Note

This page was generated from totalVI.ipynb. Interactive online version: Colab badge. Some tutorial content may look better in light mode.

CITE-seq analysis with totalVI#

With totalVI, we can produce a joint latent representation of cells, denoised data for both protein and RNA, integrate datasets, and compute differential expression of RNA and protein. Here we demonstrate this functionality with an integrated analysis of PBMC10k and PBMC5k, datasets of peripheral blood mononuclear cells publicly available from 10X Genomics subset to the 14 shared proteins between them. The same pipeline would generally be used to analyze a single CITE-seq dataset.

If you use totalVI, please consider citing:

  • Gayoso, A., Steier, Z., Lopez, R., Regier, J., Nazor, K. L., Streets, A., & Yosef, N. (2021). Joint probabilistic modeling of single-cell multi-omic data with totalVI. Nature Methods, 18(3), 272-282.

[ ]:

from scvi_colab import install

!pip install --quiet scvi-colab

install()

[1]:

import anndata as ad
import matplotlib.pyplot as plt
import mudata as md
import muon
import scanpy as sc
import scvi

Imports and data loading#

[1]:

sc.set_figure_params(figsize=(4, 4))

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

/home/adam/miniconda3/envs/scvi-tools-dev/lib/python3.10/site-packages/pytorch_lightning/utilities/seed.py:48: LightningDeprecationWarning: `pytorch_lightning.utilities.seed.seed_everything` has been deprecated in v1.8.0 and will be removed in v1.10.0. Please use `lightning_lite.utilities.seed.seed_everything` instead.
  rank_zero_deprecation(
Global seed set to 0
/home/adam/miniconda3/envs/scvi-tools-dev/lib/python3.10/site-packages/pytorch_lightning/loggers/base.py:24: LightningDeprecationWarning: The `pytorch_lightning.loggers.base.rank_zero_experiment` is deprecated in v1.7 and will be removed in v1.9. Please use `pytorch_lightning.loggers.logger.rank_zero_experiment` instead.
  rank_zero_deprecation(

This dataset was filtered as described in the totalVI manuscript (low quality cells, doublets, lowly expressed genes, etc.).

We run the standard workflow for keeping count and normalized data together.

[2]:

adata = scvi.data.pbmcs_10x_cite_seq()
adata.layers["counts"] = adata.X.copy()
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
adata.obs_names_make_unique()

INFO     File data/pbmc_10k_protein_v3.h5ad already downloaded
INFO     File data/pbmc_5k_protein_v3.h5ad already downloaded

/home/adam/miniconda3/envs/scvi-tools-dev/lib/python3.10/site-packages/anndata/_core/anndata.py:1828: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.
  utils.warn_names_duplicates("obs")

[3]:

adata

[3]:

AnnData object with n_obs × n_vars = 10849 × 15792
    obs: 'n_genes', 'percent_mito', 'n_counts', 'batch'
    uns: 'log1p'
    obsm: 'protein_expression'
    layers: 'counts'

In this tutorial we will show totalVI’s compatibility with the MuData format, which is a container for multiple AnnData objects. AnnData alone can also be used by storing the protein count data in .obsm, which is how it already is. For the AnnData-only workflow, see the documentation for setup_anndata in scvi.model.TOTALVI.

Note

MuData objects can be read from the outputs of CellRanger using muon.read_10x_h5

[4]:

protein_adata = ad.AnnData(adata.obsm["protein_expression"])
protein_adata.obs_names = adata.obs_names
del adata.obsm["protein_expression"]
mdata = md.MuData({"rna": adata, "protein": protein_adata})
mdata

/tmp/ipykernel_17433/3155427964.py:1: FutureWarning: X.dtype being converted to np.float32 from int64. In the next version of anndata (0.9) conversion will not be automatic. Pass dtype explicitly to avoid this warning. Pass `AnnData(X, dtype=X.dtype, ...)` to get the future behavour.
  protein_adata = ad.AnnData(adata.obsm["protein_expression"])
/home/adam/miniconda3/envs/scvi-tools-dev/lib/python3.10/site-packages/mudata/_core/mudata.py:578: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  data_mod.loc[:, colname] = col
/home/adam/miniconda3/envs/scvi-tools-dev/lib/python3.10/site-packages/mudata/_core/mudata.py:578: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  data_mod.loc[:, colname] = col

[4]:

