scSVC reconstructs whole-genome profiles of Xenium datasets and defines fine-grained subtypes of T cells
Notebook Guide
Purpose. Analyze reconstructed T-cell states and compare sc-SVC signals against raw Xenium measurements.
Inputs. Executed T-cell reconstruction outputs under ../../output/sc_SVC_case/P5CRC_Xenium/T/ plus raw Xenium data.
Outputs. Subtype spatial maps, marker dot plots, volcano plots, and enrichment summaries displayed inline and saved to disk.
Reading order.
Load reconstructed SVCs
Visualize T-cell subtypes
Quantify differential genes and pathways
Compare raw and reconstructed signals
Reproducibility note. revise imports are standard package imports from the installed revise-svc distribution; this notebook does not modify sys.path to import the repository source tree.
[1]:
import os
os.environ.setdefault("TQDM_DISABLE", "1")
os.environ.setdefault("TQDM_MININTERVAL", "60")
try:
from IPython import get_ipython
_ipython = get_ipython()
if _ipython is not None:
_ipython.run_line_magic("matplotlib", "inline")
except Exception:
pass
output_dir = "../../output/sc_SVC_case/P2CRC_Xenium"
select_ct = "T"
Load Reconstructed SVCs
Load the executed reconstruction outputs that drive the downstream figures.
[2]:
import os
import scanpy as sc
from revise.backend.runners.sc_svc_application import ScSVCAnalysis
import pandas as pd
import gseapy as gp
import revise.analysis.bio as _revise_bio
_EMPTY_ENRICHMENT_COLUMNS = [
"Gene_set",
"Term",
"Overlap",
"P-value",
"Adjusted P-value",
"Old P-value",
"Old Adjusted P-value",
"Odds Ratio",
"Combined Score",
"Genes",
]
def _get_enrichment_human_compatible(deg_genes, geneset_file, cutoff=0.05):
if not deg_genes:
return pd.DataFrame(columns=_EMPTY_ENRICHMENT_COLUMNS)
try:
enr = gp.enrichr(
gene_list=deg_genes,
gene_sets=geneset_file,
organism="human",
cutoff=cutoff,
)
return enr.results
except Exception as exc:
print(f"Skipping enrichment analysis: {type(exc).__name__}: {exc}")
return pd.DataFrame(columns=_EMPTY_ENRICHMENT_COLUMNS)
_revise_bio.get_enrichment = _get_enrichment_human_compatible
svc_save_dir = f"{output_dir}/{select_ct}"
sc_svc_expr = sc.read_h5ad(f"{svc_save_dir}/sc_SVC_expr.h5ad")
sc_svc_spatial = sc.read_h5ad(f"{svc_save_dir}/sc_SVC_spatial.h5ad")
sc_svc_analysis = ScSVCAnalysis(sc_svc_spatial, sc_svc_expr,
"SVC_cluster")
# Legacy notebooks use these aliases in downstream result and plotting cells.
sc_SVC_adata = sc_svc_analysis.sc_SVC_adata_spatial.copy()
adata_sp = sc_svc_analysis.sc_SVC_adata_spatial.copy()
adata_sc = sc_svc_analysis.sc_SVC_adata_expr.copy()
/cpfs01/projects-HDD/cfff-c7cd658afc74_HDD/jiaoyifeng/miniconda3/envs/brainbeacon/lib/python3.9/site-packages/numba/core/decorators.py:246: RuntimeWarning: nopython is set for njit and is ignored
warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)
Conducting differential expression analysis...
