Omnipath¶
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from bengrn.base import train_classifier
from bengrn import compute_genie3, BenGRN
from grnndata import utils as grnutils
import pandas as pd
import scanpy as sc
from anndata.utils import make_index_unique
%load_ext autoreload
%autoreload 2
import torch
torch.set_float32_matmul_precision('medium')
from bengrn.base import train_classifier
from bengrn import compute_genie3, BenGRN
from grnndata import utils as grnutils
import pandas as pd
import scanpy as sc
from anndata.utils import make_index_unique
%load_ext autoreload
%autoreload 2
import torch
torch.set_float32_matmul_precision('medium')
/pasteur/appa/homes/jkalfon/miniconda3/envs/scprint17/lib/python3.10/site-packages/torch/cuda/__init__.py:546: UserWarning: Can't initialize NVML
warnings.warn("Can't initialize NVML")
/pasteur/appa/homes/jkalfon/miniconda3/envs/scprint17/lib/python3.10/site-packages/bitsandbytes/cextension.py:31: UserWarning: The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.
warn("The installed version of bitsandbytes was compiled without GPU support. "
/pasteur/appa/homes/jkalfon/miniconda3/envs/scprint17/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cpu.so: undefined symbol: cadam32bit_grad_fp32
💡 connected lamindb: jkobject/scprint
/pasteur/appa/homes/jkalfon/miniconda3/envs/scprint17/lib/python3.10/site-packages/umap/__init__.py:9: ImportWarning: Tensorflow not installed; ParametricUMAP will be unavailable warn(
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NUM_GENES = 5000
MAXCELLS = 1024
NUM_GENES = 5000
MAXCELLS = 1024
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CELLTYPES = [
'kidney distal convoluted tubule epithelial cell',
'kidney loop of Henle thick ascending limb epithelial cell',
'kidney collecting duct principal cell',
'mesangial cell',
'blood vessel smooth muscle cell',
'podocyte',
'macrophage',
'leukocyte',
'kidney interstitial fibroblast',
# 'endothelial cell'
]
CELLTYPES = [
'kidney distal convoluted tubule epithelial cell',
'kidney loop of Henle thick ascending limb epithelial cell',
'kidney collecting duct principal cell',
'mesangial cell',
'blood vessel smooth muscle cell',
'podocyte',
'macrophage',
'leukocyte',
'kidney interstitial fibroblast',
# 'endothelial cell'
]
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#adata = sc.read_h5ad('/home/ml4ig1/scprint/.lamindb/yBCKp6HmXuHa0cZptMo7.h5ad')
adata = sc.read_h5ad('../data/yBCKp6HmXuHa0cZptMo7.h5ad')
adata.var["isTF"] = False
adata.var.loc[adata.var.symbol.isin(grnutils.TF), "isTF"] = True
adata
#adata = sc.read_h5ad('/home/ml4ig1/scprint/.lamindb/yBCKp6HmXuHa0cZptMo7.h5ad')
adata = sc.read_h5ad('../data/yBCKp6HmXuHa0cZptMo7.h5ad')
adata.var["isTF"] = False
adata.var.loc[adata.var.symbol.isin(grnutils.TF), "isTF"] = True
adata
Out[4]:
View of AnnData object with n_obs × n_vars = 15728 × 23149
obs: 'donor_id', 'self_reported_ethnicity_ontology_term_id', 'organism_ontology_term_id', 'sample_uuid', 'sample_preservation_method', 'tissue_ontology_term_id', 'development_stage_ontology_term_id', 'tissue_section_uuid', 'tissue_section_thickness', 'library_uuid', 'assay_ontology_term_id', 'mapped_reference_annotation', 'is_primary_data', 'cell_type_ontology_term_id', 'author_cell_type', 'disease_ontology_term_id', 'sex_ontology_term_id', 'suspension_type', 'cell_type', 'assay', 'disease', 'organism', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage', 'cell_culture', 'nnz', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_20_genes', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'log1p_total_counts_ribo', 'pct_counts_ribo', 'total_counts_hb', 'log1p_total_counts_hb', 'pct_counts_hb', 'outlier', 'mt_outlier', 'leiden_3', 'leiden_2', 'leiden_1', 'dpt_group', 'heat_diff', 'proliferation', 'apoptosis', 'normalized_growth'
var: 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype', 'ncbi_gene_id', 'biotype', 'description', 'synonyms', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'symbol', 'isTF'
uns: 'T_fwd_params', 'diffmap_evals', 'iroot', 'leiden', 'neighbors', 'umap', 'unseen_genes'
obsm: 'X_diffmap', 'X_pca', 'X_umap', 'clean_pca'
layers: 'clean'
obsp: 'T_fwd', 'connectivities', 'distances'
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adata.obs['current'] = "other"
adata.obs.loc[adata.obs['cell_type']=="podocyte", 'current'] = "podocyte"
adata.obs['current'] = "other"
adata.obs.loc[adata.obs['cell_type']=="podocyte", 'current'] = "podocyte"
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sc.pl.umap(adata, color='current', palette=['grey', 'green'])
sc.pl.umap(adata, color='current', palette=['grey', 'green'])
... storing 'current' as categorical
/pasteur/appa/homes/jkalfon/miniconda3/envs/scprint17/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1234: FutureWarning: The default value of 'ignore' for the `na_action` parameter in pandas.Categorical.map is deprecated and will be changed to 'None' in a future version. Please set na_action to the desired value to avoid seeing this warning color_vector = pd.Categorical(values.map(color_map))
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sc.pl.umap(adata, color=['cell_type'])
sc.pl.umap(adata, color=['cell_type'])
/pasteur/appa/homes/jkalfon/miniconda3/envs/scprint17/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:1234: FutureWarning: The default value of 'ignore' for the `na_action` parameter in pandas.Categorical.map is deprecated and will be changed to 'None' in a future version. Please set na_action to the desired value to avoid seeing this warning color_vector = pd.Categorical(values.map(color_map))
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sc.tl.rank_genes_groups(
adata, groupby="cell_type"
)
adata.var['ensembl_id'] = adata.var.index
adata = adata[adata.X.sum(1) > 500]
shapes = []
metrics = {}
for celltype in CELLTYPES:
to_use = adata.uns["rank_genes_groups"]["names"][celltype][
: NUM_GENES
].tolist()
subadata = adata[adata.obs.cell_type == celltype][:MAXCELLS, adata.var.index.isin(to_use)]
genie_grn = compute_genie3(
subadata, nthreads=20, regulators=adata.var[adata.var.isTF].index.tolist())
genie_grn.var.index = make_index_unique(
genie_grn.var['symbol'].astype(str))
print(celltype, genie_grn.shape)
metrics['genie3_tf_'+celltype] = BenGRN(genie_grn,
do_auc=True, doplot=True).scprint_benchmark()
genie_grn = compute_genie3(subadata, nthreads=20)
genie_grn.var.index = make_index_unique(
genie_grn.var['symbol'].astype(str))
metrics['genie3_'+celltype] = BenGRN(genie_grn,
do_auc=True, doplot=True).scprint_benchmark()
shapes.append(genie_grn.shape[1])
sc.tl.rank_genes_groups(
adata, groupby="cell_type"
)
adata.var['ensembl_id'] = adata.var.index
adata = adata[adata.X.sum(1) > 500]
shapes = []
metrics = {}
for celltype in CELLTYPES:
to_use = adata.uns["rank_genes_groups"]["names"][celltype][
: NUM_GENES
].tolist()
subadata = adata[adata.obs.cell_type == celltype][:MAXCELLS, adata.var.index.isin(to_use)]
genie_grn = compute_genie3(
subadata, nthreads=20, regulators=adata.var[adata.var.isTF].index.tolist())
genie_grn.var.index = make_index_unique(
genie_grn.var['symbol'].astype(str))
print(celltype, genie_grn.shape)
metrics['genie3_tf_'+celltype] = BenGRN(genie_grn,
do_auc=True, doplot=True).scprint_benchmark()
genie_grn = compute_genie3(subadata, nthreads=20)
genie_grn.var.index = make_index_unique(
genie_grn.var['symbol'].astype(str))
metrics['genie3_'+celltype] = BenGRN(genie_grn,
do_auc=True, doplot=True).scprint_benchmark()
shapes.append(genie_grn.shape[1])
WARNING: Default of the method has been changed to 't-test' from 't-test_overestim_var'
WARNING: It seems you use rank_genes_groups on the raw count data. Please logarithmize your data before calling rank_genes_groups.
