Visium HD: What Changes When Resolution Goes Subcellular

Spatial transcriptomics · Visium HD · Xenium · CosMx · platform comparison

Why Resolution Matters (and Why It Is Not Everything)

Visium HD delivers 2 µm bins — roughly single-cell resolution — while the original Visium captures 55 µm spots that each contain 5–20 cells. That 27x improvement in resolution sounds decisive. In practice, the right platform depends on your biological question, not just the spec sheet.

This post covers what Visium HD actually changes analytically, where it outperforms its predecessor, and when Xenium or CosMx is the better choice despite having a fixed gene panel.

Figure 1. Left: spatial resolution comparison — Visium HD achieves 2 µm bins vs. 55 µm spots for Visium v1. Right: capability trade-offs across platforms normalized to maximum observed values.

What Visium HD Changes: The Bin Model

Visium HD does not capture individual cells — it captures 2 µm square bins that tile the tissue continuously. The key workflow difference is binning strategy: you choose a bin size at analysis time, not at capture time.

import squidpy as sq
import scanpy as sc

# Load Visium HD at 8 µm bins (good starting point — balances resolution and sparsity)
adata = sq.read.visium(
    path="visium_hd_output/",
    counts_file="filtered_feature_bc_matrix.h5",
    library_id="your_sample_id",
    bin_size=8,  # options: 2, 8, 16 micrometers
)

print(adata)
# AnnData: n_obs (bins) depends heavily on bin_size
# bin_size=2:  ~500,000 bins (very sparse, ~2-5 UMI/bin)
# bin_size=8:  ~30,000 bins (workable, ~50-100 UMI/bin)
# bin_size=16: ~8,000 bins (Visium-like density)

Start with 8 µm bins for exploratory analysis. At 2 µm, most bins will have fewer than 5 UMI — too sparse for reliable clustering without heavy smoothing. Only use 2 µm bins if you need subcellular resolution for a specific question (e.g., resolving nuclear vs. cytoplasmic transcript localization).

The Critical Difference: Deconvolution Is Optional

With Visium v1, cell2location or RCTD is mandatory to get cell type proportions per spot. With Visium HD at 8 µm bins, many bins will contain a single dominant cell type and can be annotated directly by marker expression — though mixed bins still exist at tissue interfaces.

# Standard clustering works directly on HD bins
sc.pp.filter_cells(adata, min_counts=10)
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata, n_top_genes=3000)
sc.tl.pca(adata)
sc.pp.neighbors(adata, n_neighbors=15)
sc.tl.leiden(adata, resolution=0.4)

# Annotate by marker expression (same workflow as scRNA-seq)
sc.pl.dotplot(adata, markers_dict, groupby="leiden")

# Optional: if you want single-cell precision at 2µm bins,
# still use cell2location with matched scRNA-seq reference

Visium HD vs. Xenium: Choosing the Right Tool

Choose Visium HD when:

• You need whole-transcriptome coverage (not a fixed panel)
• Your question involves discovery — finding unexpected cell states or pathways
• You have FFPE tissue (Visium HD supports FFPE; Xenium also does)
• You want spatial multi-omics (Visium HD + CUT&TAG or protein co-detection)

Choose Xenium/CosMx when:

• You need subcellular resolution (mRNA localization within a cell)
• Your target genes are already known and a panel of 100–500 genes is sufficient
• You need higher sensitivity per transcript (in-situ amplification vs. sequencing)
• Your tissue is thick or 3D (Xenium handles z-stacking)

Squidpy Compatibility

Visium HD AnnData loads exactly like Visium v1 from Squidpy's perspective. All tools covered in this series work without modification:

# Everything from earlier posts works identically on Visium HD
sq.gr.spatial_neighbors(adata, coord_type="generic", n_neighs=6)
sq.gr.nhood_enrichment(adata, cluster_key="leiden")
sq.gr.ligrec(adata, n_perms=500, cluster_key="leiden")
sq.gr.spatial_autocorr(adata, mode="moran")

# The only practical difference: more bins = longer runtime
# nhood_enrichment on 30,000 bins takes ~10 min vs ~2 min for Visium v1

One Practical Warning: Tissue Coverage

Visium HD captures a 6.5 mm × 6.5 mm area — identical to Visium v1. This is enough for most tissue sections but smaller than a typical whole-slide image. If your question requires comparing multiple tumor regions across a large section, you will need multiple capture areas or switch to a platform with larger footprint (some CosMx runs cover full slides).

Interested in what the immune cell types you are mapping at single-cell resolution are actually doing in the tumor? NeoantigenLab covers T cell exhaustion, checkpoint inhibitor mechanisms, and CAR-T biology at the molecular level.

Get the complete pack

SpatiaBio Squidpy Pack 1 — 16 Notebooks ($19)

All 16 notebooks — neighborhood enrichment, co-occurrence, ligand-receptor, spatial autocorrelation, publication figures, and the complete workflow. Includes memory tips for Visium HD. Ready to run on your own data.

Get it for $19 →

---

From the NeoantigenLab sister blog

Working with tumor transcriptomics for neoantigen research?

NeoantigenLab covers neoantigen biology, WES pipelines, pVACseq, and HLA typing for experimental researchers — the biology behind what you're sequencing.

Visit NeoantigenLab →

Spatial Trajectory Analysis: PAGA and Diffusion Pseudotime vs. Spateo

Spatial transcriptomics · trajectory analysis · pseudotime · Squidpy · Spateo

The Problem: Clustering Loses Spatial Continuity

Standard spatial analysis assigns each spot to a discrete cluster. But tissue biology is continuous — there is no hard boundary between stroma and tumor, between hypoxic core and normoxic periphery, between vascularized and avascular zones. Trajectory analysis reconstructs this continuum by ordering spots along a path through gene expression space, anchored to their physical location in tissue.

