__init__.py

import numpy from Bio.Cluster.cluster import * def _treesort(order, nodeorder, nodecounts, tree): # Find the order of the nodes consistent with the hierarchical clustering # tree, taking into account the preferred order of nodes. nNodes = len(tree) nElements = nNodes + 1 neworder = numpy.zeros(nElements) clusterids = numpy.arange(nElements) for i in range(nNodes): i1 = tree[i].left i2 = tree[i].right if i1 < 0: order1 = nodeorder[-i1-1] count1 = nodecounts[-i1-1] else: order1 = order[i1] count1 = 1 if i2 < 0: order2 = nodeorder[-i2-1] count2 = nodecounts[-i2-1] else: order2 = order[i2] count2 = 1 # If order1 and order2 are equal, their order is determined # by the order in which they were clustered if i1 < i2: if order1 < order2: increase = count1 else: increase = count2 for j in range(nElements): clusterid = clusterids[j] if clusterid == i1 and order1 >= order2: neworder[j] += increase if clusterid == i2 and order1 < order2: neworder[j] += increase if clusterid == i1 or clusterid == i2: clusterids[j] = -i-1 else: if order1 <= order2: increase = count1 else: increase = count2 for j in range(nElements): clusterid = clusterids[j] if clusterid == i1 and order1 > order2: neworder[j] += increase if clusterid == i2 and order1 <= order2: neworder[j] += increase if clusterid == i1 or clusterid == i2: clusterids[j] = -i-1 return numpy.argsort(neworder) def _savetree(jobname, tree, order, transpose): # Save the hierarchical clustering solution given by the tree, following # the specified order, in a file whose name is based on jobname. if transpose == 0: extension = ".gtr" keyword = "GENE" else: extension = ".atr" keyword = "ARRY" nnodes = len(tree) outputfile = open(jobname+extension, "w") nodeindex = 0 nodeID = [''] * nnodes nodecounts = numpy.zeros(nnodes, int) nodeorder = numpy.zeros(nnodes) nodedist = numpy.array([node.distance for node in tree]) for nodeindex in range(nnodes): min1 = tree[nodeindex].left min2 = tree[nodeindex].right nodeID[nodeindex] = "NODE%dX" % (nodeindex+1) outputfile.write(nodeID[nodeindex]) outputfile.write("\t") if min1 < 0: index1 = -min1-1 order1 = nodeorder[index1] counts1 = nodecounts[index1] outputfile.write(nodeID[index1]+"\t") nodedist[nodeindex] = max(nodedist[nodeindex],nodedist[index1]) else: order1 = order[min1] counts1 = 1 outputfile.write("%s%dX\t" % (keyword, min1)) if min2 < 0: index2 = -min2-1 order2 = nodeorder[index2] counts2 = nodecounts[index2] outputfile.write(nodeID[index2]+"\t") nodedist[nodeindex] = max(nodedist[nodeindex],nodedist[index2]) else: order2 = order[min2] counts2 = 1 outputfile.write("%s%dX\t" % (keyword, min2)) outputfile.write(str(1.0-nodedist[nodeindex])) outputfile.write("\n") counts = counts1 + counts2 nodecounts[nodeindex] = counts nodeorder[nodeindex] = (counts1*order1+counts2*order2) / counts outputfile.close() # Now set up order based on the tree structure index = _treesort(order, nodeorder, nodecounts, tree) return index 00111 class Record(object): """Store gene expression data. A Record stores the gene expression data and related information contained in a data file following the file format defined for Michael Eisen's Cluster/TreeView program. A Record has the following members: data: a matrix containing the gene expression data mask: a matrix containing only 1's and 0's, denoting which values are present (1) or missing (0). If all elements of mask are one (no missing data), then mask is set to None. geneid: a list containing a unique identifier for each gene (e.g., ORF name) genename: a list containing an additional description for each gene (e.g., gene name) gweight: the weight to be used for each gene when calculating the distance gorder: an array of real numbers indicating the preferred order of the genes in the output file expid: a list containing a unique identifier for each experimental condition eweight: the weight to be used for each experimental condition when calculating the distance eorder: an array of real numbers indication the preferred order in the output file of the experimental conditions uniqid: the string that was used instead of UNIQID in the input file. """ 00140 def __init__(self, handle=None): """Read gene expression data from the file handle and return a Record. The file should be in the format defined for Michael Eisen's Cluster/TreeView program. """ self.data = None self.mask = None self.geneid = None self.genename = None self.gweight = None self.gorder = None self.expid = None self.eweight = None self.eorder = None self.uniqid = None if not handle: return line = handle.readline().strip("\r\n").split("\t") n = len(line) self.uniqid = line[0] self.expid = [] cols = {0: "GENEID"} for word in line[1:]: if word == "NAME": cols[line.index(word)] = word self.genename = [] elif word == "GWEIGHT": cols[line.index(word)] = word self.gweight = [] elif word=="GORDER": cols[line.index(word)] = word self.gorder = [] else: