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 scatter_1by2(
adata,
genes,
filename=None,
figdir=None,
selection=None,
add_marker_edges=True,
label_va="top",
label_ha="left",
markersize=3,
):
gs = plt.GridSpec(1, 2, wspace=0, hspace=0)
fig = plt.figure(figsize=(4, 4))
ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[0, 1])
axs = (ax1, ax2)
plot_data = adata
plot_params = dict(use_raw=False,
show=False,
title="",
size=markersize,
color_map=red_colormap)
if selection is not None:
plot_data = adata[selection]
plot_params["size"] = 12
x = dict(left=0.05, right=0.95)[label_ha]
y = dict(bottom=0.05, top=0.95)[label_va]
for ax, gene in zip(axs, genes):
sc.pl.umap(plot_data, color=gene, ax=ax, **plot_params)
ax.text(x,
y,
gene,
ha=label_ha,
va=label_va,
size="small",
transform=ax.transAxes)
filtered_children = list(
filter(lambda c: isinstance(c, matplotlib.axes.Axes),
fig.get_children()))
for k, child in enumerate(filtered_children[len(axs):]):
fig.delaxes(child)
for ax in axs:
ax.set_xlabel("")
ax.set_ylabel("")
fix_aspect_scatter_with_legend(fig)
if add_marker_edges:
add_scatter_borders(ax)
ax1.set_ylabel("UMAP2")
ax1.set_xlabel("UMAP1")
ax2.set_xlabel("UMAP1")
fig.tight_layout()
fig.subplots_adjust(wspace=0.0)
fig.tight_layout(rect=(0.025, 0.025, 0.975, 0.975))
if (filename is not None) and (figdir is not None):
save_figure(fig, figdir, filename)
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_S8():
neuronal_final = load_adata("neuronal")
genes = pd.Index([
"Rprm", "Pitx2", "Cdh11",
"Cpne9", "Gpr83", "Sim1",
"Ctxn3", "Lhx1", "Sim2",
"Fam19a1", "Lhx5", "Nr4a2",
"Nkx2-1",
])
for gene in genes:
fig, ax = plt.subplots(figsize=(2, 2))
sc.pl.umap(
neuronal_final, color=gene,
size=3, cmap=red_colormap, title="",
ax=ax, show=False, use_raw=False
)
fig.delaxes(fig.get_children()[-1])
ax.text(0.025, 0.975, gene, ha="left", va="top", size="small", style="italic", transform=ax.transAxes)
ax.set_xlabel("")
ax.set_ylabel("")
fix_aspect_scatter_with_legend(fig)
save_figure(fig, "figure_S08", f"fig_S8_neuronal-UMAP_{gene}")
del fig
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_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_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 genes_umi_violins(adata,
cluster_key,
palette,
filename=None,
figdir=None,
ylims=[50000, 10000]):
obs_to_plot = ["total_counts", "n_genes_by_counts"]
obs_names = ["UIMs", "Genes"]
yticks = [np.linspace(0, ylims[0], 6), np.linspace(0, ylims[1], 6)]
L = len(obs_names)
fig, axs = plt.subplots(2, 1, figsize=(4, 2))
violin_data = pd.DataFrame(adata.obs[obs_to_plot].values,
columns=obs_names)
violin_data["Cluster"] = adata.obs[cluster_key].values
for k, (ax, obs, ylim,
ytick) in enumerate(zip(axs.flat, obs_names, ylims, yticks)):
ax.grid(axis="y", lw=0.25, color="0.6")
sns.violinplot(
data=violin_data,
x="Cluster",
y=obs,
palette=palette,
ax=ax,
inner=None,
