Mapping human lymph node cell types to 10X Visium with Cell2location#
This contributed tutorial shows how to use cell2location method for spatially resolving fine-grained cell types by integrating 10X Visium data with scRNA-seq reference of cell types. Cell2location is a principled Bayesian model that estimates which combination of cell types in which cell abundance could have given the mRNA counts in the spatial data, while modelling technical effects (platform/technology effect, contaminating RNA, unexplained variance).
Important
Cell2location is accessible via its own package, but is powered by scvi-tools. Please visit https://cell2location.readthedocs.io/en/latest/.
In this tutorial, we analyse a publicly available Visium dataset of the human lymph node from 10X Genomics, and spatially map a comprehensive atlas of 34 reference cell types derived by integration of scRNA-seq datasets from human secondary lymphoid organs.
Cell2location provides high sensitivity and resolution by borrowing statistical strength across locations. This is achieved by modelling similarity of location patterns between cell types using a hierarchical factorisation of cell abundance into tissue zones as a prior (see paper methods).
Using our statistical method based on Negative Binomial regression to robustly combine scRNA-seq reference data across technologies and batches results in improved spatial mapping accuracy. Given cell type annotation for each cell, the corresponding reference cell type signatures \(g_{f,g}\), which represent the average mRNA count of each gene \(g\) in each cell type \(f\), can be estimated from sc/snRNA-seq data using either 1) NB regression or 2) a hard-coded computation of per-cluster average mRNA counts for individual genes. We generally recommend using NB regression. This notebook shows use a dataset composed on multiple batches and technologies.When the batch effects are small, a faster hard-coded method of computing per cluster averages provides similarly high accuracy. We also recommend the hard-coded method for non-UMI technologies such as Smart-Seq 2.
Cell2location needs untransformed unnormalised spatial mRNA counts as input.
You also need to provide cell2location with the expected average cell abundance per location which is used as a prior to guide estimation of absolute cell abundance. This value depends on the tissue and can be estimated by counting nuclei for a few locations in the paired histology image but can be approximate (see paper methods for more guidance).
Workflow diagram#
Loading packages #
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 os
# os.system("pip install git+https://github.com/BayraktarLab/cell2location.git")
!pip install --quiet git+https://github.com/BayraktarLab/cell2location.git
import tempfile
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import scanpy as sc
import scvi
import seaborn as sns
import torch
from cell2location.models import Cell2location, RegressionModel
from cell2location.plt import plot_spatial
from cell2location.utils import select_slide
from cell2location.utils.filtering import filter_genes
scvi.settings.seed = 0
print("Last run with scvi-tools version:", scvi.__version__)
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"
First, let’s define where we save the results of our analysis:
results_folder = "./results/lymph_nodes_analysis/"
# create paths and names to results folders for reference regression and cell2location models
ref_run_name = f"{results_folder}/reference_signatures"
run_name = f"{results_folder}/cell2location_map"
Loading Visium and scRNA-seq reference data #
First let’s read spatial Visium data from 10X Space Ranger output. Here we use lymph node data generated by 10X and presented in Kleshchevnikov et al (section 4, Fig 4). This dataset can be conveniently downloaded and imported using scanpy. See this tutorial for a more extensive and practical example of data loading (multiple visium samples).
adata_vis = sc.datasets.visium_sge(sample_id="V1_Human_Lymph_Node")
adata_vis.obs["sample"] = list(adata_vis.uns["spatial"].keys())[0]
You can still plot gene expression by name using standard scanpy functions as follows:
sc.pl.spatial(color="PTPRC", gene_symbols="SYMBOL", ...)
adata_vis.var["SYMBOL"] = adata_vis.var_names
adata_vis.var.set_index("gene_ids", drop=True, inplace=True)
Note
Mitochondia-encoded genes (gene names start with prefix mt- or MT-) are irrelevant for spatial mapping because their expression represents technical artifacts in the single cell and nucleus data rather than biological abundance of mitochondria. Yet these genes compose 15-40% of mRNA in each location. Hence, to avoid mapping artifacts we strongly recommend removing mitochondrial genes.