MuData object with n_obs × n_vars = 10849 × 15806
  2 modalities
    rna:    10849 x 15792
      obs:  'n_genes', 'percent_mito', 'n_counts', 'batch'
      uns:  'log1p'
      layers:       'counts'
    protein:        10849 x 14
[5]:

sc.pp.highly_variable_genes(
    mdata.mod["rna"],
    n_top_genes=4000,
    flavor="seurat_v3",
    batch_key="batch",
    layer="counts",
)
# Place subsetted counts in a new modality
mdata.mod["rna_subset"] = mdata.mod["rna"][
    :, mdata.mod["rna"].var["highly_variable"]
].copy()

[6]:

mdata.update()

/home/adam/miniconda3/envs/scvi-tools-dev/lib/python3.10/site-packages/mudata/_core/mudata.py:458: UserWarning: Cannot join columns with the same name because var_names are intersecting.
  warnings.warn(
/home/adam/miniconda3/envs/scvi-tools-dev/lib/python3.10/site-packages/mudata/_core/mudata.py:578: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  data_mod.loc[:, colname] = col
/home/adam/miniconda3/envs/scvi-tools-dev/lib/python3.10/site-packages/mudata/_core/mudata.py:578: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  data_mod.loc[:, colname] = col

Setup mudata#

Now we run setup_mudata, which is the MuData analog to setup_anndata. The caveat of this workflow is that we need to provide this function which modality of the mdata object contains each piece of data. So for example, the batch information is in mdata.mod["rna"].obs["batch"]. Therefore, in the modalities argument below we specify that the batch_key can be found in the "rna_subset" modality of the MuData object.

Notably, we provide protein_layer=None. This means scvi-tools will pull information from .X from the modality specified in modalities ("protein" in this case). In the case of RNA, we want to use the counts, which we stored in mdata.mod["rna"].layers["counts"].

[7]:

scvi.model.TOTALVI.setup_mudata(
    mdata,
    rna_layer="counts",
    protein_layer=None,
    batch_key="batch",
    modalities={
        "rna_layer": "rna_subset",
        "protein_layer": "protein",
        "batch_key": "rna_subset",
    },
)

Info

Specify the modality of each argument via the modalities dictionary, which maps layer/key arguments to MuData modalities.

Prepare and run model#

[8]:

vae = scvi.model.TOTALVI(mdata)

INFO     Computing empirical prior initialization for protein background.

[9]:

vae.train()

GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]

Epoch 273/400:  68%|██████▊   | 273/400 [02:27<01:10,  1.80it/s, loss=1.21e+03, v_num=1]Epoch 00273: reducing learning rate of group 0 to 2.4000e-03.
Epoch 313/400:  78%|███████▊  | 313/400 [02:48<00:45,  1.91it/s, loss=1.22e+03, v_num=1]Epoch 00313: reducing learning rate of group 0 to 1.4400e-03.
Epoch 349/400:  87%|████████▋ | 349/400 [03:08<00:28,  1.81it/s, loss=1.23e+03, v_num=1]Epoch 00349: reducing learning rate of group 0 to 8.6400e-04.
Epoch 363/400:  91%|█████████ | 363/400 [03:15<00:19,  1.85it/s, loss=1.21e+03, v_num=1]
Monitored metric elbo_validation did not improve in the last 45 records. Best score: 1241.049. Signaling Trainer to stop.

[10]:

fig, ax = plt.subplots(1, 1)
vae.history["elbo_train"].plot(ax=ax, label="train")
vae.history["elbo_validation"].plot(ax=ax, label="validation")
ax.set(title="Negative ELBO over training epochs", ylim=(1200, 1400))
ax.legend()

[10]:

<matplotlib.legend.Legend at 0x7f9e3daa7ac0>
../../_images/tutorials_notebooks_totalVI_20_1.png

Analyze outputs#

We use Scanpy and muon for clustering and visualization after running totalVI. It’s also possible to save totalVI outputs for an R-based workflow.

[11]:

rna = mdata.mod["rna_subset"]
protein = mdata.mod["protein"]
# arbitrarily store latent in rna modality
rna.obsm["X_totalVI"] = vae.get_latent_representation()

rna_denoised, protein_denoised = vae.get_normalized_expression(
    n_samples=25, return_mean=True, transform_batch=["PBMC10k", "PBMC5k"]
)

(
    rna.layers["denoised_rna"],
    protein.layers["denoised_protein"],
) = (rna_denoised, protein_denoised)

protein.layers["protein_foreground_prob"] = vae.get_protein_foreground_probability(
    n_samples=25, return_mean=True, transform_batch=["PBMC10k", "PBMC5k"]
)
parsed_protein_names = [p.split("_")[0] for p in protein.var_names]
protein.var["clean_names"] = parsed_protein_names
mdata.update()

