def write_MOHGP_results(organ, strain, path, fit, geneID): """ A helper function akin to 'write_gene_model_results' to save MOHGP results to disk Arguments ========= organ - Blood/Spleen strain - AS/CB path - a structured dictionary of paths returned by config fit - a Mixture of Hierarchical Gaussian Process model geneID - a pandas data frame containing ordered probeID/geneSymbols NOTE: this is different from probesToCluster order Returns ========= None - data saved to disk """ # Extract the cluster assigned to each probe clustNum = np.argmax(fit.phi, axis=1) + 1 # cluster number clustName = [strain + '_' + organ[:2] + '_%02d' % i for i in clustNum] # cluster name geneID['Cluster'] = clustName # add to data frame # Extract the gene and probe list geneList = []; probeList = []; header = [] for name in np.unique(clustName): bWant = geneID['Cluster'] == name geneList.append(list(geneID.loc[bWant, 'Symbol'])) probeList.append(list(geneID.loc[bWant, 'ProbeID'])) header.append(name) # Save to disk io.write_list_to_csv(os.path.join(path['Clust']['GeneList'], organ + strain + '.csv'), header, geneList) io.write_list_to_csv(os.path.join(path['Clust']['ProbeList'], organ + strain + '.csv'), header, probeList) # Probe list # Save model and standard plot io.save_pickle(os.path.join(path['Clust']['Model'], organ + strain + ".pickle"), fit) io.save_pdf(os.path.join(path['Clust']['Plot'], organ + strain + ".pdf"), standard_plot(fit)) # Compute cluster predictions for xTest where xTest is taken from SmoothExprs data = pd.read_csv(os.path.join(path['GPFit']['SmoothExprs'], organ + strain + ".csv")) xTest = data.drop(['ProbeID', 'Symbol'], axis=1).columns.values.astype('float64')[:, None] mu, var = fit.predict_components(xTest) # Compute posterior mean and posterior variance # Write to disk (mu row ordering is biggest to smallest cluster) df = pd.DataFrame(data=np.array(mu), columns=list(map(str, xTest.flatten()))) df['Cluster'] = header # header = cluster name df.to_csv(os.path.join(path['Clust']['Centres'], organ + strain + '.csv'), index=False) clustCentre = df # for readability # Merge smooth expression data frame with gene ID smoothExprs = pd.merge(geneID, data, how='left', on=['ProbeID', 'Symbol']) # Produce alternate plot hFig = alternate_plot(smoothExprs, clustCentre, config.COL[organ]) io.save_pdf(os.path.join(path['Clust']['Plot'], organ + strain + '2.pdf'), hFig)
def fit_plot_save(k, smoothExprs, day, probeID, geneSymbol, organ, strain, path): """ Fit k-means, plot and save results Arguments ========= k - no. of clusters smoothExprs - gene expression rows = genes, columns = day day - day probeID - probeID geneSymbol - geneSymbol path - path Returns ========= None - results are plotted and saved """ model = KMeans(n_clusters=k) model.fit(smoothExprs) clustCentre = model.cluster_centers_ # Plot results plot_silhouette(silhouette_samples(smoothExprs, model.labels_), model.labels_) clust.multi_plot(smoothExprs, clustCentre, day, model.labels_) # Hierarchical clustering # Ward + Euclidean header = ["Cluster%i" % label for label in np.unique(model.labels_)] hclust = hc.linkage(clustCentre, method='ward', metric='euclidean') plt.figure(); plt.title("Hclust() Ward + Euclidean") hc.dendrogram(hclust, color_threshold=0.0, labels=header) #seed=101 #embedding = tsne.tsne(smoothExprs, no_dims = 3, initial_dims = 20, perplexity = 30.0, seed=seed) # low dimensional embedding #tsne.plot(embedding, model.labels_) # Save model io.save_pickle(os.path.join(path['Clust']['Model'], organ + strain + ".pickle"), model) # Save Gene/Probe List geneList = clust.get_gene_list(model.labels_, geneSymbol) probeList = clust.get_gene_list(model.labels_, probeID) io.write_list_to_csv(os.path.join(path['Clust']['GeneList'], organ + strain + ".csv"), header, geneList) # Gene list io.write_list_to_csv(os.path.join(path['Clust']['ProbeList'], organ + strain + ".csv"), header, probeList) # Probe list # Save Cluster "centres" dataMatrix = np.hstack((np.array(header)[:, None], clustCentre)) header = list(itertools.chain.from_iterable([["Cluster"], list(day)])) io.write_to_csv(os.path.join(path['Clust']['Centres'], organ + strain + ".csv"), header, dataMatrix) # Cluster "centres" # Save Alternate plot hFig = clust.multi_plot(smoothExprs, clustCentre, day, model.labels_) io.save_pdf(os.path.join(path['Clust']['Plot'], organ + strain + "2.pdf"), hFig) # Plot
