import gtk import system.workflow as wf from system.workflow import Stage, Function import pickle from scipy import log2,transpose,dot,divide,shape,mean,resize,zeros from scipy.linalg import svd,inv,norm,get_blas_funcs,eig from system import dataset, logger, plots class PCAWorkflow(wf.Workflow): name = 'PCA Workflow' ident = 'pca' description = 'PCA workflow. Uses real microarray data from a study of diabetes (Mootha et al.).' def __init__(self, app): wf.Workflow.__init__(self, app) #self.add_project(app.project) #logger.log('notice','Current project added to: %s' %self.name) load = Stage('load', 'Load Data') load.add_function(LoadMoothaData()) self.add_stage(load) preproc = Stage('preprocess', 'Preprocessing') preproc.add_function(Log2Function()) self.add_stage(preproc) annot = Stage('annot', 'Affy annotations') annot.add_function(LoadAnnotationsFunction()) self.add_stage(annot) model = Stage('model', 'Model') model.add_function(PCAFunction(self)) self.add_stage(model) logger.log('debug', '\tPCA\'s workflow is now active') class LoadAnnotationsFunction(Function): def __init__(self): Function.__init__(self, 'load', 'Load Annotations') 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() ### Reading and parsing here annot = read_affy_annot(self.file) 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: pathways = description[i_want] if not pathways[0][0]=='--': pass D = [] return [D] class PCAFunction(Function): def __init__(self,workflow): Function.__init__(self, 'pca', 'PCA') self.output = None self.workflow = workflow def run(self,data): logger.log('debug', 'datatype: %s' % type(data)) if not isinstance(data,dataset.Dataset): return None #logger.log('debug', 'dimensions: %s' % data.dims) ## calculations 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: row_ids = data.get_identifiers('genes') col_ids = data.get_identifiers('samples') T = dataset.Dataset(T,[('samples',col_ids) ,comp_def],name='T2') P = dataset.Dataset(P,[('genes',row_ids),comp_def],name='P') E = dataset.Dataset(E,[('samples',col_ids),('genes',row_ids)],name='E') #tsq = dataset.Dataset(tsq,[singel_def,data_ids[1]) ## plots loading_plot1 = plots.ScatterPlot(P,P,'genes','comp','1','2') loading_plot2 = plots.ScatterPlot(P,P,'genes','comp','3','4') score_plot = plots.ScatterPlot(T,T,'samples','comp','1','2') return [T,P,E,loading_plot1,loading_plot2,score_plot] 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 """ 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 def mat_center(self,X,axis=0,ret_mn=False): """Mean center matrix along axis. 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" if axis==0: mnX = mean(X,axis) Xs = X - resize(mnX,(rows,cols)) 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()) gene_ids = [] x = zeros((nSamps,nVars),typecode) for i,(id,desc) in enumerate(data.items()): gene_ids.append(id) x[:,i] = desc[0].astype(typecode) gene_def = ('genes',gene_ids) sample_def = ('samples', sample_names) X = dataset.Dataset(x,identifiers=[sample_def,gene_def]) # samples x genes return [X] class Log2Function(Function): def __init__(self): Function.__init__(self, 'log', 'Log2') def run(self,data): x = log2(data._array) #pull out identifiers ids = [] for dim in data.get_dim_names(): ids.append((dim,data.get_identifiers(dim))) return [dataset.Dataset(x,identifiers=ids,name='Log2_X')] PCAWorkflow.name = 'PCA Workflow'