newexp.seqdict:
hs.Debug(8,'found %s again' % cseq)
cseq=cseq+'-'+str(idx)
newseqs.append(cseq)
newexp.seqdict[cseq]=idx
newexp.seqs=newseqs
return(newexp)
def cleantaxonomy(self):
items=self.bMainList.selectedItems()
if len(items)!=1:
print("Need 1 item")
return
for citem in items:
cname=str(citem.text())
cexp=self.explist[cname]
ctwin = CleanTaxonomyWindow(cexp)
res=ctwin.exec_()
if res==QtGui.QDialog.Accepted:
newexp=hs.copyexp(cexp)
if ctwin.cMitochondria.checkState():
newexp=hs.filtertaxonomy(newexp,'mitochondria',exclude=True)
if ctwin.cChloroplast.checkState():
newexp=hs.filtertaxonomy(newexp,'Streptophyta',exclude=True)
newexp=hs.filtertaxonomy(newexp,'Chloroplast',exclude=True)
if ctwin.cUnknown.checkState():
newexp=hs.filtertaxonomy(newexp,'nknown',exclude=True)
if ctwin.cBacteria.checkState():
newexp=hs.filtertaxonomy(newexp,'Bacteria;',exclude=True,exact=True)
newexp=hs.normalizereads(newexp)
newexp.studyname=cexp.studyname+'.ct'
self.addexp(newexp)
def changemapval(expdat,newfield,newval,oldfield,vals,inplace=False): """ change values of a field in the mapping file according to another field input: expdat : Experiment newfield : name of the field to change the values in (from addmapfield?) newval : the new value to put oldfield : the field with the values to test vals : a list of values, so newfield is set to newval only if the the value of oldfield is in the list inplace : bool True to overwrite current experiment, False (default) to copy """ if inplace: newexp=expdat else: newexp=hs.copyexp(expdat) for csamp in newexp.samples: if newexp.smap[csamp][oldfield] in vals: newexp.smap[csamp][newfield]=newval return newexp
def cleantaxonomy(expdat,mitochondria=True,chloroplast=True,bacteria=True,unknown=True,exclude=True):
"""
remove common non-16s sequences from the experiment and renormalize
input:
expdat : Experiment
mitochondria : bool
remove mitochondrial sequences
chloroplast : bool
remove chloroplast sequences
bacteria : bool
remove sequences only identified as "Bacteria" (no finer identification)
unknown : bool
remove unknown sequences
exclude : bool
True (default) to remove these sequecnes, False to keep them and throw other
output:
newexp : Experiment
the renormalized experiment without these bacteria
"""
params=locals()
newexp=hs.copyexp(expdat)
if mitochondria:
if exclude:
newexp=hs.filtertaxonomy(newexp,'mitochondria',exclude=True)
else:
ne1=hs.filtertaxonomy(newexp,'mitochondria',exclude=False)
if chloroplast:
if exclude:
newexp=hs.filtertaxonomy(newexp,'Streptophyta',exclude=True)
newexp=hs.filtertaxonomy(newexp,'Chloroplast',exclude=True)
else:
ne2=hs.filtertaxonomy(newexp,'Streptophyta',exclude=False)
ne3=hs.filtertaxonomy(newexp,'Chloroplast',exclude=False)
if unknown:
if exclude:
newexp=hs.filtertaxonomy(newexp,'Unknown',exclude=True)
newexp=hs.filtertaxonomy(newexp,'Unclassified;',exclude=True,exact=True)
else:
ne4=hs.filtertaxonomy(newexp,'Unknown',exclude=False)
ne5=hs.filtertaxonomy(newexp,'Unclassified;',exclude=False,exact=True)
if bacteria:
if exclude:
newexp=hs.filtertaxonomy(newexp,'Bacteria;',exclude=True,exact=True)
else:
ne6=hs.filtertaxonomy(newexp,'Bacteria;',exclude=False,exact=True)
if exclude:
newexp=hs.normalizereads(newexp)
else:
allseqs=[]
allseqs+=(ne1.seqs)
allseqs+=(ne2.seqs)
allseqs+=(ne3.seqs)
allseqs+=(ne4.seqs)
allseqs+=(ne5.seqs)
allseqs+=(ne6.seqs)
allseqs=list(set(allseqs))
newexp=hs.filterseqs(newexp,allseqs)
newexp.filters.append('Clean Taxonomy (remove mitochondria etc.)')