linewidth=0.4,
scale="width",
cut=0,
)
ax.set_ylabel(obs,
rotation=90,
ha="center",
va="center",
size="medium")
ax.set_yticks(ytick)
ax.set_ylim(0, ylim)
ax.yaxis.set_tick_params(labelsize="small")
if k < (L - 1):
ax.set_xticks([])
ax.set_xlabel("")
else:
ax.xaxis.set_tick_params(labelsize="small")
ax.set_xlabel("Cluster", size="medium")
sns.despine(ax=ax, left=False)
ax.set_axisbelow(True)
fig.tight_layout()
fig.subplots_adjust(hspace=0.25, wspace=0)
if (filename is not None) and (figdir is not None):
save_figure(fig, figdir, filename)
def marker_violins(
adata,
genes,
cluster_key,
palette,
cluster_name="Cluster",
filename=None,
figdir=None,
):
L = len(genes)
N = len(adata.obs[cluster_key].unique())
fig, axs = plt.subplots(L, 1, figsize=(0.5 + 0.25 * N, 0.32 * L))
violin_data = pd.DataFrame(adata[:, genes].X.toarray(), columns=genes)
violin_data[cluster_name] = adata.obs[cluster_key].values
for k, (ax, gene) in enumerate(zip(axs.flat, genes)):
sns.violinplot(
data=violin_data,
x=cluster_name,
y=gene,
palette=palette,
ax=ax,
inner=None,
linewidth=0.4,
scale="width",
cut=0,
)
ax.set_ylabel(gene,
rotation=0,
ha="right",
va="top",
size="small",
style="italic")
ax.set_yticks([])
ax.set_ylim(0, ax.get_ylim()[1])
if k < (L - 1):
ax.set_xticks([])
ax.set_xlabel(None)
ax.xaxis.label.set_visible(False)
else:
ax.xaxis.set_tick_params(labelsize="small")
sns.despine(ax=ax, left=True)
fig.tight_layout()
fig.subplots_adjust(hspace=0, wspace=0)
if (filename is not None) and (figdir is not None):
save_figure(fig, figdir, filename)
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_1f():
full_no_dub = load_adata("global_no_dub")
fig, ax = plt.subplots(1, 1, figsize=(4, 4))
sc.pl.umap(
full_no_dub,
color="cluster_revised",
palette=main_palette,
title="",
show=False,
ax=ax,
)
legend = ax.legend(
bbox_to_anchor=(1, 1), ncol=1, frameon=False, loc="upper left", fontsize=7.6
)
for leg in legend.legendHandles:
leg._sizes = [20]
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_01", "fig1f_all-clusters")
def figure_S1b():
full_no_dub = load_adata("global_no_dub")
neuronal_markers = ["Snap25", "Tubb3", "Elavl2", "Syp"]
full_no_dub.obs["neuronal_average_exp"] = full_no_dub[:, neuronal_markers].X.mean(
axis=1
)
fig, ax = plt.subplots(1, 1, figsize=(4, 4))
sc.pl.umap(
full_no_dub,
color="neuronal_average_exp",
palette=neuronal_nonneuronal_palette,
title="Neuronal marker mean expression",
show=False,
color_map=red_colormap,
ax=ax,
)
sns.despine(fig)
fig.get_children()[-1].set_ylabel("Log-normalized expression")
fix_aspect_scatter_with_cbar(fig)
fig.tight_layout(rect=(0.025, 0.025, 0.975, 0.975))
save_figure(fig, "figure_S01", "figS1b_neuronal-expression")
def figure_S1a():
full_no_dub = load_adata("global_with_dub")
full_comb = full_no_dub.copy()
sc.pp.normalize_per_cell(full_comb)
sc.pp.log1p(full_comb)
sc.pp.highly_variable_genes(full_comb, n_top_genes=2500, flavor="cell_ranger")
