Using SHAP values and IntegratedGradients for cell type classification interpretabilty#
Previously we saw semi-supervised models, like SCANVI being used for tasks like cell type classification, enabling researchers to uncover complex biological patterns. However, as these models become more sophisticated, it is essential to understand not just the predictions they make, but why they make them. This is where interpretability methods like SHAP (SHapley Additive exPlanations) and CAPTUM IntegratedGradients come into play. By providing insights into the influence of individual features on model predictions, these methods help us trust and validate our models in critical biological contexts.
In this tutorial, we’ll explore the significance of interpretability techniques in supervised cell classification using ScanVI, which are now avialble as part of SCVI-Tools.
Note
Running the following cell will install tutorial dependencies on Google Colab only. It will have no effect on environments other than Google Colab.
!pip install --quiet scvi-colab
from scvi_colab import install
install()
import matplotlib.pyplot as plt
import numpy as np
import scanpy as sc
import scvi
import seaborn as sns
import torch
torch.set_float32_matmul_precision("high")
Load data and tain scanvi#
In this tutorial we will be using the dataset of peripheral blood mononuclear cells from 10x Genomics, PBMC dataset
adata = scvi.data.pbmc_dataset()
adata.layers["counts"] = adata.X.copy()
adata.obs["batch"] = adata.obs["batch"].astype("category")
adata
INFO File data/gene_info_pbmc.csv already downloaded
INFO File data/pbmc_metadata.pickle already downloaded
INFO File data/pbmc8k/filtered_gene_bc_matrices.tar.gz already downloaded
INFO Extracting tar file
INFO Removing extracted data at data/pbmc8k/filtered_gene_bc_matrices
INFO File data/pbmc4k/filtered_gene_bc_matrices.tar.gz already downloaded
INFO Extracting tar file
INFO Removing extracted data at data/pbmc4k/filtered_gene_bc_matrices
AnnData object with n_obs × n_vars = 11990 × 3346
obs: 'n_counts', 'batch', 'labels', 'str_labels'
var: 'gene_symbols', 'n_counts-0', 'n_counts-1', 'n_counts'
uns: 'cell_types'
obsm: 'design', 'raw_qc', 'normalized_qc', 'qc_pc'
layers: 'counts'
adata.var_names = adata.var["gene_symbols"]
adata.obs.str_labels.value_counts() # list of classes and their observations
str_labels
CD4 T cells 4996
CD14+ Monocytes 2227
B cells 1621
CD8 T cells 1448
Other 463
NK cells 457
FCGR3A+ Monocytes 351
Dendritic Cells 339
Megakaryocytes 88
Name: count, dtype: int64
print("# cells, # genes before filtering:", adata.shape)
sc.pp.filter_genes(adata, min_counts=3)
sc.pp.filter_cells(adata, min_counts=3)
# cells, # genes before filtering: (11990, 3346)
# We select a small number of genes here, so our later interpretabilty analysis will be fast
sc.pp.highly_variable_genes(
adata,
n_top_genes=200,
subset=True,
layer="counts",
flavor="seurat_v3",
batch_key="batch",
)
print("# cells, # genes after filtering:", adata.shape)
# cells, # genes after filtering: (11990, 200)
scvi.model.SCANVI.setup_anndata(
adata,
layer="counts",
batch_key="batch",
labels_key="str_labels",
unlabeled_category="unknown",
)
model = scvi.model.SCANVI(adata)
model
ScanVI Model with the following params: unlabeled_category: unknown, n_hidden: 128, n_latent: 10, n_layers: 1, dropout_rate: 0.1, dispersion: gene, gene_likelihood: zinb Training status: Not Trained Model's adata is minified?: False
model.train(
max_epochs=100,
early_stopping=True,
check_val_every_n_epoch=1,
train_size=0.8,
validation_size=0.2,
accelerator="gpu",
devices=-1,
strategy="ddp_notebook_find_unused_parameters_true",
)
INFO Training for 100 epochs.
Inspect scanvi training and test performance#
adata.obsm["X_scANVI"] = model.get_latent_representation()
# use scVI latent space for UMAP generation
sc.pp.neighbors(adata, use_rep="X_scANVI", n_neighbors=30)
sc.tl.umap(adata, min_dist=0.3)
sc.pl.umap(adata, color=["str_labels", "batch"], ncols=2, wspace=0.4)
Next we will apply the 2 techniques for features interpretabilty and compare between them
Integrated Gradients#
Integrated Gradients is a robust interpretability technique that attributes the output of a model to its input features by calculating the cumulative sum of gradients along a path from a baseline (typically zero or a neutral input) to the actual input. This approach provides a way to measure how each feature contributes to the model’s output in a smooth and consistent manner.