Tree method: RF K: sqrt Number of trees: 100 running jobs on 20 threads
Elapsed time: 74.96 seconds base enrichment
/pasteur/appa/homes/jkalfon/miniconda3/envs/scprint17/lib/python3.10/multiprocessing/pool.py:265: ResourceWarning: unclosed running multiprocessing pool <multiprocessing.pool.Pool state=RUN pool_size=20>
_warn(f"unclosed running multiprocessing pool {self!r}",
Top central genes: []
_________________________________________ TF specific enrichment
found some significant results for 14.705882352941176 % TFs _________________________________________ loading GT, omnipath
intersection of 3565 genes intersection pct: 0.713 precision: 0.004541789382676131 recall: 0.33513633669235326 random precision: 0.001062203773751226
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/pasteur/appa/homes/jkalfon/benGRN/bengrn/base.py:750: RuntimeWarning: invalid value encountered in scalar divide precision = (grn[true] > threshold).sum() / (grn > threshold).sum() 100%|██████████| 128/128 [00:02<00:00, 46.20it/s]
Average Precision (AP): 0.0028506585986099184 Area Under Precision-Recall Curve (AUPRC): 0.00252355640477646 EPR: 5.849284104972232
Tree method: RF K: sqrt Number of trees: 100 running jobs on 20 threads
Elapsed time: 262.67 seconds
/pasteur/appa/homes/jkalfon/miniconda3/envs/scprint17/lib/python3.10/multiprocessing/pool.py:265: ResourceWarning: unclosed running multiprocessing pool <multiprocessing.pool.Pool state=RUN pool_size=20>
_warn(f"unclosed running multiprocessing pool {self!r}",
base enrichment
Top central genes: []
_________________________________________ TF specific enrichment
found some significant results for 2.9411764705882355 % TFs _________________________________________ loading GT, omnipath
intersection of 3565 genes intersection pct: 0.713 precision: 0.0011096698483979172 recall: 0.6956135151155898 random precision: 0.001062203773751226
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/pasteur/appa/homes/jkalfon/benGRN/bengrn/base.py:750: RuntimeWarning: invalid value encountered in scalar divide precision = (grn[true] > threshold).sum() / (grn > threshold).sum() 100%|██████████| 128/128 [00:02<00:00, 45.34it/s]
Average Precision (AP): 0.001095405878733279 Area Under Precision-Recall Curve (AUPRC): 0.0010760455741965266
EPR: 1.7466859320820376
Tree method: RF K: sqrt Number of trees: 100 running jobs on 20 threads
Elapsed time: 40.44 seconds
/pasteur/appa/homes/jkalfon/miniconda3/envs/scprint17/lib/python3.10/multiprocessing/pool.py:265: ResourceWarning: unclosed running multiprocessing pool <multiprocessing.pool.Pool state=RUN pool_size=20>
_warn(f"unclosed running multiprocessing pool {self!r}",
base enrichment
Top central genes: []
_________________________________________ TF specific enrichment
found some significant results for 10.0 % TFs _________________________________________ loading GT, omnipath
intersection of 3514 genes intersection pct: 0.7028 precision: 0.006271091258494464 recall: 0.40673661698857183 random precision: 0.0012121008868434197
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/pasteur/appa/homes/jkalfon/benGRN/bengrn/base.py:750: RuntimeWarning: invalid value encountered in scalar divide precision = (grn[true] > threshold).sum() / (grn > threshold).sum() 100%|██████████| 128/128 [00:02<00:00, 47.68it/s]
Average Precision (AP): 0.0038068710056600934 Area Under Precision-Recall Curve (AUPRC): 0.0033103141109521303 EPR: 4.897761556952193
Tree method: RF K: sqrt Number of trees: 100 running jobs on 20 threads
Elapsed time: 64.58 seconds
/pasteur/appa/homes/jkalfon/miniconda3/envs/scprint17/lib/python3.10/multiprocessing/pool.py:265: ResourceWarning: unclosed running multiprocessing pool <multiprocessing.pool.Pool state=RUN pool_size=20>
_warn(f"unclosed running multiprocessing pool {self!r}",
base enrichment
Top central genes: []
_________________________________________ TF specific enrichment
found some significant results for 2.5 % TFs _________________________________________ loading GT, omnipath
intersection of 3514 genes intersection pct: 0.7028 precision: 0.001285625479006546 recall: 0.32881106729933834 random precision: 0.0012121008868434197
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/pasteur/appa/homes/jkalfon/benGRN/bengrn/base.py:750: RuntimeWarning: invalid value encountered in scalar divide precision = (grn[true] > threshold).sum() / (grn > threshold).sum() 100%|██████████| 128/128 [00:02<00:00, 47.36it/s]
Average Precision (AP): 0.0012326445926554342 Area Under Precision-Recall Curve (AUPRC): 0.0012041101257049699 EPR: 0.7162872764286753
Tree method: RF K: sqrt Number of trees: 100 running jobs on 20 threads
Elapsed time: 46.47 seconds
/pasteur/appa/homes/jkalfon/miniconda3/envs/scprint17/lib/python3.10/multiprocessing/pool.py:265: ResourceWarning: unclosed running multiprocessing pool <multiprocessing.pool.Pool state=RUN pool_size=20>
_warn(f"unclosed running multiprocessing pool {self!r}",
base enrichment
Top central genes: []
_________________________________________ TF specific enrichment
found some significant results for 1.4285714285714286 % TFs _________________________________________ loading GT, omnipath
intersection of 3594 genes intersection pct: 0.7188 precision: 0.007092883035904675 recall: 0.4387900153522488 random precision: 0.0013619352909207464
