WORKFLOW 05: Gene & Pathway Enrichment Analysis#
This workflow demonstrates how to identify genes and pathways associated with each archetype:
Compute gene associations (differential expression per archetype)
Compute pathway associations (enrichment analysis)
The gene_associations function returns a DataFrame with columns:
gene: Gene identifier
archetype: Which archetype (0, 1, 2, …)
n_archetype_cells: Number of cells in this archetype
n_other_cells: Number of cells in other archetypes
mean_archetype: Mean expression in archetype cells
mean_other: Mean expression in other cells
log_fold_change: Log fold change (archetype vs others)
statistic: Test statistic
pvalue: Raw p-value
fdr_pvalue: FDR-adjusted p-value
significant: Boolean significance flag
direction: ‘up’ or ‘down’
Example usage: python WORKFLOW_05.py
Requirements: - peach - scanpy - Trained model with archetype assignments (from WORKFLOW_04)
[12]:
import scanpy as sc
import peach as pc
from pathlib import Path
Configuration#
[ ]:
# Data path
data_path = Path("~/data/hsc_10k.h5ad")
# Training parameters (from WORKFLOW_03-04)
n_archetypes = 5
hidden_dims = [256, 128, 64]
n_epochs = 50
seed = 42
# Gene association parameters
top_n_genes = 50 # Number of top genes to display per archetype
p_value_threshold = 0.05 # Significance threshold
Step 1: Prepare Data with Model and Assignments (Prerequisites)#
[14]:
print("Preparing data (loading, training, assigning)...")
adata = sc.read_h5ad(data_path)
print(f" Shape: {adata.n_obs:,} cells × {adata.n_vars:,} genes")
# Ensure PCA exists
if 'X_pca' not in adata.obsm:
print(" Running PCA...")
sc.tl.pca(adata, n_comps=13)
# Train model
print(f" Training model ({n_archetypes} archetypes)...")
pc.tl.train_archetypal(
adata,
n_archetypes=n_archetypes,
n_epochs=n_epochs,
model_config={'hidden_dims': hidden_dims},
seed=seed,
device='cpu',
)
Preparing data (loading, training, assigning)...
Shape: 10,000 cells × 2,500 genes
Training model (5 archetypes)...
[OK] Using specified PCA coordinates: adata.obsm['X_pca'] (10000, 50)
[STATS] DataLoader created: 10000 cells × 50 PCA components
Config: batch_size=128, workers=0 (Apple Silicon)
Archetypes parameter registered: True
Archetypes requires_grad: True
Deep_AA (Deep Archetypal Analysis) initialized:
- Single-stage architecture (like Deep_2)
- Inflation factor: 1.5
- Direct archetypal coordinates (no bottleneck)
Initializing with PCHA + inflation_factor=1.5...
Consolidated Archetype Initialization
PCHA: True, Inflation: True (factor: 1.5)
Test inflation: False
Running PCHA initialization...
Input shape: (1000, 50)
Target archetypes: 5
Running PCHA with 5 archetypes...
Data shape for PCHA: (50, 1000)
PCHA Results:
Archetypes shape: (5, 50)
Archetype R²: 0.4446
SSE: 182614.4223
PCHA archetype R²: 0.4446
Archetype shape: (5, 50)
[OK] Initialized 5 archetypes using PCHA
Scalar Archetypal Inflation (factor: 1.50)
[OK] Inflation complete
[STATS] Positioning verification:
Data radius (max): 82.083
Data radius (mean): 17.643
Archetype distances: 15.444 to 28.705
Archetypes outside data: 0/5
Min archetype separation: 28.561
[OK] Archetype initialization complete (success: True)
[OK] PCHA + inflation initialization successful!
Starting training for 50 epochs...
Device: cpu
Archetypal weight: 1.0, KLD weight: 0.0, Reconstruction weight: 0.0
(Model configured: arch=1.0, kld=0.0)
Tracking stability: True, Validating constraints: True
Epoch 0 Debug:
z row sums (should be ~1.0): 1.0000 ± 0.0000
z stats: min=0.0018, max=0.9853, mean=0.2000
Batch reconstruction MSE: 7.2206
Archetype stats: min=-14.5121, max=26.3165
Archetype gradients: norm=0.028331, mean=0.001400
Epoch 1/50
Average loss: 5.8391
Archetypal loss: 5.8391
KLD loss: 30.0765
Reconstruction loss: 5.8391
Archetype R²: 0.1296
fc_Y grad norm: 0.000000
Constraints satisfied: True
Constraint violation rate: 0.000
Archetype drift (mean): 0.000000
Epoch 2 Debug:
z row sums (should be ~1.0): 1.0000 ± 0.0000
z stats: min=0.0003, max=0.9958, mean=0.2000
Batch reconstruction MSE: 4.7002
Archetype stats: min=-14.6200, max=26.3834
Archetype change since last debug: 1.320670
Archetype gradients: norm=0.056029, mean=0.002796
Epoch 4 Debug:
z row sums (should be ~1.0): 1.0000 ± 0.0000
z stats: min=0.0008, max=0.9873, mean=0.2000
Batch reconstruction MSE: 4.2932
Archetype stats: min=-14.6421, max=26.4233
Archetype change since last debug: 0.779623
Archetype gradients: norm=0.046320, mean=0.002401
