Introduction to scvi-tools

In this introductory tutorial, we go through the different steps of an scvi-tools workflow.

While we focus on scVI in this tutorial, the API is consistent across all models.

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()

WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.

import os
import tempfile

import scanpy as sc
import scvi
import seaborn as sns
import torch

scvi.settings.seed = 0
print("Last run with scvi-tools version:", scvi.__version__)

Last run with scvi-tools version: 1.1.6

Note

You can modify save_dir below to change where the data files for this tutorial are saved.

sc.set_figure_params(figsize=(6, 6), frameon=False)
sns.set_theme()
torch.set_float32_matmul_precision("high")
save_dir = tempfile.TemporaryDirectory()

%config InlineBackend.print_figure_kwargs={"facecolor": "w"}
%config InlineBackend.figure_format="retina"

Loading and preparing data

Let us first load a subsampled version of the heart cell atlas dataset described in Litviňuková et al. (2020). scvi-tools has many “built-in” datasets as well as support for loading arbitrary .csv, .loom, and .h5ad (AnnData) files. Please see our tutorial on data loading for more examples.

• Litviňuková, M., Talavera-López, C., Maatz, H., Reichart, D., Worth, C. L., Lindberg, E. L., … & Teichmann, S. A. (2020). Cells of the adult human heart. Nature, 588(7838), 466-472.

Important

All scvi-tools models require AnnData objects as input.

adata = scvi.data.heart_cell_atlas_subsampled(save_path=save_dir.name)
adata

INFO     Downloading file at /tmp/tmpho7i61if/hca_subsampled_20k.h5ad

AnnData object with n_obs × n_vars = 18641 × 26662
    obs: 'NRP', 'age_group', 'cell_source', 'cell_type', 'donor', 'gender', 'n_counts', 'n_genes', 'percent_mito', 'percent_ribo', 'region', 'sample', 'scrublet_score', 'source', 'type', 'version', 'cell_states', 'Used'
    var: 'gene_ids-Harvard-Nuclei', 'feature_types-Harvard-Nuclei', 'gene_ids-Sanger-Nuclei', 'feature_types-Sanger-Nuclei', 'gene_ids-Sanger-Cells', 'feature_types-Sanger-Cells', 'gene_ids-Sanger-CD45', 'feature_types-Sanger-CD45', 'n_counts'
    uns: 'cell_type_colors'

Now we preprocess the data to remove, for example, genes that are very lowly expressed and other outliers. For these tasks we prefer the Scanpy preprocessing module.

sc.pp.filter_genes(adata, min_counts=3)

In scRNA-seq analysis, it’s popular to normalize the data. These values are not used by scvi-tools, but given their popularity in other tasks as well as for visualization, we store them in the anndata object separately (via the .raw attribute).

Important

Unless otherwise specified, scvi-tools models require the raw counts (not log library size normalized). scvi-tools models will run for non-negative real-valued data, but we strongly suggest checking that these possibly non-count values are intended to represent pseudocounts (e.g. SoupX-corrected counts), and not some other normalized data, in which the variance/covariance structure of the data has changed dramatically.

adata.layers["counts"] = adata.X.copy()  # preserve counts
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
adata.raw = adata  # freeze the state in `.raw`

Finally, we perform feature selection, to reduce the number of features (genes in this case) used as input to the scvi-tools model. For best practices of how/when to perform feature selection, please refer to the model-specific tutorial. For scVI, we recommend anywhere from 1,000 to 10,000 HVGs, but it will be context-dependent.

sc.pp.highly_variable_genes(
    adata,
    n_top_genes=1200,
    subset=True,
    layer="counts",
    flavor="seurat_v3",
    batch_key="cell_source",
)

Now it’s time to run setup_anndata(), which alerts scvi-tools to the locations of various matrices inside the anndata. It’s important to run this function with the correct arguments so scvi-tools is notified that your dataset has batches, annotations, etc. For example, if batches are registered with scvi-tools, the subsequent model will correct for batch effects. See the full documentation for details.