# find mitochondria-encoded (MT) genes
adata_vis.var["MT_gene"] = [gene.startswith("MT-") for gene in adata_vis.var["SYMBOL"]]
# remove MT genes for spatial mapping (keeping their counts in the object)
adata_vis.obsm["MT"] = adata_vis[:, adata_vis.var["MT_gene"].values].X.toarray()
adata_vis = adata_vis[:, ~adata_vis.var["MT_gene"].values]
Published scRNA-seq datasets of lymph nodes have typically lacked an adequate representation of germinal centre-associated immune cell populations due to age of patient donors. We, therefore, include scRNA-seq datasets spanning lymph nodes, spleen and tonsils in our single-cell reference to ensure that we captured the full diversity of immune cell states likely to exist in the spatial transcriptomic dataset.
Here we download this dataset, import into anndata and change variable names to ENSEMBL gene identifiers.
# Read data
adata_ref = sc.read(
"./data/sc.h5ad",
backup_url="https://cell2location.cog.sanger.ac.uk/paper/integrated_lymphoid_organ_scrna/RegressionNBV4Torch_57covariates_73260cells_10237genes/sc.h5ad",
)
Warning
Here we rename genes to ENSEMBL ID for correct matching between single cell and spatial data.
adata_ref.var["SYMBOL"] = adata_ref.var.index
adata_ref.var.set_index("GeneID-2", drop=True, inplace=True)
# delete unnecessary raw slot (to be removed in a future version of the tutorial)
del adata_ref.raw
Note
Before we estimate the reference cell type signature we recommend to perform very permissive genes selection. We prefer this to standard highly-variable-gene selection because our procedure keeps markers of rare genes while removing most of the uninformative genes.
In this 2D histogram, orange rectangle highlights genes excluded based on the combination of number of cells expressing that gene (Y-axis) and average RNA count for cells where the gene was detected (X-axis).
In this case, the downloaded dataset was already filtered using this method, hence no density under the orange rectangle (to be changed in the future version of the tutorial).
selected = filter_genes(
adata_ref, cell_count_cutoff=5, cell_percentage_cutoff2=0.03, nonz_mean_cutoff=1.12
)
# filter the object
adata_ref = adata_ref[:, selected].copy()
Estimation of reference cell type signatures (NB regression) #
The signatures are estimated from scRNA-seq data, accounting for batch effect, using a Negative Binomial regression model.
# prepare anndata for the regression model
RegressionModel.setup_anndata(
adata=adata_ref,
# 10X reaction / sample / batch
batch_key="Sample",
# cell type, covariate used for constructing signatures
labels_key="Subset",
# multiplicative technical effects (platform, 3' vs 5', donor effect)
categorical_covariate_keys=["Method"],
)
mod = RegressionModel(adata_ref)
mod.view_anndata_setup()
# Use all data for training (validation not implemented yet, train_size=1)
mod.train(
max_epochs=250,
batch_size=2500,
train_size=1,
lr=0.002,
)
Anndata setup with scvi-tools version 0.16.2.
Setup via `RegressionModel.setup_anndata` with arguments:
{ │ 'layer': None, │ 'batch_key': 'Sample', │ 'labels_key': 'Subset', │ 'categorical_covariate_keys': ['Method'], │ 'continuous_covariate_keys': None }
Summary Statistics ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┓ ┃ Summary Stat Key ┃ Value ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━┩ │ n_vars │ 10237 │ │ n_cells │ 73260 │ │ n_batch │ 23 │ │ n_labels │ 34 │ │ n_extra_categorical_covs │ 1 │ │ n_extra_continuous_covs │ 0 │ └──────────────────────────┴───────┘