Epoch 6/50
Average loss: 3.6891
Archetypal loss: 3.6891
KLD loss: 5.8762
Reconstruction loss: 3.6891
Archetype R²: 0.4506
fc_Y grad norm: 0.000000
Epoch 6 Debug:
z row sums (should be ~1.0): 1.0000 ± 0.0000
z stats: min=0.0007, max=0.9711, mean=0.2000
Batch reconstruction MSE: 4.0701
Archetype stats: min=-14.6506, max=26.4296
Archetype change since last debug: 0.214943
Archetype gradients: norm=0.085434, mean=0.004041
Epoch 8 Debug:
z row sums (should be ~1.0): 1.0000 ± 0.0000
z stats: min=0.0037, max=0.9733, mean=0.2000
Batch reconstruction MSE: 4.0687
Archetype stats: min=-14.6544, max=26.4321
Archetype change since last debug: 0.086395
Archetype gradients: norm=0.090197, mean=0.004353
Epoch 10 Debug:
z row sums (should be ~1.0): 1.0000 ± 0.0000
z stats: min=0.0023, max=0.9590, mean=0.2000
Batch reconstruction MSE: 3.8472
Archetype stats: min=-14.6561, max=26.4312
Archetype change since last debug: 0.062218
Archetype gradients: norm=0.113335, mean=0.005214
Epoch 11/50
Average loss: 3.6966
Archetypal loss: 3.6966
KLD loss: 6.9209
Reconstruction loss: 3.6966
Archetype R²: 0.4508
fc_Y grad norm: 0.000000
Constraints satisfied: True
Constraint violation rate: 0.000
Archetype drift (mean): 0.015289
Epoch 12 Debug:
z row sums (should be ~1.0): 1.0000 ± 0.0000
z stats: min=0.0067, max=0.8718, mean=0.2000
Batch reconstruction MSE: 3.7125
Archetype stats: min=-14.6509, max=26.4254
Archetype change since last debug: 0.066543
Archetype gradients: norm=0.050003, mean=0.002553
Epoch 14 Debug:
z row sums (should be ~1.0): 1.0000 ± 0.0000
z stats: min=0.0059, max=0.9096, mean=0.2000
Batch reconstruction MSE: 3.8448
Archetype stats: min=-14.6543, max=26.4262
Archetype change since last debug: 0.043014
Archetype gradients: norm=0.097463, mean=0.004409
Epoch 16/50
Average loss: 3.6612
Archetypal loss: 3.6612
KLD loss: 8.1732
Reconstruction loss: 3.6612
Archetype R²: 0.4541
fc_Y grad norm: 0.000000
Epoch 16 Debug:
z row sums (should be ~1.0): 1.0000 ± 0.0000
z stats: min=0.0063, max=0.8508, mean=0.2000
Batch reconstruction MSE: 4.2753
Archetype stats: min=-14.6532, max=26.4227
Archetype change since last debug: 0.042768
Archetype gradients: norm=0.053411, mean=0.002580
Epoch 18 Debug:
z row sums (should be ~1.0): 1.0000 ± 0.0000
z stats: min=0.0088, max=0.8276, mean=0.2000
Batch reconstruction MSE: 3.5648
Archetype stats: min=-14.6525, max=26.4206
Archetype change since last debug: 0.033115
Archetype gradients: norm=0.036683, mean=0.001715
Epoch 20 Debug:
z row sums (should be ~1.0): 1.0000 ± 0.0000
z stats: min=0.0109, max=0.8799, mean=0.2000
Batch reconstruction MSE: 3.6455
Archetype stats: min=-14.6530, max=26.4171
Archetype change since last debug: 0.031581
Archetype gradients: norm=0.040524, mean=0.001658
Epoch 21/50
Average loss: 3.6740
Archetypal loss: 3.6740
KLD loss: 8.6371
Reconstruction loss: 3.6740
Archetype R²: 0.4532
fc_Y grad norm: 0.000000
Constraints satisfied: True
Constraint violation rate: 0.000
Archetype drift (mean): 0.014879
Epoch 22 Debug:
z row sums (should be ~1.0): 1.0000 ± 0.0000
z stats: min=0.0071, max=0.9162, mean=0.2000
Batch reconstruction MSE: 3.3092
Archetype stats: min=-14.6509, max=26.4102
Archetype change since last debug: 0.047052
Archetype gradients: norm=0.138397, mean=0.004879
Epoch 24 Debug:
z row sums (should be ~1.0): 1.0000 ± 0.0000
z stats: min=0.0102, max=0.8957, mean=0.2000
Batch reconstruction MSE: 4.0135
Archetype stats: min=-14.6432, max=26.4066
Archetype change since last debug: 0.088294
Archetype gradients: norm=0.075356, mean=0.003671
Epoch 26/50
Average loss: 3.6786
Archetypal loss: 3.6786
KLD loss: 9.8887
Reconstruction loss: 3.6786
Archetype R²: 0.4540
fc_Y grad norm: 0.000000
Epoch 26 Debug:
z row sums (should be ~1.0): 1.0000 ± 0.0000
z stats: min=0.0094, max=0.8422, mean=0.2000
Batch reconstruction MSE: 3.4788
Archetype stats: min=-14.6452, max=26.4036
Archetype change since last debug: 0.047760
Archetype gradients: norm=0.046662, mean=0.002226
Epoch 28 Debug:
z row sums (should be ~1.0): 1.0000 ± 0.0000
z stats: min=0.0116, max=0.8346, mean=0.2000
Batch reconstruction MSE: 3.4514
Archetype stats: min=-14.6430, max=26.3961
Archetype change since last debug: 0.039553
Archetype gradients: norm=0.038909, mean=0.001910
Epoch 30 Debug:
z row sums (should be ~1.0): 1.0000 ± 0.0000
z stats: min=0.0135, max=0.7947, mean=0.2000
Batch reconstruction MSE: 3.5813
Archetype stats: min=-14.6439, max=26.3971
Archetype change since last debug: 0.009621