In this dataset, there is a “cell_source” categorical covariate, and within each “cell_source”, multiple “donors”, “gender” and “age_group”. There are also two continuous covariates we’d like to correct for: “percent_mito” and “percent_ribo”. These covariates can be registered using the categorical_covariate_keys argument. If you only have one categorical covariate, you can also use the batch_key argument instead.

scvi.model.SCVI.setup_anndata(
    adata,
    layer="counts",
    categorical_covariate_keys=["cell_source", "donor"],
    continuous_covariate_keys=["percent_mito", "percent_ribo"],
)

Warning

If the adata is modified after running setup_anndata, please run setup_anndata again, before creating an instance of a model.

Creating and training a model

While we highlight the scVI model here, the API is consistent across all scvi-tools models and is inspired by that of scikit-learn. For a full list of options, see the scvi documentation.

model = scvi.model.SCVI(adata)

We can see an overview of the model by printing it.

model

SCVI model with the following parameters: 
n_hidden: 128, n_latent: 10, n_layers: 1, dropout_rate: 0.1, dispersion: gene, gene_likelihood: zinb, 
latent_distribution: normal.
Training status: Not Trained
Model's adata is minified?: False

Important

All scvi-tools models run faster when using a GPU. By default, scvi-tools will use a GPU if one is found to be available. Please see the installation page for more information about installing scvi-tools when a GPU is available.

model.train()

Saving and loading

Saving consists of saving the model neural network weights, as well as parameters used to initialize the model.

model_dir = os.path.join(save_dir.name, "scvi_model")
model.save(model_dir, overwrite=True)

model = scvi.model.SCVI.load(model_dir, adata=adata)

INFO     File /tmp/tmpho7i61if/scvi_model/model.pt already downloaded

Obtaining model outputs

It’s often useful to store the outputs of scvi-tools back into the original anndata, as it permits interoperability with Scanpy.

SCVI_LATENT_KEY = "X_scVI"

latent = model.get_latent_representation()
adata.obsm[SCVI_LATENT_KEY] = latent
latent.shape

(18641, 10)

The model.get...() functions default to using the AnnData that was used to initialize the model. It’s possible to also query a subset of the anndata, or even use a completely independent anndata object as long as the anndata is organized in an equivalent fashion.

adata_subset = adata[adata.obs.cell_type == "Fibroblast"]
latent_subset = model.get_latent_representation(adata_subset)
latent.shape

INFO     Received view of anndata, making copy.
INFO     Input AnnData not setup with scvi-tools. attempting to transfer AnnData setup

(18641, 10)

denoised = model.get_normalized_expression(adata_subset, library_size=1e4)
denoised.iloc[:5, :5]

	ISG15	TNFRSF18	VWA1	HES5	SPSB1
GTCAAGTCATGCCACG-1-HCAHeart7702879	1.092411	0.010465	2.148042	0.021961	4.239618
GAGTCATTCTCCGTGT-1-HCAHeart8287128	0.422053	0.029648	3.526198	0.056507	25.019445
CCTCTGATCGTGACAT-1-HCAHeart7702881	2.416214	0.028048	3.418624	0.087428	6.610714
CGCCATTCATCATCTT-1-H0035_apex	0.412665	0.056413	0.342542	0.044107	6.719165
TCGTAGAGTAGGACTG-1-H0015_septum	0.213223	0.018612	0.192475	0.141990	8.640146

Let’s store the normalized values back in the anndata.

SCVI_NORMALIZED_KEY = "scvi_normalized"

adata.layers[SCVI_NORMALIZED_KEY] = model.get_normalized_expression(library_size=10e4)

Interoperability with Scanpy

Scanpy is a powerful python library for visualization and downstream analysis of scRNA-seq data. We show here how to feed the objects produced by scvi-tools into a scanpy workflow.

Visualization without batch correction

Warning

We use UMAP to qualitatively assess our low-dimension embeddings of cells. We do not advise using UMAP or any similar approach quantitatively. We do recommend using the embeddings produced by scVI as a plug-in replacement of what you would get from PCA, as we show below.

First, we demonstrate the presence of nuisance variation with respect to nuclei/whole cell, age group, and donor by plotting the UMAP results of the top 30 PCA components for the raw count data.