hs.addcommand(newexp,"cleantaxonomy",params=params,replaceparams={'expdat':expdat})
hs.Debug(6,'%d sequences before filtering, %d after' % (len(expdat.seqs),len(newexp.seqs)))
return newexp
def filtersimilarsamples(expdat,field,method='mean'):
"""
join similar samples into one sample (i.e. to remove samples of same individual)
input:
expdat : Experiment
field : string
Name of the field containing the values (for which similar values will be joined)
method : string
What to do with samples with similar value. options:
'mean' - replace with a sample containing the mean of the samples
'median'- replace with a sample containing the median of the samples
'random' - replace with a single random sample out of these samples
'sum' - replace with sum of original reads in all samples, renormalized after to 10k and orignumreads updated
'fracpres' - replace with fraction of samples where the bacteria is present
output:
newexp : Experiment
like the input experiment but only one sample per unique value in field
"""
params=locals()
newexp=hs.copyexp(expdat)
if method=='sum':
newexp=hs.toorigreads(newexp)
uvals=hs.getfieldvals(expdat,field,ounique=True)
keep=[]
for cval in uvals:
cpos=hs.findsamples(expdat,field,cval)
if len(cpos)==1:
keep.append(cpos[0])
continue
if method=='random':
keep.append(cpos[np.random.randint(len(cpos))])
continue
# set the mapping file values
cmap=expdat.smap[expdat.samples[cpos[0]]]
for ccpos in cpos[1:]:
for cfield in cmap.keys():
if cmap[cfield]!=expdat.smap[expdat.samples[ccpos]][cfield]:
cmap[cfield]='NA'
if method=='mean':
cval=np.mean(expdat.data[:,cpos],axis=1)
newexp.data[:,cpos[0]]=cval
keep.append(cpos[0])
elif method=='median':
cval=np.median(expdat.data[:,cpos],axis=1)
newexp.data[:,cpos[0]]=cval
keep.append(cpos[0])
elif method=='sum':
cval=np.sum(newexp.data[:,cpos],axis=1)
newexp.data[:,cpos[0]]=cval
newexp.origreads[cpos[0]]=np.sum(hs.reorder(expdat.origreads,cpos))
keep.append(cpos[0])
elif method=='fracpres':
cval=np.sum(expdat.data[:,cpos]>0,axis=1)
newexp.data[:,cpos[0]]=cval/len(cpos)
keep.append(cpos[0])
else:
hs.Debug(9,'method %s not supported' % method)
return False
newexp.smap[expdat.samples[cpos[0]]]=cmap
newexp=hs.reordersamples(newexp,keep)
if method=='sum':
newexp=hs.normalizereads(newexp)
newexp.filters.append('Filter similar samples field %s method %s' % (field,method))
hs.addcommand(newexp,"filtersimilarsamples",params=params,replaceparams={'expdat':expdat})
hs.Debug(6,'%d samples before filtering, %d after' % (len(expdat.samples),len(newexp.samples)))
return newexp
def plotexp(exp,sortby=False,numeric=False,minreads=4,rangeall=False,seqdb=None,cdb=None,showline=True,ontofig=False,usegui=True,showxall=False,showcolorbar=False,ptitle=False,lowcutoff=1,uselog=True,showxlabel=True,colormap=False,colorrange=False):
"""
Plot an experiment
input:
exp - from load()
sortby - name of mapping file field to sort by or Flase to not sort
numeric - True if the field is numeric
minreads - minimum number of reads per bacteria in order to show it or 0 to show all
rangeall - True to show all frequencies in image scale, false to saturate at 10%
seqdb - the SRBactDB database (from bactdb.load)
cdb - the cool sequences database (from cooldb.load)
showline - if True plot lines between category values
ontofig - name of ontology to plot for bactdb or false to no plot