[3]:
cm_df = sc_svc_analysis.get_cm_df("Level2")
cm_df
/cpfs01/projects-HDD/cfff-c7cd658afc74_HDD/jiaoyifeng/miniconda3/envs/brainbeacon/lib/python3.9/site-packages/revise/backend/runners/sc_svc_application.py:67: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
grouped = self.sc_SVC_adata_expr.obs.groupby([self.cluster_col, sub_cell_type_col]).size()
[3]:
| Level2 | CD4T | CD8T | NK | Tprolif |
|---|---|---|---|---|
| SVC_cluster | ||||
| 0 | 553 | 6 | 0 | 57 |
| 1 | 22 | 3 | 0 | 262 |
| 2 | 164 | 1098 | 99 | 73 |
| 3 | 498 | 85 | 1 | 7 |
| 4 | 712 | 50 | 0 | 32 |
| 5 | 819 | 7 | 0 | 31 |
| 6 | 197 | 99 | 53 | 36 |
[4]:
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
size = None
cmap = plt.cm.get_cmap('tab20', lut=10)
palette = [mcolors.to_hex(cmap(i)) for i in range(cmap.N)]
sc.pl.scatter(sc_svc_analysis.sc_SVC_adata_spatial, x="x", y="y",
color='Level2',
size=size)
desired_order = ['1', '2', '3', '5', '4', '6', '0'] # for better visualization
current_categories = sc_svc_analysis.sc_SVC_adata_spatial.obs['SVC_cluster'].cat.categories.astype(str).tolist()
ordered_categories = [cat for cat in desired_order if cat in current_categories]
ordered_categories += [cat for cat in current_categories if cat not in ordered_categories]
sc_svc_analysis.sc_SVC_adata_spatial.obs['SVC_cluster'] = (
sc_svc_analysis.sc_SVC_adata_spatial.obs['SVC_cluster'].cat.reorder_categories(ordered_categories, ordered=True)
)
sc.pl.scatter(sc_svc_analysis.sc_SVC_adata_spatial, x="x", y="y",
color='SVC_cluster',
size=size)
/tmp/ipykernel_33723/2520313853.py:5: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.
cmap = plt.cm.get_cmap('tab20', lut=10)
Spatial Subtype Maps
Display reconstructed spatial cell states and side-by-side annotation comparisons.
[5]:
import matplotlib.pyplot as plt
def plot_sc_SVC(adata, color, title = None, file_name = None):
colors = color if isinstance(color, (list, tuple)) else [color]
titles = title if isinstance(title, (list, tuple)) else [title] * len(colors)
plt.figure(figsize=(10, 8*len(colors)))
for color_key, plot_title in zip(colors, titles):
sc.pl.scatter(
adata, x="x", y="y",
color=color_key,
title=plot_title, show=False,
)
plt.savefig(file_name, dpi = 300)
plt.show()
plt.close()
sc_SVC_file_name = f"{output_dir}/sc_SVC.png"
plot_sc_SVC(sc_svc_analysis.sc_SVC_adata_spatial, color='SVC_cluster',
file_name=sc_SVC_file_name
)
sc_SVC_file_name = f"{output_dir}/compare.png"
plot_sc_SVC(sc_svc_analysis.sc_SVC_adata_spatial, color=['Level2','SVC_cluster'],
title=["Expert anno", "sc_SVC"],
file_name=sc_SVC_file_name
)
<Figure size 1000x800 with 0 Axes>
<Figure size 1000x1600 with 0 Axes>
bioinfo analysis
[6]:
cluster_nums = ['1','3','5']
fc_threshold = 1
pathway_num = 20
gene_num = 60
geneset_file = ["MSigDB_Hallmark_2020"]
pathway_file_name = f"{output_dir}/pathway_{fc_threshold}_{pathway_num}.txt"
all_pathway = sc_svc_analysis.get_pathway_conclusion(
cluster_nums, fc_threshold=fc_threshold, pathway_num=pathway_num, gene_num=gene_num, geneset_file=geneset_file, normalize=True)
all_pathway.to_csv(pathway_file_name)
all_pathway
/cpfs01/projects-HDD/cfff-c7cd658afc74_HDD/jiaoyifeng/miniconda3/envs/brainbeacon/lib/python3.9/site-packages/scanpy/preprocessing/_normalization.py:207: UserWarning: Received a view of an AnnData. Making a copy.
view_to_actual(adata)
Conducting differential expression analysis...