# run PCA then generate UMAP plots
sc.tl.pca(adata)
sc.pp.neighbors(adata, n_pcs=30, n_neighbors=20)
sc.tl.umap(adata, min_dist=0.3)

sc.pl.umap(
    adata,
    color=["cell_type"],
    frameon=False,
)
sc.pl.umap(
    adata,
    color=["donor", "cell_source"],
    ncols=2,
    frameon=False,
)

We see that while the cell types are generally well separated, nuisance variation plays a large part in the variation of the data.

Visualization with batch correction (scVI)

Now, let us try using the scVI latent space to generate the same UMAP plots to see if scVI successfully accounts for batch effects in the data.

# use scVI latent space for UMAP generation
sc.pp.neighbors(adata, use_rep=SCVI_LATENT_KEY)
sc.tl.umap(adata, min_dist=0.3)

sc.pl.umap(
    adata,
    color=["cell_type"],
    frameon=False,
)
sc.pl.umap(
    adata,
    color=["donor", "cell_source"],
    ncols=2,
    frameon=False,
)

We can see that scVI was able to correct for nuisance variation due to nuclei/whole cell, age group, and donor, while maintaining separation of cell types.

Clustering on the scVI latent space

The user will note that we imported curated labels from the original publication. Our interface with scanpy makes it easy to cluster the data with scanpy from scVI’s latent space and then reinject them into scVI (e.g., for differential expression).

# neighbors were already computed using scVI
SCVI_CLUSTERS_KEY = "leiden_scVI"
sc.tl.leiden(adata, key_added=SCVI_CLUSTERS_KEY, resolution=0.5)

sc.pl.umap(
    adata,
    color=[SCVI_CLUSTERS_KEY],
    frameon=False,
)

Differential expression

We can also use many scvi-tools models for differential expression. For further details on the methods underlying these functions as well as additional options, please see the API docs.

adata.obs.cell_type.head()

AACTCCCCACGAGAGT-1-HCAHeart7844001                      Myeloid
ATAACGCAGAGCTGGT-1-HCAHeart7829979    Ventricular_Cardiomyocyte
GTCAAGTCATGCCACG-1-HCAHeart7702879                   Fibroblast
GGTGATTCAAATGAGT-1-HCAHeart8102858                  Endothelial
AGAGAATTCTTAGCAG-1-HCAHeart8102863                  Endothelial
Name: cell_type, dtype: category
Categories (11, object): ['Adipocytes', 'Atrial_Cardiomyocyte', 'Endothelial', 'Fibroblast', ..., 'Neuronal', 'Pericytes', 'Smooth_muscle_cells', 'Ventricular_Cardiomyocyte']

For example, a 1-vs-1 DE test is as simple as:

de_df = model.differential_expression(
    groupby="cell_type", group1="Endothelial", group2="Fibroblast"
)
de_df.head()

	proba_de	proba_not_de	bayes_factor	scale1	scale2	pseudocounts	delta	lfc_mean	lfc_median	lfc_std	...	raw_mean1	raw_mean2	non_zeros_proportion1	non_zeros_proportion2	raw_normalized_mean1	raw_normalized_mean2	is_de_fdr_0.05	comparison	group1	group2
CDH19	0.9986	0.0014	6.569875	0.000231	0.012090	0.0	0.25	-6.987106	-7.068306	2.345611	...	0.049890	3.217938	0.014359	0.672527	1.843241	137.447021	True	Endothelial vs Fibroblast	Endothelial	Fibroblast
NEGR1	0.9984	0.0016	6.436144	0.000338	0.014734	0.0	0.25	-8.349928	-9.017187	3.402418	...	0.050134	3.443618	0.015576	0.782093	1.761474	173.263184	True	Endothelial vs Fibroblast	Endothelial	Fibroblast
COL6A3	0.9982	0.0018	6.318161	0.000183	0.004512	0.0	0.25	-5.270587	-5.289909	1.795962	...	0.026284	1.228131	0.021903	0.498365	1.196546	54.222549	True	Endothelial vs Fibroblast	Endothelial	Fibroblast
SOX17	0.9980	0.0020	6.212601	0.001741	0.000061	0.0	0.25	5.253040	5.257253	1.960884	...	0.784371	0.006541	0.307617	0.004497	17.128260	0.185868	True	Endothelial vs Fibroblast	Endothelial	Fibroblast
EGFL7	0.9980	0.0020	6.212601	0.007151	0.000435	0.0	0.25	4.321842	4.286746	1.501081	...	2.376779	0.036795	0.741543	0.025756	89.509911	1.171088	True	Endothelial vs Fibroblast	Endothelial	Fibroblast