sc.pp.pca(full_comb, n_comps=25, svd_solver="arpack", use_highly_variable=True)
sc.pp.neighbors(full_comb, n_neighbors=15, metric="correlation")
sc.tl.umap(full_comb, min_dist=0.5)
full_comb.obs["chemistry"] = full_comb.obs.sampleid.map(
{"AJ18003": "v2", "AJ18004": "v2", "AJ19001": "v3", "AJ19002": "v3",}
)
full_comb.obsm["X_umap"] = full_comb.obsm["X_umap"].dot(-np.eye(2))
fig, axarr = plt.subplots(2, 3, figsize=(6, 4))
params = dict(show=False, size=2)
sc.pl.umap(
full_comb,
color="chemistry",
ax=axarr[0, 0],
palette=sns.xkcd_palette(["orange", "grass green"]),
legend_loc=None,
title="10X Chemistry",
**params,
)
sc.pl.umap(
full_no_dub,
color="chemistry",
ax=axarr[1, 0],
palette=sns.xkcd_palette(["orange", "grass green"]),
title="",
**params,
)
sc.pl.umap(
full_comb,
color="sex",
ax=axarr[0, 1],
palette=sns.xkcd_palette(["electric blue", "bright red"]),
legend_loc=None,
title="Sex",
**params,
)
sc.pl.umap(
full_no_dub,
color="sex",
ax=axarr[1, 1],
palette=sns.xkcd_palette(["electric blue", "bright red"]),
title="",
**params,
)
sc.pl.umap(
full_comb,
color="sample_name",
ax=axarr[0, 2], # palette=sns.xkcd_palette(["orange", "grass green"]),
legend_loc=None,
title="Sample",
**params,
)
sc.pl.umap(
full_no_dub,
color="sample_name",
ax=axarr[1, 2], # palette=sns.xkcd_palette(["orange", "grass green"]),
title="",
**params,
)
legend_params = dict(
bbox_to_anchor=(0.5, -0.0),
loc="upper center",
ncol=2,
frameon=False,
columnspacing=0.9,
fontsize=7,
markerscale=0.5,
)
for k in range(3):
leg = axarr[1, k].legend(**legend_params)
for ax in axarr.flat:
ax.set_xlabel("")
ax.set_ylabel("")
axarr[0, 0].set_ylabel("No batch correction", size=8)
axarr[1, 0].set_ylabel("With batch correction", size=8)
axarr[0, 0].set_title("10X Chemistry", size=9)
axarr[0, 1].set_title("Sex", size=9)
axarr[0, 2].set_title("Sample", size=9)
fix_aspect_scatter_with_legend(fig)
fig.subplots_adjust(wspace=0.0, hspace=0.0)
fig.tight_layout(rect=(0.025, 0.025, 0.975, 0.975))
save_figure(fig, "figure_S01", "figS1a_batch-correction-comparison")
def figure_7b():
def get_heatmap_data_tmp(adata, markers, cluster_key, palette):
grpd = markers.groupby(cluster_key).head(100)
genes = grpd.gene_name # .unique()
duplicated = genes.duplicated()
clusters = grpd[cluster_key].astype(int).values - 1
genes = genes[~duplicated]
clusters = clusters[~duplicated]
# heatmap_data = nonneuronal_final[nonneuronal_final.obs.sort_values("cluster_final").index, :]
data = adata[:, genes].X.toarray()
sort_order = adata.obs[cluster_key].reset_index(drop=True).sort_values().index
data = data[sort_order, :].T
col_colors = np.array(palette)[adata.obs[cluster_key].cat.codes[sort_order]]
row_colors = np.array(palette)[clusters]
weights = np.array(
[
[0, 0, 1, 0, 0],
[0, 2, 4, 2, 0],
[1, 4, 8, 4, 1],
[0, 2, 4, 2, 0],
[0, 0, 1, 0, 0],
],
dtype=np.float,
)
weights = weights / np.sum(weights[:])
