def figure_2c(): neuronal_final = load_adata("neuronal") neuronal_marker_gene_list = load_markers("neuronal") neuronal_heatmap_data, neuronal_col_colors, neuronal_row_colors = get_heatmap_data( neuronal_final, neuronal_marker_gene_list, "cluster_revised", neuronal_palette ) cg = sns.clustermap( neuronal_heatmap_data, z_score=0, vmin=-3, vmax=3, cmap=heatmap_cmap, xticklabels=False, yticklabels=False, row_cluster=False, col_cluster=False, col_colors=neuronal_col_colors, row_colors=neuronal_row_colors, robust=True, figsize=(4, 4), cbar_kws=dict(use_gridspec=True), ) cg.ax_row_colors.set_ylabel("Genes", size="small") cg.ax_col_colors.set_title("Cells", size="small", zorder=100) cg.ax_col_colors.xaxis.tick_top() fix_heatmap_colorbar(cg, neuronal_final, neuronal_heatmap_data, heatmap_cmap) # cg.fig.tight_layout(rect=(0.025, 0.025, 0.975, 0.975)) fix_heatmap_annotations(cg) save_figure(cg.fig, "figure_02", "fig2_neuronal-heatmap", dpi=600, ext="png")
def figure_S5b(): neuronal_final = load_adata("neuronal") arcuate_violin_genes = pd.Index( [ "Slc32a1", "Slc17a6", "Prlr", "Tac2", "Tmem35a", "Esr1", "Nhlh2", "Pdyn", "Mrap2", "Inhbb", "Nr5a2", "Rxfp1", "Ucp2", "Lxn", "Trp53i11", "Fndc9", "Col2a1", "Kiss1", "Kcnk2", ] ) marker_violins( neuronal_final, arcuate_violin_genes, "cluster_revised", neuronal_palette, filename="figS5b_arcuate-marker-violins", figdir="figure_S05", )
def figure_4b(): neuronal_final = load_adata("neuronal") tuberomammillary_violin_genes = pd.Index( [ "Slc32a1", "Slc17a6", "Gad1", "Slc18a2", "Hdc", "Wif1", "Maoa", "Maob", "Msrb2", "Itm2a", "Hcrtr2", "Hrh3", "Bsx", "Tspan12", "Prph", "Sncg", ] ) marker_violins( neuronal_final, tuberomammillary_violin_genes, "cluster_revised", neuronal_palette, filename="fig4_tuberomammillary-marker-violins", figdir="figure_04", )
def figure_S2a(): neuronal_final = load_adata("neuronal") neuronal_cluster_props = get_cluster_proportions( neuronal_final, "cluster_revised", sample_key="sample_name" ) fig1 = plot_celltype_proportions(neuronal_cluster_props, neuronal_palette) save_figure(fig1, "figure_S02", "figS2a_neuronal_cluster_props")
def figure_S2c(): nonneuronal_final = load_adata("nonneuronal") nonneuronal_cluster_props = get_cluster_proportions( nonneuronal_final, "cluster_final", sample_key="sample_name" ) fig1 = plot_celltype_proportions(nonneuronal_cluster_props, nonneuronal_palette) save_figure(fig1, "figure_S02", "figS2c_nonneuronal_cluster_props")
def figure_1e(): full_no_dub = load_adata("global_no_dub") full_markers = load_markers("global") full_heatmap_data, full_col_colors, full_row_colors = get_heatmap_data( full_no_dub, full_markers, "cluster_revised", main_palette ) cg = sns.clustermap( full_heatmap_data, z_score=0, vmin=-3, vmax=3, cmap=heatmap_cmap, xticklabels=False, yticklabels=False, row_cluster=False, col_cluster=False, col_colors=full_col_colors, row_colors=full_row_colors, robust=True, figsize=(4, 4), cbar_kws=dict(use_gridspec=True), ) cg.ax_row_colors.set_ylabel("Genes", size="small") cg.ax_col_colors.set_title("Cells", size="small", zorder=100) fix_heatmap_colorbar(cg, full_no_dub, full_heatmap_data, heatmap_cmap) cg.ax_col_colors.xaxis.tick_top() # cg.fig.tight_layout(rect=(0.025, 0.025, 0.975, 0.975)) fix_heatmap_annotations(cg) save_figure(cg.fig, "figure_01", "fig1_main-heatmap", dpi=600, ext="png")