usegui - True use a gui for otu summary, False just print
showxall - True to show all sample names when not sorting, False to show no more than 10
showcolorbar - True to plot the colorbar. False to not plot
ptitle - name of the figure or False to show processing history as name
lowcutoff - minimal value for read (for 0 log transform) - the minimal resolution - could be 10000*2/origreads
showxlabel : bool
True to show the x label (default), False to hide it
colormap : string or False
name of colormap or False (default) to use mpl default colormap
colorrange : [min,max] or False
[min,max] to set the colormap range, False to use data min,max (default) as specified in rangeall
output:
newexp - the plotted experiment (sorted and filtered)
ax - the plot axis
"""
hs.Debug(1,"Plot experiment %s" % exp.studyname)
hs.Debug(1,"Commands:")
for ccommand in exp.commands:
hs.Debug(1,"%s" % ccommand)
vals=[]
if sortby:
hs.Debug(1,"Sorting by field %s" % sortby)
for csamp in exp.samples:
vals.append(exp.smap[csamp][sortby])
if numeric:
hs.Debug(1,"(numeric sort)")
vals=hs.tofloat(vals)
svals,sidx=hs.isort(vals)
newexp=hs.reordersamples(exp,sidx)
else:
hs.Debug(1,"No sample sorting")
svals=hs.getfieldvals(exp,'#SampleID')
newexp=hs.copyexp(exp)
hs.Debug(1,"Filtering min reads. original bacteria - %d" % len(newexp.seqs))
if minreads>0:
newexp=hs.filterminreads(newexp,minreads,logit=uselog)
hs.Debug(1,"New number of bacteria %d" % len(newexp.seqs))
newexp.seqdb=seqdb
newexp.cdb=cdb
# ldat=ldat[:,sidx]
ldat=newexp.data
if uselog:
hs.Debug(1,"Using log, cutoff at %f" % lowcutoff)
ldat[np.where(ldat<lowcutoff)]=lowcutoff
ldat=np.log2(ldat)
oldparams=plt.rcParams
mpl.rc('keymap',back='c, backspace')
mpl.rc('keymap',forward='v')
mpl.rc('keymap',all_axes='A')
f=figure()
# set the colormap to default if not supplied
if not colormap:
colormap=plt.rcParams['image.cmap']
# plot the image
if colorrange:
hs.Debug(1,"colormap range is 0,10")
iax=imshow(ldat,interpolation='nearest',aspect='auto',clim=colorrange,cmap=plt.get_cmap(colormap))
elif rangeall:
hs.Debug(1,"colormap range is all")
iax=imshow(ldat,interpolation='nearest',aspect='auto',cmap=plt.get_cmap(colormap))
else:
hs.Debug(1,"colormap range is 0,10")
iax=imshow(ldat,interpolation='nearest',aspect='auto',clim=[0,10],cmap=plt.get_cmap(colormap))
if not ptitle:
hs.Debug(1,"Showing filters in title")
if (len(newexp.filters))>4:
cfilters=[newexp.filters[0],'...',newexp.filters[-2],newexp.filters[-1]]
else:
cfilters=newexp.filters
cfilters=hs.clipstrings(cfilters,30)
ptitle='\n'.join(cfilters)
title(ptitle,fontsize=10)
ax=iax.get_axes()
ax.autoscale(False)
if showline:
hs.Debug(1,"Showing lines")
labs=[]
labpos=[]
linepos=[]
minpos=0
svals.append('end')
for idx,cval in enumerate(svals[:-1]):
if cval==svals[idx+1]:
continue
labpos.append(minpos-0.5+float(idx+1-minpos)/2)
minpos=idx+1
linepos.append(idx+0.5)
labs.append(cval)
hs.Debug(1,"number of lines is %d" % len(linepos))
if showxlabel:
ax.set_xticks(labpos)
ax.set_xticklabels(labs,rotation=45,ha='right')
for cx in linepos:
plot([cx,cx],[-0.5,np.size(ldat,0)-0.5],'k',linewidth=2)
else:
hs.Debug(1,"Not showing lines")
if showxall or len(newexp.samples)<=10:
hs.Debug(1,"less than 10 samples, showing all sample names")
ax.set_xticklabels(svals,rotation=90)
ax.set_xticks(range(len(newexp.samples)))
tight_layout()