self.expid.append(word) self.geneid = [] self.data = [] self.mask = [] needmask = 0 for line in handle: line = line.strip("\r\n").split("\t") if len(line) != n: raise ValueError("Line with %d columns found (expected %d)" % (len(line), n)) if line[0] == "EWEIGHT": i = max(cols) + 1 self.eweight = numpy.array(line[i:], float) continue if line[0] == "EORDER": i = max(cols) + 1 self.eorder = numpy.array(line[i:], float) continue rowdata = [] rowmask = [] n = len(line) for i in range(n): word = line[i] if i in cols: if cols[i] == "GENEID": self.geneid.append(word) if cols[i] == "NAME": self.genename.append(word) if cols[i] == "GWEIGHT": self.gweight.append(float(word)) if cols[i] == "GORDER": self.gorder.append(float(word)) continue if not word: rowdata.append(0.0) rowmask.append(0) needmask = 1 else: rowdata.append(float(word)) rowmask.append(1) self.data.append(rowdata) self.mask.append(rowmask) self.data = numpy.array(self.data) if needmask: self.mask = numpy.array(self.mask, int) else: self.mask = None if self.gweight: self.gweight = numpy.array(self.gweight) if self.gorder: self.gorder = numpy.array(self.gorder) 00227 def treecluster(self, transpose=0, method='m', dist='e'): """Apply hierarchical clustering and return a Tree object. The pairwise single, complete, centroid, and average linkage hierarchical clustering methods are available. transpose: if equal to 0, genes (rows) are clustered; if equal to 1, microarrays (columns) are clustered. dist : specifies the distance function to be used: dist=='e': Euclidean distance dist=='b': City Block distance dist=='c': Pearson correlation dist=='a': absolute value of the correlation dist=='u': uncentered correlation dist=='x': absolute uncentered correlation dist=='s': Spearman's rank correlation dist=='k': Kendall's tau method : specifies which linkage method is used: method=='s': Single pairwise linkage method=='m': Complete (maximum) pairwise linkage (default) method=='c': Centroid linkage method=='a': Average pairwise linkage See the description of the Tree class for more information about the Tree object returned by this method. """ if transpose == 0: weight = self.eweight else: weight = self.gweight return treecluster(self.data, self.mask, weight, transpose, method, dist) 00261 def kcluster(self, nclusters=2, transpose=0, npass=1, method='a', dist='e', initialid=None): """Apply k-means or k-median clustering. This method returns a tuple (clusterid, error, nfound). nclusters: number of clusters (the 'k' in k-means) transpose: if equal to 0, genes (rows) are clustered; if equal to 1, microarrays (columns) are clustered. npass : number of times the k-means clustering algorithm is performed, each time with a different (random) initial condition. method : specifies how the center of a cluster is found: method=='a': arithmetic mean method=='m': median dist : specifies the distance function to be used: dist=='e': Euclidean distance dist=='b': City Block distance dist=='c': Pearson correlation dist=='a': absolute value of the correlation dist=='u': uncentered correlation dist=='x': absolute uncentered correlation dist=='s': Spearman's rank correlation dist=='k': Kendall's tau initialid: the initial clustering from which the algorithm should start. If initialid is None, the routine carries out npass repetitions of the EM algorithm, each time starting from a different random initial clustering. If initialid is given, the routine carries out the EM algorithm only once, starting from the given initial clustering and without randomizing the order in which items are assigned to clusters (i.e., using the same order as in the data matrix). In that case, the k-means algorithm is fully deterministic. Return values: clusterid: array containing the number of the cluster to which each gene/microarray was assigned in the best k-means clustering solution that was found in the npass runs; error: the within-cluster sum of distances for the returned k-means clustering solution; nfound: the number of times this solution was found. """ if transpose == 0: weight = self.eweight else: weight = self.gweight return kcluster(self.data, nclusters, self.mask, weight, transpose, npass, method, dist, initialid) 00312 def somcluster(self, transpose=0, nxgrid=2, nygrid=1, inittau=0.02, niter=1, dist='e'): """Calculate a self-organizing map on a rectangular grid. The somcluster method returns a tuple (clusterid, celldata). transpose: if equal to 0, genes (rows) are clustered; if equal to 1, microarrays (columns) are clustered. nxgrid : the horizontal dimension of the rectangular SOM map nygrid : the vertical dimension of the rectangular SOM map inittau : the initial value of tau (the neighborbood function) niter : the number of iterations dist : specifies the distance function to be used: dist=='e': Euclidean distance dist=='b': City Block distance dist=='c': Pearson correlation dist=='a': absolute