Archetype gradients: norm=0.024654, mean=0.001296
Epoch 31/50
Average loss: 3.6614
Archetypal loss: 3.6614
KLD loss: 9.7610
Reconstruction loss: 3.6614
Archetype R²: 0.4554
fc_Y grad norm: 0.000000
Constraints satisfied: True
Constraint violation rate: 0.000
Archetype drift (mean): 0.003683
Epoch 32 Debug:
z row sums (should be ~1.0): 1.0000 ± 0.0000
z stats: min=0.0130, max=0.8426, mean=0.2000
Batch reconstruction MSE: 3.8325
Archetype stats: min=-14.6440, max=26.3979
Archetype change since last debug: 0.007973
Archetype gradients: norm=0.025128, mean=0.001241
Epoch 34 Debug:
z row sums (should be ~1.0): 1.0000 ± 0.0000
z stats: min=0.0087, max=0.8153, mean=0.2000
Batch reconstruction MSE: 3.1213
Archetype stats: min=-14.6446, max=26.3984
Archetype change since last debug: 0.007511
Archetype gradients: norm=0.029240, mean=0.001477
Epoch 36/50
Average loss: 3.6483
Archetypal loss: 3.6483
KLD loss: 9.5727
Reconstruction loss: 3.6483
Archetype R²: 0.4560
fc_Y grad norm: 0.000000
Epoch 36 Debug:
z row sums (should be ~1.0): 1.0000 ± 0.0000
z stats: min=0.0125, max=0.7938, mean=0.2000
Batch reconstruction MSE: 3.7836
Archetype stats: min=-14.6453, max=26.3991
Archetype change since last debug: 0.009430
Archetype gradients: norm=0.012030, mean=0.000580
Epoch 38 Debug:
z row sums (should be ~1.0): 1.0000 ± 0.0000
z stats: min=0.0094, max=0.8136, mean=0.2000
Batch reconstruction MSE: 3.7424
Archetype stats: min=-14.6457, max=26.4001
Archetype change since last debug: 0.008207
Archetype gradients: norm=0.019250, mean=0.000959
Epoch 40 Debug:
z row sums (should be ~1.0): 1.0000 ± 0.0000
z stats: min=0.0153, max=0.8132, mean=0.2000
Batch reconstruction MSE: 3.6533
Archetype stats: min=-14.6460, max=26.4010
Archetype change since last debug: 0.007721
Archetype gradients: norm=0.024774, mean=0.000990
Epoch 41/50
Average loss: 3.6709
Archetypal loss: 3.6709
KLD loss: 9.4661
Reconstruction loss: 3.6709
Archetype R²: 0.4541
fc_Y grad norm: 0.000000
Constraints satisfied: True
Constraint violation rate: 0.000
Archetype drift (mean): 0.003977
Epoch 42 Debug:
z row sums (should be ~1.0): 1.0000 ± 0.0000
z stats: min=0.0120, max=0.8204, mean=0.2000
Batch reconstruction MSE: 3.2447
Archetype stats: min=-14.6462, max=26.4017
Archetype change since last debug: 0.008960
Archetype gradients: norm=0.018014, mean=0.000904
Epoch 44 Debug:
z row sums (should be ~1.0): 1.0000 ± 0.0000
z stats: min=0.0095, max=0.8329, mean=0.2000
Batch reconstruction MSE: 3.3984
Archetype stats: min=-14.6462, max=26.4017
Archetype change since last debug: 0.007368
Archetype gradients: norm=0.026762, mean=0.001127
Epoch 46/50
Average loss: 3.6648
Archetypal loss: 3.6648
KLD loss: 9.7107
Reconstruction loss: 3.6648
Archetype R²: 0.4546
fc_Y grad norm: 0.000000
Epoch 46 Debug:
z row sums (should be ~1.0): 1.0000 ± 0.0000
z stats: min=0.0171, max=0.8268, mean=0.2000
Batch reconstruction MSE: 3.6691
Archetype stats: min=-14.6469, max=26.4028
Archetype change since last debug: 0.009340
Archetype gradients: norm=0.028169, mean=0.001325
Epoch 48 Debug:
z row sums (should be ~1.0): 1.0000 ± 0.0000
z stats: min=0.0080, max=0.8243, mean=0.2000
Batch reconstruction MSE: 3.4960
Archetype stats: min=-14.6476, max=26.4033
Archetype change since last debug: 0.008653
Archetype gradients: norm=0.020366, mean=0.001087
Epoch 49 Debug:
z row sums (should be ~1.0): 1.0000 ± 0.0000
z stats: min=0.0165, max=0.7822, mean=0.2000
Batch reconstruction MSE: 4.0064
Archetype stats: min=-14.6478, max=26.4032
Archetype change since last debug: 0.005465
Archetype gradients: norm=0.032445, mean=0.001618
Epoch 50/50
Average loss: 3.6542
Archetypal loss: 3.6542
KLD loss: 9.8556
Reconstruction loss: 3.6542
Archetype R²: 0.4556
fc_Y grad norm: 0.000000
Constraints satisfied: True
Constraint violation rate: 0.000
Archetype drift (mean): 0.001879
============================================================
TRAINING COMPLETED
============================================================
[OK] Stored archetype coordinates in adata.uns['archetype_coordinates']: torch.Size([5, 50])
Final Performance:
loss: 3.6542 (range: 3.6477 - 5.8391)
archetypal_loss: 3.6542 (range: 3.6477 - 5.8391)
archetype_r2: 0.4556 (range: 0.1296 - 0.4567)
fc_Y Learning Summary:
Final Stability Metrics:
archetype_drift_mean: 0.001879 (range: 0.000000 - 0.528382)
archetype_variance_mean: 0.000000 (range: 0.000000 - 0.002172)
Final Constraint Status:
A matrix sum error: 0.000000