def figure_6b(): neuronal_final = load_adata("neuronal") mammillary_violin_genes = pd.Index( [ "Slc32a1", "Slc17a6", "Ctxn3", "Gpr83", "Rprm", "Cck", "Adcyap1", "Fam19a1", "Slc24a2", "Foxb1", "Cpne9", "Cox6a2", "Cnih3", "Pvalb", "Cartpt", "Nts", "Onecut2", "Tac2", "Cxcl14", ] ) marker_violins( neuronal_final, mammillary_violin_genes, "cluster_revised", neuronal_palette, filename="fig6_marker-violins", figdir="figure_06", )
def figure_5b(): neuronal_final = load_adata("neuronal") lateral_mammillary_violin_genes = pd.Index( [ "Slc32a1", "Slc17a6", "Tac2", "Tcf4", "Pvalb", "Fgf1", "Cnr1", "Cplx1", "Cbln2", "Cabp7", "Parm1", "Inf2", "Nefm", "Myo1a", "Syt2", ] ) marker_violins( neuronal_final, lateral_mammillary_violin_genes, "cluster_revised", neuronal_palette, filename="fig5_lateral-mammillary-marker-violins", figdir="figure_05", )
def figure_3b(): neuronal_final = load_adata("neuronal") premammillary_cluster7_violin_genes = pd.Index( [ "Slc32a1", "Slc17a6", "Adcyap1", "Calb2", "Foxp2", "Htr2c", "Nos1", "Glra3", "Tac1", "C1ql3", "Irs4", ] ) marker_violins( neuronal_final, premammillary_cluster7_violin_genes, "cluster_revised", neuronal_palette, filename="fig3_glut7-marker-violins", figdir="figure_03", )
def figure_S2d(): nonneuronal_final = load_adata("nonneuronal") nonneuronal_sample_props = get_sample_proportions( nonneuronal_final, "cluster_final", "sample_name" ) fig2 = plot_celltype_proportions( nonneuronal_sample_props, sns.mpl_palette("tab20", 4)[::-1] ) save_figure(fig2, "figure_S02", "figS2d_nonneuronal_sample_props")
def figure_1d(): full_no_dub = load_adata("global_no_dub") fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4)) axs = (ax1, ax2) params = dict( norm_hist=False, kde=False, hist_kws=dict(color=sns.xkcd_rgb["cerulean"], alpha=0.9), ) sns.distplot( full_no_dub.obs.total_counts, ax=ax1, bins=np.linspace(2000, 35000, 50), **params ) sns.distplot(full_no_dub.obs.n_genes_by_counts, ax=ax2, **params) median1 = full_no_dub.obs.total_counts.median().astype(int) median2 = full_no_dub.obs.n_genes_by_counts.median().astype(int) ymax = 0.85 ax1.axvline( median1, ymax=ymax, lw=1, ls="--", color="k", ) ax2.axvline(median2, ymax=ymax, lw=1, ls="--", color="k") ax1.text( median1, ymax * 1.02 * ax1.get_ylim()[1], str(median1), transform=ax1.transData, ha="center", size="small", ) ax2.text( median2, ymax * 1.02 * ax2.get_ylim()[1], str(median2), transform=ax2.transData, ha="center", size="small", ) ax1.set_xlim(0, 35000) ax2.set_xlim(0, 10000) ax1.set_xlabel("No. of transcripts") ax2.set_xlabel("No. of genes") ax1.set_ylabel("No. of cells") ax2.set_ylabel("No. of cells") for ax in axs: sns.despine(fig, ax) fig.tight_layout(rect=(0.025, 0.025, 0.975, 0.975)) save_figure(fig, "figure_01", "fig1_qc-histograms")
def figure_S3d(): nonneuronal_final = load_adata("nonneuronal") genes_umi_violins( nonneuronal_final, "cluster_final", nonneuronal_palette, figdir="figure_S03", filename="figS3d_nonneuronal-umi-genes-violins", )
def figure_6a(): neuronal_final = load_adata("neuronal") genes = ["Cartpt", "Foxb1", "Cck", "Adcyap1"] scatter_2by2( neuronal_final, genes, filename="fig6_umap-global-markers", figdir="figure_06", add_marker_edges=False, )
def figure_5a(): neuronal_final = load_adata("neuronal") genes = ["Tac2", "Tcf4", "Cplx1", "Pvalb"] scatter_2by2( neuronal_final, genes, "fig5_lateral-mammillary-umap-markers", figdir="figure_05", add_marker_edges=False, )
