8_coronal_sagittal_cc_in_out_deg_compare

[1]:

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib as mpl
mpl.rcParams['pdf.fonttype'] = 42
mpl.rcParams['ps.fonttype'] = 42

[2]:

zxw1_deg = pd.read_csv('..data/coronal_all_cc_out_vs_in_gene.csv')
zxw1_deg

[2]:
Unnamed: 0 names scores logfoldchanges pvals pvals_adj log_pval comparison
0 0 Gfap 119.552864 2.961014 0.0 0.0 inf cc-out_vs_cc_in
1 1 Fam107a 101.277850 2.966981 0.0 0.0 inf cc-out_vs_cc_in
2 2 Acsbg1 78.532550 2.523039 0.0 0.0 inf cc-out_vs_cc_in
3 3 Slc17a7 74.491930 3.045114 0.0 0.0 inf cc-out_vs_cc_in
4 4 Id4 73.924090 2.437699 0.0 0.0 inf cc-out_vs_cc_in
... ... ... ... ... ... ... ... ...
1106 1106 Grm3 -59.469433 -1.069267 0.0 0.0 inf cc-out_vs_cc_in
1107 1107 Gprc5b -61.270737 -0.411187 0.0 0.0 inf cc-out_vs_cc_in
1108 1108 Mog -64.613570 -0.375698 0.0 0.0 inf cc-out_vs_cc_in
1109 1109 Sec14l5 -80.332520 -1.933895 0.0 0.0 inf cc-out_vs_cc_in
1110 1110 Cldn11 -81.602516 0.002031 0.0 0.0 inf cc-out_vs_cc_in

1111 rows × 8 columns

[3]:

zxw3_deg = pd.read_csv('../data/sagittal_all_cc_out_vs_in_gene.csv')
zxw3_deg

[3]:
Unnamed: 0 names scores logfoldchanges pvals pvals_adj log_pval
0 0 Gfap 86.087240 3.226564 0.0 0.0 inf
1 1 Sstr1 66.189080 4.045555 0.0 0.0 inf
2 2 Fam107a 63.092552 3.008477 0.0 0.0 inf
3 3 Slc17a7 54.659230 3.349460 0.0 0.0 inf
4 4 Acsbg1 43.139275 2.211851 0.0 0.0 inf
... ... ... ... ... ... ... ...
1106 1106 Grm3 -42.883160 -1.661856 0.0 0.0 inf
1107 1107 Dscaml1 -45.509216 -2.303302 0.0 0.0 inf
1108 1108 Cldn11 -49.790493 -0.007955 0.0 0.0 inf
1109 1109 Sema6a -50.425950 -2.009346 0.0 0.0 inf
1110 1110 Sec14l5 -62.888046 -2.653704 0.0 0.0 inf

1111 rows × 7 columns

[ ]:

plt.rcParams.update({
    # 'font.family': 'sans-serif',
    # 'font.sans-serif': ['Arial'],
    'font.size': 9,
    'axes.titlesize': 11,
    'axes.labelsize': 10,
    'xtick.labelsize': 9,
    'ytick.labelsize': 9,
    'axes.facecolor': 'white',
    'figure.facecolor': 'white',
    'savefig.dpi': 300
})

common_genes = zxw1_deg['names'].tolist()

df1_aligned = zxw1_deg[zxw1_deg['names'].isin(common_genes)].set_index('names').sort_index()
df2_aligned = zxw3_deg[zxw3_deg['names'].isin(common_genes)].set_index('names').sort_index()

correlation = df1_aligned['logfoldchanges'].corr(df2_aligned['logfoldchanges'])

merged_df = pd.DataFrame({
    'Expression_1': df1_aligned['logfoldchanges'],
    'Expression_2': df2_aligned['logfoldchanges']
})

plt.figure(figsize=(3.3, 3.3))
ax = plt.gca()

