6_ctx_cc_intracellular_extracellular_compare

[1]:

import pandas as pd
import scanpy as sc
import matplotlib.pyplot as plt
import anndata as ad
import numpy as np
import warnings
import seaborn as sns
import matplotlib.colors as clr
color_self = clr.LinearSegmentedColormap.from_list('pink_green', ['#3AB370',"#EAE7CC","#FD1593"], N=256)

import matplotlib as mpl
mpl.rcParams['pdf.fonttype'] = 42
mpl.rcParams['ps.fonttype'] = 42
warnings.filterwarnings('ignore')

[2]:

adata_in = sc.read_h5ad('/mnt/Data16Tc/home/haichao/code/SpaCon/Data/N_20231213_zxw/mouse_3/adata_processed.h5ad')
allen_region = pd.read_csv('/mnt/Data16Tc/home/haichao/code/SpaCon/Data/N_20231213_zxw/mouse_3/allen_region.csv')
adata_in.obs['region'] = allen_region['region'].values
meta = pd.read_csv('/mnt/Data16Tc/home/haichao/code/SpaCon/Data/N_20231213_zxw/mouse_3/cell_metadata_with_cluster_annotation.csv')
meta = meta.set_index('cell_label')
meta = meta.loc[adata_in.obs.index.to_list()]
adata_in.obs['cell_type'] = meta['class'].to_list()

adata_out = sc.read_h5ad('/mnt/Data18Td/Data/haichao/merfish_raw_data_zxw3/out_cell_adata/adata_out_cell_distance_q0.3/after_qc/out_cell_adata_qc.h5ad')
# adata_out = sc.read_h5ad('/mnt/Data18Td/Data/haichao/merfish_raw_data_zxw3/out_cell_adata/adata_out_cell_distance_q0.3/after_qc/Zhuang-ABCA-3.001.h5ad')
adata_out.obs['region'] = adata_in.obs.loc[adata_out.obs_names]['region'].values
adata_in.obs

[2]:

	brain_section_label	x	y	z	x_ccf	y_ccf	z_ccf	region	cell_type
cell_label
198904341065180396762707397604803217407	Zhuang-ABCA-3.023	49.206853	44.877634	12.168155	4.920685	4.487763	1.216815	SSs1	33 Vascular
252199681526991424029643077826220097990	Zhuang-ABCA-3.023	48.973992	44.813761	12.179006	4.897399	4.481376	1.217901	SSs1	33 Vascular
277720971126854564514249564750701518375	Zhuang-ABCA-3.023	48.791066	44.577722	12.192707	4.879107	4.457772	1.219271	SSs1	33 Vascular
31551867344111790264292067056219852271	Zhuang-ABCA-3.023	48.830489	44.426120	12.195078	4.883049	4.442612	1.219508	SSs1	33 Vascular
131102494428104399865219008178262036485	Zhuang-ABCA-3.023	48.308843	43.028156	12.267879	4.830884	4.302816	1.226788	SSs1	34 Immune
...	...	...	...	...	...	...	...	...	...
318102106429791409781741726367984532777	Zhuang-ABCA-3.009	131.090716	69.334275	41.436743	13.109072	6.933427	4.143674	MDRNd	30 Astro-Epen
35262847161560382172299767067854387528	Zhuang-ABCA-3.009	131.216032	69.494070	41.351034	13.121603	6.949407	4.135103	MDRNd	33 Vascular
75415866509570969932943497000463821106	Zhuang-ABCA-3.009	131.415152	70.764504	40.800403	13.141515	7.076450	4.080040	sctd	24 MY Glut
12350978322417280063239916106423065862	Zhuang-ABCA-3.009	131.646167	71.182557	40.595995	13.164617	7.118256	4.059599	sctd	24 MY Glut
327554758863546024460748891922509519354	Zhuang-ABCA-3.009	131.658892	71.414675	40.501356	13.165889	7.141468	4.050136	sctd	24 MY Glut

1566842 rows × 9 columns

[3]:

sc.pp.normalize_total(adata_in, target_sum=1e4)
sc.pp.log1p(adata_in)
sc.pp.normalize_total(adata_out, target_sum=1e4)
sc.pp.log1p(adata_out)