Cluster 1:
Up-regulated genes: ['MKI67', 'TYMS', 'HIST1H1B', 'TUBB', 'HMGB2', 'HIST1H1C', 'ACTB', 'RRM2', 'LMNB1', 'TOP2A', 'HIST1H4C', 'PFN1', 'VIM', 'CFL1', 'HIST1H1D', 'TPX2', 'NUSAP1', 'NCAPG2', 'TACC3', 'HIST1H2BH', 'CENPM', 'MCM7', 'SMC4', 'KIF11', 'CRIP1', 'HNRNPA2B1', 'MCM4', 'KIFC1', 'LCP1', 'PHF19', 'NCAPG', 'HJURP', 'IDH2', 'PCLAF', 'H2AFZ', 'CEP55', 'ZWINT', 'TUBA1B', 'CDK1', 'CORO1A', 'ANP32E', 'NDC80', 'TMSB10', 'KIF22', 'TMPO', 'HMMR', 'EZH2', 'COTL1', 'TCF19', 'MCM2', 'CENPF', 'LMNB2', 'PLK1', 'BUB1', 'TK1', 'SMC2', 'FANCI', 'TUBB4B', 'HELLS', 'RRM1']
Enriched pathways: ['E2F Targets' 'G2-M Checkpoint' 'Mitotic Spindle' 'mTORC1 Signaling'
'Myc Targets V1' 'PI3K/AKT/mTOR Signaling' 'Spermatogenesis'
'Myc Targets V2' 'Pperoxisome' 'DNA Repair' 'Apoptosis' 'Glycolysis'
'Estrogen Response Late' 'Apical Junction'
'Epithelial Mesenchymal Transition' 'Protein Secretion'
'Bile Acid Metabolism' 'Oxidative Phosphorylation' 'KRAS Signaling Up'
'Complement']
Cluster 3:
Up-regulated genes: ['CCL5', 'IL7R', 'GZMA', 'MUC12', 'CEACAM5', 'TC2N', 'FABP1', 'PIGR', 'ERN1', 'KLRK1', 'APOBR', 'ITGA1', 'CD8A', 'SORL1', 'KLRG1', 'EPHA1', 'SYTL2', 'LY9', 'CD300A', 'OLFM4', 'MATK', 'GSAP', 'TRGC1', 'GPR15', 'PTGER2', 'ADRB2', 'ERMP1', 'PPIAL4G', 'CTSW', 'RALGPS1', 'AOAH', 'NELL2', 'MYO1D', 'LRFN1', 'KRT8', 'GZMK', 'GPR35', 'CAPRIN2', 'CEBPD', 'SNTB1', 'SMAGP', 'ARRDC5', 'VPS9D1', 'COLQ', 'PRR5L', 'PLAC8', 'ACVR1B', 'PI3', 'MYBL1', 'SPEF2', 'TRGC2', 'BMP1', 'SCART1', 'PLCB1', 'LGALS4', 'FCRL6', 'RNFT1', 'TRMT10B', 'FLT4', 'RNF157']
Enriched pathways: ['Inflammatory Response' 'TNF-alpha Signaling via NF-kB'
'Allograft Rejection' 'KRAS Signaling Up' 'Interferon Gamma Response'
'Complement' 'Estrogen Response Early'
'Epithelial Mesenchymal Transition' 'p53 Pathway' 'Hypoxia'
'Xenobiotic Metabolism' 'IL-2/STAT5 Signaling' 'Fatty Acid Metabolism'
'Androgen Response' 'Unfolded Protein Response'
'PI3K/AKT/mTOR Signaling' 'Apical Junction' 'IL-6/JAK/STAT3 Signaling'
'Coagulation' 'mTORC1 Signaling']
Cluster 5:
Up-regulated genes: ['FOXP3', 'IGHG3', 'LTB', 'MAF', 'TBC1D4', 'TIGIT', 'CCR4', 'ICOS', 'RTKN2', 'TNFRSF18', 'CTLA4', 'PIM2', 'DGKA', 'IL2RA', 'CCR8', 'SELL', 'LAYN', 'TNFRSF4', 'NABP1', 'IL1R1', 'CD27', 'ZC3H12D', 'CCR6', 'STAM', 'LINC02694', 'CD80', 'CCDC141', 'SOCS3', 'CSF1', 'SESN3', 'MAST4', 'IKZF2', 'TTN', 'ADAT2', 'PLCL1', 'F5', 'COL9A2', 'ENTPD1', 'IKZF4', 'KCNC3', 'CSF2RB', 'CD177', 'IL1R2', 'MCF2L2', 'WHRN', 'PITPNM2', 'ACAD11', 'NTNG2', 'BCL2L11', 'MAP1LC3A', 'PLA2G4C', 'SYNGAP1', 'CYSLTR1', 'SLC35D2', 'INPP5F', 'TSHZ2', 'FBLN7', 'CYP7B1', 'SOGA3', 'NINJ1']