Data Registry ┏━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ Registry Key ┃ scvi-tools Location ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ │ X │ adata.X │ │ batch │ adata.obs['_scvi_batch'] │ │ labels │ adata.obs['_scvi_labels'] │ │ extra_categorical_covs │ adata.obsm['_scvi_extra_categorical_covs'] │ │ ind_x │ adata.obs['_indices'] │ └────────────────────────┴────────────────────────────────────────────┘
batch State Registry ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓ ┃ Source Location ┃ Categories ┃ scvi-tools Encoding ┃ ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩ │ adata.obs['Sample'] │ 4861STDY7135913 │ 0 │ │ │ 4861STDY7135914 │ 1 │ │ │ 4861STDY7208412 │ 2 │ │ │ 4861STDY7208413 │ 3 │ │ │ 4861STDY7462253 │ 4 │ │ │ 4861STDY7462254 │ 5 │ │ │ 4861STDY7462255 │ 6 │ │ │ 4861STDY7462256 │ 7 │ │ │ 4861STDY7528597 │ 8 │ │ │ 4861STDY7528598 │ 9 │ │ │ 4861STDY7528599 │ 10 │ │ │ 4861STDY7528600 │ 11 │ │ │ BCP002_Total │ 12 │ │ │ BCP003_Total │ 13 │ │ │ BCP004_Total │ 14 │ │ │ BCP005_Total │ 15 │ │ │ BCP006_Total │ 16 │ │ │ BCP008_Total │ 17 │ │ │ BCP009_Total │ 18 │ │ │ Human_colon_16S7255677 │ 19 │ │ │ Human_colon_16S7255678 │ 20 │ │ │ Human_colon_16S8000484 │ 21 │ │ │ Pan_T7935494 │ 22 │ └─────────────────────┴────────────────────────┴─────────────────────┘
labels State Registry ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓ ┃ Source Location ┃ Categories ┃ scvi-tools Encoding ┃ ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩ │ adata.obs['Subset'] │ B_Cycling │ 0 │ │ │ B_GC_DZ │ 1 │ │ │ B_GC_LZ │ 2 │ │ │ B_GC_prePB │ 3 │ │ │ B_IFN │ 4 │ │ │ B_activated │ 5 │ │ │ B_mem │ 6 │ │ │ B_naive │ 7 │ │ │ B_plasma │ 8 │ │ │ B_preGC │ 9 │ │ │ DC_CCR7+ │ 10 │ │ │ DC_cDC1 │ 11 │ │ │ DC_cDC2 │ 12 │ │ │ DC_pDC │ 13 │ │ │ Endo │ 14 │ │ │ FDC │ 15 │ │ │ ILC │ 16 │ │ │ Macrophages_M1 │ 17 │ │ │ Macrophages_M2 │ 18 │ │ │ Mast │ 19 │ │ │ Monocytes │ 20 │ │ │ NK │ 21 │ │ │ NKT │ 22 │ │ │ T_CD4+ │ 23 │ │ │ T_CD4+_TfH │ 24 │ │ │ T_CD4+_TfH_GC │ 25 │ │ │ T_CD4+_naive │ 26 │ │ │ T_CD8+_CD161+ │ 27 │ │ │ T_CD8+_cytotoxic │ 28 │ │ │ T_CD8+_naive │ 29 │ │ │ T_TIM3+ │ 30 │ │ │ T_TfR │ 31 │ │ │ T_Treg │ 32 │ │ │ VSMC │ 33 │ └─────────────────────┴──────────────────┴─────────────────────┘
extra_categorical_covs State Registry ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓ ┃ Source Location ┃ Categories ┃ scvi-tools Encoding ┃ ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩ │ adata.obs['Method'] │ 3GEX │ 0 │ │ │ 5GEX │ 1 │ │ │ │ │ └─────────────────────┴────────────┴─────────────────────┘
Epoch 1/250: 0%| | 0/250 [00:00<?, ?it/s]
Epoch 250/250: 100%|██████████| 250/250 [19:59<00:00, 4.80s/it, v_num=1, elbo_train=2.88e+8]
Here, we plot ELBO loss history during training, removing first 20 epochs from the plot.
This plot should have a decreasing trend and level off by the end of training. If it is still decreasing, increase max_epochs.
mod.plot_history(20)
# In this section, we export the estimated cell abundance
# (summary of the posterior distribution).
adata_ref = mod.export_posterior(
adata_ref,
sample_kwargs={"num_samples": 1000, "batch_size": 2500},
)
# Save model
mod.save(f"{ref_run_name}", overwrite=True)
# Save anndata object with results
adata_file = f"{ref_run_name}/sc.h5ad"
adata_ref.write(adata_file)
adata_file
Sampling local variables, batch: 0%| | 0/30 [00:00<?, ?it/s]
Sampling global variables, sample: 100%|██████████| 999/999 [00:17<00:00, 56.01it/s]
'./results/lymph_nodes_analysis//reference_signatures/sc.h5ad'
Reconstruction accuracy to assess if there are any issues with inference.