B matrix sum error: 0.000000
Constraints satisfied: True
[14]:
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'final_model': Deep_AA(
(encoder): Sequential(
(0): Sequential(
(0): Linear(in_features=50, out_features=256, bias=True)
(1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Linear(in_features=256, out_features=128, bias=True)
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Linear(in_features=128, out_features=64, bias=True)
(1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): LeakyReLU(negative_slope=0.01)
)
)
(fc_mu): Linear(in_features=64, out_features=5, bias=True)
(fc_var): Linear(in_features=64, out_features=5, bias=True)
(decoder_input): Linear(in_features=5, out_features=64, bias=True)
(decoder): Sequential(
(0): Sequential(
(0): Linear(in_features=64, out_features=128, bias=True)
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Linear(in_features=128, out_features=256, bias=True)
(1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): LeakyReLU(negative_slope=0.01)
)
)
(final_layer): Sequential(
(0): Linear(in_features=256, out_features=50, bias=True)
)
(archetype_transform): Sequential(
(0): Linear(in_features=50, out_features=50, bias=True)
(1): LeakyReLU(negative_slope=0.01)
(2): Linear(in_features=50, out_features=50, bias=True)
)
),
'final_optimizer': Adam (
Parameter Group 0
amsgrad: False
betas: (0.9, 0.999)
capturable: False
differentiable: False
eps: 1e-08
foreach: None
fused: None
lr: 0.0001
maximize: False
weight_decay: 0
),
'final_analysis': {'final_constraint_validation': {'A_sum_error': 3.166496753692627e-08,
'A_negative_fraction': 0.0,
'B_sum_error': 0.0,
'B_negative_fraction': 0.0,
'constraints_satisfied': 1.0},
'archetypal_weights': {'A_matrix': {'mean_weights': array([0.08769251, 0.2293152 , 0.285479 , 0.21785782, 0.17965545],
dtype=float32),
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dtype=float32),
'max_weights': array([0.5699687 , 0.7181422 , 0.83219475, 0.7141526 , 0.8052886 ],
dtype=float32),
'min_weights': array([0.00909145, 0.0368204 , 0.01877142, 0.0185516 , 0.02189638],
dtype=float32),
'dominant_archetype': array([0.0703125, 0.3046875, 0.2890625, 0.1640625, 0.171875 ],
dtype=float32)},
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dtype=float32),
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'max_weights': array([0.0078125, 0.0078125, 0.0078125, 0.0078125, 0.0078125],
dtype=float32),
'min_weights': array([0.0078125, 0.0078125, 0.0078125, 0.0078125, 0.0078125],
dtype=float32)}},
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'training_config': {'n_epochs': 50,
'actual_epochs': 50,
'early_stop_triggered': False,
'archetypal_weight': 1.0,
'kld_weight': 0.0,
'reconstruction_weight': 0.0,
'activation_func': 'relu',
'seed': 42,
'constraint_tolerance': 0.001,
'stability_history_size': 20,
'early_stopping': False,
'early_stopping_patience': None,
'early_stopping_metric': None},
'model': Deep_AA(
(encoder): Sequential(
(0): Sequential(
(0): Linear(in_features=50, out_features=256, bias=True)
(1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Linear(in_features=256, out_features=128, bias=True)
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): LeakyReLU(negative_slope=0.01)
)
(2): Sequential(
(0): Linear(in_features=128, out_features=64, bias=True)
(1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): LeakyReLU(negative_slope=0.01)
)
)
(fc_mu): Linear(in_features=64, out_features=5, bias=True)
(fc_var): Linear(in_features=64, out_features=5, bias=True)
(decoder_input): Linear(in_features=5, out_features=64, bias=True)
(decoder): Sequential(
(0): Sequential(
(0): Linear(in_features=64, out_features=128, bias=True)
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): LeakyReLU(negative_slope=0.01)
)
(1): Sequential(
(0): Linear(in_features=128, out_features=256, bias=True)
(1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): LeakyReLU(negative_slope=0.01)
)
)
(final_layer): Sequential(
(0): Linear(in_features=256, out_features=50, bias=True)
)
(archetype_transform): Sequential(
(0): Linear(in_features=50, out_features=50, bias=True)
(1): LeakyReLU(negative_slope=0.01)
(2): Linear(in_features=50, out_features=50, bias=True)
)
),
'final_archetype_r2': 0.45562073852442486,
'final_rmse': 1.9098130374015132,
'final_loss': 3.654239461391787,
'convergence_epoch': 50}
[15]:
pc.tl.archetypal_coordinates(adata)
Computing archetype distances in PCA space...