Enriched pathways: ['IL-2/STAT5 Signaling' 'IL-6/JAK/STAT3 Signaling'
'TNF-alpha Signaling via NF-kB' 'Allograft Rejection'
'Inflammatory Response' 'Interferon Alpha Response'
'Interferon Gamma Response' 'Estrogen Response Early' 'KRAS Signaling Dn'
'Xenobiotic Metabolism' 'UV Response Up' 'p53 Pathway'
'Protein Secretion' 'Androgen Response' 'Bile Acid Metabolism'
'Unfolded Protein Response' 'Apical Junction' 'Apoptosis'
'Mitotic Spindle' 'Adipogenesis']
[6]:
| Gene_set | Term | Overlap | P-value | Adjusted P-value | Old P-value | Old Adjusted P-value | Odds Ratio | Combined Score | Genes | group | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | MSigDB_Hallmark_2020 | E2F Targets | 22/200 | 3.112157e-29 | 6.224314e-28 | 0 | 0 | 64.276168 | 4219.065275 | TOP2A;TCF19;HELLS;RRM2;MCM7;TUBB;PLK1;HMGB2;KI... | 1 |
| 1 | MSigDB_Hallmark_2020 | G2-M Checkpoint | 19/200 | 5.976739e-24 | 5.976739e-23 | 0 | 0 | 50.589004 | 2705.204861 | TOP2A;PLK1;KIF11;KIF22;HMMR;MKI67;SMC4;NDC80;L... | 1 |
| 2 | MSigDB_Hallmark_2020 | Mitotic Spindle | 12/199 | 6.214391e-13 | 4.142927e-12 | 0 | 0 | 26.407754 | 742.235837 | TOP2A;TPX2;CENPF;PLK1;NUSAP1;CDK1;KIF11;KIF22;... | 1 |
| 3 | MSigDB_Hallmark_2020 | mTORC1 Signaling | 6/200 | 2.963485e-05 | 1.185394e-04 | 0 | 0 | 11.309278 | 117.916863 | RRM2;PLK1;MCM4;CORO1A;BUB1;MCM2 | 1 |
| 4 | MSigDB_Hallmark_2020 | Myc Targets V1 | 6/200 | 2.963485e-05 | 1.185394e-04 | 0 | 0 | 11.309278 | 117.916863 | RRM1;MCM7;HNRNPA2B1;MCM4;TYMS;MCM2 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 19 | MSigDB_Hallmark_2020 | Apical Junction | 1/200 | 4.533326e-01 | 4.533326e-01 | 0 | 0 | 1.681373 | 1.330183 | LAYN | 5 |
| 16 | MSigDB_Hallmark_2020 | Apoptosis | 1/161 | 3.847172e-01 | 4.533326e-01 | 0 | 0 | 2.095339 | 2.001566 | BCL2L11 | 5 |
| 17 | MSigDB_Hallmark_2020 | Mitotic Spindle | 1/199 | 4.516711e-01 | 4.533326e-01 | 0 | 0 | 1.689950 | 1.343174 | BCL2L11 | 5 |
| 18 | MSigDB_Hallmark_2020 | Adipogenesis | 1/200 | 4.533326e-01 | 4.533326e-01 | 0 | 0 | 1.681373 | 1.330183 | NABP1 | 5 |
| 20 | MSigDB_Hallmark_2020 | Complement | 1/200 | 4.533326e-01 | 4.533326e-01 | 0 | 0 | 1.681373 | 1.330183 | F5 | 5 |
61 rows × 11 columns
Marker And Differential Programs
Summarize marker genes, differential genes, and pathway-level signals.
[7]:
cluster_nums = ['1', '3', '5']
degs = sc_svc_analysis.get_svc_degs(cluster_nums, fc_threshold=1)
marker_dict = (
degs.groupby('group')['gene']
.apply(lambda x: x.head(5).tolist())
.to_dict()
)
sc_svc_analysis.get_dot_plot(cluster_nums, marker_dict)
Conducting differential expression analysis...