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/pasteur/appa/homes/jkalfon/benGRN/bengrn/base.py:750: RuntimeWarning: invalid value encountered in scalar divide precision = (grn[true] > threshold).sum() / (grn > threshold).sum() 100%|██████████| 128/128 [00:02<00:00, 44.55it/s]
Average Precision (AP): 0.005337285240454103 Area Under Precision-Recall Curve (AUPRC): 0.004757467010786939 EPR: 8.781634597124883
Tree method: RF K: sqrt Number of trees: 100 running jobs on 20 threads
Elapsed time: 94.85 seconds
/pasteur/appa/homes/jkalfon/miniconda3/envs/scprint17/lib/python3.10/multiprocessing/pool.py:265: ResourceWarning: unclosed running multiprocessing pool <multiprocessing.pool.Pool state=RUN pool_size=20>
_warn(f"unclosed running multiprocessing pool {self!r}",
base enrichment
Top central genes: []
_________________________________________ TF specific enrichment
found some significant results for 2.857142857142857 % TFs _________________________________________ loading GT, omnipath
intersection of 3594 genes intersection pct: 0.7188 precision: 0.001539952451821791 recall: 0.4742707681810428 random precision: 0.0013619352909207464
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/pasteur/appa/homes/jkalfon/benGRN/bengrn/base.py:750: RuntimeWarning: invalid value encountered in scalar divide precision = (grn[true] > threshold).sum() / (grn > threshold).sum() 100%|██████████| 128/128 [00:02<00:00, 44.92it/s]
Average Precision (AP): 0.0014811949583253049 Area Under Precision-Recall Curve (AUPRC): 0.0014320894483849911 EPR: 1.5890254176610075
Tree method: RF K: sqrt Number of trees: 100 running jobs on 20 threads
Elapsed time: 22.75 seconds
/pasteur/appa/homes/jkalfon/miniconda3/envs/scprint17/lib/python3.10/multiprocessing/pool.py:265: ResourceWarning: unclosed running multiprocessing pool <multiprocessing.pool.Pool state=RUN pool_size=20>
_warn(f"unclosed running multiprocessing pool {self!r}",
base enrichment
Top central genes: []
_________________________________________ TF specific enrichment
found some significant results for 2.5641025641025643 % TFs _________________________________________ loading GT, omnipath
intersection of 2542 genes intersection pct: 0.7242165242165243 precision: 0.011580584843104287 recall: 0.1789703739679456 random precision: 0.0012750761624232764
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/pasteur/appa/homes/jkalfon/benGRN/bengrn/base.py:750: RuntimeWarning: invalid value encountered in scalar divide precision = (grn[true] > threshold).sum() / (grn > threshold).sum() 100%|██████████| 128/128 [00:01<00:00, 90.85it/s]
Average Precision (AP): 0.003349531418887418 Area Under Precision-Recall Curve (AUPRC): 0.0030541973899464257 EPR: 9.434251628175465
Tree method: RF K: sqrt Number of trees: 100 running jobs on 20 threads
Elapsed time: 23.17 seconds
/pasteur/appa/homes/jkalfon/miniconda3/envs/scprint17/lib/python3.10/multiprocessing/pool.py:265: ResourceWarning: unclosed running multiprocessing pool <multiprocessing.pool.Pool state=RUN pool_size=20>
_warn(f"unclosed running multiprocessing pool {self!r}",
base enrichment
Top central genes: []
_________________________________________ TF specific enrichment
found some significant results for 2.5641025641025643 % TFs _________________________________________ loading GT, omnipath
intersection of 2542 genes intersection pct: 0.7242165242165243 precision: 0.0016484163428705993 recall: 0.0305973773676542 random precision: 0.0012750761624232764
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/pasteur/appa/homes/jkalfon/benGRN/bengrn/base.py:750: RuntimeWarning: invalid value encountered in scalar divide precision = (grn[true] > threshold).sum() / (grn > threshold).sum() 100%|██████████| 128/128 [00:01<00:00, 90.72it/s]
Average Precision (AP): 0.001177156569385212 Area Under Precision-Recall Curve (AUPRC): 0.0012825989724762198 EPR: 1.3342748933585344
Tree method: RF K: sqrt Number of trees: 100 running jobs on 20 threads
Elapsed time: 36.26 seconds
/pasteur/appa/homes/jkalfon/miniconda3/envs/scprint17/lib/python3.10/multiprocessing/pool.py:265: ResourceWarning: unclosed running multiprocessing pool <multiprocessing.pool.Pool state=RUN pool_size=20>
_warn(f"unclosed running multiprocessing pool {self!r}",
base enrichment
Top central genes: []
_________________________________________ TF specific enrichment
found some significant results for 0.0 % TFs _________________________________________ loading GT, omnipath
intersection of 3595 genes intersection pct: 0.7264093756314407 precision: 0.00896463904832305 recall: 0.37216471129514606 random precision: 0.0016822195546123465
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/pasteur/appa/homes/jkalfon/benGRN/bengrn/base.py:750: RuntimeWarning: invalid value encountered in scalar divide precision = (grn[true] > threshold).sum() / (grn > threshold).sum() 100%|██████████| 128/128 [00:02<00:00, 44.18it/s]
Average Precision (AP): 0.0054905113019332215 Area Under Precision-Recall Curve (AUPRC): 0.004916303339642281 EPR: 5.978256210614143
Tree method: RF K: sqrt Number of trees: 100 running jobs on 20 threads
Elapsed time: 47.64 seconds
/pasteur/appa/homes/jkalfon/miniconda3/envs/scprint17/lib/python3.10/multiprocessing/pool.py:265: ResourceWarning: unclosed running multiprocessing pool <multiprocessing.pool.Pool state=RUN pool_size=20>
_warn(f"unclosed running multiprocessing pool {self!r}",
base enrichment
Top central genes: []
