2006-04-20 17:30:29 +02:00
|
|
|
import gtk
|
|
|
|
import logger
|
2006-04-24 18:05:54 +02:00
|
|
|
import workflow as wf
|
|
|
|
from workflow import Stage, Function
|
2006-04-24 11:53:07 +02:00
|
|
|
import pickle
|
|
|
|
from scipy import log2,transpose,dot,divide,shape,mean,resize,zeros
|
|
|
|
from scipy.linalg import svd,inv,norm,get_blas_funcs,eig
|
2006-04-20 17:30:29 +02:00
|
|
|
import plots
|
2006-04-21 11:23:05 +02:00
|
|
|
import dataset
|
2006-04-20 17:30:29 +02:00
|
|
|
|
2006-04-24 18:05:54 +02:00
|
|
|
class PCAWorkflow(wf.Workflow):
|
2006-04-24 11:53:07 +02:00
|
|
|
name = 'PCA Workflow'
|
|
|
|
description = 'PCA workflow. Uses real microarray data from a study of diabetes (Mootha et al.).'
|
2006-04-20 17:30:29 +02:00
|
|
|
|
|
|
|
def __init__(self, app):
|
2006-04-24 18:05:54 +02:00
|
|
|
wf.Workflow.__init__(self, app)
|
2006-04-24 11:53:07 +02:00
|
|
|
#self.add_project(app.project)
|
|
|
|
#logger.log('notice','Current project added to: %s' %self.name)
|
|
|
|
|
2006-04-20 17:30:29 +02:00
|
|
|
load = Stage('load', 'Load Data')
|
2006-04-21 11:23:05 +02:00
|
|
|
load.add_function(LoadMoothaData())
|
2006-04-20 17:30:29 +02:00
|
|
|
self.add_stage(load)
|
|
|
|
|
|
|
|
preproc = Stage('preprocess', 'Preprocessing')
|
2006-04-24 11:53:07 +02:00
|
|
|
preproc.add_function(Log2Function())
|
2006-04-20 17:30:29 +02:00
|
|
|
self.add_stage(preproc)
|
|
|
|
|
|
|
|
annot = Stage('annot', 'Affy annotations')
|
|
|
|
annot.add_function(LoadAnnotationsFunction())
|
|
|
|
self.add_stage(annot)
|
|
|
|
|
|
|
|
model = Stage('model', 'Model')
|
2006-04-24 11:53:07 +02:00
|
|
|
model.add_function(PCAFunction(self))
|
2006-04-20 17:30:29 +02:00
|
|
|
self.add_stage(model)
|
2006-04-24 11:53:07 +02:00
|
|
|
|
2006-04-20 17:30:29 +02:00
|
|
|
logger.log('debug', '\tPCA\'s workflow is now active')
|
|
|
|
|
|
|
|
class LoadAnnotationsFunction(Function):
|
|
|
|
|
|
|
|
def __init__(self):
|
|
|
|
Function.__init__(self, 'load', 'Load Annotations')
|
2006-04-24 11:53:07 +02:00
|
|
|
|
2006-04-20 17:30:29 +02:00
|
|
|
def load_affy_file(self, filename):
|
|
|
|
f = open(filename)
|
|
|
|
logger.log('notice', 'Loading annotation file: %s' % filename)
|
|
|
|
self.file = f
|
|
|
|
|
|
|
|
def on_response(self, dialog, response):
|
|
|
|
if response == gtk.RESPONSE_OK:
|
|
|
|
logger.log('notice', 'Reading file: %s' % dialog.get_filename())
|
|
|
|
self.load_affy_file(dialog.get_filename())
|
|
|
|
|
|
|
|
def run(self, data):
|
|
|
|
btns = ('Open', gtk.RESPONSE_OK, \
|
|
|
|
'Cancel', gtk.RESPONSE_CANCEL)
|
|
|
|
dialog = gtk.FileChooserDialog('Open Affy Annotation File',
|
|
|
|
buttons=btns)
|
|
|
|
dialog.connect('response', self.on_response)
|
|
|
|
dialog.run()
|
|
|
|
dialog.destroy()
|
|
|
|
|
2006-04-21 10:29:43 +02:00
|
|
|
### Reading and parsing here
|
2006-04-20 17:30:29 +02:00
|
|
|
annot = read_affy_annot(self.file)
|
2006-04-21 10:29:43 +02:00
|
|
|
i_want = 'Pathway'
|
|
|
|
nothing = '---'
|
|
|
|
ids_in_data = set(data.names('genes')) #assuming we have genes
|
|
|
|
sanity_check = set(annot.keys())
|
|
|
|
if not ids_in_data.intersection(sanity_check) == ids_in_data:
|
|
|
|
logger.log('debug','Some identifers in data does not exist in affy file!')