smoothed = convolve(data, weights, mode="constant")
return smoothed, col_colors, row_colors
neuronal_final = load_adata("neuronal")
mammillary = neuronal_final[
neuronal_final.obs.cluster_revised.isin(list("12345")), :
].copy()
mammillary_heatmap_genes = pd.Index(
[
"Nts",
"Alcam",
"Col25a1",
"Ctxn3",
"Spock3",
"Serpini1",
"Tshz2",
"Trp53i11",
"Grin3a",
"Hpcal1",
"Zbtb20",
"Onecut2",
"Rbms3",
"Slc24a2",
"Pvalb",
"Calb1",
"Nos1",
"Tac2",
"Cadm1",
"Cxcl14",
]
)
mammillary_marker_genes = pd.DataFrame(
{
"gene_name": mammillary_heatmap_genes,
"cluster_revised": ["1"] * 4
+ ["2"] * 7
+ ["3"] * 4
+ ["4"] * 2
+ ["5"] * 3,
"garbage": np.arange(len(mammillary_heatmap_genes)),
}
)
(
mammillary_heatmap_data,
mammillary_col_colors,
mammillary_row_colors,
) = get_heatmap_data_tmp(
mammillary, mammillary_marker_genes, "cluster_revised", neuronal_palette
)
cg = sns.clustermap(
mammillary_heatmap_data,
z_score=0,
vmin=-3,
vmax=3,
cmap=heatmap_cmap,
xticklabels=False,
yticklabels=mammillary_heatmap_genes,
row_cluster=False,
col_cluster=False,
col_colors=mammillary_col_colors,
row_colors=mammillary_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_heatmap.tick_params(axis="y", which="major", labelsize="xx-small")
cg.ax_col_colors.xaxis.tick_top()
fix_heatmap_colorbar(cg, mammillary, mammillary_heatmap_data, heatmap_cmap)
cg.fig.subplots_adjust(right=0.78)
# cg.fig.tight_layout(rect=(0.025, 0.025, 0.975, 0.975))
fix_heatmap_annotations(cg)
save_figure(cg.fig, "figure_07", "fig7_mammillary-heatmap", dpi=600, ext="png")
def figure_2a():
neuronal_final = load_adata("neuronal")
gs = plt.GridSpec(1, 5)
fig = plt.figure(figsize=(18, 4))
ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[0, 1])
ax3 = fig.add_subplot(gs[0, 2])
ax4 = fig.add_subplot(gs[0, 3])
ax6 = fig.add_subplot(gs[0, 4])
axs = (ax1, ax2, ax3, ax4, ax6)
genes = ["Slc17a6", "Slc32a1", "Gad1", "Hdc"]
for ax, gene in zip(axs, genes):
sc.pl.umap(
neuronal_final,
color=gene,
use_raw=False,
show=False,
ax=ax,
title="",
color_map=red_colormap,
size=10,
)
ax.text(
0.05, 0.95, gene, ha="left", va="top", size="large", transform=ax.transAxes
)
sc.pl.umap(
neuronal_final,
color="classification",
ax=ax6,
show=False,
title="",
legend_loc="right margin",
palette=gaba_vs_glut_vs_hdc_palette,
size=10,
)
ax6.legend(
frameon=False,
loc="upper right",
labelspacing=0,
borderaxespad=0.1,
markerscale=0.7,
)
filtered_children = list(
filter(lambda c: isinstance(c, matplotlib.axes.Axes), fig.get_children())
)
for k, child in enumerate(filtered_children[len(axs) :]):
fig.delaxes(child)
for ax in axs:
ax.set_xlabel("")
ax.set_ylabel("")
fix_aspect_scatter_with_legend(fig)
ax1.set_xlabel("UMAP1")
ax2.set_xlabel("UMAP1")
ax3.set_xlabel("UMAP1")
ax4.set_xlabel("UMAP1")
ax6.set_xlabel("UMAP1")
ax6.set_ylabel("UMAP2")
ax6.yaxis.set_label_position("right")
fig.subplots_adjust(wspace=0.0)
save_figure(fig, "figure_02", "fig2_gaba-vs-glut")