[8]:
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rcParams['pdf.fonttype'] = 42
mpl.rcParams['ps.useafm'] = False
plt.figure(figsize=(8, 6))
marker_dict = {
'1': ['MKI67', 'TYMS', 'RRM2'],
'3': ['CCL5', 'IL7R', 'GZMA'],
'5': ['FOXP3', 'CTLA4', 'MAF']
}
sc_svc_analysis.get_dot_plot(cluster_nums, marker_dict, normalize=True)
plt.savefig(f"{output_dir}/sc_SVC_dotplot.pdf", dpi=300, bbox_inches='tight')
plt.show()
plt.close()
<Figure size 800x600 with 0 Axes>
<Figure size 640x480 with 0 Axes>
sc_SVC
[9]:
# tumor_cluster_num = '1' # Proliferation
tumor_cluster_num = '5' # Regulation
# tumor_cluster = '0' # Median
normal_cluster = '3' # normal
if tumor_cluster_num == '1':
tumor_cluster = 'Proliferation'
elif tumor_cluster_num == '5':
tumor_cluster = 'Regulation'
[10]:
import pandas as pd
# for statical numbers of degs and pathways
pvals_adj = 0.01
logfoldchanges = 1
num_df = pd.DataFrame(0, index=['sp_SC', 'raw_Xenium'], columns=['DEG', 'Pathway'])
cluster_nums = [normal_cluster, tumor_cluster_num]
replace_cols = {normal_cluster: 'Normal-infiltrated', tumor_cluster_num: 'Tumor-infiltrated'}
degs = sc_svc_analysis.get_volcano_plot(cluster_nums, target_group="Tumor-infiltrated", replace_cols=replace_cols, fc_threshold=None, log_fold_changes=10, logfc_threshold=1, padj_threshold=1e-6, top_k=10)
deg_num = ((degs['pvals_adj'] < pvals_adj) & (degs['logfoldchanges'].abs() > logfoldchanges)).sum()
print( f"sc_SVC {tumor_cluster} degs num: ", deg_num )
num_df.loc['sp_SC', 'DEG'] = deg_num
plt.savefig(f"{output_dir}/sc_SVC/{tumor_cluster}_volcano.pdf", dpi=300, bbox_inches='tight')
plt.show()
/cpfs01/projects-HDD/cfff-c7cd658afc74_HDD/jiaoyifeng/miniconda3/envs/brainbeacon/lib/python3.9/site-packages/revise/backend/runners/sc_svc_application.py:134: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
select_adata.obs['SVC_cluster'].replace(replace_cols, inplace=True)
/cpfs01/projects-HDD/cfff-c7cd658afc74_HDD/jiaoyifeng/miniconda3/envs/brainbeacon/lib/python3.9/site-packages/revise/backend/runners/sc_svc_application.py:134: FutureWarning: The behavior of Series.replace (and DataFrame.replace) with CategoricalDtype is deprecated. In a future version, replace will only be used for cases that preserve the categories. To change the categories, use ser.cat.rename_categories instead.
select_adata.obs['SVC_cluster'].replace(replace_cols, inplace=True)
Conducting differential expression analysis...