The estimated expression signatures are distinct from mean expression in each cluster because of batch effects. For scRNA-seq datasets which do not suffer from batch effect (this dataset does), cluster average expression can be used instead of estimating signatures with a model. When this plot is very different from a diagonal plot (e.g. very low values on Y-axis, density everywhere) it indicates problems with signature estimation.
mod.plot_QC()
The model and output h5ad can be loaded later like this:
mod = cell2location.models.RegressionModel.load(f"{ref_run_name}", adata_ref)
adata_file = f"{ref_run_name}/sc.h5ad"
adata_ref = sc.read_h5ad(adata_file)
# export estimated expression in each cluster
if "means_per_cluster_mu_fg" in adata_ref.varm.keys():
inf_aver = adata_ref.varm["means_per_cluster_mu_fg"][
[f"means_per_cluster_mu_fg_{i}" for i in adata_ref.uns["mod"]["factor_names"]]
].copy()
else:
inf_aver = adata_ref.var[
[f"means_per_cluster_mu_fg_{i}" for i in adata_ref.uns["mod"]["factor_names"]]
].copy()
inf_aver.columns = adata_ref.uns["mod"]["factor_names"]
inf_aver.iloc[0:5, 0:5]
| B_Cycling | B_GC_DZ | B_GC_LZ | B_GC_prePB | B_IFN | |
|---|---|---|---|---|---|
| GeneID-2 | |||||
| ENSG00000188976 | 0.422873 | 0.238854 | 0.304739 | 0.340573 | 0.148950 |
| ENSG00000188290 | 0.002055 | 0.000721 | 0.000750 | 0.055309 | 0.040174 |
| ENSG00000187608 | 0.384433 | 0.212866 | 0.274928 | 0.512807 | 3.943279 |
| ENSG00000186891 | 0.019475 | 0.000767 | 0.054012 | 0.067262 | 0.010874 |
| ENSG00000186827 | 0.007602 | 0.000532 | 0.006271 | 0.029203 | 0.011210 |
Cell2location: spatial mapping #
# find shared genes and subset both anndata and reference signatures
intersect = np.intersect1d(adata_vis.var_names, inf_aver.index)
adata_vis = adata_vis[:, intersect].copy()
inf_aver = inf_aver.loc[intersect, :].copy()
Important
To use cell2location spatial mapping model, you need to specify 2 user-provided hyperparameters (N_cells_per_location and detection_alpha) - for detailed guidance on setting these hyperparameters and their impact see the flow diagram and the note.
Choosing hyperparameter N_cells_per_location!
It is useful to adapt the expected cell abundance N_cells_per_location to every tissue. This value can be estimated from paired histology images and as described in the note above. Change the value presented in this tutorial (N_cells_per_location=30) to the value observed in your your tissue.
Choosing hyperparameter detection_alpha!
You can often use the default value of detection_alpha hyperparameter (detection_alpha=200), but for datasets where you observe within-batch variation in total RNA count that cannot be explained by tissue containing more cells (histological examination) use detection_alpha=20.
Cell2location.setup_anndata(adata=adata_vis, batch_key="sample")
mod = Cell2location(
adata_vis,
cell_state_df=inf_aver,
# the expected average cell abundance: tissue-dependent
# hyper-prior which can be estimated from paired histology:
N_cells_per_location=30,
# hyperparameter controlling normalisation of
# within-experiment variation in RNA detection (using default here):
detection_alpha=200,
)
mod.view_anndata_setup()
mod.train(
max_epochs=30000,
# train using full data (batch_size=None)
batch_size=None,
# use all data points in training because
# we need to estimate cell abundance at all locations
train_size=1,
)
# plot ELBO loss history during training, removing first 100 epochs from the plot
mod.plot_history(1000)
plt.legend(labels=["full data training"]);
Anndata setup with scvi-tools version 0.16.2.