def MOHGP(probesToCluster, organ, strain, prefix, K, alpha, path, seed=0): """ Word cloud plot for all clusters in a dataset Arguments ========= probesToCluster - a set of unique probeIDs to cluster organ - blood/spleen strain - AS/CB prefix - all/common/only K - init no. of clusters alpha - concentration parameter/strength parameter of the Dirichlet Process Prior path - dictionary with all results paths seed - to reproduce results due to multiple local optima Returns ========= None - a Mixture of Hierarchical Gaussian Process model is fitted and saved to disk """ # To reproduce results np.random.seed(seed) # Load gene expression data data = pandas.read_csv(os.path.join(path['RawData']['Log2FC'], organ + strain + ".csv"), sep=",") # read data probeID = np.array(data['ProbeID']) yTrain = data.values[:, 2:].astype('float') # 45,281 genes x S samples xTrain = np.floor(data.columns.values[2:].astype('float64'))[:, None] # floor to get int 0, 0, 2, 2, ..., 12 # Subset the data by keeping only probesToCluster bWant = np.array([probeID[i] in probesToCluster for i in xrange(len(probeID))]) # simply creates a vector of T, F, T yTrain = yTrain[bWant, :] probeID = np.array(data['ProbeID'][bWant]) geneSymbol = np.array(data['GeneSymbol'][bWant]) # MOHGP fitting # Define the covariance functions for the hierarchical GP structure # The model of any cluster of genes has a hierarchical structure, with the unknown cluster-specific # mean drawn from a GP, and then each gene in that cluster being drawn from a GP with said unknown mean function. # Covariance function for the latent function that describes EACH cluster. covFunCluster = GPy.kern.RBF(input_dim=1, variance=np.var(yTrain.ravel()), lengthscale=LENGTHSCALE) # Covariance function that describes how EACH time-course (gene) deviates from the cluster covFunGene = GPy.kern.RBF(input_dim=1, variance=np.var(yTrain.ravel())/10, lengthscale=LENGTHSCALE) + \ GPy.kern.White(1, variance=NOISE_VARIANCE) # Set-up the clustering problem NB: For large alpha P resembles Po (i.e the base distribution) fit = GPclust.MOHGP(X=xTrain, kernF=covFunCluster, kernY=covFunGene, Y=yTrain, K=K, prior_Z='DP', alpha=alpha) # Constrain lengthscales (to avoid very short lengthscales as per Topa et al. (2012) on arXiv) fit.rbf.lengthscale.constrain_bounded(LOWER_BOUND_LENGTHSCALE, UPPER_BOUND_LENGTHSCALE , warning=False) fit.add.rbf.lengthscale.constrain_bounded(LOWER_BOUND_LENGTHSCALE, UPPER_BOUND_LENGTHSCALE , warning=False) fit.hyperparam_opt_interval = 1000 # how often to optimize the hyperparameters # Optimise hyperparameters fit.optimize() fit.systematic_splits(verbose=False) # Name and reorder fit fit.name = prefix + organ + strain fit.reorder() labels = np.argmax(fit.phi, axis=1) + 1 # cluster number # Compute cluster prediction for xTest where xTest is taken from SmoothExprs data = pandas.read_csv(os.path.join(path['GPFit']['SmoothExprs'], organ + strain + ".csv"), sep=",") # read data smoothExprs = data.values[:, 2:].astype('float')[bWant, :] xTest = data.columns.values[2:].astype('float64')[:, None] mu, var = fit.predict_components(xTest) clustCentre = np.empty((len(mu), xTest.shape[0])) for iClust in xrange(len(mu)): clustCentre[iClust, :] = mu[iClust] # Save model and plot io.save_pickle(os.path.join(path['Clust']['Model'], prefix + organ + strain + ".pickle"), fit) io.save_pdf(os.path.join(path['Clust']['Plot'], prefix + organ + strain + ".pdf"), plot(fit)) # Save Gene/Probe List geneList = get_gene_list(labels, geneSymbol) probeList = get_gene_list(labels, probeID) header = ["Cluster%i" % label for label in np.unique(labels)] io.write_list_to_csv(os.path.join(path['Clust']['GeneList'], prefix + organ + strain + ".csv"), header, geneList) # Gene list io.write_list_to_csv(os.path.join(path['Clust']['ProbeList'], prefix + organ + strain + ".csv"), header, probeList) # Probe list # Save Cluster "centres" dataMatrix = np.hstack((np.array(header)[:, None], clustCentre)) header = list(itertools.chain.from_iterable([["Cluster"], list(xTest.ravel())])) io.write_to_csv(os.path.join(path['Clust']['Centres'], prefix + organ + strain + ".csv"), header, dataMatrix) # Cluster "centres" # Save Alternate plot hFig = multi_plot(smoothExprs, clustCentre, xTest, labels) io.save_pdf(os.path.join(path['Clust']['Plot'], prefix + organ + strain + "2.pdf"), hFig) # Plot # Word cloud #vis.word_cloud_plot(organ, strain, prefix, path) # Heatmap vis.heatmap_plot_by_clusters(organ, strain, prefix, path)