[4]:

adata_in.obs['ct'] = adata_in.obs['cell_type']
adata_in.obs.loc[adata_in.obs['cell_type'].str.contains('Glut'), 'ct'] = 'Glut'
adata_in.obs.loc[adata_in.obs['cell_type'].str.contains('GABA'), 'ct'] = 'GABA'
adata_in = adata_in[adata_in.obs['ct'] != '32 OEC']
adata_in.obs['ct'].unique()

[4]:

array(['33 Vascular', '34 Immune', '31 OPC-Oligo', '30 Astro-Epen',
       'GABA', 'Glut', '21 MB Dopa', '22 MB-HB Sero'], dtype=object)

[5]:

ctx_43_regions = ['FRP', 'MOp', 'MOs', 'SSp-n', 'SSp-bfd', 'SSp-ll', 'SSp-m', 'SSp-ul', 'SSp-tr', 'SSp-un', 'SSs', 'GU', 'VISC', 'AUDd', 'AUDp', 'AUDpo', 'AUDv', 'VISal', 'VISam', 'VISl', 'VISp', 'VISpl', 'VISpm', 'VISli', 'VISpor', 'ACAd', 'ACAv', 'PL', 'ILA', 'ORBl', 'ORBm', 'ORBvl', 'AId', 'AIp', 'AIv', 'RSPagl', 'RSPd', 'RSPv', 'VISa', 'VISrl', 'TEa', 'PERI', 'ECT']

[6]:

adata_cc_in = adata_in[(adata_in.obs['region'].str.startswith('cc')) & (~adata_in.obs['cell_type'].str.contains('GABA|Glut'))]
adata_cc_in

[6]:

View of AnnData object with n_obs × n_vars = 15599 × 1122
    obs: 'brain_section_label', 'x', 'y', 'z', 'x_ccf', 'y_ccf', 'z_ccf', 'region', 'cell_type', 'ct'
    uns: 'log1p'

[7]:

adata_ctx = adata_in[adata_in.obs['region'].str.startswith(tuple(ctx_43_regions))]
adata_ctx = adata_ctx[adata_ctx.obs['cell_type'].str.contains('Glut')]
adata_ctx

[7]:

View of AnnData object with n_obs × n_vars = 148119 × 1122
    obs: 'brain_section_label', 'x', 'y', 'z', 'x_ccf', 'y_ccf', 'z_ccf', 'region', 'cell_type', 'ct'
    uns: 'log1p'

[8]:

adata_cc_out = adata_out[adata_out.obs['region'].str.startswith('cc')]
adata_cc_out

[8]:

View of AnnData object with n_obs × n_vars = 14435 × 1147
    obs: 'totalRNA', 'brain_section_label', 'x', 'y', 'z', 'n_genes_by_counts', 'total_counts', 'region'
    uns: 'log1p'
    obsm: 'spatial'

[9]:

common_cells = adata_cc_in.obs_names.intersection(adata_cc_out.obs_names)
adata_cc_in = adata_cc_in[common_cells]
adata_cc_out = adata_cc_out[common_cells]

[10]:

adata_cc_in.obs['cell_type'].value_counts()

[10]:

31 OPC-Oligo     9402
30 Astro-Epen    2340
33 Vascular       894
34 Immune         526
Name: cell_type, dtype: int64

[11]:

adata_cc_in.obsm['spatial'] = adata_cc_in.obs[['x', 'y']].values
adata_cc_out.obsm['spatial'] = adata_cc_out.obs[['x', 'y']].values
adata_in.obsm['spatial'] = adata_in.obs[['x', 'y']].values

[12]:

adata_ctx.obs['DEG'] = 'ctx_glut_in'
adata_cc_out.obs['DEG'] = 'cc_out'
adata_cc_in.obs['DEG'] = 'cc_in'
adata_ctx_cc = ad.concat([adata_ctx, adata_cc_in, adata_cc_out])
adata_ctx_cc.obs