value of the correlation dist=='u': uncentered correlation dist=='x': absolute uncentered correlation dist=='s': Spearman's rank correlation dist=='k': Kendall's tau Return values: clusterid: array with two columns, while the number of rows is equal to the number of genes or the number of microarrays depending on whether genes or microarrays are being clustered. Each row in the array contains the x and y coordinates of the cell in the rectangular SOM grid to which the gene or microarray was assigned. celldata: an array with dimensions (nxgrid, nygrid, number of microarrays) if genes are being clustered, or (nxgrid, nygrid, number of genes) if microarrays are being clustered. Each element [ix][iy] of this array is a 1D vector containing the gene expression data for the centroid of the cluster in the SOM grid cell with coordinates (ix, iy). """ if transpose == 0: weight = self.eweight else: weight = self.gweight return somcluster(self.data, self.mask, weight, transpose, nxgrid, nygrid, inittau, niter, dist) 00357 def clustercentroids(self, clusterid=None, method='a', transpose=0): """Calculate the cluster centroids and return a tuple (cdata, cmask). The centroid is defined as either the mean or the median over all elements for each dimension. data : nrows x ncolumns array containing the expression data mask : nrows x ncolumns array of integers, showing which data are missing. If mask[i][j]==0, then data[i][j] is missing. transpose: if equal to 0, gene (row) clusters are considered; if equal to 1, microarray (column) clusters are considered. clusterid: array containing the cluster number for each gene or microarray. The cluster number should be non-negative. method : specifies how the centroid is calculated: method=='a': arithmetic mean over each dimension. (default) method=='m': median over each dimension. Return values: cdata : 2D array containing the cluster centroids. If transpose==0, then the dimensions of cdata are nclusters x ncolumns. If transpose==1, then the dimensions of cdata are nrows x nclusters. cmask : 2D array of integers describing which elements in cdata, if any, are missing. """ return clustercentroids(self.data, self.mask, clusterid, method, transpose) 00386 def clusterdistance(self, index1=[0], index2=[0], method='a', dist='e', transpose=0): """Calculate the distance between two clusters. index1 : 1D array identifying which genes/microarrays belong to the first cluster. If the cluster contains only one gene, then index1 can also be written as a single integer. index2 : 1D array identifying which genes/microarrays belong to the second cluster. If the cluster contains only one gene, then index2 can also be written as a single integer. transpose: if equal to 0, genes (rows) are clustered; if equal to 1, microarrays (columns) are clustered. dist : specifies the distance function to be used: dist=='e': Euclidean distance dist=='b': City Block distance dist=='c': Pearson correlation dist=='a': absolute value of the correlation dist=='u': uncentered correlation dist=='x': absolute uncentered correlation dist=='s': Spearman's rank correlation dist=='k': Kendall's tau method : specifies how the distance between two clusters is defined: method=='a': the distance between the arithmetic means of the two clusters method=='m': the distance between the medians of the two clusters method=='s': the smallest pairwise distance between members of the two clusters method=='x': the largest pairwise distance between members of the two clusters method=='v': average of the pairwise distances between members of the clusters transpose: if equal to 0: clusters of genes (rows) are considered; if equal to 1: clusters of microarrays (columns) are considered. """ if transpose == 0: weight = self.eweight else: weight = self.gweight return clusterdistance(self.data, self.mask, weight, index1, index2, method, dist, transpose) 00431 def distancematrix(self, transpose=0, dist='e'): """Calculate the distance matrix and return it as a list of arrays transpose: if equal to 0: calculate the distances between genes (rows); if equal to 1: calculate the distances beteeen microarrays (columns). dist : specifies the distance function to be used: dist=='e': Euclidean distance dist=='b': City Block distance dist=='c': Pearson correlation dist=='a': absolute value of the correlation dist=='u': uncentered correlation dist=='x': absolute uncentered correlation dist=='s': Spearman's rank correlation dist=='k': Kendall's tau Return value: The distance matrix is returned as a list of 1D arrays containing the distance matrix between the gene expression data. The number of columns in each row is equal to the row number. Hence, the first row has zero elements. An example of the return value is matrix = [[], array([1.]), array([7., 3.]), array([4., 2., 6.])] This corresponds to the distance matrix [0., 1., 7., 4.] [1., 0., 3., 2.] [7., 3., 0., 6.] [4., 