""" 3. Boxplot of summary stats by group: plot boxplots of all groups - magnitude and breadth for each protein """ amp.plot_summary_stat_boxplots_by_exp_groups(arr_df, arr_summary_stats, sample_inds=None, fig_path=fig_path) """Clustering plots""" """ 1. Dendrograms: """ #amp.plot_clustering_dendrograms(Z_struct=Z_struct, prot_names=prot_names, labels=arr_df.index[time_dict['D21']], fig_path=None) amp.plot_clustering_dendrograms(Z_struct=Z_struct, prot_names=['EBOV-GP'], labels=arr_df.group, fig_path=fig_path, fig_prefix='') """ 2. Raw responses by cluster: """ # for p, s in zip(['EBOV-GP'], ['EBOV-GP']): # amp.plot_raw_responses_by_clusters(raw_arr_df, ind_dict[p], num_clusters, clusters=clusters[p], # fig_path=fig_path, fig_prefix=s + '_Raw', fig_size=(18,11), y_lims=[0, 20000]) """ 3. Median responses by cluster: """ for p, s in zip(['EBOV-GP'], ['EBOV-GP']): amp.plot_median_responses_by_clusters(arr_df, ind_dict[p], num_clusters, clusters=clusters[p], fig_path=fig_path, fig_prefix=s + '_Raw', fig_size=(18,11), y_lims=[0, 10000])
""" 3. Boxplot of summary stats by group: plot boxplots of all groups - magnitude and breadth for each protein """ amp.plot_summary_stat_boxplots_by_exp_groups(raw_arr_df, arr_summary_stats, sample_inds=None, fig_path=fig_path) """Clustering plots""" """ 1. Dendrograms: """ #amp.plot_clustering_dendrograms(Z_struct=Z_struct, prot_names=prot_names, labels=arr_df.index[time_dict['D21']], fig_path=None) amp.plot_clustering_dendrograms(Z_struct=Z_struct, prot_names=['VN1203', 'Indo05'], labels=arr_df.index, fig_path=fig_path, fig_prefix='Raw_') """ 2. Raw responses by cluster: """ for p, s in zip(['VN1203', 'Indo05'], ['VN1203', 'Indo05']): amp.plot_raw_responses_by_clusters(raw_arr_df, ind_dict[p], num_clusters, clusters=clusters[p], fig_path=fig_path, fig_prefix=s + '_Raw', fig_size=(18,11), y_lims=[0, 50000]) """ 3. Median responses by cluster: """ for p, s in zip(['VN1203', 'Indo05'], ['VN1203', 'Indo05']): amp.plot_median_responses_by_clusters(raw_arr_df, ind_dict[p], num_clusters, clusters=clusters[p], fig_path=fig_path, fig_prefix=s + '_Raw', fig_size=(18,11), y_lims=y_lims)
mbp.myboxplot_by_labels(curr_df[curr_time_dict[t]][assay], curr_df[curr_time_dict[t]]['group']) axarr.set_title("".join([t, " ", assay.replace('_', ' '), " responses"]), fontsize=16) axarr.tick_params(axis='both', which='major', labelsize=14) axarr.set_yscale('log') filename = "".join([FIG_PATH, t, "_", assay, "_boxplots_by_groups.png"]) f.savefig(filename, dpi=200) filename = "".join([FIG_PATH, t, "_", assay, "_boxplots_by_groups.eps"]) f.savefig(filename, dpi=1000) # Figure 4, 5 and 6 - clustering dendrograms, median responses and summary stats of WT vs. Obese for each group: for a in ['Vac', 'AS03']: curr_inds = group_inds['Ob_post_' + a].append(group_inds['WT_post_' + a]) amp.plot_clustering_dendrograms(Z_struct=Z_struct[a], prot_names=['SHA_ha'], labels=arr_df.loc[curr_inds].group, fig_prefix=a + '_', fig_path=FIG_PATH) amp.plot_median_responses_by_clusters(arr_df=arr_df.loc[curr_inds], antigen_inds=ind_dict['SHA_ha'], num_clusters=4, clusters=clusters[a]['SHA_ha'], y_lims=[0, 25000], fig_prefix=a, fig_path=FIG_PATH) #amp.plot_raw_responses_by_clusters(arr_df=arr_df.loc[curr_inds], antigen_inds=ind_dict['SHA_ha'], num_clusters=4, clusters=clusters[a]['SHA_ha']) amp.plot_summary_stat_boxplots_by_clusters(arr_df=arr_df.loc[curr_inds], clusters=clusters[a], prot_names=['SHA_ha'], arr_summary_stats=['H7_breadth', 'H7_mag'], fig_prefix = a + '_', fig_path=FIG_PATH) # Export all post data to excel: # now generate dataframes for sig antigens from proteins of interest: writer = pd.ExcelWriter(FIG_PATH + 'H7H9_WT_OB_raw_data.xlsx') for p, p_name in zip(prot_names, prot_strs): curr_df = arr_df.loc[time_dict['Post']][ind_dict[p]] curr_df.to_excel(writer, sheet_name='post_' + p_name)