[12]:

	brain_section_label	x	y	z	region	DEG
207252950882079766503645227815929952400	Zhuang-ABCA-3.023	50.597984	41.393473	12.239274	SSs2/3	ctx_glut_in
311894855078226645952213910865897976013	Zhuang-ABCA-3.023	50.420950	41.271525	12.251970	SSs1	ctx_glut_in
125208524519663791324346814779771999476	Zhuang-ABCA-3.023	50.959183	43.276307	12.158869	SSs2/3	ctx_glut_in
12594778395225515056477813574460470379	Zhuang-ABCA-3.023	49.836112	42.209685	12.238386	SSs1	ctx_glut_in
148621603142722639702356861951538418099	Zhuang-ABCA-3.023	51.023440	42.722536	12.174236	SSs2/3	ctx_glut_in
...	...	...	...	...	...	...
79089257071572594359780469736093814949	Zhuang-ABCA-3.009	74.951639	13.604024	38.481071	ccs	cc_out
92146094126984864052663841225116312773	Zhuang-ABCA-3.009	75.154326	14.108468	38.486841	ccs	cc_out
93045082915409019668907757310610222929	Zhuang-ABCA-3.009	73.220268	13.598299	38.481447	ccs	cc_out
95615023825791199971975610754292794950	Zhuang-ABCA-3.009	74.127226	13.866381	38.486890	ccs	cc_out
204077232911649662213478306036479568155	Zhuang-ABCA-3.009	75.364054	13.718454	38.480330	ccs	cc_out

174443 rows × 6 columns

[13]:

sc.tl.rank_genes_groups(adata_ctx_cc, 'DEG', groups=['ctx_glut_in'], reference='cc_in', method='wilcoxon')
a_vs_c = sc.get.rank_genes_groups_df(adata_ctx_cc, group='ctx_glut_in')

sc.tl.rank_genes_groups(adata_ctx_cc, 'DEG', groups=['cc_out'], reference='cc_in', method='wilcoxon')
b_vs_c = sc.get.rank_genes_groups_df(adata_ctx_cc, group='cc_out')

# Find out the gene of expressing closer to A in B
similar_genes = []
for gene in b_vs_c['names']:
    if gene in a_vs_c['names'].values:
        a_logfc = a_vs_c[a_vs_c['names'] == gene]['logfoldchanges'].values[0]
        b_logfc = b_vs_c[b_vs_c['names'] == gene]['logfoldchanges'].values[0]
        # if np.sign(a_logfc) == np.sign(b_logfc) and abs(a_logfc - b_logfc) < 0.4 and a_logfc>2 and b_logfc>2:
        if np.sign(a_logfc) == np.sign(b_logfc) and a_logfc > 2 and b_logfc > 2:
            similar_genes.append(gene)

print(f"similar_genes: {len(similar_genes)}")
print(similar_genes)

B中与A更相似的基因: 26
['Slc17a7', 'Ppp1r1b', 'Stac2', 'Syt17', 'Ccnd2', 'Hs3st4', 'Sv2b', 'Fgf13', 'Caln1', 'Kcnh3', 'Neurod2', 'Pde1a', 'Nxph3', 'Vgf', 'Scn4b', 'Kcnj4', 'Bcl11b', 'Oprm1', 'Gpr88', 'Hrh3', 'Egr3', 'Doc2b', '4930452B06Rik', 'Ankrd63', 'Actn2', 'Cckar']

[15]:

def plot_volcano(df, title, highlight_genes=None):
    plt.figure(figsize=(10, 3))

    # -log10(p-value)
    df['log_pval'] = -np.log10(df['pvals'])

    # Set point color
    colors = np.where((df['logfoldchanges'] > 1)&(df['log_pval'] > 3), '#fa7f6f',
                      np.where((df['logfoldchanges'] < -1)&(df['log_pval'] > 3), '#82b0d2', 'grey'))

    # Draw all points
    plt.scatter(df['logfoldchanges'], df['log_pval'], c=colors, alpha=0.8)

    # If you provide a list of highlighting genes, draw these genes
    if highlight_genes:
        highlight_df = df[df['names'].isin(highlight_genes)]
        plt.scatter(highlight_df['logfoldchanges'], highlight_df['log_pval'], color='red')

        # Add tags to highlight points
        for _, row in highlight_df.iterrows():
            plt.annotate(row['names'], (row['logfoldchanges'], row['log_pval']))

    plt.xlabel('Log2 Fold Change')
    plt.ylabel('-Log10(p-value)')
    # plt.title(title)

    # Add threshold line
    plt.axhline(y=-np.log10(0.005), color='black', linestyle='--')
    plt.axvline(x=1, color='black', linestyle='--')
    plt.axvline(x=-1, color='black', linestyle='--')

