# This code is part of the Biopython distribution and governed by its # license. Please see the LICENSE file that should have been included # as part of this package. # 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) with open(jobname + extension, "w") as outputfile: 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 # Now set up order based on the tree structure index = _treesort(order, nodeorder, nodecounts, tree) return index 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. """ 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) 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) 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) 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) 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) 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) 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) 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 is None: gorder = numpy.arange(ngenes) else: gorder = self.gorder if self.eorder is None: eorder = numpy.arange(nexps) else: eorder = self.eorder if geneclusters is not None and expclusters is not 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 isinstance(geneclusters, Tree): # This is a hierarchical clustering result. geneindex = _savetree(jobname, geneclusters, gorder, 0) gid = 1 elif geneclusters is not 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 isinstance(expclusters, Tree): # This is a hierarchical clustering result. expindex = _savetree(jobname, expclusters, eorder, 1) aid = 1 elif expclusters is not 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 with open(filename, "w") as outputfile: 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 return sortedindex def _savedata(self, jobname, gid, aid, geneindex, expindex): # Save the clustered data. if self.genename is None: genename = self.geneid else: genename = self.genename (ngenes, nexps) = numpy.shape(self.data) with open(jobname + '.cdt', 'w') as outputfile: if self.mask is not None: mask = self.mask else: mask = numpy.ones((ngenes, nexps), int) if self.gweight is not None: gweight = self.gweight else: gweight = numpy.ones(ngenes) if self.eweight is not 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') 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)