Canonical reference: adata.obs.index (10000 cells)
Found PCA coordinates: X_pca (10000, 50)
Found archetype coordinates: archetype_coordinates (5, 50)
Computing pairwise distances in PCA space...
[OK] Distance computation complete
Distance matrix shape: (10000, 5)
[OK] Stored in AnnData:
adata.obsm['archetype_distances']: (10000, 5) distance matrix
adata.uns['archetype_positions']: (5, 50) archetype positions
adata.uns['archetype_distance_info']: distance computation metadata
[STATS] Distance Statistics:
Nearest archetype distribution:
Archetype 0: 785 cells (7.8%), mean distance: 15.1040
Archetype 1: 1486 cells (14.9%), mean distance: 17.0829
Archetype 2: 3987 cells (39.9%), mean distance: 16.8960
Archetype 3: 3127 cells (31.3%), mean distance: 15.3948
Archetype 4: 615 cells (6.2%), mean distance: 26.0079
Overall statistics:
Mean nearest distance: 16.8741
Distance range: [7.578, 87.574]
[15]:
| cell_id | cell_idx | archetype_0_distance | archetype_1_distance | archetype_2_distance | archetype_3_distance | archetype_4_distance | nearest_archetype | nearest_distance | mean_distance | std_distance | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | young4_TCAATCTGTACATCCA-1 | 0 | 30.868059 | 34.729964 | 10.722053 | 27.453603 | 33.294571 | 2 | 10.722053 | 27.413650 | 8.702386 |
| 1 | Oetjen_A_TATTACCTCTGGAGCC-1 | 1 | 34.567422 | 33.966304 | 26.612939 | 14.541995 | 35.431334 | 3 | 14.541995 | 29.023999 | 7.896221 |
| 2 | young4_CACAAACTCAAACCGT-1 | 2 | 20.907336 | 34.237827 | 18.358518 | 27.472408 | 34.075530 | 2 | 18.358518 | 27.010324 | 6.549316 |
| 3 | MantonBM4_HiSeq_6-TTGTAGGTCTGTTGAG-1 | 3 | 36.648242 | 16.944750 | 27.587736 | 30.461982 | 34.720264 | 1 | 16.944750 | 29.272595 | 6.932625 |
| 4 | BMMC_10x_GREENLEAF_REP2:GTGGTTAGTGTAAACA-1 | 4 | 34.135876 | 35.857042 | 25.095644 | 14.220676 | 34.981425 | 3 | 14.220676 | 28.858133 | 8.279438 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 9995 | Oetjen_P_CGTCCATTCGCCCTTA-1 | 9995 | 32.290439 | 33.789671 | 25.598921 | 11.978496 | 34.261287 | 3 | 11.978496 | 27.583763 | 8.399040 |
| 9996 | elderly2_GAAGCAGCAGCATGAG-1 | 9996 | 30.041344 | 31.902685 | 13.483154 | 24.355426 | 32.360471 | 2 | 13.483154 | 26.428616 | 7.072302 |
| 9997 | MantonBM1_HiSeq_5-CGAGAAGAGCAGCCTC-1 | 9997 | 33.951706 | 33.183307 | 25.140412 | 13.244904 | 34.424874 | 3 | 13.244904 | 27.989040 | 8.117364 |
| 9998 | BMMC_10x_GREENLEAF_REP1:CCCGAAGAGACGACTG-1 | 9998 | 44.436055 | 22.912607 | 38.325610 | 37.708057 | 44.468478 | 1 | 22.912607 | 37.570161 | 7.876051 |
| 9999 | young2_ACGGGCTAGGCACATG-1 | 9999 | 32.243800 | 32.221607 | 21.336163 | 28.777127 | 27.148466 | 2 | 21.336163 | 28.345432 | 4.024200 |
10000 rows × 11 columns
[16]:
pc.tl.assign_archetypes(adata)
AnnData-centric archetype binning...
Distance matrix: (10000, 5) (from adata.obsm['archetype_distances'])
Canonical cell reference: adata.obs.index (10000 cells)
Selecting top 1000 cells (10.0%) per archetype
INCLUDING central archetype_0 (generalist cells)
Archetype 0 (central): 1000 cells, centroid distance range: [25.4429, 26.7755], mean: 26.3968
Archetype 1: 1000 cells, distance range: [7.5782, 24.5794], mean: 16.7386
Archetype 2: 1000 cells, distance range: [9.6427, 18.2831], mean: 14.8333
Archetype 3: 1000 cells, distance range: [8.0890, 12.9625], mean: 11.5525
Archetype 4: 1000 cells, distance range: [9.4175, 13.6764], mean: 12.7843
Archetype 5: 1000 cells, distance range: [18.2666, 29.6810], mean: 26.2502
[STATS] Assignment Summary:
Total cells: 10000
Archetype 0 (central): 1000 cells (10.0%)
Archetype 1: 1000 cells (10.0%)
Archetype 2: 1000 cells (10.0%)
Archetype 3: 1000 cells (10.0%)
Archetype 4: 1000 cells (10.0%)
Archetype 5: 1000 cells (10.0%)
No archetype: 4637 cells (46.4%)
[WARNING] Overlapping assignments: 1273 cells
[OK] Stored assignments in adata.obs['archetypes']:
Categories: ['archetype_0', 'archetype_1', 'archetype_2', 'archetype_3', 'archetype_4', 'archetype_5', 'no_archetype']
no_archetype: 4637 cells (46.4%)
archetype_2: 1000 cells (10.0%)
archetype_1: 989 cells (9.9%)
archetype_5: 988 cells (9.9%)
archetype_4: 930 cells (9.3%)
archetype_3: 764 cells (7.6%)
archetype_0: 692 cells (6.9%)
[17]:
print("\nComputing gene associations...")