Setup via `Cell2location.setup_anndata` with arguments:
{ │ 'layer': None, │ 'batch_key': 'sample', │ 'labels_key': None, │ 'categorical_covariate_keys': None, │ 'continuous_covariate_keys': None }
Summary Statistics ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┓ ┃ Summary Stat Key ┃ Value ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━┩ │ n_vars │ 10217 │ │ n_cells │ 4035 │ │ n_batch │ 1 │ │ n_labels │ 1 │ │ n_extra_categorical_covs │ 0 │ │ n_extra_continuous_covs │ 0 │ └──────────────────────────┴───────┘
Data Registry ┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ Registry Key ┃ scvi-tools Location ┃ ┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ │ X │ adata.X │ │ batch │ adata.obs['_scvi_batch'] │ │ labels │ adata.obs['_scvi_labels'] │ │ ind_x │ adata.obs['_indices'] │ └──────────────┴───────────────────────────┘
batch State Registry ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓ ┃ Source Location ┃ Categories ┃ scvi-tools Encoding ┃ ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩ │ adata.obs['sample'] │ V1_Human_Lymph_Node │ 0 │ └─────────────────────┴─────────────────────┴─────────────────────┘
labels State Registry ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓ ┃ Source Location ┃ Categories ┃ scvi-tools Encoding ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩ │ adata.obs['_scvi_labels'] │ 0 │ 0 │ └───────────────────────────┴────────────┴─────────────────────┘
Epoch 28502/30000: 95%|█████████▌| 28501/30000 [1:06:03<03:27, 7.23it/s, v_num=1, elbo_train=4.54e+7]
# In this section, we export the estimated cell abundance (summary of the posterior distribution).
adata_vis = mod.export_posterior(
adata_vis,
sample_kwargs={"num_samples": 1000, "batch_size": mod.adata.n_obs},
)
# Save model
mod.save(f"{run_name}", overwrite=True)
# mod = cell2location.models.Cell2location.load(f"{run_name}", adata_vis)
# Save anndata object with results
adata_file = f"{run_name}/sp.h5ad"
adata_vis.write(adata_file)
adata_file
The model and output h5ad can be loaded later like this:
mod = cell2location.models.Cell2location.load(f"{run_name}", adata_vis)
adata_file = f"{run_name}/sp.h5ad"
adata_vis = sc.read_h5ad(adata_file)
mod.plot_QC()
When intergrating multiple spatial batches and when working with datasets that have substantial variation of detected RNA within slides (that cannot be explained by high cellular density in the histology), it is important to assess whether cell2location normalised those effects. You expect to see similar total cell abundance across batches but distinct RNA detection sensitivity (both estimated by cell2location). You expect total cell abundance to mirror high cellular density in the histology.
fig = mod.plot_spatial_QC_across_batches()
Visualising cell abundance in spatial coordinates #
Note
We use 5% quantile of the posterior distribution, representing the value of cell abundance that the model has high confidence in (aka ‘at least this amount is present’).
# add 5% quantile, representing confident cell abundance, 'at least this amount is present',
# to adata.obs with nice names for plotting
adata_vis.obs[adata_vis.uns["mod"]["factor_names"]] = adata_vis.obsm["q05_cell_abundance_w_sf"]
slide = select_slide(adata_vis, "V1_Human_Lymph_Node")
# plot in spatial coordinates
with mpl.rc_context({"axes.facecolor": "black", "figure.figsize": [4.5, 5]}):
colors = [
"B_Cycling",
"B_GC_LZ",
"T_CD4+_TfH_GC",
"FDC",
"B_naive",
"T_CD4+_naive",
"B_plasma",
"Endo",
]
sc.pl.spatial(
slide,
cmap="magma",
# show first 8 cell types
color=colors,
ncols=2,
size=1.3,
img_key="hires",
# limit color scale at 99.2% quantile of cell abundance
vmin=0,
vmax="p99.2",
)
# select up to 6 clusters
clust_labels = ["T_CD4+_naive", "B_naive", "FDC"]
clust_col = ["" + str(i) for i in clust_labels] # in case column names differ from labels
slide = select_slide(adata_vis, "V1_Human_Lymph_Node")
with mpl.rc_context({"figure.figsize": (15, 15)}):
fig = plot_spatial(
adata=slide,
# labels to show on a plot
color=clust_col,
labels=clust_labels,
show_img=True,
# 'fast' (white background) or 'dark_background'
style="fast",
# limit color scale at 99.2% quantile of cell abundance
max_color_quantile=0.992,
# size of locations (adjust depending on figure size)
circle_diameter=6,
colorbar_position="right",
)