def figure_4a(): neuronal_final = load_adata("neuronal") genes = ["Hdc", "Slc18a2", "Wif1", "Maob"] scatter_2by2( neuronal_final, genes, filename="fig4_tuberomammillary-umap-markers", figdir="figure_04", add_marker_edges=False, )
def figure_3a(): neuronal_final = load_adata("neuronal") genes = ["Tac1", "Nos1", "Calb2", "Foxp2"] scatter_2by2( neuronal_final, genes, filename="fig3_glut7-umap-markers", figdir="figure_03", add_marker_edges=False, )
def figure_2e(): neuronal_final = load_adata("neuronal") genes_umi_violins( neuronal_final, "cluster_revised", neuronal_palette, filename="fig2_neuronal-umi-genes-violins", figdir="figure_02", ylims=[80000, 10000], )
def figure_S7a(): neuronal_final = load_adata("neuronal") genes = ["Cck", "Synpr", "Tac1", "Nos1"] scatter_2by2( neuronal_final, genes, filename="figS7a_premammillary-umap-markers", figdir="figure_S07", add_marker_edges=False, )
def figure_S5a(): neuronal_final = load_adata("neuronal") genes = ["Tac2", "Pdyn", "Esr1", "Prlr"] scatter_2by2( neuronal_final, genes, filename="figS5a_arcuate-umap-markers", figdir="figure_S05", add_marker_edges=False, )
def figure_S6c(): supramammillary = load_adata("supramammillary") genes = ["Sema3c", "Inhba", "Rxfp1", "Cpne7"] scatter_2by2( supramammillary, genes, filename="figS6c_cluster8_key-genes-2", figdir="figure_S06", add_marker_edges=False, label_va="top", label_ha="left", markersize=16, )
def figure_S6a(): supramammillary = load_adata("supramammillary") genes = ["Gad1", "Gad2", "Slc32a1", "Slc17a6"] scatter_2by2( supramammillary, genes, filename="figS6a_cluster8_key-genes-1", figdir="figure_S06", add_marker_edges=False, label_va="top", label_ha="left", markersize=16, )
def figure_S4a(): nonneuronal_final = load_adata("nonneuronal") fig, ax = plt.subplots(1, 1, figsize=(4, 4)) sc.pl.umap( nonneuronal_final[nonneuronal_final.obs.cluster_final.isin(list("123456"))], color="cluster_final", show=False, ax=ax, title="", palette=nonneuronal_palette, ) ax.legend(bbox_to_anchor=(1.0, 0.5), loc="center left", ncol=1, frameon=False) fix_aspect_scatter_with_legend(fig) fig.tight_layout(rect=(0.025, 0.025, 0.975, 0.975)) save_figure(fig, "figure_S04", "figS4a_oligo-lineage")
def figure_S3a(): nonneuronal_final = load_adata("nonneuronal") fig, ax = plt.subplots(1, 1, figsize=(4, 4)) sc.pl.umap( nonneuronal_final, color="cluster_final", show=False, ax=ax, title="Nonneuronal clusters", palette=nonneuronal_palette, ) ax.legend(bbox_to_anchor=(1.0, 0.5), loc="center left", ncol=1, frameon=False) fix_aspect_scatter_with_legend(fig) fig.tight_layout(rect=(0.025, 0.025, 0.975, 0.975)) save_figure(fig, "figure_S03", "fig3a_nonneuronal-clusters")
def figure_7a(): neuronal_final = load_adata("neuronal") fig, ax = plt.subplots(1, 1, figsize=(4, 4)) sc.pl.umap( neuronal_final[neuronal_final.obs.cluster_revised.isin(list("12345"))], color="cluster_revised", show=False, ax=ax, title="Mammillary clusters", palette=neuronal_palette, ) ax.legend(bbox_to_anchor=(1.0, 0.5), loc="center left", ncol=1, frameon=False) fix_aspect_scatter_with_legend(fig) # add_scatter_borders(ax) fig.tight_layout(rect=(0.025, 0.025, 0.975, 0.975)) save_figure(fig, "figure_07", "fig7_mammillary-umap-clusters")