_________________________________________ TF specific enrichment
found some significant results for 3.5714285714285716 % TFs _________________________________________ loading GT, omnipath
intersection of 3595 genes intersection pct: 0.7264093756314407 precision: 0.002206980823429709 recall: 0.2576029445594663 random precision: 0.0016822195546123465
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/pasteur/appa/homes/jkalfon/benGRN/bengrn/base.py:750: RuntimeWarning: invalid value encountered in scalar divide precision = (grn[true] > threshold).sum() / (grn > threshold).sum() 100%|██████████| 128/128 [00:02<00:00, 44.75it/s]
Average Precision (AP): 0.0018285890904070012 Area Under Precision-Recall Curve (AUPRC): 0.0017771005963575573 EPR: 1.1218100192058453
Tree method: RF K: sqrt Number of trees: 100 running jobs on 20 threads
Elapsed time: 41.59 seconds
/pasteur/appa/homes/jkalfon/miniconda3/envs/scprint17/lib/python3.10/multiprocessing/pool.py:265: ResourceWarning: unclosed running multiprocessing pool <multiprocessing.pool.Pool state=RUN pool_size=20>
_warn(f"unclosed running multiprocessing pool {self!r}",
base enrichment
Top central genes: []
_________________________________________ TF specific enrichment
found some significant results for 0.0 % TFs _________________________________________ loading GT, omnipath
intersection of 3603 genes intersection pct: 0.7206 precision: 0.008509893153340102 recall: 0.4291732223519261 random precision: 0.001518183918238287
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/pasteur/appa/homes/jkalfon/benGRN/bengrn/base.py:750: RuntimeWarning: invalid value encountered in scalar divide precision = (grn[true] > threshold).sum() / (grn > threshold).sum() 100%|██████████| 128/128 [00:02<00:00, 44.44it/s]
Average Precision (AP): 0.0052484975615239675 Area Under Precision-Recall Curve (AUPRC): 0.0046805414565712425 EPR: 6.145020274594569
Tree method: RF K: sqrt Number of trees: 100 running jobs on 20 threads
Elapsed time: 85.88 seconds
/pasteur/appa/homes/jkalfon/miniconda3/envs/scprint17/lib/python3.10/multiprocessing/pool.py:265: ResourceWarning: unclosed running multiprocessing pool <multiprocessing.pool.Pool state=RUN pool_size=20>
_warn(f"unclosed running multiprocessing pool {self!r}",
base enrichment
Top central genes: []
_________________________________________ TF specific enrichment
found some significant results for 4.25531914893617 % TFs _________________________________________ loading GT, omnipath
intersection of 3603 genes intersection pct: 0.7206 precision: 0.001674633348973699 recall: 0.4119169669593463 random precision: 0.001518183918238287
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/pasteur/appa/homes/jkalfon/benGRN/bengrn/base.py:750: RuntimeWarning: invalid value encountered in scalar divide precision = (grn[true] > threshold).sum() / (grn > threshold).sum() 100%|██████████| 128/128 [00:02<00:00, 43.72it/s]
Average Precision (AP): 0.0015823538224594797 Area Under Precision-Recall Curve (AUPRC): 0.001539994484667556
EPR: 0.7348799242950723
Tree method: RF K: sqrt Number of trees: 100 running jobs on 20 threads
Elapsed time: 31.20 seconds
/pasteur/appa/homes/jkalfon/miniconda3/envs/scprint17/lib/python3.10/multiprocessing/pool.py:265: ResourceWarning: unclosed running multiprocessing pool <multiprocessing.pool.Pool state=RUN pool_size=20>
_warn(f"unclosed running multiprocessing pool {self!r}",
base enrichment
Top central genes: []
_________________________________________ TF specific enrichment
found some significant results for 2.4390243902439024 % TFs _________________________________________ loading GT, omnipath
intersection of 3411 genes intersection pct: 0.7128526645768025 precision: 0.009331147617220653 recall: 0.25440598453150753 random precision: 0.0013561437852866911
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/pasteur/appa/homes/jkalfon/benGRN/bengrn/base.py:750: RuntimeWarning: invalid value encountered in scalar divide precision = (grn[true] > threshold).sum() / (grn > threshold).sum() 100%|██████████| 128/128 [00:02<00:00, 49.52it/s]
Average Precision (AP): 0.003925004962797144 Area Under Precision-Recall Curve (AUPRC): 0.0033775019219795498 EPR: 6.5477109026987135
Tree method: RF K: sqrt Number of trees: 100 running jobs on 20 threads
Elapsed time: 31.60 seconds
/pasteur/appa/homes/jkalfon/miniconda3/envs/scprint17/lib/python3.10/multiprocessing/pool.py:265: ResourceWarning: unclosed running multiprocessing pool <multiprocessing.pool.Pool state=RUN pool_size=20>
_warn(f"unclosed running multiprocessing pool {self!r}",
base enrichment
Top central genes: []
_________________________________________ TF specific enrichment
found some significant results for 0.0 % TFs _________________________________________ loading GT, omnipath
intersection of 3411 genes intersection pct: 0.7128526645768025 precision: 0.001856214279002524 recall: 0.07138328895651071 random precision: 0.0013561437852866911
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/pasteur/appa/homes/jkalfon/benGRN/bengrn/base.py:750: RuntimeWarning: invalid value encountered in scalar divide precision = (grn[true] > threshold).sum() / (grn > threshold).sum() 100%|██████████| 128/128 [00:02<00:00, 50.54it/s]
Average Precision (AP): 0.001384961126647782 Area Under Precision-Recall Curve (AUPRC): 0.0013751209299609028 EPR: 1.2630649585124507
Tree method: RF K: sqrt Number of trees: 100 running jobs on 20 threads