ax.set_ylim(-0.5,np.size(ldat,0)+0.5)
if showcolorbar:
hs.Debug(1,"Showing colorbar")
cb=colorbar(ticks=list(np.log2([2,10,100,500,1000])))
cb.ax.set_yticklabels(['<0.02%','0.1%','1%','5%','>10%'])
# create the plot
ax.expdat=newexp
ax.lastselect=-1
ax.sampline=''
ax.ofig=f
ax.labelson=False
ax.labelnames=[]
f.canvas.mpl_connect('button_press_event', onplotmouseclick)
f.canvas.mpl_connect('key_press_event', onplotkeyclick)
# show()
plt.rcParams=oldparams
# if want the ontology analysis for a given category:
if ontofig:
hs.Debug(1,"Ontofig is set")
newexp.ontofigname=ontofig
else:
newexp.ontofigname=False
# if we want gui, open it
if usegui:
hs.Debug(1,"Using the GUI window")
import heatsequer.plots.plotwingui
guiwin = heatsequer.plots.plotwingui.PlotGUIWindow(newexp)
# from heatsequer.plots import plotwingui
# guiwin = plotwingui.PlotGUIWindow(newexp)
ax.guiwin=guiwin
guiwin.plotfig=f
guiwin.plotax=ax
guiwin.show()
else:
ax.guiwin=False
hs.Debug(7,'Not using gui')
if newexp.plotmetadata:
hs.Debug(1,"Experiment has metadata attached for plotting (%d points)" % len(newexp.plotmetadata))
for cmet in newexp.plotmetadata:
addplotmetadata(newexp,field=cmet[0],value=cmet[1],color=cmet[2],inverse=cmet[3],beforesample=cmet[4])
show()
return newexp,ax
def plotexp(exp,sortby=False,numeric=False,minreads=4,rangeall=False,seqdb=None,cdb=None,showline=True,ontofig=False,usegui=True,showxall=False,showcolorbar=False,ptitle=False,lowcutoff=1,uselog=True,showxlabel=True,colormap=False,colorrange=False,linewidth=2,subline='',showhline=True,newfig=True,fixfont=False,fontsize=None,nosort=False,zeroisnone=False,xlabelrotation=45,showtaxnames=False):
"""
Plot an experiment
input:
exp - from load()
sortby - name of mapping file field to sort by or Flase to not sort
numeric - True if the field is numeric
minreads - minimum number of reads per bacteria in order to show it or 0 to show all
rangeall - True to show all frequencies in image scale, false to saturate at 10%
seqdb - the SRBactDB database (from bactdb.load)
cdb - the cool sequences database (from cooldb.load), or None (default) to use the heatsequer loaded cdb
showline - if True plot lines between category values
ontofig - name of ontology to plot for bactdb or false to no plot
usegui - True use a gui for otu summary, False just print
showxall - True to show all sample names when not sorting, False to show no more than 10
showcolorbar - True to plot the colorbar. False to not plot
ptitle : str (optional)
'' to show o show processing history as name, None to not show title, or str of name of the figure
lowcutoff - minimal value for read (for 0 log transform) - the minimal resolution - could be 10000*2/origreads
showxlabel : bool
True to show the x label (default), False to hide it
colormap : string or False
name of colormap or False (default) to use mpl default colormap
colorrange : [min,max] or False
[min,max] to set the colormap range, False to use data min,max (default) as specified in rangeall
subline : str
Name of category for subline plotting or '' (Default) for no sublines
showhline : bool
True (default) to plot the horizontal lines listed in exp.hlines. False to not plot them
newfig : bool
True (default) to open figure in new window, False to use current
fixfont : bool (optional)
False (default) to use fixedfont, True to use fixed width font
fontsize : int or None (optional)