print(" (Differential expression per archetype)")
gene_assoc = pc.tl.gene_associations(
adata,
obs_key='archetypes', # Column with archetype assignments
test_method='mannwhitneyu', # or 'wilcoxon' for non-parametric
fdr_scope='global', # Global FDR correction
verbose=True,
)
print(f"\n Results DataFrame shape: {gene_assoc.shape}")
print(f" Columns: {list(gene_assoc.columns)}")
# Show top genes for each archetype
print(f"\nTop genes per archetype:")
for archetype in sorted(gene_assoc['archetype'].unique()):
arch_genes = gene_assoc[gene_assoc['archetype'] == archetype]
# Filter by significance
sig_genes = arch_genes[arch_genes['fdr_pvalue'] < p_value_threshold]
# Sort by fold change
top_genes = sig_genes.nlargest(5, 'log_fold_change')
print(f"\n Archetype {archetype} ({len(sig_genes)} significant genes):")
if len(top_genes) > 0:
for idx, row in top_genes.iterrows():
gene = row['gene']
fc = row['log_fold_change']
pval = row['fdr_pvalue']
print(f" {gene}: logFC={fc:.2f}, fdr_p={pval:.2e}")
else:
print(f" No significant genes found")
Computing gene associations...
(Differential expression per archetype)
🧪 Testing archetype-gene associations (AnnData-centric)...
Method: mannwhitneyu
FDR correction: benjamini_hochberg (global scope)
Test direction: two-sided
Bin proportion: 0.1 (closest cells to each archetype)
Minimum cells per archetype: 10
Comparison group: all
[WARNING] Using adata.X - assuming data is already log-transformed
[OK] AnnData validation passed:
Distance matrix: (10000, 5) from adata.obsm['archetype_distances']
Archetype assignments: 10000 cells from adata.obs['archetypes']
Assignment categories: ['archetype_0', 'archetype_1', 'archetype_2', 'archetype_3', 'archetype_4', 'archetype_5', 'no_archetype']
Using adata.X
Expression data: 10000 cells × 2500 genes
Sparse matrix: 95.5% zeros
Original format: csr
Using AnnData archetype assignments for binning...
Found 6 archetype categories: ['archetype_0', 'archetype_1', 'archetype_2', 'archetype_3', 'archetype_4', 'archetype_5']
no_archetype: 4637 cells (46.4%)
archetype_2: 1000 cells (10.0%)
archetype_1: 989 cells (9.9%)
archetype_5: 988 cells (9.9%)
archetype_4: 930 cells (9.3%)
archetype_3: 764 cells (7.6%)
archetype_0: 692 cells (6.9%)
archetype_0: 692 assigned cells
archetype_1: 989 assigned cells
archetype_2: 1000 assigned cells
archetype_3: 764 assigned cells
archetype_4: 930 assigned cells
archetype_5: 988 assigned cells
[OK] Assignment-based binning completed (6 valid archetypes)
Testing archetype_0...
Archetype cells: 692, Comparison cells: 9308
Extracting expression data for 692 archetype + 9308 other cells...
[OK] Expression extraction completed - testing 2500 genes...
Tested 2500 genes
Testing archetype_1...
Archetype cells: 989, Comparison cells: 9011
Extracting expression data for 989 archetype + 9011 other cells...
[OK] Expression extraction completed - testing 2500 genes...
Tested 2500 genes
Testing archetype_2...
Archetype cells: 1000, Comparison cells: 9000
Extracting expression data for 1000 archetype + 9000 other cells...
[OK] Expression extraction completed - testing 2500 genes...
Tested 2500 genes
Testing archetype_3...
Archetype cells: 764, Comparison cells: 9236
Extracting expression data for 764 archetype + 9236 other cells...
[OK] Expression extraction completed - testing 2500 genes...
Tested 2500 genes
Testing archetype_4...
Archetype cells: 930, Comparison cells: 9070
Extracting expression data for 930 archetype + 9070 other cells...
[OK] Expression extraction completed - testing 2500 genes...
Tested 2500 genes
Testing archetype_5...
Archetype cells: 988, Comparison cells: 9012
Extracting expression data for 988 archetype + 9012 other cells...
[OK] Expression extraction completed - testing 2500 genes...
Tested 2500 genes
Applying FDR correction (benjamini_hochberg, global scope)...
Total tests performed: 6693
[STATS] FDR Correction Statistics:
Method: benjamini_hochberg
Number of tests: 6693
Raw p-values < 0.05: 3007
Min p-value: 0.00e+00
Median p-value: 1.61e-01
FDR-corrected p-values < 0.05: 2803
Correction ratio: 2803/3007 = 0.932
Note: BH assumes independence or positive dependence among tests
Applied global FDR correction: 2803/6693 significant
[OK] Gene association testing completed!