    # plt.show()
    plt.tight_layout()
    # plt.savefig('./in_out_compare/cc_in_out_deg.pdf', format='pdf')

# Use function
plot_volcano(b_vs_c, 'B vs C Differential Expression', highlight_genes=None)

_images/6_ctx_cc_intracellular_extracellular_compare_14_0.png

[16]:

# adata_ctx = adata_in[adata_in.obs['region'].str.startswith(tuple(ctx_43_regions))]
adata_ctx = adata_in[(adata_in.obs['region'].str.startswith('cc')) | (adata_in.obs['region'].str.startswith(tuple(ctx_43_regions)))]
# plt.figure(figsize=(10,6))
sc.pl.heatmap(adata_ctx, b_vs_c[(b_vs_c['logfoldchanges'] >1)&(b_vs_c['log_pval'] > 3)]['names'].tolist(),'ct',var_group_rotation=True, figsize=(12, 8), show=False)
# plt.savefig('./in_out_compare/down_gene_heatmap.png', dpi=500, bbox_inches='tight')

WARNING: Gene labels are not shown when more than 50 genes are visualized. To show gene labels set `show_gene_labels=True`

[16]:

{'heatmap_ax': <AxesSubplot:>, 'groupby_ax': <AxesSubplot:ylabel='ct'>}

_images/6_ctx_cc_intracellular_extracellular_compare_15_2.png

[21]:

sc.pl.heatmap(adata_ctx, ['Kit','Erbb4', 'Parm1', 'Sulf2', 'Sox2', 'Zfp536', 'Dscaml1'],'ct',var_group_rotation=True, show=False)
plt.tight_layout()
# plt.savefig('./in_out_compare/gaba_oligo_maker.png', dpi=500, bbox_inches='tight')

_images/6_ctx_cc_intracellular_extracellular_compare_16_0.png

[23]:

gene_exp = adata_ctx[:, b_vs_c[(b_vs_c['logfoldchanges'] <-1)&(b_vs_c['log_pval'] > 3)]['names'].tolist()].to_df()
gene_exp['celltype'] = adata_ctx.obs['ct']
gene_mean = gene_exp.groupby('celltype').mean()
df_long = gene_mean.reset_index().melt(id_vars='celltype', var_name='Gene', value_name='Expression')
plt.figure(figsize=(5, 7))
sns.barplot(x='celltype', y='Expression', data=df_long)
# sns.swarmplot(x='celltype', y='Expression', data=df_long)
# plt.xlabel('Row Index')
# plt.ylabel('Expression Level')
# plt.title('Violin Plots for Each Row')
plt.xticks(rotation=45)
# plt.show()
plt.tight_layout()
# plt.legend()
# plt.savefig('./in_out_compare/cc_in_out_deg_down_gene_in_ctx.pdf', format='pdf')

_images/6_ctx_cc_intracellular_extracellular_compare_17_0.png

gene exp plot

[120]:

gene = ['Slc17a7', 'Fgf13', 'Calb1']
df = pd.DataFrame(index=['in_cc', 'out'], columns=gene, dtype=float)
for g in gene:
    # in_cell_ctx = adata_ctx[:, g].X.mean()
    in_cell_cc = adata_cc_in[:, g].X.mean()
    out_cell = adata_cc_out[:, g].X.mean()
    all = in_cell_cc + out_cell
    df.loc['in_cc', g] = in_cell_cc / all
    # df.loc['in_ctx', g] = in_cell_ctx / all
    df.loc['out', g] = out_cell / all

# Set data
categories = gene
colors = ['#eab883', '#b7b7eb']
data = df.values

# Create a chart
fig, ax = plt.subplots(figsize=(3, 6))

# Draw a stack of stacks
x = np.arange(len(categories))
bottom = np.zeros(len(categories))
width=0.6
for i, row in enumerate(data):
    ax.bar(x, row, bottom=bottom, color=colors[i], edgecolor='white',width=width)

    # Add percentage tags above each part
    # for j in range(len(categories)):
    #     percent = row[j] * 100  # Convert the percentage to the percentage system
    #     ax.text(x[j], bottom[j] + row[j] / 2, f'{percent:.1f}%',
    #             ha='center', va='center', color='black', fontsize=10, rotation=90)

    bottom += row

# Add full -coverage ribbon
for j in range(len(categories) - 1):
    left = x[j]+width/2
    right = x[j+1]-width/2
    left_data = data[:, j]
    right_data = data[:, j+1]

    left_cumsum = np.cumsum(left_data)
    right_cumsum = np.cumsum(right_data)