Elapsed time: 15.86 seconds
/pasteur/appa/homes/jkalfon/miniconda3/envs/scprint17/lib/python3.10/multiprocessing/pool.py:265: ResourceWarning: unclosed running multiprocessing pool <multiprocessing.pool.Pool state=RUN pool_size=20>
_warn(f"unclosed running multiprocessing pool {self!r}",
base enrichment
Top central genes: []
_________________________________________ TF specific enrichment
found some significant results for 0.0 % TFs _________________________________________ loading GT, omnipath
intersection of 1545 genes intersection pct: 0.6845369960124058 precision: 0.009188551561432917 recall: 0.37841984147276914 random precision: 0.001639502322383755
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/pasteur/appa/homes/jkalfon/benGRN/bengrn/base.py:750: RuntimeWarning: invalid value encountered in scalar divide precision = (grn[true] > threshold).sum() / (grn > threshold).sum() 100%|██████████| 128/128 [00:00<00:00, 248.94it/s]
Average Precision (AP): 0.006097495201627464 Area Under Precision-Recall Curve (AUPRC): 0.005781176553272736 EPR: 9.454522889486277
Tree method: RF K: sqrt Number of trees: 100 running jobs on 20 threads
Elapsed time: 16.70 seconds base enrichment
/pasteur/appa/homes/jkalfon/miniconda3/envs/scprint17/lib/python3.10/multiprocessing/pool.py:265: ResourceWarning: unclosed running multiprocessing pool <multiprocessing.pool.Pool state=RUN pool_size=20>
_warn(f"unclosed running multiprocessing pool {self!r}",
Top central genes: []
_________________________________________ TF specific enrichment
found some significant results for 10.526315789473685 % TFs _________________________________________ loading GT, omnipath
intersection of 1545 genes intersection pct: 0.6845369960124058 precision: 0.0022959552969107387 recall: 0.2027614420864229 random precision: 0.001639502322383755
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/pasteur/appa/homes/jkalfon/benGRN/bengrn/base.py:750: RuntimeWarning: invalid value encountered in scalar divide precision = (grn[true] > threshold).sum() / (grn > threshold).sum() 100%|██████████| 128/128 [00:00<00:00, 259.84it/s]
Average Precision (AP): 0.0016960689439870014 Area Under Precision-Recall Curve (AUPRC): 0.0017903071540811078 EPR: 1.248658655459195
Tree method: RF K: sqrt Number of trees: 100 running jobs on 20 threads
Elapsed time: 31.63 seconds
/pasteur/appa/homes/jkalfon/miniconda3/envs/scprint17/lib/python3.10/multiprocessing/pool.py:265: ResourceWarning: unclosed running multiprocessing pool <multiprocessing.pool.Pool state=RUN pool_size=20>
_warn(f"unclosed running multiprocessing pool {self!r}",
base enrichment
Top central genes: []
_________________________________________ TF specific enrichment
found some significant results for 6.382978723404255 % TFs _________________________________________ loading GT, omnipath
intersection of 3406 genes intersection pct: 0.7138964577656676 precision: 0.009544498926019955 recall: 0.32796150187454537 random precision: 0.0015409448472635749
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/pasteur/appa/homes/jkalfon/benGRN/bengrn/base.py:750: RuntimeWarning: invalid value encountered in scalar divide precision = (grn[true] > threshold).sum() / (grn > threshold).sum() 100%|██████████| 128/128 [00:02<00:00, 50.25it/s]
Average Precision (AP): 0.005047515883367648 Area Under Precision-Recall Curve (AUPRC): 0.004712568423019291 EPR: 5.934936091576408
Tree method: RF K: sqrt Number of trees: 100 running jobs on 20 threads
Elapsed time: 36.69 seconds
/pasteur/appa/homes/jkalfon/miniconda3/envs/scprint17/lib/python3.10/multiprocessing/pool.py:265: ResourceWarning: unclosed running multiprocessing pool <multiprocessing.pool.Pool state=RUN pool_size=20>
_warn(f"unclosed running multiprocessing pool {self!r}",
base enrichment
Top central genes: []
_________________________________________ TF specific enrichment
found some significant results for 6.382978723404255 % TFs _________________________________________ loading GT, omnipath
intersection of 3406 genes intersection pct: 0.7138964577656676 precision: 0.002308445385969307 recall: 0.19646354428963125 random precision: 0.0015409448472635749
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/pasteur/appa/homes/jkalfon/benGRN/bengrn/base.py:750: RuntimeWarning: invalid value encountered in scalar divide precision = (grn[true] > threshold).sum() / (grn > threshold).sum() 100%|██████████| 128/128 [00:02<00:00, 50.30it/s]
Average Precision (AP): 0.0015709317218276543 Area Under Precision-Recall Curve (AUPRC): 0.0016294208207601668 EPR: 1.2720250886940248
In [9]:
Copied!
shapes
shapes
Out[9]:
[5000, 5000, 5000, 3510, 4949, 5000, 4785, 2257, 4771]
In [10]:
Copied!
metrics
metrics
Out[10]:
{'genie3_tf_kidney distal convoluted tubule epithelial cell': {'enriched_terms_Central': ['0__TFs',
'celltype.gmt__Embryonic stem cells',
'celltype.gmt__Enteric neurons',
'celltype.gmt__Melanocytes'],
'TF_enr': True,
'enriched_terms_Targets': ['celltype.gmt__Melanocytes'],
'significant_enriched_TFtargets': 14.705882352941176,
'precision': 0.004541789382676131,
'recall': 0.33513633669235326,
'rand_precision': 0.001062203773751226,
'auprc': 0.00252355640477646,
'ap': 0.0028506585986099184,
'epr': 5.849284104972232},
'genie3_kidney distal convoluted tubule epithelial cell': {'enriched_terms_Central': ['celltype.gmt__Distal tubule cells',
'celltype.gmt__-intercalated cells (Collecting duct system)',