5 rows × 22 columns

We can also do a 1-vs-all DE test, which compares each cell type with the rest of the dataset:

de_df = model.differential_expression(
    groupby="cell_type",
)
de_df.head()

	proba_de	proba_not_de	bayes_factor	scale1	scale2	pseudocounts	delta	lfc_mean	lfc_median	lfc_std	...	raw_mean1	raw_mean2	non_zeros_proportion1	non_zeros_proportion2	raw_normalized_mean1	raw_normalized_mean2	is_de_fdr_0.05	comparison	group1	group2
PLIN1	0.9994	0.0006	7.417964	0.004854	0.000044	0.0	0.25	7.981709	7.803561	2.994635	...	2.799999	0.004379	0.806897	0.004325	52.928539	0.196444	True	Adipocytes vs Rest	Adipocytes	Rest
GPAM	0.9992	0.0008	7.130086	0.025241	0.000211	0.0	0.25	7.289426	7.275628	2.268540	...	17.372416	0.035791	0.896552	0.031520	280.350311	1.566058	True	Adipocytes vs Rest	Adipocytes	Rest
CIDEC	0.9990	0.0010	6.906745	0.002038	0.000022	0.0	0.25	7.458050	7.458266	2.505853	...	1.137931	0.001406	0.510345	0.001406	21.810789	0.057569	True	Adipocytes vs Rest	Adipocytes	Rest
ARHGAP20	0.9982	0.0018	6.318161	0.001961	0.000063	0.0	0.25	5.562314	5.489888	2.050780	...	1.117241	0.008651	0.475862	0.007894	20.218712	0.378039	True	Adipocytes vs Rest	Adipocytes	Rest
PNPLA2	0.9980	0.0020	6.212601	0.004086	0.000337	0.0	0.25	3.565027	3.605001	1.221045	...	2.765516	0.093481	0.793103	0.082937	54.447849	2.788166	True	Adipocytes vs Rest	Adipocytes	Rest

5 rows × 22 columns

We now extract top markers for each cluster using the DE results.

markers = {}
cats = adata.obs.cell_type.cat.categories
for c in cats:
    cid = f"{c} vs Rest"
    cell_type_df = de_df.loc[de_df.comparison == cid]

    cell_type_df = cell_type_df[cell_type_df.lfc_mean > 0]

    cell_type_df = cell_type_df[cell_type_df["bayes_factor"] > 3]
    cell_type_df = cell_type_df[cell_type_df["non_zeros_proportion1"] > 0.1]

    markers[c] = cell_type_df.index.tolist()[:3]

sc.tl.dendrogram(adata, groupby="cell_type", use_rep="X_scVI")

sc.pl.dotplot(
    adata,
    markers,
    groupby="cell_type",
    dendrogram=True,
    color_map="Blues",
    swap_axes=True,
    use_raw=True,
    standard_scale="var",
)

We can also visualize the scVI normalized gene expression values with the layer option.

sc.pl.heatmap(
    adata,
    markers,
    groupby="cell_type",
    layer="scvi_normalized",
    standard_scale="var",
    dendrogram=True,
    figsize=(8, 12),
)

Logging information

Verbosity varies in the following way:

• logger.setLevel(logging.WARNING) will show a progress bar.

• logger.setLevel(logging.INFO) will show global logs including the number of jobs done.

• logger.setLevel(logging.DEBUG) will show detailed logs for each training (e.g the parameters tested).

This function’s behaviour can be customized, please refer to its documentation for information about the different parameters available.

In general, you can use scvi.settings.verbosity to set the verbosity of the scvi package. Note that verbosity corresponds to the logging levels of the standard python logging module. By default, that verbosity level is set to INFO (=20). As a reminder the logging levels are:

Level	Numeric value
CRITICAL	50
ERROR	40
WARNING	30
INFO	20
DEBUG	10
NOTSET	0