scatter = sns.regplot(
    x='Expression_1',
    y='Expression_2',
    data=merged_df,
    scatter_kws={
        's': 20,
        'color': '#1f77b4',
        'alpha': 0.6,
        'edgecolor': 'w',
        'linewidth': 0.3
    },
    line_kws={
        'color': '#d62728',
        'linewidth': 1.2
    },
    ci=95
)

ax.spines['bottom'].set_color('black')
ax.spines['left'].set_color('black')
ax.spines['bottom'].set_linewidth(0.5)
ax.spines['left'].set_linewidth(0.5)
sns.despine(trim=True, offset=2)

plt.title(f'r = {correlation:.2f}', pad=10, fontweight='medium')
plt.xlabel('Dataset 1 (log$_2$FC)', labelpad=5)
plt.ylabel('Dataset 2 (log$_2$FC)', labelpad=5)

ax.tick_params(width=0.5, length=3, which='both')

# ax.grid(True, linestyle='--', alpha=0.3, linewidth=0.5)

plt.tight_layout(pad=1.5)

# plt.show()
# plt.savefig('/mnt/Data16Tc/home/haichao/code/sainsc-study-main/cluster_result/paper_fig2/cc_in_out_deg_compare.pdf', bbox_inches='tight', format='pdf')

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_images/8_coronal_sagittal_cc_in_out_deg_compare_4_1.png 
[4]:

zxw1_deg_top100 = zxw1_deg.nlargest(100, 'logfoldchanges')
zxw3_deg_top100 = zxw3_deg.nlargest(100, 'logfoldchanges')
zxw1_deg_top100

[4]:
Unnamed: 0 names scores logfoldchanges pvals pvals_adj log_pval comparison
3 3 Slc17a7 74.491930 3.045114 0.000000e+00 0.000000e+00 inf cc-out_vs_cc_in
1 1 Fam107a 101.277850 2.966981 0.000000e+00 0.000000e+00 inf cc-out_vs_cc_in
0 0 Gfap 119.552864 2.961014 0.000000e+00 0.000000e+00 inf cc-out_vs_cc_in
24 24 Stac2 43.515114 2.741199 0.000000e+00 0.000000e+00 inf cc-out_vs_cc_in
57 57 Kcnh3 28.302246 2.647537 3.242797e-176 4.740457e-175 175.489080 cc-out_vs_cc_in
... ... ... ... ... ... ... ... ...
145 145 Syndig1 12.873200 1.738300 6.370649e-38 3.867645e-37 37.195816 cc-out_vs_cc_in
398 398 Fibcd1 3.639619 1.737264 2.730421e-04 6.594559e-04 3.563770 cc-out_vs_cc_in
371 371 Acvr1c 4.170446 1.735889 3.040033e-05 7.872906e-05 4.517122 cc-out_vs_cc_in
135 135 Calb1 14.095745 1.733454 4.033682e-45 2.605477e-44 44.394298 cc-out_vs_cc_in
46 46 Coro1a 31.388968 1.731327 2.861894e-216 5.046928e-215 215.543346 cc-out_vs_cc_in

100 rows × 8 columns

[5]:

zxw1_deg_small100 = zxw1_deg.nsmallest(100, 'logfoldchanges')
zxw3_deg_small100 = zxw3_deg.nsmallest(100, 'logfoldchanges')
zxw1_deg_small100

[5]:
Unnamed: 0 names scores logfoldchanges pvals pvals_adj log_pval comparison
1109 1109 Sec14l5 -80.332520 -1.933895 0.000000 0.000000 inf cc-out_vs_cc_in
1100 1100 Adamtsl1 -38.866173 -1.699647 0.000000 0.000000 inf cc-out_vs_cc_in
1103 1103 Dscaml1 -50.880287 -1.499207 0.000000 0.000000 inf cc-out_vs_cc_in
1101 1101 Tnfaip6 -46.938343 -1.410299 0.000000 0.000000 inf cc-out_vs_cc_in
1105 1105 Sema6a -58.445510 -1.381543 0.000000 0.000000 inf cc-out_vs_cc_in
... ... ... ... ... ... ... ... ...
989 989 Kcnh8 -0.429622 -0.261252 0.667471 0.747636 0.175568 cc-out_vs_cc_in
985 985 Ptprc -0.355616 -0.259794 0.722128 0.786554 0.141386 cc-out_vs_cc_in
1014 1014 Lama3 -1.007572 -0.249090 0.313660 0.436978 0.503541 cc-out_vs_cc_in
1017 1017 Gpr4 -1.041164 -0.246525 0.297799 0.422803 0.526076 cc-out_vs_cc_in
1016 1016 Fat4 -1.024630 -0.246279 0.305538 0.429965 0.514935 cc-out_vs_cc_in