Total tests: 6693
Significant associations: 2803 (41.9%)
Significant by direction: {'two-sided': 2803}
Raw significant (p<0.05): 3007
FDR correction impact: 3007 → 2803
[STATS] Top significant associations per archetype:
archetype_2:
↑ ENSG00000163220: FC=4.45, p=0.00e+00 (higher in archetype) (two-sided)
↑ ENSG00000090382: FC=4.35, p=0.00e+00 (higher in archetype) (two-sided)
↑ ENSG00000143546: FC=4.15, p=0.00e+00 (higher in archetype) (two-sided)
archetype_1:
↑ ENSG00000244734: FC=4.30, p=0.00e+00 (higher in archetype) (two-sided)
↑ ENSG00000169877: FC=2.77, p=0.00e+00 (higher in archetype) (two-sided)
↑ ENSG00000223609: FC=2.67, p=0.00e+00 (higher in archetype) (two-sided)
archetype_5:
↑ ENSG00000019582: FC=2.18, p=0.00e+00 (higher in archetype) (two-sided)
↑ ENSG00000204287: FC=2.02, p=0.00e+00 (higher in archetype) (two-sided)
↑ ENSG00000196126: FC=1.26, p=9.58e-203 (higher in archetype) (two-sided)
archetype_3:
↑ ENSG00000128040: FC=1.82, p=0.00e+00 (higher in archetype) (two-sided)
↑ ENSG00000185198: FC=1.18, p=1.09e-194 (higher in archetype) (two-sided)
↑ ENSG00000226777: FC=0.91, p=6.69e-159 (higher in archetype) (two-sided)
archetype_4:
↑ ENSG00000177954: FC=1.28, p=0.00e+00 (higher in archetype) (two-sided)
↑ ENSG00000227507: FC=1.79, p=4.17e-279 (higher in archetype) (two-sided)
↑ ENSG00000008517: FC=1.74, p=6.28e-279 (higher in archetype) (two-sided)
archetype_0:
↓ ENSG00000197956: FC=-0.67, p=1.63e-35 (lower in archetype) (two-sided)
↓ ENSG00000075624: FC=-0.41, p=1.89e-33 (lower in archetype) (two-sided)
↑ ENSG00000128040: FC=0.59, p=5.68e-29 (higher in archetype) (two-sided)
Results DataFrame shape: (6693, 14)
Columns: ['gene', 'archetype', 'n_archetype_cells', 'n_other_cells', 'mean_archetype', 'mean_other', 'log_fold_change', 'statistic', 'pvalue', 'test_direction', 'direction', 'passes_lfc_threshold', 'fdr_pvalue', 'significant']
Top genes per archetype:
Archetype archetype_0 (291 significant genes):
ENSG00000128040: logFC=0.59, fdr_p=5.68e-29
ENSG00000185198: logFC=0.37, fdr_p=1.78e-17
ENSG00000109321: logFC=0.27, fdr_p=2.76e-07
ENSG00000177954: logFC=0.26, fdr_p=4.62e-11
ENSG00000277734: logFC=0.24, fdr_p=1.08e-04
Archetype archetype_1 (491 significant genes):
ENSG00000244734: logFC=4.30, fdr_p=0.00e+00
ENSG00000206172: logFC=3.02, fdr_p=1.27e-228
ENSG00000169877: logFC=2.77, fdr_p=0.00e+00
ENSG00000188536: logFC=2.68, fdr_p=9.32e-171
ENSG00000223609: logFC=2.67, fdr_p=0.00e+00
Archetype archetype_2 (634 significant genes):
ENSG00000163220: logFC=4.45, fdr_p=0.00e+00
ENSG00000090382: logFC=4.35, fdr_p=0.00e+00
ENSG00000143546: logFC=4.15, fdr_p=0.00e+00
ENSG00000085265: logFC=2.75, fdr_p=0.00e+00
ENSG00000100097: logFC=2.46, fdr_p=0.00e+00
Archetype archetype_3 (384 significant genes):
ENSG00000128040: logFC=1.82, fdr_p=0.00e+00
ENSG00000185198: logFC=1.18, fdr_p=1.09e-194
ENSG00000109321: logFC=1.02, fdr_p=1.05e-115
ENSG00000226777: logFC=0.91, fdr_p=6.69e-159
ENSG00000128322: logFC=0.78, fdr_p=6.99e-70
Archetype archetype_4 (517 significant genes):
ENSG00000227507: logFC=1.79, fdr_p=4.17e-279
ENSG00000008517: logFC=1.74, fdr_p=6.28e-279
ENSG00000277734: logFC=1.48, fdr_p=7.21e-263
ENSG00000167286: logFC=1.44, fdr_p=1.33e-277
ENSG00000169442: logFC=1.29, fdr_p=8.36e-188
Archetype archetype_5 (486 significant genes):
ENSG00000019582: logFC=2.18, fdr_p=0.00e+00
ENSG00000204287: logFC=2.02, fdr_p=0.00e+00
ENSG00000105369: logFC=1.34, fdr_p=1.19e-202
ENSG00000196126: logFC=1.26, fdr_p=9.58e-203
ENSG00000211592: logFC=1.19, fdr_p=9.42e-99
There’s a lot added to .uns at this point (‘archetype_coordinates’, ‘trained_model’, ‘archetype_positions’, and ‘archetype_distance_info’) but the archetype-cell assignments (default 10%) are stored in .obs for convenience.