/home/adam/miniconda3/envs/scvi-tools-dev/lib/python3.10/site-packages/mudata/_core/mudata.py:458: UserWarning: Cannot join columns with the same name because var_names are intersecting.
  warnings.warn(
/home/adam/miniconda3/envs/scvi-tools-dev/lib/python3.10/site-packages/mudata/_core/mudata.py:578: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  data_mod.loc[:, colname] = col
/home/adam/miniconda3/envs/scvi-tools-dev/lib/python3.10/site-packages/mudata/_core/mudata.py:578: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  data_mod.loc[:, colname] = col

Now we can compute clusters and visualize the latent space.

[12]:

sc.pp.neighbors(rna, use_rep="X_totalVI")
sc.tl.umap(rna)
sc.tl.leiden(rna, key_added="leiden_totalVI")

[13]:

mdata.update()

/home/adam/miniconda3/envs/scvi-tools-dev/lib/python3.10/site-packages/mudata/_core/mudata.py:458: UserWarning: Cannot join columns with the same name because var_names are intersecting.
  warnings.warn(
/home/adam/miniconda3/envs/scvi-tools-dev/lib/python3.10/site-packages/mudata/_core/mudata.py:578: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  data_mod.loc[:, colname] = col

We can now use muon plotting functions which can pull data from either modality of the MuData object.

[14]:

muon.pl.embedding(
    mdata,
    basis="rna_subset:X_umap",
    color=["rna_subset:leiden_totalVI", "rna_subset:batch"],
    frameon=False,
    ncols=1,
)

/home/adam/miniconda3/envs/scvi-tools-dev/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
/home/adam/miniconda3/envs/scvi-tools-dev/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
../../_images/tutorials_notebooks_totalVI_27_1.png

Visualize denoised protein values#

[15]:

muon.pl.embedding(
    mdata,
    basis="rna_subset:X_umap",
    color=protein.var_names,
    frameon=False,
    ncols=3,
    vmax="p99",
    wspace=0.1,
    layer="denoised_protein",
)
../../_images/tutorials_notebooks_totalVI_29_0.png

Visualize probability of foreground#

Here we visualize the probability of foreground for each protein and cell (projected on UMAP). Some proteins are easier to disentangle than others. Some proteins end up being “all background”. For example, CD15 does not appear to be captured well, when looking at the denoised values above we see little localization in the monocytes.

Note

While the foreground probability could theoretically be used to identify cell populations, we recommend using the denoised protein expression, which accounts for the foreground/background probability, but preserves the dynamic range of the protein measurements. Consequently, the denoised values are on the same scale as the raw data and it may be desirable to take a transformation like log or square root.

By viewing the foreground probability, we can get a feel for the types of cells in our dataset. For example, it’s very easy to see a population of monocytes based on the CD14 foregroud probability.

[16]:

muon.pl.embedding(
    mdata,
    basis="rna_subset:X_umap",
    layer="protein_foreground_prob",
    color=protein.var_names,
    frameon=False,
    ncols=3,
    vmax="p99",
    wspace=0.1,
    color_map="cividis",
)
../../_images/tutorials_notebooks_totalVI_33_0.png

Differential expression#

Here we do a one-vs-all DE test, where each cluster is tested against all cells not in that cluster. The results for each of the one-vs-all tests is concatenated into one DataFrame object. Inividual tests can be sliced using the “comparison” column. Genes and proteins are included in the same DataFrame.

Important

We do not recommend using totalVI denoised values in other differential expression tools, as denoised values are a summary of a random quantity. The totalVI DE test takes into account the full uncertainty of the denoised quantities.

[17]:

de_df = vae.differential_expression(
    groupby="rna_subset:leiden_totalVI", delta=0.5, batch_correction=True
)
de_df.head(5)

/home/adam/Documents/software/scvi-tools/scvi/model/_totalvi.py:1066: UserWarning: Make sure the registered protein expression in anndata contains unnormalized count data.
  warnings.warn(

DE...:   6%|▌         | 1/18 [00:01<00:17,  1.03s/it]

/home/adam/Documents/software/scvi-tools/scvi/model/_totalvi.py:1066: UserWarning: Make sure the registered protein expression in anndata contains unnormalized count data.
  warnings.warn(

DE...:  11%|█         | 2/18 [00:02<00:16,  1.01s/it]

/home/adam/Documents/software/scvi-tools/scvi/model/_totalvi.py:1066: UserWarning: Make sure the registered protein expression in anndata contains unnormalized count data.
  warnings.warn(