|
|
|
|
for affy_id,description in annot:
|
|
|
|
if affy_id in ids_in_data:
|
2006-04-21 10:34:08 +02:00
|
|
|
pathways = description[i_want]
|
|
|
|
if not pathways[0][0]=='--':
|
|
|
|
pass
|
2006-04-24 11:53:07 +02:00
|
|
|
D = []
|
|
|
|
return [D]
|
2006-04-20 17:30:29 +02:00
|
|
|
|
|
|
|
class PCAFunction(Function):
|
|
|
|
|
2006-04-24 11:53:07 +02:00
|
|
|
def __init__(self,workflow):
|
|
|
|
Function.__init__(self, 'pca', 'PCA')
|
2006-04-20 17:30:29 +02:00
|
|
|
self.output = None
|
2006-04-24 11:53:07 +02:00
|
|
|
self.workflow = workflow
|
|
|
|
|
2006-04-20 17:30:29 +02:00
|
|
|
def run(self, data):
|
|
|
|
logger.log('debug', 'datatype: %s' % type(data))
|
|
|
|
if not isinstance(data,dataset.Dataset):
|
|
|
|
return None
|
2006-04-24 11:53:07 +02:00
|
|
|
#logger.log('debug', 'dimensions: %s' % data.dims)
|
2006-04-20 17:30:29 +02:00
|
|
|
|
|
|
|
## calculations
|
2006-04-24 11:53:07 +02:00
|
|
|
T,P,E,tsq = self.pca(data._array,5,tsq_loads=False)
|
|
|
|
comp_def = ('comp',('1','2','3','4','5'))
|
|
|
|
singel_def = ('1',('s'))
|
|
|
|
|
|
|
|
# pull out input identifiers:
|
2006-04-24 13:23:30 +02:00
|
|
|
row_ids = data.get_identifiers('genes')
|
|
|
|
col_ids = data.get_identifiers('samples')
|
2006-04-24 11:53:07 +02:00
|
|
|
|
2006-04-24 13:23:30 +02:00
|
|
|
T = dataset.Dataset(T,[('samples',col_ids) ,comp_def])
|
|
|
|
P = dataset.Dataset(P,[('genes',row_ids),comp_def])
|
2006-04-24 14:51:43 +02:00
|
|
|
E = dataset.Dataset(E,[('samples',col_ids),('genes',row_ids)])
|
2006-04-24 11:53:07 +02:00
|
|
|
#tsq = dataset.Dataset(tsq,[singel_def,data_ids[1])
|
2006-04-20 17:30:29 +02:00
|
|
|
|
|
|
|
## plots
|
2006-04-24 16:42:45 +02:00
|
|
|
loading_plot1 = plots.ScatterPlot(P,'genes','comp','1','2')
|
|
|
|
loading_plot2 = plots.ScatterPlot(P,'genes','comp','3','4')
|
|
|
|
score_plot = plots.ScatterPlot(T,'samples','comp','1','2')
|
2006-04-20 17:30:29 +02:00
|
|
|
|
2006-04-24 11:53:07 +02:00
|
|
|
return [T,P,E,loading_plot1,loading_plot2,score_plot]
|
2006-04-20 17:30:29 +02:00
|
|
|
|
2006-04-24 11:53:07 +02:00
|
|
|
def pca(self,X,a_opt,cent=True,scale='loads',tsq_loads=False):
|
|
|
|
"""Principal component analysis
|
|
|
|
|
|
|
|
input:
|
|
|
|
Xc -- matrix, data