def figure_S1c(): full_no_dub = load_adata("global_no_dub") fig, ax = plt.subplots(1, 1, figsize=(4, 4)) sc.pl.umap( full_no_dub, color="classification_cluster", palette=neuronal_nonneuronal_palette, title="Neuronal Classification", show=False, ax=ax, ) ax.legend(bbox_to_anchor=(0.5, -0.2), ncol=2, frameon=False, loc="lower center") sns.despine(fig) fix_aspect_scatter_with_legend(fig) fig.tight_layout(rect=(0.025, 0.025, 0.975, 0.975)) save_figure(fig, "figure_S01", "figS1c_neuronal-classification")
def figure_2b(): neuronal_final = load_adata("neuronal") fig, ax = plt.subplots(1, 1, figsize=(8, 8)) sc.pl.umap( neuronal_final, color="cluster_revised", show=False, ax=ax, size=40, title="Neuronal clusters", palette=neuronal_palette, ) ax.legend(bbox_to_anchor=(1.0, 0.5), loc="center left", ncol=1, frameon=False) fix_aspect_scatter_with_legend(fig) fig.tight_layout(rect=(0.025, 0.025, 0.975, 0.975)) save_figure(fig, "figure_02", "fig2_neuronal-clusters")
def figure_1c(): full_no_dub = load_adata("global_no_dub") fig, axarr = plt.subplots( 1, 2, gridspec_kw=dict(hspace=0, wspace=0), sharex=True, sharey=True, figsize=(7, 4), ) params = dict(size=3, show=False, alpha=1.0, legend_fontsize=8) legend_params = dict( loc="upper center", bbox_to_anchor=(0.5, 0.0), ncol=2, frameon=False, fontsize="small", ) # by sex colors = sns.xkcd_palette(["electric blue", "bright red"]) sc.pl.umap( full_no_dub, color="sex", title="", ax=axarr[0], palette=colors, **params ) axarr[0].legend(**legend_params) # by 10x chemistry colors = sns.xkcd_palette(["orange", "grass green"]) sc.pl.umap( full_no_dub, color="chemistry", title="", ax=axarr[1], palette=colors, **params ) axarr[1].legend(**legend_params) for ax in axarr.flat: ax.set_xlabel("") ax.set_ylabel("") ax.set_aspect(1) fix_aspect_scatter_with_legend(fig) fig.tight_layout(rect=(0.025, 0.025, 0.975, 0.975)) save_figure(fig, "figure_01", "fig1_umap-sex-chemistry")
def figure_S6b(): supramammillary = load_adata("supramammillary") fig, ax = plt.subplots(1, 1, figsize=(2, 2)) sc.pl.umap( supramammillary, color="cluster_revised", show=False, ax=ax, title="", size=16 ) leg = ax.legend( bbox_to_anchor=(1.0, 0.5), loc="center left", ncol=1, frameon=False, fontsize="xx-small", ) for handle in leg.legendHandles: handle._sizes = [16] fix_aspect_scatter_with_legend(fig) # add_scatter_borders(ax) fig.tight_layout(rect=(0.025, 0.025, 0.975, 0.975)) save_figure(fig, "figure_S06", "fig6b_cluster8_subclusters")
def figure_S4b(): nonneuronal_final = load_adata("nonneuronal") gene_list = ( ("Cspg4", "Mal"), ("Pdgfra", "Tspan2"), ("Fyn", "Opalin"), ("Tcf7l2", "Apod"), ("Ctps", "Klk6"), ) for genes in gene_list: gene_label = "-".join(genes) scatter_1by2( nonneuronal_final, genes, selection=nonneuronal_final.obs.cluster_final.isin(list("123456")), label_ha="right", filename=f"figS4b_oligo-lineage_{gene_label}", figdir="figure_S04", add_marker_edges=False, )
def figure_S3b(): nonneuronal_final = load_adata("nonneuronal") nonneuronal_violin_genes = [ "Ascl1", "Pdgfra", "Fyn", "Mag", "Mobp", "Mal", "Tspan2", "Ptgds", "Hcn2", "Fth1", "Agt", "Aqp4", "Gfap", "Rax", "Ccdc153", "Mrc1", "Tmem119", "Cx3cr1", "Rgs5", "Acta2", "Fxyd5", "Slc47a1", "Col1a1", "Dcn", "Igfbp2", "Pecam1", "Slc38a5", ] marker_violins( nonneuronal_final, nonneuronal_violin_genes, "cluster_final", nonneuronal_palette, filename="figS3b_nonneuronal-marker-violins", figdir="figure_S03", )