'celltype.gmt__Loop of Henle cells',
'celltype.gmt__Connecting tubule cells',
'celltype.gmt__Proximal tubule cells',
'celltype.gmt__Podocytes',
'celltype.gmt__Hepatic stellate cells',
'celltype.gmt__Melanocytes',
'celltype.gmt__Ductal cells',
'celltype.gmt__Ionocytes',
'celltype.gmt__Embryonic stem cells',
'celltype.gmt__Cholangiocytes',
'celltype.gmt__Enterocytes'],
'TF_enr': False,
'enriched_terms_Targets': ['celltype.gmt__Distal tubule cells',
'celltype.gmt__Cholangiocytes',
'celltype.gmt__-intercalated cells (Collecting duct system)',
'celltype.gmt__Loop of Henle cells',
'celltype.gmt__Ductal cells',
'celltype.gmt__Connecting tubule cells',
'celltype.gmt__Proximal tubule cells',
'celltype.gmt__Melanocytes',
'celltype.gmt__Hepatic stellate cells',
'celltype.gmt__Hepatocytes',
'celltype.gmt__Enterocytes',
'celltype.gmt__Podocytes',
'celltype.gmt__Ionocytes',
'celltype.gmt__Embryonic stem cells',
'celltype.gmt__Beta cells',
'celltype.gmt__Delta cells',
'celltype.gmt__Principal cells (Collecting duct system)'],
'significant_enriched_TFtargets': 2.9411764705882355,
'precision': 0.0011096698483979172,
'recall': 0.6956135151155898,
'rand_precision': 0.001062203773751226,
'auprc': 0.0010760455741965266,
'ap': 0.001095405878733279,
'epr': 1.7466859320820376},
'genie3_tf_kidney loop of Henle thick ascending limb epithelial cell': {'enriched_terms_Central': ['0__TFs',
'celltype.gmt__Loop of Henle cells',
'celltype.gmt__Embryonic stem cells',
'celltype.gmt__Delta cells',
'celltype.gmt__Distal tubule cells',
'celltype.gmt__Proximal tubule cells',
'celltype.gmt__Microfold cells',
'celltype.gmt__Gamma (PP) cells',
'celltype.gmt__Alpha cells',
'celltype.gmt__Renal Vesicle cells',
'celltype.gmt__Enterocytes'],
'TF_enr': True,
'significant_enriched_TFtargets': 10.0,
'precision': 0.006271091258494464,
'recall': 0.40673661698857183,
'rand_precision': 0.0012121008868434197,
'auprc': 0.0033103141109521303,
'ap': 0.0038068710056600934,
'epr': 4.897761556952193},
'genie3_kidney loop of Henle thick ascending limb epithelial cell': {'enriched_terms_Central': ['celltype.gmt__Distal tubule cells',
'celltype.gmt__Loop of Henle cells',
'celltype.gmt__Proximal tubule cells',
'celltype.gmt__Ductal cells',
'celltype.gmt__-intercalated cells (Collecting duct system)',
'celltype.gmt__Hepatocytes'],
'TF_enr': False,
'enriched_terms_Targets': ['celltype.gmt__Distal tubule cells',
'celltype.gmt__Loop of Henle cells',
'celltype.gmt__Ductal cells',
'celltype.gmt__Proximal tubule cells',
'celltype.gmt__-intercalated cells (Collecting duct system)',
'celltype.gmt__Podocytes'],
'significant_enriched_TFtargets': 2.5,
'precision': 0.001285625479006546,
'recall': 0.32881106729933834,
'rand_precision': 0.0012121008868434197,
'auprc': 0.0012041101257049699,
'ap': 0.0012326445926554342,
'epr': 0.7162872764286753},
'genie3_tf_kidney collecting duct principal cell': {'enriched_terms_Central': ['0__TFs',
'celltype.gmt__Microfold cells',
'celltype.gmt__Melanocytes',
'celltype.gmt__Embryonic stem cells',
'celltype.gmt__Distal tubule cells',
'celltype.gmt__Satellite cells',
'celltype.gmt__Principal cells (Collecting duct system)',
'celltype.gmt__Hepatic stellate cells',
'celltype.gmt__Keratinocytes',
'celltype.gmt__Mesangial cells',
'celltype.gmt__-intercalated cells (Collecting duct system)',
'celltype.gmt__Renal Vesicle cells',
'celltype.gmt__Ionocytes',
'celltype.gmt__Tuft cells'],
'TF_enr': True,
'significant_enriched_TFtargets': 1.4285714285714286,
'precision': 0.007092883035904675,
'recall': 0.4387900153522488,
'rand_precision': 0.0013619352909207464,
'auprc': 0.004757467010786939,
'ap': 0.005337285240454103,
'epr': 8.781634597124883},
'genie3_kidney collecting duct principal cell': {'enriched_terms_Central': ['celltype.gmt__Distal tubule cells',
'celltype.gmt__Principal cells (Collecting duct system)',
'celltype.gmt__-intercalated cells (Collecting duct system)',
'celltype.gmt__Connecting tubule cells',
'celltype.gmt__Ductal cells',
'celltype.gmt__Proximal tubule cells',
'celltype.gmt__Loop of Henle cells',
'celltype.gmt__Foveolar cells',
'celltype.gmt__Podocytes',
'celltype.gmt__Gamma (PP) cells',
'celltype.gmt__Hepatic stellate cells',
'celltype.gmt__Alpha cells',
'celltype.gmt__Cholangiocytes'],
'TF_enr': False,
'enriched_terms_Targets': ['celltype.gmt__Principal cells (Collecting duct system)',
'celltype.gmt__-intercalated cells (Collecting duct system)',
'celltype.gmt__Distal tubule cells',
'celltype.gmt__Ductal cells',
'celltype.gmt__Connecting tubule cells',
'celltype.gmt__Cholangiocytes',
'celltype.gmt__Hepatic stellate cells',
'celltype.gmt__Gamma (PP) cells',
'celltype.gmt__Loop of Henle cells',
'celltype.gmt__Foveolar cells',
'celltype.gmt__Podocytes',
'celltype.gmt__Proximal tubule cells',
'celltype.gmt__Acinar cells',
'celltype.gmt__Alpha cells',
'celltype.gmt__Pulmonary alveolar type I cells',
'celltype.gmt__Ionocytes'],
'significant_enriched_TFtargets': 2.857142857142857,
'precision': 0.001539952451821791,
'recall': 0.4742707681810428,
'rand_precision': 0.0013619352909207464,
'auprc': 0.0014320894483849911,
'ap': 0.0014811949583253049,
'epr': 1.5890254176610075},
'genie3_tf_mesangial cell': {'enriched_terms_Central': ['0__TFs',
'celltype.gmt__Mesangial cells'],
'TF_enr': True,
'significant_enriched_TFtargets': 2.5641025641025643,
'precision': 0.011580584843104287,
'recall': 0.1789703739679456,
'rand_precision': 0.0012750761624232764,