100 rows × 8 columns

[22]:

len(set(zxw3_deg_small100['names'].tolist()) & set(zxw1_deg_small100['names'].tolist()))

[22]:

83

[23]:

len(set(zxw3_deg_top100['names'].tolist()) & set(zxw1_deg_top100['names'].tolist()))

[23]:

78

[ ]:

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from adjustText import adjust_text

# Data preparation
zxw1_deg['comparison'] = 'coronal'
zxw3_deg['comparison'] = 'sagittal'
df = pd.concat([zxw1_deg, zxw3_deg])

df['regulation'] = df['logfoldchanges'].apply(lambda x: 'Up' if x > 0 else 'Down')

# For coronal data (zxw1_deg), select the top 100 and bottom 100 genes by logfoldchanges
coronal_top = zxw1_deg.nlargest(100, 'logfoldchanges')['names']
coronal_bottom = zxw1_deg.nsmallest(100, 'logfoldchanges')['names']
coronal_highlight = set(coronal_top).union(set(coronal_bottom))

# For sagittal data (zxw3_deg), select the top 100 and bottom 100 genes by logfoldchanges
sagittal_top = zxw3_deg.nlargest(100, 'logfoldchanges')['names']
sagittal_bottom = zxw3_deg.nsmallest(100, 'logfoldchanges')['names']
sagittal_highlight = set(sagittal_top).union(set(sagittal_bottom))

# Set 'highlight' column based on 'comparison'
def highlight_flag(row):
    if row['comparison'] == 'coronal':
        return row['names'] in coronal_highlight
    else:
        return row['names'] in sagittal_highlight

df['highlight'] = df.apply(highlight_flag, axis=1)

# Reset index to ensure uniqueness
df = df.reset_index(drop=True)

# Specify genes to label: top and bottom 5 genes for each group
# Label for coronal data
coronal_data = df[df['comparison'] == 'coronal'].copy()
coronal_top_indices = coronal_data.nlargest(5, 'logfoldchanges').index
coronal_bottom_indices = coronal_data.nsmallest(5, 'logfoldchanges').index

# Label for sagittal data
sagittal_data = df[df['comparison'] == 'sagittal'].copy()
sagittal_top_indices = sagittal_data.nlargest(5, 'logfoldchanges').index
sagittal_bottom_indices = sagittal_data.nsmallest(5, 'logfoldchanges').index

# Initialize labeling column
df['to_label'] = False
df.loc[coronal_top_indices, 'to_label'] = True
df.loc[coronal_bottom_indices, 'to_label'] = True
df.loc[sagittal_top_indices, 'to_label'] = True
df.loc[sagittal_bottom_indices, 'to_label'] = True

# Verify number of labeled genes per group
coronal_labeled = df[df['to_label'] & (df['comparison'] == 'coronal')]
sagittal_labeled = df[df['to_label'] & (df['comparison'] == 'sagittal')]

# Prepare jittered x-coordinates for visualization
df['jitter_x'] = df['comparison'].map({'coronal': 0, 'sagittal': 1})
np.random.seed(39)
df['jitter_x'] = df['jitter_x'] + np.random.uniform(-0.2, 0.2, len(df))

df_label = df[df['to_label']].copy()
df_nonlabel = df[~df['to_label']].copy()