2., 6., 0.] """ if transpose == 0: weight = self.eweight else: weight = self.gweight return distancematrix(self.data, self.mask, weight, transpose, dist) 00469 def save(self, jobname, geneclusters=None, expclusters=None): """Save the clustering results. The saved files follow the convention for the Java TreeView program, which can therefore be used to view the clustering result. Arguments: jobname: The base name of the files to be saved. The filenames are jobname.cdt, jobname.gtr, and jobname.atr for hierarchical clustering, and jobname-K*.cdt, jobname-K*.kgg, jobname-K*.kag for k-means clustering results. geneclusters=None: For hierarchical clustering results, geneclusters is a Tree object as returned by the treecluster method. For k-means clustering results, geneclusters is a vector containing ngenes integers, describing to which cluster a given gene belongs. This vector can be calculated by kcluster. expclusters=None: For hierarchical clustering results, expclusters is a Tree object as returned by the treecluster method. For k-means clustering results, expclusters is a vector containing nexps integers, describing to which cluster a given experimental condition belongs. This vector can be calculated by kcluster. """ (ngenes,nexps) = numpy.shape(self.data) if self.gorder == None: gorder = numpy.arange(ngenes) else: gorder = self.gorder if self.eorder == None: eorder = numpy.arange(nexps) else: eorder = self.eorder if geneclusters!=None and expclusters!=None and \ type(geneclusters) != type(expclusters): raise ValueError("found one k-means and one hierarchical " + "clustering solution in geneclusters and " + "expclusters") gid = 0 aid = 0 filename = jobname postfix = "" if type(geneclusters) == Tree: # This is a hierarchical clustering result. geneindex = _savetree(jobname, geneclusters, gorder, 0) gid = 1 elif geneclusters!=None: # This is a k-means clustering result. filename = jobname + "_K" k = max(geneclusters+1) kggfilename = "%s_K_G%d.kgg" % (jobname, k) geneindex = self._savekmeans(kggfilename, geneclusters, gorder, 0) postfix = "_G%d" % k else: geneindex = numpy.argsort(gorder) if type(expclusters) == Tree: # This is a hierarchical clustering result. expindex = _savetree(jobname, expclusters, eorder, 1) aid = 1 elif expclusters!=None: # This is a k-means clustering result. filename = jobname + "_K" k = max(expclusters+1) kagfilename = "%s_K_A%d.kag" % (jobname, k) expindex = self._savekmeans(kagfilename, expclusters, eorder, 1) postfix += "_A%d" % k else: expindex = numpy.argsort(eorder) filename = filename + postfix self._savedata(filename,gid,aid,geneindex,expindex) def _savekmeans(self, filename, clusterids, order, transpose): # Save a k-means clustering solution if transpose == 0: label = self.uniqid names = self.geneid else: label = "ARRAY" names = self.expid try: outputfile = open(filename, "w") except IOError: raise IOError("Unable to open output file") outputfile.write(label + "\tGROUP\n") index = numpy.argsort(order) n = len(names) sortedindex = numpy.zeros(n, int) counter = 0 cluster = 0 while counter < n: for j in index: if clusterids[j] == cluster: outputfile.write("%s\t%s\n" % (names[j], cluster)) sortedindex[counter] = j counter += 1 cluster += 1 outputfile.close() return sortedindex def _savedata(self, jobname, gid, aid, geneindex, expindex): # Save the clustered data. if self.genename == None: genename = self.geneid else: genename = self.genename (ngenes, nexps) = numpy.shape(self.data) try: outputfile = open(jobname+'.cdt', 'w') except IOError: raise IOError("Unable to open output file") if self.mask!=None: mask = self.mask else: mask = numpy.ones((ngenes,nexps), int) if self.gweight!=None: gweight = self.gweight else: gweight = numpy.ones(ngenes) if self.eweight!=None: eweight = self.eweight else: eweight = numpy.ones(nexps) if gid: outputfile.write('GID\t') outputfile.write(self.uniqid) outputfile.write('\tNAME\tGWEIGHT') # Now add headers for data columns. for j in expindex: outputfile.write('\t%s' % self.expid[j]) outputfile.write('\n') if aid: outputfile.write("AID") if gid: outputfile.write('\t') outputfile.write("\t\t") for j in expindex: outputfile.write('\tARRY%dX' % j) outputfile.write('\n') outputfile.write('EWEIGHT') if gid: outputfile.write('\t') outputfile.write('\t\t') for j in expindex: outputfile.write('\t%f' % eweight[j]) outputfile.write('\n') for i in geneindex: if gid: outputfile.write('GENE%dX\t' % i) outputfile.write("%s\t%s\t%f" % (self.geneid[i], genename[i], gweight[i])) for j in expindex: outputfile.write('\t') if mask[i,j]: outputfile.write(str(self.data[i,j])) outputfile.write('\n') outputfile.close() def read(handle): """Read gene expression data from the file handle and return a Record. The file should be in the file format defined for Michael Eisen's Cluster/TreeView program. """ return Record(handle)

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