None (default) to use default font size, number to use that font size
nosort : bool (optional)
False (default) to sort by the sort field, True to skip the sorting
zeroisnone : bool (optional)
False (default) to plot zeros as 0, True to assign None (white color)
xlabelrotation : int (optional)
the rotation of the xtick labels
showtaxnames : book (optional)
False (default) to not show tax names (need to press 'h' to show)
True to show the taxonomy names
output:
newexp - the plotted experiment (sorted and filtered)
ax - the plot axis
"""
hs.Debug(1,"Plot experiment %s" % exp.studyname)
hs.Debug(1,"Commands:")
for ccommand in exp.commands:
hs.Debug(1,"%s" % ccommand)
if exp.sparse:
hs.Debug(9,'Sparse matrix - converting to dense')
exp=hs.copyexp(exp,todense=True)
vals=[]
if cdb is None:
cdb=hs.cdb
if seqdb is None:
seqdb=hs.bdb
if sortby:
if not nosort:
hs.Debug(1,"Sorting by field %s" % sortby)
for csamp in exp.samples:
vals.append(exp.smap[csamp][sortby])
if numeric:
hs.Debug(1,"(numeric sort)")
vals=hs.tofloat(vals)
svals,sidx=hs.isort(vals)
newexp=hs.reordersamples(exp,sidx)
else:
hs.Debug(1,"no sorting but showing columns")
svals=hs.getfieldvals(exp,sortby)
newexp=hs.copyexp(exp)
else:
hs.Debug(1,"No sample sorting")
svals=hs.getfieldvals(exp,'#SampleID')
newexp=hs.copyexp(exp)
hs.Debug(1,"Filtering min reads. original bacteria - %d" % len(newexp.seqs))
if minreads>0:
newexp=hs.filterminreads(newexp,minreads,logit=uselog)
hs.Debug(1,"New number of bacteria %d" % len(newexp.seqs))
newexp.seqdb=seqdb
newexp.cdb=cdb
newexp.scdb=hs.scdb
# if usegui:
# hs.Debug(1,"Using the GUI window")
# import heatsequer.plots.plotwingui
# from PyQt4 import QtGui
# app = QtGui.QApplication(sys.argv)
# guiwin = heatsequer.plots.plotwingui.PlotGUIWindow(newexp)
# ldat=ldat[:,sidx]
ldat=newexp.data
if zeroisnone:
ldat[ldat==0]=None
if uselog:
hs.Debug(1,"Using log, cutoff at %f" % lowcutoff)
ldat[np.where(ldat<lowcutoff)]=lowcutoff
ldat=np.log2(ldat)
oldparams=plt.rcParams
mpl.rc('keymap',back='c, backspace')
mpl.rc('keymap',forward='v')
mpl.rc('keymap',all_axes='A')
if newfig:
f=plt.figure(tight_layout=True)
else:
f=plt.gcf()
# set the colormap to default if not supplied
if not colormap:
colormap=plt.rcParams['image.cmap']
# plot the image
if colorrange:
hs.Debug(1,"colormap range is 0,10")
iax=plt.imshow(ldat,interpolation='nearest',aspect='auto',clim=colorrange,cmap=plt.get_cmap(colormap))
elif rangeall:
hs.Debug(1,"colormap range is all")
iax=plt.imshow(ldat,interpolation='nearest',aspect='auto',cmap=plt.get_cmap(colormap))
else:
hs.Debug(1,"colormap range is 0,10")
iax=plt.imshow(ldat,interpolation='nearest',aspect='auto',clim=[0,10],cmap=plt.get_cmap(colormap))
if ptitle is not None:
if not ptitle:
hs.Debug(1,"Showing filters in title")
if (len(newexp.filters))>4:
cfilters=[newexp.filters[0],'...',newexp.filters[-2],newexp.filters[-1]]
else:
cfilters=newexp.filters
cfilters=hs.clipstrings(cfilters,30)
ptitle='\n'.join(cfilters)
plt.title(ptitle,fontsize=10)
ax=iax.get_axes()
ax.autoscale(False)
# plot the sublines (smaller category lines)
if subline:
slval=hs.getfieldvals(newexp,subline)
prevval=slval[0]
for idx,cval in enumerate(slval):
if cval!=prevval:
xpos=idx-0.5
plt.plot([xpos,xpos],[-0.5,np.size(ldat,0)-0.5],'w:')
prevval=cval
if showline:
hs.Debug(1,"Showing lines")
labs=[]
labpos=[]