|
|
|
|
a_opt -- scalar, max number of comp. to calculate
|
|
|
|
cent -- bool, centering [True]
|
|
|
|
crit -- string, pc criteria ['exp_var',['ief','rpv','average']]
|
|
|
|
scale -- string, scaling ['loads',['scores']]
|
|
|
|
tsq_loads -- bool, calculate t-squared? [True]
|
|
|
|
reg -- float, covariance regularizer for tsq calculations [0.2]
|
|
|
|
output:
|
|
|
|
T,P,E,r
|
2006-04-20 17:30:29 +02:00
|
|
|
|
2006-04-24 11:53:07 +02:00
|
|
|
"""
|
|
|
|
nSamples,nVarX = shape(X)
|
|
|
|
if cent:
|
|
|
|
Xc = self.mat_center(X)
|
|
|
|
else:
|
|
|
|
Xc = X
|
|
|
|
u,s,vh = self.esvd(Xc)
|
|
|
|
if scale=='scores':
|
|
|
|
T = u*s
|
|
|
|
T = T[:,:a_opt]
|
|
|
|
P = transpose(vh)
|
|
|
|
P = P[:,:a_opt]
|
|
|
|
elif scale=='loads':
|
|
|
|
T = u
|
|
|
|
T = T[:,:a_opt]
|
|
|
|
P = transpose(vh)*s
|
|
|
|
P = P[:,:a_opt]
|
|
|
|
|
|
|
|
E = Xc - dot(T,transpose(P))
|
|
|
|
varEach = s**2
|
|
|
|
totVar = sum(varEach)
|
|
|
|
r = divide(varEach,totVar)*100
|
|
|
|
return T,P,E,r
|
2006-04-21 10:30:37 +02:00
|
|
|
|
2006-04-24 11:53:07 +02:00
|
|
|
def mat_center(self,X,axis=0,ret_mn=False):
|
|
|
|
"""Mean center matrix along axis.
|
2006-04-21 10:30:37 +02:00
|
|
|
|
2006-04-24 11:53:07 +02:00
|
|
|
input:
|
|
|
|
X -- matrix, data
|
|
|
|
axis -- dim,
|
|
|
|
ret_mn -- bool, return mean
|
|
|
|
output:
|
|
|
|
Xc, [mnX]
|
|
|
|
|
|
|
|
NB: axis = 1 is column-centering, axis=0=row-centering
|
|
|
|
default is row centering (axis=0)
|
|
|
|
"""
|
|
|
|
try:
|
|
|
|
rows,cols = shape(X)
|
|
|
|
except ValueError:
|
|
|
|
print "The X data needs to be two-dimensional"
|
2006-04-21 10:30:37 +02:00
|
|
|
|
2006-04-24 11:53:07 +02:00
|
|
|
if axis==0:
|
|
|
|
mnX = mean(X,axis)
|
|
|
|
Xs = X - resize(mnX,(rows,cols))
|
2006-04-21 10:30:37 +02:00
|
|
|
|
2006-04-24 11:53:07 +02:00
|
|
|
elif axis==1:
|
|
|
|
mnX = mean(X,axis)
|
|
|
|
Xs = transpose(transpose(X) - resize(mnX,(cols,rows)))
|
|
|
|
if ret_mn:
|
|
|
|
return Xs,mnX
|
|
|
|
else:
|
|
|
|
return Xs
|
|
|
|
|
|
|
|
def esvd(self,data,economy=1):
|
|
|
|
"""SVD with the option of economy sized calculation
|
|
|
|
Calculate subspaces of X'X or XX' depending on the shape
|
|
|
|
of the matrix.
|
|
|
|
Good for extreme fat or thin matrices.