'auprc': 0.0030541973899464257,
'ap': 0.003349531418887418,
'epr': 9.434251628175465},
'genie3_mesangial cell': {'enriched_terms_Central': ['celltype.gmt__Hepatic stellate cells',
'celltype.gmt__Myoepithelial cells',
'celltype.gmt__Perivascular cells',
'celltype.gmt__Myofibroblasts',
'celltype.gmt__Podocytes',
'celltype.gmt__Loop of Henle cells'],
'TF_enr': False,
'enriched_terms_Targets': ['celltype.gmt__Hepatic stellate cells',
'celltype.gmt__Podocytes',
'celltype.gmt__Myofibroblasts',
'celltype.gmt__Principal cells (Collecting duct system)',
'celltype.gmt__Pulmonary alveolar type I cells',
'celltype.gmt__Perivascular cells',
'celltype.gmt__Myoepithelial cells',
'celltype.gmt__Renal interstitium (Mesenchymal cells)',
'celltype.gmt__Smooth Muscle cells'],
'significant_enriched_TFtargets': 2.5641025641025643,
'precision': 0.0016484163428705993,
'recall': 0.0305973773676542,
'rand_precision': 0.0012750761624232764,
'auprc': 0.0012825989724762198,
'ap': 0.001177156569385212,
'epr': 1.3342748933585344},
'genie3_tf_blood vessel smooth muscle cell': {'enriched_terms_Central': ['0__TFs',
'celltype.gmt__Embryonic stem cells',
'celltype.gmt__Perivascular cells',
'celltype.gmt__Microfold cells',
'celltype.gmt__Myofibroblasts',
'celltype.gmt__Satellite cells',
'celltype.gmt__Smooth Muscle cells',
'celltype.gmt__Gamma (PP) cells',
'celltype.gmt__Cap mesenchyme cells (Mesenchymal cells)',
'celltype.gmt__Mesenchymal stem cells',
'celltype.gmt__Mesangial cells'],
'TF_enr': True,
'significant_enriched_TFtargets': 0.0,
'precision': 0.00896463904832305,
'recall': 0.37216471129514606,
'rand_precision': 0.0016822195546123465,
'auprc': 0.004916303339642281,
'ap': 0.0054905113019332215,
'epr': 5.978256210614143},
'genie3_blood vessel smooth muscle cell': {'enriched_terms_Central': ['celltype.gmt__Hepatic stellate cells',
'celltype.gmt__Myofibroblasts',
'celltype.gmt__Perivascular cells',
'celltype.gmt__Smooth Muscle cells',
'celltype.gmt__Smooth muscle cells',
'celltype.gmt__Mesangial cells',
'celltype.gmt__Mesenchymal stem cells',
'celltype.gmt__Myoepithelial cells',
'celltype.gmt__Vascular smooth muscle cells',
'celltype.gmt__Fibroblasts',
'celltype.gmt__Cap mesenchyme cells (Mesenchymal cells)',
'celltype.gmt__Pericytes',
'celltype.gmt__Leydig cells',
'celltype.gmt__Radial glial cells',
'celltype.gmt__Glycinergic amacrine cells',
'celltype.gmt__Adipocytes',
'celltype.gmt__Renal interstitium (Mesenchymal cells)',
'celltype.gmt__Microfold cells',
'celltype.gmt__Pro-B cells',
'celltype.gmt__Endometrium'],
'TF_enr': False,
'enriched_terms_Targets': ['celltype.gmt__Hepatic stellate cells',
'celltype.gmt__Myofibroblasts',
'celltype.gmt__Smooth Muscle cells',
'celltype.gmt__Perivascular cells',
'celltype.gmt__Smooth muscle cells',
'celltype.gmt__Myoepithelial cells',
'celltype.gmt__Renal interstitium (Mesenchymal cells)',
'celltype.gmt__Mesangial cells',
'celltype.gmt__Vascular smooth muscle cells',
'celltype.gmt__Pericytes',
'celltype.gmt__Leydig cells',
'celltype.gmt__Fibroblasts',
'celltype.gmt__Mesenchymal stem cells',
'celltype.gmt__Cap mesenchyme cells (Mesenchymal cells)',
'celltype.gmt__Radial glial cells',
'celltype.gmt__Melanocytes'],
'significant_enriched_TFtargets': 3.5714285714285716,
'precision': 0.002206980823429709,
'recall': 0.2576029445594663,
'rand_precision': 0.0016822195546123465,
'auprc': 0.0017771005963575573,
'ap': 0.0018285890904070012,
'epr': 1.1218100192058453},
'genie3_tf_podocyte': {'enriched_terms_Central': ['0__TFs',
'celltype.gmt__Embryonic stem cells',
'celltype.gmt__Podocytes',
'celltype.gmt__Hepatic stellate cells',
'celltype.gmt__Microfold cells',
'celltype.gmt__Enteric neurons',
'celltype.gmt__Mesangial cells',
'celltype.gmt__Gamma (PP) cells',
'celltype.gmt__Glycinergic amacrine cells',
'celltype.gmt__Cap mesenchyme cells (Mesenchymal cells)',
'celltype.gmt__Myocytes'],
'TF_enr': True,
'significant_enriched_TFtargets': 0.0,
'precision': 0.008509893153340102,
'recall': 0.4291732223519261,
'rand_precision': 0.001518183918238287,
'auprc': 0.0046805414565712425,
'ap': 0.0052484975615239675,
'epr': 6.145020274594569},
'genie3_podocyte': {'enriched_terms_Central': ['celltype.gmt__Podocytes',
'celltype.gmt__Hepatic stellate cells',
'celltype.gmt__Myofibroblasts',
'celltype.gmt__Pulmonary alveolar type I cells',
'celltype.gmt__Mesangial cells'],
'TF_enr': False,
'enriched_terms_Targets': ['celltype.gmt__Podocytes',
'celltype.gmt__Hepatic stellate cells',
'celltype.gmt__Pulmonary alveolar type I cells',
'celltype.gmt__Myoepithelial cells',
'celltype.gmt__Myofibroblasts',
'celltype.gmt__Endometrium',
'celltype.gmt__Stromal cells',
'celltype.gmt__Myocytes',
'celltype.gmt__Embryonic stem cells',
'celltype.gmt__Immune cells',
'celltype.gmt__Smooth muscle cells'],
'significant_enriched_TFtargets': 4.25531914893617,
'precision': 0.001674633348973699,
'recall': 0.4119169669593463,
'rand_precision': 0.001518183918238287,
'auprc': 0.001539994484667556,
'ap': 0.0015823538224594797,
'epr': 0.7348799242950723},
'genie3_tf_macrophage': {'enriched_terms_Central': ['0__TFs',
'celltype.gmt__Tuft cells'],
'TF_enr': True,
'significant_enriched_TFtargets': 2.4390243902439024,
'precision': 0.009331147617220653,
'recall': 0.25440598453150753,
'rand_precision': 0.0013561437852866911,
'auprc': 0.0033775019219795498,
'ap': 0.003925004962797144,
'epr': 6.5477109026987135},
'genie3_macrophage': {'enriched_terms_Central': ['celltype.gmt__Myofibroblasts',