    # Add all ribbons from the bottom to the top
    for i in range(len(colors)):
        left_bottom = left_cumsum[i-1] if i > 0 else 0
        left_top = left_cumsum[i]
        right_bottom = right_cumsum[i-1] if i > 0 else 0
        right_top = right_cumsum[i]

        ax.fill([left, right, right, left],
                [left_bottom, right_bottom, right_top, left_top],
                color=colors[i], alpha=0.3)

# Set chart style
# ax.set_title('Full Coverage Ribbon Stacked Bar Chart')
# ax.set_xlabel('Categories')
ax.set_ylabel('Expression percentage')
plt.xticks(x, rotation=45)
ax.set_xticklabels(categories)
ax.set_yticklabels([])
ax.set_yticks([])

# Remove the coordinate shaft border
for spine in ax.spines.values():
    spine.set_visible(False)

# Display chart
plt.tight_layout()
# plt.savefig('./in_out_compare/in_out_exp.pdf', format='pdf')

_images/6_ctx_cc_intracellular_extracellular_compare_19_0.png

ctx layer maker gene in cc

[87]:

# adata_ctx.obs['layer'] = adata_ctx.obs['DEG'].astype(str)
adata_ctx = adata_in[adata_in.obs['region'].str.startswith(tuple(ctx_43_regions))]
adata_ctx = adata_ctx[adata_ctx.obs['cell_type'].str.contains('Glut')]

adata_ctx = adata_ctx[:,adata_ctx_cc.var_names]
adata_ctx.obs['layer'] = None
adata_ctx.obs.loc[adata_ctx.obs['region'].str.contains('1'), 'layer'] = 'L1'
adata_ctx.obs.loc[adata_ctx.obs['region'].str.contains('2/3'), 'layer'] = 'L2/3'
adata_ctx.obs.loc[adata_ctx.obs['region'].str.contains('4'), 'layer'] = 'L4'
adata_ctx.obs.loc[adata_ctx.obs['region'].str.contains('5'), 'layer'] = 'L5'
adata_ctx.obs.loc[adata_ctx.obs['region'].str.contains('6'), 'layer'] = 'L6'
# adata.obs.loc[adata.obs['region'].isin(['LP', 'LD']), 'layer'] = 'th'
# adata.obs.loc[adata.obs['region'].str.contains('VISp'), 'layer'] = 'VISp'
# adata_ctx_cc.uns['layer_colors'] = ["#FF420E", "#FFBB00", "#4CB5F5", "#89DA59", "#878787", "#B037C4", '#8C6D31']
adata_ctx = adata_ctx[adata_ctx.obs['layer'] != 'L1']
# adata_ctx = adata_ctx[adata_ctx.obs['layer'] is not None]

[90]:

sc.tl.rank_genes_groups(adata_ctx, 'layer', method='wilcoxon')

sc.pl.rank_genes_groups_heatmap(adata_ctx, n_genes=8, show=False)
plt.tight_layout()
plt.savefig('./in_out_compare/ctx_maker.png', dpi=400, bbox_inches='tight')

WARNING: dendrogram data not found (using key=dendrogram_UMAP). Running `sc.tl.dendrogram` with default parameters. For fine tuning it is recommended to run `sc.tl.dendrogram` independently.
WARNING: You’re trying to run this on 1111 dimensions of `.X`, if you really want this, set `use_rep='X'`.
         Falling back to preprocessing with `sc.pp.pca` and default params.

_images/6_ctx_cc_intracellular_extracellular_compare_22_1.png

[95]:

l23 = ['Calb1', 'Cux2', 'Lamp5', 'Gucy1a1', 'Syt17', 'Pdzrn3', 'Otof', 'Lhx2']
l4 = ['Rorb', 'Rspo1', 'Cnih3', 'Tshz1', 'Boc', 'A830036E02Rik', 'Kcnab3', 'Pamr1']
l5 = ['Sulf2', 'Fezf2', 'Tox', 'Cnih3', 'Etv1', 'Coro6', 'Hrh3', 'Fras1']
l6 = ['Grik3', 'Hs3st4', 'Hs3st2', 'Syt6', 'Rprm', 'Col6a1', 'Chrna4', 'Nxph3']
sc.pl.heatmap(
    adata_ctx,
    var_names=l23 + l4 + l5 + l6,
    groupby='layer',
    # cmap='plasma',
    swap_axes=False,
    # dendrogram=True
    show=False
)
# plt.savefig('./in_out_compare/ctx_maker.png', dpi=400, bbox_inches='tight')