linepos=[]
minpos=0
svals.append('end')
for idx,cval in enumerate(svals[:-1]):
if cval==svals[idx+1]:
continue
labpos.append(minpos-0.5+float(idx+1-minpos)/2)
minpos=idx+1
linepos.append(idx+0.5)
labs.append(cval)
hs.Debug(1,"number of lines is %d" % len(linepos))
if showxlabel:
ax.set_xticks(labpos)
ax.set_xticklabels(labs,rotation=xlabelrotation,ha='right')
for cx in linepos:
plt.plot([cx,cx],[-0.5,np.size(ldat,0)-0.5],'k',linewidth=linewidth)
plt.plot([cx,cx],[-0.5,np.size(ldat,0)-0.5],'w:',linewidth=linewidth)
else:
hs.Debug(1,"Not showing lines")
if showxall or len(newexp.samples)<=10:
hs.Debug(1,"less than 10 samples, showing all sample names")
ax.set_xticklabels(svals,rotation=90)
ax.set_xticks(range(len(newexp.samples)))
# f.tight_layout()
ax.set_ylim(-0.5,np.size(ldat,0)-0.5)
if fixfont:
fontProperties = {'family':'monospace'}
ax.set_yticklabels(ax.get_yticks(), fontProperties)
if showcolorbar:
hs.Debug(1,"Showing colorbar")
cb=plt.colorbar(ticks=list(np.log2([2,10,100,500,1000])))
cb.ax.set_yticklabels(['<0.02%','0.1%','1%','5%','>10%'])
# create the plot
ax.expdat=newexp
ax.lastselect=-1
ax.sampline=''
ax.ofig=f
ax.labelson=False
ax.labelnames=[]
f.canvas.mpl_connect('button_press_event', onplotmouseclick)
f.canvas.mpl_connect('key_press_event', onplotkeyclick)
# show()
plt.rcParams=oldparams
# if want the ontology analysis for a given category:
if ontofig:
hs.Debug(1,"Ontofig is set")
newexp.ontofigname=ontofig
else:
newexp.ontofigname=False
# if we want gui, open it
if usegui:
hs.Debug(1,"Using the GUI window")
import heatsequer.plots.plotwingui
# from PyQt4 import QtGui
# app = QtGui.QApplication(sys.argv)
guiwin = heatsequer.plots.plotwingui.PlotGUIWindow(newexp)
from heatsequer.plots import plotwingui
guiwin = plotwingui.PlotGUIWindow(newexp)
ax.guiwin=guiwin
guiwin.plotfig=f
guiwin.plotax=ax
guiwin.show()
else:
ax.guiwin=False
hs.Debug(7,'Not using gui')
ax.plot_labelsize=fontsize
if newexp.plotmetadata:
hs.Debug(1,"Experiment has metadata attached for plotting (%d points)" % len(newexp.plotmetadata))
for cmet in newexp.plotmetadata:
addplotmetadata(newexp,field=cmet[0],value=cmet[1],color=cmet[2],inverse=cmet[3],beforesample=cmet[4])
if showhline:
if newexp.hlines:
for cpos in newexp.hlines:
plt.plot([0,np.shape(newexp.data)[1]],[cpos-0.5,cpos-0.5],'g')
plt.show()
if showtaxnames:
showtaxonomies(newexp,ax,showdb=False,showcontam=False)
# if usegui:
# app.exec_()
return newexp,ax
def joinexperiments(exp1,exp2,missingval='NA',origfieldname='origexp',addbefore=False):
"""
join 2 experiments into a new experiment. adding a new field origfieldname
input:
exp1,exp2 - the experiments to join
missingval - string to put when field not in mapping file of one of the experiments
origfieldname - name of the new field to add which contains the original experiment name
addbefore : bool (optional)
False (default) to add '-1'/'-2' after sampleid if similar ids in both experiments
True to add '-1'/'-2' after sampleid if similar ids in both experiments
"""
params=locals()
# test if same sampleid exists in both experiments. if so, add "-1" and "-2" to sampleid
samp1=set(exp1.samples)
samp2=set(exp2.samples)
if len(samp1.intersection(samp2))>0:
hs.Debug(6,'same sampleID - renaming samples')
exp1=hs.renamesamples(exp1,'-1',addbefore=addbefore)
exp2=hs.renamesamples(exp2,'-2',addbefore=addbefore)