'celltype.gmt__Hepatic stellate cells'],
'TF_enr': False,
'enriched_terms_Targets': ['celltype.gmt__Myofibroblasts'],
'significant_enriched_TFtargets': 0.0,
'precision': 0.001856214279002524,
'recall': 0.07138328895651071,
'rand_precision': 0.0013561437852866911,
'auprc': 0.0013751209299609028,
'ap': 0.001384961126647782,
'epr': 1.2630649585124507},
'genie3_tf_leukocyte': {'enriched_terms_Central': ['0__TFs'],
'TF_enr': True,
'significant_enriched_TFtargets': 0.0,
'precision': 0.009188551561432917,
'recall': 0.37841984147276914,
'rand_precision': 0.001639502322383755,
'auprc': 0.005781176553272736,
'ap': 0.006097495201627464,
'epr': 9.454522889486277},
'genie3_leukocyte': {'TF_enr': False,
'significant_enriched_TFtargets': 10.526315789473685,
'precision': 0.0022959552969107387,
'recall': 0.2027614420864229,
'rand_precision': 0.001639502322383755,
'auprc': 0.0017903071540811078,
'ap': 0.0016960689439870014,
'epr': 1.248658655459195},
'genie3_tf_kidney interstitial fibroblast': {'enriched_terms_Central': ['0__TFs',
'celltype.gmt__Perivascular cells',
'celltype.gmt__Embryonic stem cells',
'celltype.gmt__Smooth Muscle cells',
'celltype.gmt__Myofibroblasts',
'celltype.gmt__Mesangial cells'],
'TF_enr': True,
'enriched_terms_Targets': ['celltype.gmt__Mesangial cells'],
'significant_enriched_TFtargets': 6.382978723404255,
'precision': 0.009544498926019955,
'recall': 0.32796150187454537,
'rand_precision': 0.0015409448472635749,
'auprc': 0.004712568423019291,
'ap': 0.005047515883367648,
'epr': 5.934936091576408},
'genie3_kidney interstitial fibroblast': {'enriched_terms_Central': ['celltype.gmt__Myofibroblasts',
'celltype.gmt__Mesangial cells',
'celltype.gmt__Perivascular cells',
'celltype.gmt__Smooth Muscle cells',
'celltype.gmt__Hepatic stellate cells',
'celltype.gmt__Myoepithelial cells',
'celltype.gmt__Cap mesenchyme cells (Mesenchymal cells)',
'celltype.gmt__Vascular smooth muscle cells',
'celltype.gmt__Embryonic stem cells',
'celltype.gmt__Plasmacytoid Dendritic cells',
'celltype.gmt__Mesenchymal stem cells',
'celltype.gmt__Smooth muscle cells',
'celltype.gmt__Myeloid Dendritic cells',
'celltype.gmt__Endothelial cells'],
'TF_enr': False,
'enriched_terms_Targets': ['celltype.gmt__Hepatic stellate cells',
'celltype.gmt__Myofibroblasts',
'celltype.gmt__Smooth Muscle cells',
'celltype.gmt__Perivascular cells',
'celltype.gmt__Myoepithelial cells',
'celltype.gmt__Mesangial cells',
'celltype.gmt__Renal interstitium (Mesenchymal cells)',
'celltype.gmt__Vascular smooth muscle cells',
'celltype.gmt__Smooth muscle cells',
'celltype.gmt__Mesenchymal stem cells',
'celltype.gmt__Myocytes',
'celltype.gmt__Endothelial'],
'significant_enriched_TFtargets': 6.382978723404255,
'precision': 0.002308445385969307,
'recall': 0.19646354428963125,
'rand_precision': 0.0015409448472635749,
'auprc': 0.0016294208207601668,
'ap': 0.0015709317218276543,
'epr': 1.2720250886940248}}
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In [12]:
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res = []
for k, v in metrics.items():
res.append([k.split('_')[-1], v['epr'], v['auprc'], v['rand_precision'], v['significant_enriched_TFtargets'], v.get('TF_enr', False), 'tf_' in k])
df = pd.DataFrame(res, columns=['name','EPR', 'AUPRC', 'RAND', 'TF_targ', 'TF_enr', 'TF_only'])
df
res = []
for k, v in metrics.items():
res.append([k.split('_')[-1], v['epr'], v['auprc'], v['rand_precision'], v['significant_enriched_TFtargets'], v.get('TF_enr', False), 'tf_' in k])
df = pd.DataFrame(res, columns=['name','EPR', 'AUPRC', 'RAND', 'TF_targ', 'TF_enr', 'TF_only'])
df
Out[12]:
| name | EPR | AUPRC | RAND | TF_targ | TF_enr | TF_only | |
|---|---|---|---|---|---|---|---|
| 0 | kidney distal convoluted tubule epithelial cell | 5.849284 | 0.002524 | 0.001062 | 14.705882 | True | True |
| 1 | kidney distal convoluted tubule epithelial cell | 1.746686 | 0.001076 | 0.001062 | 2.941176 | False | False |
| 2 | kidney loop of Henle thick ascending limb epit... | 4.897762 | 0.003310 | 0.001212 | 10.000000 | True | True |
| 3 | kidney loop of Henle thick ascending limb epit... | 0.716287 | 0.001204 | 0.001212 | 2.500000 | False | False |
| 4 | kidney collecting duct principal cell | 8.781635 | 0.004757 | 0.001362 | 1.428571 | True | True |
| 5 | kidney collecting duct principal cell | 1.589025 | 0.001432 | 0.001362 | 2.857143 | False | False |
| 6 | mesangial cell | 9.434252 | 0.003054 | 0.001275 | 2.564103 | True | True |
| 7 | mesangial cell | 1.334275 | 0.001283 | 0.001275 | 2.564103 | False | False |
| 8 | blood vessel smooth muscle cell | 5.978256 | 0.004916 | 0.001682 | 0.000000 | True | True |
| 9 | blood vessel smooth muscle cell | 1.121810 | 0.001777 | 0.001682 | 3.571429 | False | False |
| 10 | podocyte | 6.145020 | 0.004681 | 0.001518 | 0.000000 | True | True |
| 11 | podocyte | 0.734880 | 0.001540 | 0.001518 | 4.255319 | False | False |
| 12 | macrophage | 6.547711 | 0.003378 | 0.001356 | 2.439024 | True | True |
| 13 | macrophage | 1.263065 | 0.001375 | 0.001356 | 0.000000 | False | False |
| 14 | leukocyte | 9.454523 | 0.005781 | 0.001640 | 0.000000 | True | True |
| 15 | leukocyte | 1.248659 | 0.001790 | 0.001640 | 10.526316 | False | False |
| 16 | kidney interstitial fibroblast | 5.934936 | 0.004713 | 0.001541 | 6.382979 | True | True |
| 17 | kidney interstitial fibroblast | 1.272025 | 0.001629 | 0.001541 | 6.382979 | False | False |