It is availble for any semi supervised model in SCVI-Tools by passing the ig_interpretability=True flag to the predict function.
predictions, attributions = model.predict(ig_interpretability=True)
The method works relatievely fast and we can then plot the gene table with their importnace mean and variance, overall for all cell - types
n_plot = 15
attributions.head(n_plot)
| gene | gene_idx | attribution_mean | attribution_std | cells | |
|---|---|---|---|---|---|
| 0 | S100A4 | 18 | 0.451401 | 0.615315 | 11990 |
| 1 | TYROBP | 179 | 0.179418 | 0.325539 | 11990 |
| 2 | S100A6 | 17 | 0.159837 | 0.249992 | 11990 |
| 3 | S100A9 | 15 | 0.130546 | 0.352899 | 11990 |
| 4 | NKG7 | 186 | 0.125753 | 0.293756 | 11990 |
| 5 | LGALS2 | 196 | 0.108985 | 0.229961 | 11990 |
| 6 | IL32 | 148 | 0.102592 | 0.174431 | 11990 |
| 7 | GZMK | 62 | 0.085330 | 0.262654 | 11990 |
| 8 | HLA-DPB1 | 81 | 0.084460 | 0.273015 | 11990 |
| 9 | CSTA | 41 | 0.069941 | 0.174467 | 11990 |
| 10 | CD79B | 162 | 0.069681 | 0.229186 | 11990 |
| 11 | CST3 | 166 | 0.064049 | 0.197501 | 11990 |
| 12 | CD79A | 180 | 0.053341 | 0.169086 | 11990 |
| 13 | CTSW | 112 | 0.047010 | 0.127112 | 11990 |
| 14 | GPX1 | 39 | 0.045533 | 0.093305 | 11990 |
df = attributions.head(n_plot)
ci = 1.96 * df["attribution_std"] / np.sqrt(df["cells"])
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(5, 2), dpi=200)
sns.barplot(ax=ax, data=df, x="gene", y="attribution_mean", hue="gene", dodge=False)
ax.set_yticks([])
plt.tick_params(axis="x", which="major", labelsize=8, labelrotation=90)
ax.errorbar(
df["gene"].values,
df["attribution_mean"].values,
yerr=ci,
ecolor="black",
fmt="none",
)
if ax.get_legend() is not None:
ax.get_legend().remove()
We can repeat for specific class (‘Dendritic Cells’):
predictions_class, attributions_class = model.predict(
indices=np.where(adata.obs.str_labels == "Dendritic Cells")[0].tolist(),
ig_interpretability=True,
)
attributions_class.head(n_plot)
| gene | gene_idx | attribution_mean | attribution_std | cells | |
|---|---|---|---|---|---|
| 0 | S100A4 | 18 | 1.152516 | 0.386783 | 339 |
| 1 | HLA-DPB1 | 81 | 1.113057 | 0.300992 | 339 |
| 2 | CST3 | 166 | 0.608530 | 0.287984 | 339 |
| 3 | LGALS2 | 196 | 0.606281 | 0.250443 | 339 |
| 4 | TYROBP | 179 | 0.496806 | 0.232269 | 339 |
| 5 | FCER1A | 21 | 0.379987 | 0.279442 | 339 |
| 6 | COTL1 | 154 | 0.251771 | 0.143787 | 339 |
| 7 | S100A6 | 17 | 0.232539 | 0.186305 | 339 |
| 8 | GSTP1 | 113 | 0.202374 | 0.120685 | 339 |
| 9 | FGL2 | 87 | 0.187787 | 0.156393 | 339 |
| 10 | FCER1G | 22 | 0.154540 | 0.109214 | 339 |
| 11 | CD74 | 70 | 0.132185 | 0.414647 | 339 |
| 12 | CSTA | 41 | 0.129151 | 0.135080 | 339 |
| 13 | LGALS3 | 141 | 0.117984 | 0.122187 | 339 |
| 14 | PHACTR1 | 75 | 0.101498 | 0.101565 | 339 |
df_class = attributions_class.head(n_plot)
ci = 1.96 * df["attribution_std"] / np.sqrt(df["cells"])
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(5, 2), dpi=200)
sns.barplot(ax=ax, data=df_class, x="gene", y="attribution_mean", hue="gene", dodge=False)
ax.set_yticks([])
plt.tick_params(axis="x", which="major", labelsize=8, labelrotation=90)
ax.errorbar(
df_class["gene"].values,
df_class["attribution_mean"].values,
yerr=ci,
ecolor="black",
fmt="none",
)
if ax.get_legend() is not None:
ax.get_legend().remove()
As expected, for a specific class, we can see different important genes, altough S100A4 is still the top contributer
SHAP#
