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",
)