DE...:  17%|█▋        | 3/18 [00:03<00:15,  1.02s/it]

/home/adam/Documents/software/scvi-tools/scvi/model/_totalvi.py:1066: UserWarning: Make sure the registered protein expression in anndata contains unnormalized count data.
  warnings.warn(

DE...:  22%|██▏       | 4/18 [00:04<00:14,  1.02s/it]

/home/adam/Documents/software/scvi-tools/scvi/model/_totalvi.py:1066: UserWarning: Make sure the registered protein expression in anndata contains unnormalized count data.
  warnings.warn(

DE...:  28%|██▊       | 5/18 [00:05<00:13,  1.01s/it]

/home/adam/Documents/software/scvi-tools/scvi/model/_totalvi.py:1066: UserWarning: Make sure the registered protein expression in anndata contains unnormalized count data.
  warnings.warn(

DE...:  33%|███▎      | 6/18 [00:06<00:12,  1.00s/it]

/home/adam/Documents/software/scvi-tools/scvi/model/_totalvi.py:1066: UserWarning: Make sure the registered protein expression in anndata contains unnormalized count data.
  warnings.warn(

DE...:  39%|███▉      | 7/18 [00:07<00:11,  1.01s/it]

/home/adam/Documents/software/scvi-tools/scvi/model/_totalvi.py:1066: UserWarning: Make sure the registered protein expression in anndata contains unnormalized count data.
  warnings.warn(

DE...:  44%|████▍     | 8/18 [00:08<00:10,  1.01s/it]

/home/adam/Documents/software/scvi-tools/scvi/model/_totalvi.py:1066: UserWarning: Make sure the registered protein expression in anndata contains unnormalized count data.
  warnings.warn(

DE...:  50%|█████     | 9/18 [00:09<00:09,  1.01s/it]

/home/adam/Documents/software/scvi-tools/scvi/model/_totalvi.py:1066: UserWarning: Make sure the registered protein expression in anndata contains unnormalized count data.
  warnings.warn(

DE...:  56%|█████▌    | 10/18 [00:10<00:08,  1.00s/it]

/home/adam/Documents/software/scvi-tools/scvi/model/_totalvi.py:1066: UserWarning: Make sure the registered protein expression in anndata contains unnormalized count data.
  warnings.warn(

DE...:  61%|██████    | 11/18 [00:11<00:07,  1.01s/it]

/home/adam/Documents/software/scvi-tools/scvi/model/_totalvi.py:1066: UserWarning: Make sure the registered protein expression in anndata contains unnormalized count data.
  warnings.warn(

DE...:  67%|██████▋   | 12/18 [00:12<00:06,  1.01s/it]

/home/adam/Documents/software/scvi-tools/scvi/model/_totalvi.py:1066: UserWarning: Make sure the registered protein expression in anndata contains unnormalized count data.
  warnings.warn(

DE...:  72%|███████▏  | 13/18 [00:13<00:05,  1.01s/it]

/home/adam/Documents/software/scvi-tools/scvi/model/_totalvi.py:1066: UserWarning: Make sure the registered protein expression in anndata contains unnormalized count data.
  warnings.warn(

DE...:  78%|███████▊  | 14/18 [00:14<00:04,  1.00s/it]

/home/adam/Documents/software/scvi-tools/scvi/model/_totalvi.py:1066: UserWarning: Make sure the registered protein expression in anndata contains unnormalized count data.
  warnings.warn(

DE...:  83%|████████▎ | 15/18 [00:15<00:02,  1.00it/s]

/home/adam/Documents/software/scvi-tools/scvi/model/_totalvi.py:1066: UserWarning: Make sure the registered protein expression in anndata contains unnormalized count data.
  warnings.warn(

DE...:  89%|████████▉ | 16/18 [00:16<00:02,  1.00s/it]

/home/adam/Documents/software/scvi-tools/scvi/model/_totalvi.py:1066: UserWarning: Make sure the registered protein expression in anndata contains unnormalized count data.
  warnings.warn(

DE...:  94%|█████████▍| 17/18 [00:17<00:01,  1.01s/it]

/home/adam/Documents/software/scvi-tools/scvi/model/_totalvi.py:1066: UserWarning: Make sure the registered protein expression in anndata contains unnormalized count data.
  warnings.warn(

DE...: 100%|██████████| 18/18 [00:18<00:00,  1.01s/it]