SHAP (SHapley Additive exPlanations) values are a popular interpretability technique based on cooperative game theory. The core idea is to fairly allocate the “credit” for a model’s prediction to each feature, by considering all possible combinations of features and their impact on the prediction. SHAP values are additive, meaning the sum of the SHAP values for all features equals the difference between the model’s output and the average prediction. This method works for any model type, providing a consistent way to explain individual predictions, making it highly versatile and widely applicable. Deep SHAP is an extension of the SHAP method designed specifically for deep learning models, such as the ones in SCVI-Tools. For more information see this
Calcualtion of SHAP for SC data usually takes a lot of time. In SCVI-Tools we are running an approximation of SHAP in order to reduce runtime, where we use just 100 cells at each iteration.
shap_values = model.shap_predict()
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INFO Input AnnData not setup with scvi-tools. attempting to transfer AnnData setup
INFO Received view of anndata, making copy.
INFO Input AnnData not setup with scvi-tools. attempting to transfer AnnData setup
INFO Input AnnData not setup with scvi-tools. attempting to transfer AnnData setup
INFO Received view of anndata, making copy.
INFO Input AnnData not setup with scvi-tools. attempting to transfer AnnData setup
INFO Input AnnData not setup with scvi-tools. attempting to transfer AnnData setup
INFO Received view of anndata, making copy.
INFO Input AnnData not setup with scvi-tools. attempting to transfer AnnData setup
INFO Input AnnData not setup with scvi-tools. attempting to transfer AnnData setup
INFO Received view of anndata, making copy.
INFO Input AnnData not setup with scvi-tools. attempting to transfer AnnData setup
INFO Input AnnData not setup with scvi-tools. attempting to transfer AnnData setup
INFO Received view of anndata, making copy.
INFO Input AnnData not setup with scvi-tools. attempting to transfer AnnData setup
INFO Input AnnData not setup with scvi-tools. attempting to transfer AnnData setup
INFO Received view of anndata, making copy.
INFO Input AnnData not setup with scvi-tools. attempting to transfer AnnData setup
INFO Input AnnData not setup with scvi-tools. attempting to transfer AnnData setup
INFO Received view of anndata, making copy.
INFO Input AnnData not setup with scvi-tools. attempting to transfer AnnData setup
INFO Input AnnData not setup with scvi-tools. attempting to transfer AnnData setup
adata.obs.str_labels.cat.categories
Index(['B cells', 'CD4 T cells', 'CD8 T cells', 'CD14+ Monocytes',
'Dendritic Cells', 'FCGR3A+ Monocytes', 'Megakaryocytes', 'NK cells',
'Other'],
dtype='object')
# select the class label we wish to understand
shap_top_features = model.get_ranked_genes(attrs=shap_values[:, :, 4]).head(n_plot)
print(shap_top_features)
gene gene_idx attribution_mean attribution_std cells
0 IL2RB 195 0.000012 0.000041 100
1 RNASE6 134 0.000008 0.000043 100
2 DNASE1L3 40 0.000006 0.000041 100
3 TSPAN13 86 0.000006 0.000039 100
4 CXCL3 52 0.000006 0.000042 100
5 SMPD3 153 0.000005 0.000036 100
6 CD1C 19 0.000005 0.000038 100
7 FGL2 87 0.000005 0.000043 100
8 HIST1H2AC 76 0.000005 0.000040 100
9 CSF1R 69 0.000005 0.000039 100
10 NRGN 115 0.000004 0.000031 100
11 CXCL10 55 0.000004 0.000040 100
12 AKR1C3 116 0.000004 0.000037 100
13 ALOX5AP 132 0.000004 0.000039 100
14 MT2A 152 0.000003 0.000041 100
And we can see some overlapping genes from the 2 methods for this specific group of cells
Work in progress: please check back in the next release!