# join the sequences of both experiments
# ASSUMING SAME SEQ LENGTH!!!!
allseqs=list(set(exp1.seqs) | set(exp2.seqs))
alldict={}
alltax=[]
allids=[]
for idx,cseq in enumerate(allseqs):
alldict[cseq]=idx
# make the new joined data for each experiment
dat1=np.zeros((len(allseqs),np.size(exp1.data,1)))
for idx,cseq in enumerate(allseqs):
if cseq in exp1.seqdict:
dat1[idx,:]=exp1.data[exp1.seqdict[cseq],:]
alltax.append(exp1.tax[exp1.seqdict[cseq]])
allids.append(exp1.sids[exp1.seqdict[cseq]])
else:
alltax.append(exp2.tax[exp2.seqdict[cseq]])
allids.append(exp2.sids[exp2.seqdict[cseq]])
dat2=np.zeros((len(allseqs),np.size(exp2.data,1)))
for idx,cseq in enumerate(allseqs):
if cseq in exp2.seqdict:
dat2[idx,:]=exp2.data[exp2.seqdict[cseq],:]
newexp=hs.copyexp(exp1)
# concatenate the reads
newexp.data=np.concatenate((dat1,dat2), axis=1)
newexp.seqdict=alldict
newexp.seqs=allseqs
newexp.tax=alltax
newexp.sids=allids
newexp.sids=newexp.seqs
newexp.samples = list(exp1.samples) + list(exp2.samples)
newexp.origreads=exp1.origreads+exp2.origreads
newexp.scalingfactor=np.hstack([exp1.scalingfactor,exp2.scalingfactor])
newexp.fields=list(set(exp1.fields+exp2.fields))
for cfield in newexp.fields:
if cfield in exp1.fields:
continue
for csamp in exp1.samples:
newexp.smap[csamp][cfield]=missingval
for csamp in exp2.samples:
newexp.smap[csamp]={}
for cfield in newexp.fields:
if cfield in exp2.fields:
newexp.smap[csamp][cfield]=exp2.smap[csamp][cfield]
else:
newexp.smap[csamp][cfield]=missingval
for csamp in exp1.samples:
if origfieldname in exp1.fields:
cname=exp1.smap[csamp][origfieldname]
else:
cname=exp1.studyname
newexp.smap[csamp][origfieldname]=cname
for csamp in exp2.samples:
if origfieldname in exp2.fields:
cname=exp2.smap[csamp][origfieldname]
else:
cname=exp2.studyname
newexp.smap[csamp][origfieldname]=cname
if origfieldname not in newexp.fields:
newexp.fields.append(origfieldname)
newexp.filters.append('joined with %s' % exp2.studyname)
hs.addcommand(newexp,"joinexperiments",params=params,replaceparams={'exp1':exp1,'exp2':exp2})
return newexp
def filterwave(expdat,field=False,numeric=True,minfold=2,minlen=3,step=1,direction='up',posloc='start'):
"""
filter bacteria, keeping only ones that show a consecutive region of samples with higher/lower mean than other samples
Done by scanning all windowlen/startpos options for each bacteria
input:
expdat : Experiment
field : string
The field to sort by or False to skip sorting
numeric : bool
For the sorting according to field (does not matter if field is False)
minfold : float
The minimal fold change for the window compared to the rest in order to keep
step : int
The skip between tested windows (to make it faster use a larger skip)
minlen : int
The minimal window len for over/under expression testing
direction : string
'both' - test both over and under expression in the window
'up' - only overexpressed
'down' - only underexpressed
posloc : string
The position to measure the beginning ('maxstart') or middle ('maxmid') of maximal wave
or 'gstart' to use beginning of first window with >=minfold change
output:
newexp : Experiment
The filtered experiment, sorted according to window start samples position
"""
params=locals()
# sort if needed
if field:
newexp=hs.sortsamples(expdat,field,numeric=numeric)
else:
newexp=hs.copyexp(expdat)
dat=newexp.data
dat[dat<1]=1
dat=np.log2(dat)
numsamples=len(newexp.samples)
numbact=len(newexp.seqs)
maxdiff=np.zeros([numbact])
maxpos=np.zeros([numbact])-1