# Plotting
plt.figure(figsize=(9, 5))

# Plot non-labeled points (regular circles, colored by regulation, excluding highlighted genes)
ax = sns.scatterplot(
    x='jitter_x',
    y='logfoldchanges',
    hue='regulation',
    data=df_nonlabel[~df_nonlabel['highlight']],
    palette={'Up': '#E64B35', 'Down': '#3182BD'},
    s=20,
    alpha=0.3,
    edgecolor='none',
    marker='o'
)

# Plot highlighted genes (darker colors), only for non-labeled data
sns.scatterplot(
    x='jitter_x',
    y='logfoldchanges',
    hue='regulation',
    data=df_nonlabel[df_nonlabel['highlight']],
    palette={'Up': '#8B0000', 'Down': '#00008B'},
    s=40,
    alpha=0.6,
    marker='o',
    edgecolor='black',
    linewidth=0.5,
    ax=ax,
    legend=False
)

# Plot labeled points (diamond markers)
for idx, row in df_label.iterrows():
    is_up = row['logfoldchanges'] > 0
    is_highlight = row['highlight']
    # Determine color based on regulation and highlight status
    color = '#8B0000' if is_up and is_highlight else '#00008B' if (not is_up and is_highlight) else '#E64B35' if is_up else '#3182BD'
    marker = 'D'
    size = 55
    ax.scatter(
        row['jitter_x'],
        row['logfoldchanges'],
        s=size,
        color=color,
        marker=marker,
        alpha=0.8,
        linewidth=0.5,
        zorder=10
    )

# Add text annotations
texts = []
for idx, row in df_label.iterrows():
    gene_name = row['names']
    offset_x = 0.1 if row['logfoldchanges'] > 0 else -0.1
    ha = 'left' if row['logfoldchanges'] > 0 else 'right'
    text = ax.text(
        row['jitter_x'] + offset_x,
        row['logfoldchanges'],
        gene_name,
        fontsize=8,
        color='black',
        fontweight='bold',
        ha=ha,
        va='center',
        bbox=dict(boxstyle='round,pad=0.2', facecolor='white', edgecolor='gray', alpha=0.7)
    )
    texts.append(text)

# Adjust text to avoid overlaps
adjust_text(
    texts,
    arrowprops=dict(arrowstyle='->', color='gray', lw=0.5, alpha=0.7),
    expand_points=(0.5, 0.5),
    expand_text=(0.5, 0.5),
    force_text=(0.4, 1.3)
)

# Add horizontal line at y=0
ax.axhline(0, color='black', linestyle='--', linewidth=1, alpha=0.5, zorder=0)

# Axis customization
ax.set_xticks([0, 1])
ax.set_xticklabels(['Coronal', 'Sagittal'])
ax.set_xlabel('Comparison', labelpad=10)
ax.set_ylabel('log$_2$ Fold Change', labelpad=10)
sns.despine(trim=True)

# Custom legend
legend_elements = [
    plt.Line2D([0], [0], marker='o', color='w', label='Up-regulated',
               markerfacecolor='#E64B35', markersize=8, alpha=0.6),
    plt.Line2D([0], [0], marker='o', color='w', label='Down-regulated',
               markerfacecolor='#3182BD', markersize=8, alpha=0.6),
    plt.Line2D([0], [0], marker='o', color='w', label='Common up-regulated',
               markerfacecolor='#8B0000', markersize=8, alpha=0.8),
    plt.Line2D([0], [0], marker='o', color='w', label='Common down-regulated',
               markerfacecolor='#00008B', markersize=8, alpha=0.8),
    plt.Line2D([0], [0], marker='D', color='w', label='Top/bottom 5 genes',
               markerfacecolor='gray', markersize=8, alpha=0.8)
]

ax.legend(handles=legend_elements,
          bbox_to_anchor=(1.05, 1),
          frameon=False,
          title='Gene Types')

# Adjust layout
plt.tight_layout()
# plt.show()
# plt.savefig('/mnt/Data16Tc/home/haichao/code/sainsc-study-main/cluster_result/paper_fig2/cc_in_out_deg.pdf', bbox_inches='tight', format='pdf')
_images/8_coronal_sagittal_cc_in_out_deg_compare_9_0.png