sc_SVC Regulation degs num: 89
<Figure size 640x480 with 0 Axes>
[11]:
# for enrichment analysis
from revise.analysis.bio import get_enrichment, pathway_barplot, pathway_network_plot
degs = degs[degs['logfoldchanges'] > 0]
degs.reset_index(drop = True, inplace = True)
deg_genes = degs["gene"][:60].tolist()
pathway = get_enrichment(deg_genes, geneset_file)
pathway.to_csv(f"{output_dir}/sc_SVC/{tumor_cluster}_pathway.csv", index=False)
pathway_barplot(pathway)
pathway_network_plot(pathway,
top_term = 6,
save_file_name = f'{output_dir}/sc_SVC/{tumor_cluster}_network.pdf')
raw
[12]:
from revise.analysis.bio import get_degs
os.makedirs(f"{output_dir}/raw_Xenium", exist_ok=True)
sc_SVC_adata = sc_svc_analysis.sc_SVC_adata_spatial.copy()
raw_select_adata = sc_SVC_adata[sc_SVC_adata.obs['SVC_cluster'].isin([normal_cluster, tumor_cluster_num])]
raw_select_adata.obs['SVC_cluster'].replace({normal_cluster: 'Normal-infiltrated', tumor_cluster_num: 'Tumor-infiltrated'}, inplace=True)
raw_deg_df = get_degs(raw_select_adata, groupby='SVC_cluster', method='t-test', fc_threshold=None)
raw_deg_df = raw_deg_df[raw_deg_df['group'] == "Tumor-infiltrated"]
raw_deg_df.reset_index(drop = True, inplace = True)
raw_deg_df.to_csv(f"{output_dir}/raw_Xenium/{tumor_cluster}_degs.csv", index=False)
from revise.analysis.bio import plot_volcano
plot_volcano(raw_deg_df, logfc_threshold=1, padj_threshold=1e-6,
top_k=10, save_file_name = f"{output_dir}/raw_Xenium/{tumor_cluster}_volcano.pdf")
/tmp/ipykernel_33723/3530571688.py:5: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
raw_select_adata.obs['SVC_cluster'].replace({normal_cluster: 'Normal-infiltrated', tumor_cluster_num: 'Tumor-infiltrated'}, inplace=True)
/tmp/ipykernel_33723/3530571688.py:5: FutureWarning: The behavior of Series.replace (and DataFrame.replace) with CategoricalDtype is deprecated. In a future version, replace will only be used for cases that preserve the categories. To change the categories, use ser.cat.rename_categories instead.
raw_select_adata.obs['SVC_cluster'].replace({normal_cluster: 'Normal-infiltrated', tumor_cluster_num: 'Tumor-infiltrated'}, inplace=True)
Conducting differential expression analysis...
[13]:
# for enrichment analysis
from revise.analysis.bio import get_enrichment
raw_deg_df = raw_deg_df[raw_deg_df['logfoldchanges'] > 0] # postive for up-regulated pathway
raw_deg_df.reset_index(drop = True, inplace = True)
deg_genes = raw_deg_df["gene"][:60].tolist()
pathway = get_enrichment(deg_genes, geneset_file)
pathway.to_csv(f"{output_dir}/raw_Xenium/{tumor_cluster}_pathway.csv", index=False)
raw_pathway_num = (pathway['Adjusted P-value'] < pvals_adj).sum()
print( f"Raw Xenium {tumor_cluster} pathway num: ", raw_pathway_num )
num_df.loc['raw_Xenium', 'Pathway'] = raw_pathway_num
num_df.to_csv(f"{output_dir}/sc_SVC/{tumor_cluster}_num.csv", index=False)
pathway_barplot(pathway)
pathway_network_plot(pathway,
top_term = 6,
save_file_name = f'{output_dir}/raw_Xenium/{tumor_cluster}_network.pdf')
Raw Xenium Regulation pathway num: 8
[14]:
# for enrichment analysis
from revise.analysis.bio import get_enrichment
raw_deg_df = raw_deg_df[raw_deg_df['logfoldchanges'] > 0] # postive for up-regulated pathway
raw_deg_df.reset_index(drop = True, inplace = True)
deg_genes = raw_deg_df["gene"][:60].tolist()
pathway = get_enrichment(deg_genes, geneset_file)
pathway.to_csv(f"{output_dir}/raw_Xenium/{tumor_cluster}_pathway.csv", index=False)
raw_pathway_num = (pathway['Adjusted P-value'] < pvals_adj).sum()
print( f"Raw Xenium {tumor_cluster} pathway num: ", raw_pathway_num )
num_df.loc['raw_Xenium', 'Pathway'] = raw_pathway_num
num_df.to_csv(f"{output_dir}/sc_SVC/{tumor_cluster}_num.csv", index=False)
pathway_barplot(pathway)
pathway_network_plot(pathway,
top_term = 6,
save_file_name = f'{output_dir}/raw_Xenium/{tumor_cluster}_network.pdf')
Raw Xenium Regulation pathway num: 8