|
|
|
|
|
|
|
|
"""
|
|
|
|
mm = self.mm
|
|
|
|
m,n = shape(data)
|
|
|
|
if m>=n:
|
|
|
|
u,s,v = svd(mm(data,data,trans_a=1))
|
|
|
|
u = mm(data,v,trans_b=1)
|
|
|
|
for i in xrange(n):
|
|
|
|
s[i] = norm(u[:,i])
|
|
|
|
u[:,i] = u[:,i]/s[i]
|
|
|
|
else:
|
|
|
|
u,s,v = svd(mm(data,data,trans_b=1))
|
|
|
|
v = mm(u,data,trans_a=1)
|
|
|
|
for i in xrange(m):
|
|
|
|
s[i] = norm(v[i,:])
|
|
|
|
v[i,:] = v[i,:]/s[i]
|
|
|
|
return u,s,v
|
|
|
|
|
|
|
|
def mm(self,a,b, alpha=1.0, beta=0.0, c=None, trans_a=0,
|
|
|
|
trans_b=0):
|
|
|
|
"""Fast matrix multiplication
|
|
|
|
|
|
|
|
Return alpha*(a*b) + beta*c.
|
|
|
|
|
|
|
|
a,b,c : matrices
|
|
|
|
alpha, beta: scalars
|
|
|
|
trans_a : 0 (a not transposed),
|
|
|
|
1 (a transposed),
|
|
|
|
2 (a conjugate transposed)
|
|
|
|
trans_b : 0 (b not transposed),
|
|
|
|
1 (b transposed),
|
|
|
|
2 (b conjugate transposed)
|
|
|
|
"""
|
|
|
|
if c:
|
|
|
|
gemm,= get_blas_funcs(('gemm',),(a,b,c))
|
|
|
|
else:
|
|
|
|
gemm,= get_blas_funcs(('gemm',),(a,b))
|
|
|
|
|
|
|
|
return gemm(alpha, a, b, beta, c, trans_a, trans_b)
|
|
|
|
|
|
|
|
|
|
|
|
class LoadMoothaData(Function):
|
|
|
|
def __init__(self):
|
|
|
|
Function.__init__(self, 'load', 'Load diabetes data')
|
|
|
|
|
|
|
|
def run(self,data):
|
|
|
|
data_file = open('full_data.pickle','r')
|
|
|
|
data = pickle.load(data_file)
|
|
|
|
data_file.close()
|
|
|
|
sample_file = open('sample_labels.pickle','r')
|
|
|
|
sample_names = pickle.load(sample_file)
|
|
|
|
sample_file.close()
|
|
|
|
typecode='f'
|
|
|
|
nSamps = len(sample_names)
|
|
|
|
nVars = len(data.keys())
|
2006-04-21 11:23:05 +02:00
|
|
|
gene_ids = []
|
2006-04-24 11:53:07 +02:00
|
|
|
x = zeros((nSamps,nVars),typecode)
|
|
|
|
for i,(id,desc) in enumerate(data.items()):
|
2006-04-21 11:23:05 +02:00
|
|
|
gene_ids.append(id)
|
|
|
|
x[:,i] = desc[0].astype(typecode)
|
2006-04-24 11:53:07 +02:00
|
|
|
gene_def = ('genes',gene_ids)
|
|
|
|
sample_def = ('samples', sample_names)
|
|
|
|
X = dataset.Dataset(x,identifiers=[sample_def,gene_def]) # samples x genes
|
2006-04-21 11:23:05 +02:00
|
|
|
return [X]
|
2006-04-22 23:46:44 +02:00
|
|
|
|
2006-04-24 11:53:07 +02:00
|
|
|
class Log2Function(Function):
|
|
|
|
def __init__(self):
|
|
|
|
Function.__init__(self, 'log', 'Log2')
|
|
|
|
|
|
|
|
def run(self,data):
|
|
|
|
x = log2(data._array)
|
2006-04-24 14:51:43 +02:00
|
|
|
#pull out identifiers
|
|
|
|
ids = []
|
|
|
|
for dim in data.get_dim_names():
|
|
|
|
ids.append((dim,data.get_identifiers(dim)))
|
2006-04-24 11:53:07 +02:00
|
|
|
return [dataset.Dataset(x,identifiers=ids,name='Log2_X')]
|
2006-04-22 23:46:44 +02:00
|
|
|
PCAWorkflow.name = 'PCA Workflow'
|