[17]:
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
C9ORF139 0.9734 0.0266 3.599884 5.925353e-07 0.000043 0.0 0.5 -5.723970 -6.055791 3.122658 ... 0.000526 0.075324 0.000526 0.067054 0.003434 0.433609 True 0 vs Rest 0 Rest
ASCL2 0.9726 0.0274 3.569430 4.377473e-07 0.000114 0.0 0.5 -7.013683 -7.373041 3.694981 ... 0.000000 0.187193 0.000000 0.142713 0.000000 1.200019 True 0 vs Rest 0 Rest
HLA-DQA2 0.9706 0.0294 3.496919 1.478030e-06 0.000273 0.0 0.5 -6.050211 -6.293546 3.877910 ... 0.000000 0.581024 0.000000 0.186746 0.000000 3.187069 True 0 vs Rest 0 Rest
REG4 0.9700 0.0300 3.476098 4.301558e-06 0.000001 0.0 0.5 5.233232 5.638005 3.539875 ... 0.007891 0.004135 0.007365 0.003800 0.084258 0.040130 True 0 vs Rest 0 Rest
ADTRP 0.9682 0.0318 3.415972 1.013814e-04 0.000009 0.0 0.5 6.716998 7.128677 3.925131 ... 0.163072 0.010393 0.139400 0.008829 1.981167 0.120156 True 0 vs Rest 0 Rest

5 rows × 22 columns

Now we filter the results such that we retain features above a certain Bayes factor (which here is on the natural log scale) and genes with greater than 10% non-zero entries in the cluster of interest.

[19]:

filtered_pro = {}
filtered_rna = {}
cats = rna.obs.leiden_totalVI.cat.categories
for i, c in enumerate(cats):
    cid = f"{c} vs Rest"
    cell_type_df = de_df.loc[de_df.comparison == cid]
    cell_type_df = cell_type_df.sort_values("lfc_median", ascending=False)

    cell_type_df = cell_type_df[cell_type_df.lfc_median > 0]

    pro_rows = cell_type_df.index.str.contains("TotalSeqB")
    data_pro = cell_type_df.iloc[pro_rows]
    data_pro = data_pro[data_pro["bayes_factor"] > 0.7]

    data_rna = cell_type_df.iloc[~pro_rows]
    data_rna = data_rna[data_rna["bayes_factor"] > 3]
    data_rna = data_rna[data_rna["non_zeros_proportion1"] > 0.1]

    filtered_pro[c] = data_pro.index.tolist()[:3]
    filtered_rna[c] = data_rna.index.tolist()[:2]

We can also use general scanpy visualization functions

[21]:

sc.tl.dendrogram(rna, groupby="leiden_totalVI", use_rep="X_totalVI")
# This is a bit of a hack to be able to use scanpy dendrogram with the protein data
protein.obs["leiden_totalVI"] = rna.obs["leiden_totalVI"]
protein.obsm["X_totalVI"] = rna.obsm["X_totalVI"]
sc.tl.dendrogram(protein, groupby="leiden_totalVI", use_rep="X_totalVI")

[22]:

sc.pl.dotplot(
    rna,
    filtered_rna,
    groupby="leiden_totalVI",
    dendrogram=True,
    standard_scale="var",
    swap_axes=True,
)

/home/adam/miniconda3/envs/scvi-tools-dev/lib/python3.10/site-packages/scanpy/plotting/_dotplot.py:749: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored
  dot_ax.scatter(x, y, **kwds)
../../_images/tutorials_notebooks_totalVI_42_1.png

Matrix plot displays totalVI denoised protein expression per leiden cluster.

[23]:

sc.pl.matrixplot(
    protein,
    protein.var["clean_names"],
    groupby="leiden_totalVI",
    gene_symbols="clean_names",
    dendrogram=True,
    swap_axes=True,
    layer="denoised_protein",
    cmap="Greens",
    standard_scale="var",
)
../../_images/tutorials_notebooks_totalVI_44_0.png

This is a selection of some of the markers that turned up in the RNA DE test.

[24]:

sc.pl.umap(
    rna,
    color=[
        "leiden_totalVI",
        "IGHD",
        "FCER1A",
        "SCT",
        "GZMH",
        "NOG",
        "FOXP3",
        "CD8B",
        "C1QA",
        "SIGLEC1",
        "XCL2",
        "GZMK",
    ],
    legend_loc="on data",
    frameon=False,
    ncols=3,
    layer="denoised_rna",
    wspace=0.2,
)

/home/adam/miniconda3/envs/scvi-tools-dev/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
../../_images/tutorials_notebooks_totalVI_46_1.png