[18]:
adata
[18]:
AnnData object with n_obs × n_vars = 10000 × 2500
obs: 'AuthorCellType', 'AuthorCellType_Broad', 'Shannon.Diversity.Normalized', 'nCount_RNA', 'nFeature_RNA', 'Study', 'donor_id', 'Sorting', 'S.Score', 'G2M.Score', 'CyclePhase', 'scrublet_scores', 'assay_ontology_term_id', 'development_stage_ontology_term_id', 'disease_ontology_term_id', 'is_primary_data', 'self_reported_ethnicity_ontology_term_id', 'suspension_type', 'tissue_ontology_term_id', 'sex_ontology_term_id', 'cell_type_ontology_term_id', 'Donor_Age_Group', 'tissue_type', 'cell_type', 'assay', 'disease', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage', 'observation_joinid', 'archetypes'
var: 'HCA_Hay2018', 'Oetjen2018', 'Granja2019', 'Mende2022', 'Setty2019', 'Ainciburu2023', 'HVG_intersect3000', 'nCells_Detected', 'nDatasets_Detected', 'gene_symbols', 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype', 'feature_length', 'feature_type', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'
uns: 'Study_colors', 'citation', 'hvg', 'organism', 'organism_ontology_term_id', 'schema_reference', 'schema_version', 'title', 'archetype_coordinates', 'trained_model', 'archetype_positions', 'archetype_distance_info'
obsm: 'X_harmony', 'X_pca', 'X_umap', 'archetype_distances'
Step 2: Compute Gene Associations#
[19]:
print("\nExporting results...")
# Save gene associations to CSV
output_file = "gene_associations.csv"
gene_assoc.to_csv(output_file, index=False)
print(f" Saved: {output_file}")
# Summary statistics
print(f"\nSummary statistics:")
print(f" Total genes tested: {gene_assoc['gene'].nunique()}")
print(f" Archetypes analyzed: {gene_assoc['archetype'].nunique()}")
print(f" Significant associations (fdr_p < {p_value_threshold}): {(gene_assoc['fdr_pvalue'] < p_value_threshold).sum()}")
Exporting results...
Saved: gene_associations.csv
Summary statistics:
Total genes tested: 1397
Archetypes analyzed: 6
Significant associations (fdr_p < 0.05): 2803
Note that this uses the 1-vs-all Wilcoxon rank sum test previously used in archetype analysis papers where each archetype is independently treated as the ‘high’ group against all other cells. Because this test rotates to each archetype, you may wind up with the same genes significantly associated with multiple archetypes (same for pathway associations below). This can lead to 3,000-4,000 significant genes per archetype, which is difficult to interpret, so you may want to limit the genes or pathways you are testing against to subsets of interest. I’m not particularly fond of this test, so future versions may include a more discriminative per-archetype characterization method.
Step 3: Compute Pathway Associations (Optional)#
[20]:
print("\n" + "="*70)
print("WORKFLOW 05 COMPLETE")
print("="*70)
print(f"Gene associations computed:")
print(f" • DataFrame with {len(gene_assoc):,} gene-archetype associations")
print(f" • Columns: gene, archetype, log_fold_change, pvalue, fdr_pvalue, significant")
print(f" • Exported to: {output_file}")
print(f"\nNext workflows:")
print(f" • WORKFLOW_06: CellRank Integration (requires RNA velocity)")
print(f" • WORKFLOW_08: Comprehensive Visualization")
print("="*70)
======================================================================
WORKFLOW 05 COMPLETE
======================================================================
Gene associations computed:
• DataFrame with 6,693 gene-archetype associations
• Columns: gene, archetype, log_fold_change, pvalue, fdr_pvalue, significant
• Exported to: gene_associations.csv
Next workflows:
• WORKFLOW_06: CellRank Integration (requires RNA velocity)
• WORKFLOW_08: Comprehensive Visualization
======================================================================
See 08_visualization.ipynb for dotplot and filtering options for results interpretation.
Step 4: Export Results#
[21]:
print("\nExporting results...")
# Save gene associations to CSV
output_file = "gene_associations.csv"
gene_assoc.to_csv(output_file, index=False)
print(f" Saved: {output_file}")
# Summary statistics
print(f"\nSummary statistics:")
print(f" Total genes tested: {gene_assoc['gene'].nunique()}")
print(f" Archetypes analyzed: {gene_assoc['archetype'].nunique()}")
print(f" Significant associations (fdr_p < {p_value_threshold}): {(gene_assoc['fdr_pvalue'] < p_value_threshold).sum()}")
Exporting results...
Saved: gene_associations.csv
Summary statistics:
Total genes tested: 1397
Archetypes analyzed: 6
Significant associations (fdr_p < 0.05): 2803
Summary#
[22]:
print("\n" + "="*70)
print("WORKFLOW 05 COMPLETE")
print("="*70)
print(f"Gene associations computed:")
print(f" • DataFrame with {len(gene_assoc):,} gene-archetype associations")
print(f" • Columns: gene, archetype, log_fold_change, pvalue, fdr_pvalue, significant")
print(f" • Exported to: {output_file}")
print(f"\nNext workflows:")
print(f" • WORKFLOW_06: CellRank Integration (requires RNA velocity)")
print(f" • WORKFLOW_08: Comprehensive Visualization")
print("="*70)
======================================================================
WORKFLOW 05 COMPLETE
======================================================================
Gene associations computed:
• DataFrame with 6,693 gene-archetype associations
• Columns: gene, archetype, log_fold_change, pvalue, fdr_pvalue, significant
• Exported to: gene_associations.csv
Next workflows:
• WORKFLOW_06: CellRank Integration (requires RNA velocity)
• WORKFLOW_08: Comprehensive Visualization
======================================================================