_images/6_ctx_cc_intracellular_extracellular_compare_23_0.png

[ ]:

out_exp_mean = []
in_exp_mean = []
for g, layer in zip([l23, l4, l5, l6], ['23', '4', '5', '6']):
        out_exp = adata_cc_out[:, g].to_df().mean()
        in_exp = adata_cc_in[:, g].to_df().mean()
        out_p = out_exp/(in_exp + out_exp)
        in_p = in_exp/(in_exp + out_exp)
        out_exp_mean.append(out_exp.values)
        in_exp_mean.append(in_exp.values)

        categories = in_exp.index
        values1 = in_p.values
        values2 = out_p.values

        width = 0.6
        fig, ax = plt.subplots(figsize=(8,5))

        ax.bar(categories, values1, width, label='in', color='#6bad6b')
        ax.bar(categories, values2, width, bottom=values1, label='out', color='#e56f5e')

        ax.bar([len(categories)], [in_p.values.mean()], width=1, color='#6bad6b')
        ax.bar([len(categories)], [out_p.values.mean()], width=1, bottom=in_p.values.mean(), color='#e56f5e')
        ax.text(len(categories), in_p.values.mean()/2, f'{in_p.values.mean():.2f}',
                ha='center', va='center', color='black', fontweight='bold')
        ax.text(len(categories), out_p.values.mean()/2+in_p.values.mean(), f'{out_p.values.mean():.2f}',
                ha='center', va='center', color='black', fontweight='bold')

        ax.axhline(y=0.5, color='black', linestyle='--', linewidth=2, alpha=0.5)
        ax.set_ylabel('Expression Proportion')
        ax.set_xlabel('Gene')
        all_categories = list(categories) + ['average']
        ax.set_xticks(range(len(all_categories)))
        ax.set_xticklabels(all_categories, rotation=0)

        ax.set_title(f'Layer{layer}')
        ax.legend( bbox_to_anchor=(1, 1), loc='upper left')
        plt.show()
        plt.close()

[92]:

df = pd.DataFrame({
    'Layer2/3': in_exp_mean[0],
    'Layer4': in_exp_mean[1],
    'Layer5': in_exp_mean[2],
    'Layer6': in_exp_mean[3]
})

# Convert the data box to a long format
df_long = df.melt(var_name='Group', value_name='Value')

# Set graphic style and size
# plt.figure(figsize=(12, 6))
colors = ['#9cd2ed', '#a992c0', '#ea9994', '#f2c396']
sns.barplot(x='Group', y='Value', data=df_long, palette=colors)
sns.stripplot(x='Group', y='Value', data=df_long, color='black', alpha=0.5)

# Set the title and label
plt.title('ctx layer maker gene in cc intracellular', fontsize=16)
plt.xlabel('Layer', fontsize=12)
plt.ylabel('mean expression', fontsize=12)
plt.ylim(0, 2.5)

plt.savefig('./in_out_compare/layer_cc_in.pdf', format='pdf')

_images/6_ctx_cc_intracellular_extracellular_compare_25_0.png

[93]:

df = pd.DataFrame({
    'Layer2/3': out_exp_mean[0],
    'Layer4': out_exp_mean[1],
    'Layer5': out_exp_mean[2],
    'Layer6': out_exp_mean[3]
})

# Convert the data box to a long format
df_long2 = df.melt(var_name='Group', value_name='Value')

# Set graphic style and size
# plt.figure(figsize=(4, 6))
colors = ['#9cd2ed', '#a992c0', '#ea9994', '#f2c396']
sns.barplot(x='Group', y='Value', data=df_long2, palette=colors)
sns.stripplot(x='Group', y='Value', data=df_long2, color='black', alpha=0.5)

# Set the title and label
plt.title('ctx layer maker gene in cc extracellular', fontsize=16)
plt.xlabel('Layer', fontsize=12)
plt.ylabel('mean expression', fontsize=12)
plt.ylim(0, 2.5)

plt.savefig('./in_out_compare/layer_cc_out.pdf', format='pdf')

_images/6_ctx_cc_intracellular_extracellular_compare_26_0.png