maxlen=np.zeros([numbact])
for startpos in range(numsamples-minlen):
for cwin in np.arange(minlen,numsamples-startpos,step):
meanin=np.mean(dat[:,startpos:startpos+cwin],axis=1)
nowin=[]
if startpos>0:
nonwin=np.arange(startpos-1)
if startpos<numsamples:
nowin=np.hstack([nowin,np.arange(startpos,numsamples-1)])
nowin=nowin.astype(int)
meanout=np.mean(dat[:,nowin],axis=1)
cdiff=meanin-meanout
if direction=='both':
cdiff=np.abs(cdiff)
elif direction=='down':
cdiff=-cdiff
if posloc=='gstart':
usepos=np.logical_and(cdiff>=minfold,maxpos==-1)
maxpos[usepos]=startpos
elif posloc=='start':
maxpos[cdiff>maxdiff]=startpos
elif posloc=='mid':
maxpos[cdiff>maxdiff]=startpos+int(cwin/2)
else:
hs.Debug('posloc nut supported %s' % posloc)
return False
maxlen[cdiff>maxdiff]=cwin
maxdiff=np.maximum(maxdiff,cdiff)
keep=np.where(maxdiff>=minfold)[0]
keeppos=maxpos[keep]
si=np.argsort(keeppos)
keep=keep[si]
for ci in keep:
hs.Debug(6,'bacteria %s startpos %d len %d diff %f' % (newexp.tax[ci],maxpos[ci],maxlen[ci],maxdiff[ci]))
newexp=hs.reorderbacteria(newexp,keep)
newexp.filters.append('Filter wave field=%s minlen=%d' % (field,minlen))
hs.addcommand(newexp,"filterwave",params=params,replaceparams={'expdat':expdat})
hs.Debug(6,'%d samples before filtering, %d after' % (len(expdat.samples),len(newexp.samples)))
return newexp
def joinexperiments(exp1,exp2,missingval='NA',origfieldname='origexp'):
"""
join 2 experiments into a new experiment. adding a new field origfieldname
input:
exp1,exp2 - the experiments to join
missingval - string to put when field not in mapping file of one of the experiments
origfieldname - name of the new field to add which contains the original experiment name
"""
params=locals()
# join the sequences of both experiments
# ASSUMING SAME SEQ LENGTH!!!!
allseqs=list(set(exp1.seqs) | set(exp2.seqs))
alldict={}
alltax=[]
allids=[]
for idx,cseq in enumerate(allseqs):
alldict[cseq]=idx
# make the new joined data for each experiment
dat1=np.zeros((len(allseqs),np.size(exp1.data,1)))
for idx,cseq in enumerate(allseqs):
if cseq in exp1.seqdict:
dat1[idx,:]=exp1.data[exp1.seqdict[cseq],:]
alltax.append(exp1.tax[exp1.seqdict[cseq]])
allids.append(exp1.sids[exp1.seqdict[cseq]])
else:
alltax.append(exp2.tax[exp2.seqdict[cseq]])
allids.append(exp2.sids[exp2.seqdict[cseq]])
dat2=np.zeros((len(allseqs),np.size(exp2.data,1)))
for idx,cseq in enumerate(allseqs):
if cseq in exp2.seqdict:
dat2[idx,:]=exp2.data[exp2.seqdict[cseq],:]
newexp=hs.copyexp(exp1)
# concatenate the reads
newexp.data=np.concatenate((dat1,dat2), axis=1)
newexp.seqdict=alldict
newexp.seqs=allseqs
newexp.tax=alltax
newexp.sids=allids
newexp.sids=newexp.seqs
newexp.samples = list(exp1.samples) + list(exp2.samples)
newexp.origreads=exp1.origreads+exp2.origreads
newexp.fields=list(set(exp1.fields+exp2.fields))
for cfield in newexp.fields:
if cfield in exp1.fields:
continue
for csamp in exp1.samples:
newexp.smap[csamp][cfield]=missingval
for csamp in exp2.samples:
newexp.smap[csamp]={}
for cfield in newexp.fields:
if cfield in exp2.fields:
newexp.smap[csamp][cfield]=exp2.smap[csamp][cfield]
else:
newexp.smap[csamp][cfield]=missingval
for csamp in exp1.samples:
newexp.smap[csamp][origfieldname]=exp1.studyname
for csamp in exp2.samples:
newexp.smap[csamp][origfieldname]=exp2.studyname
newexp.fields.append(origfieldname)
newexp.filters.append('joined with %s' % exp2.studyname)
hs.addcommand(newexp,"joinexperiments",params=params,replaceparams={'exp1':exp1,'exp2':exp2})
return newexp