The result is pseudotime: a one-dimensional coordinate that captures how far each spot is along a biological transition. Paired with spatial coordinates, it reveals not just that gene expression changes, but exactly where in the tissue those changes happen.

Figure 1. Left: spatial trajectory colored by pseudotime from stroma (early) to tumor core (late). Right: gene expression dynamics along the trajectory — ECM genes decline, proliferation and immune markers peak at different positions.

Two Approaches: PAGA + Diffusion Pseudotime vs. Spateo

You have two practical options for spatial trajectory analysis:

PAGA + DPT (Scanpy/Squidpy) — Works on the spatial graph. Mature, well-documented. Best when you already have a spatial graph and want trajectories between defined regions. Requires a root cell/spot (the biological starting point).

Spateo — Purpose-built for spatial trajectories. Handles 3D spatial data and multiple trajectory branches. More flexible root selection. Heavier dependency chain.

For most Visium workflows, start with PAGA + DPT — it integrates cleanly with your existing Squidpy AnnData.

Running PAGA + DPT on Visium Data

import scanpy as sc
import squidpy as sq
import numpy as np

# Start from annotated Visium AnnData
adata = sc.read_h5ad("visium_annotated.h5ad")

# 1. Build neighborhood graph on HVGs (not spatial — for PAGA topology)
sc.pp.neighbors(adata, n_neighbors=15, use_rep="X_pca")

# 2. Run PAGA to get cluster connectivity
sc.tl.paga(adata, groups="spatial_domain")
sc.pl.paga(adata, threshold=0.1, node_size_scale=2)

# 3. Initialize UMAP from PAGA for better trajectory layout
sc.tl.umap(adata, init_pos="paga")

# 4. Set root: spot with highest stroma marker expression
stroma_score = adata[:, "COL1A1"].X.toarray().flatten()
adata.uns["iroot"] = int(np.argmax(stroma_score))

# 5. Run diffusion pseudotime
sc.tl.dpt(adata)
# Result: adata.obs["dpt_pseudotime"]

Spatial Visualization of Pseudotime

Once pseudotime is computed, project it back onto tissue coordinates:

sq.pl.spatial_scatter(
    adata,
    color="dpt_pseudotime",
    cmap="plasma",
    size=1.5,
    title="Pseudotime — stroma to tumor core",
)

# Overlay with cell type to validate the trajectory makes biological sense
sq.pl.spatial_scatter(
    adata,
    color=["dpt_pseudotime", "spatial_domain"],
    ncols=2,
    size=1.5,
)

If pseudotime increases smoothly from stroma toward tumor core, the trajectory is capturing real biology. If it looks noisy or inverted, try a different root spot — pseudotime direction is set by your root choice, not by the algorithm.

Finding Genes That Change Along the Trajectory

The most useful output is the list of genes whose expression correlates with pseudotime — these are candidates for signals driving the stroma-to-tumor transition:

from scipy.stats import spearmanr
import pandas as pd

pt = adata.obs["dpt_pseudotime"].values
gene_matrix = adata.X.toarray()  # or adata.layers["normalized"]

correlations = []
for i, gene in enumerate(adata.var_names):
    rho, pval = spearmanr(pt, gene_matrix[:, i])
    correlations.append({"gene": gene, "rho": rho, "pval": pval})

corr_df = pd.DataFrame(correlations)
corr_df["qval"] = corr_df["pval"] * len(corr_df)  # Bonferroni
corr_df = corr_df[corr_df["qval"] < 0.05].sort_values("rho", ascending=False)

print("Top increasing along trajectory:")
print(corr_df.head(10)[["gene", "rho"]])

print("
Top decreasing:")
print(corr_df.tail(10)[["gene", "rho"]])

Interpreting the Results

From the figure, four biologically coherent patterns emerge along the stroma-to-tumor trajectory:

• COL1A1 (ECM marker): Highest in stroma, decays toward tumor core — validates that the trajectory direction is correct
• CD8A (cytotoxic T cells): Peaks at the interface zone, consistent with T cell exclusion from the tumor core by immunosuppressive signals
• CXCL10 (interferon response): Transient peak at the interface — the zone of active immune engagement
• MKI67 (proliferation): Rises sharply in the tumor core, confirming that high-pseudotime spots correspond to actively dividing cancer cells

Combining with Ligand-Receptor Analysis

Trajectory analysis and LR analysis are complementary. Once you know which spatial zones correspond to early vs. late pseudotime, you can ask: which ligand-receptor interactions are active specifically at the interface? Filter spots by pseudotime bin before running gr.ligrec:

# Subset to interface zone (pseudotime 0.4–0.6)
interface_mask = (adata.obs["dpt_pseudotime"] > 0.4) & (adata.obs["dpt_pseudotime"] < 0.6)
adata_interface = adata[interface_mask].copy()

sq.gr.spatial_neighbors(adata_interface, coord_type="generic", n_neighs=6)
sq.gr.ligrec(adata_interface, n_perms=500, cluster_key="cell_type")

This reveals whether the CD8A peak and CXCL10 burst at the interface are accompanied by specific T cell - cancer cell interactions that might be candidates for checkpoint blockade potentiation.

For the immunotherapy biology behind the interface dynamics — why T cells stall at the tumor boundary and what checkpoint inhibitors do about it — see NeoantigenLab.

---

From the NeoantigenLab sister blog

Working with tumor transcriptomics for neoantigen research?

NeoantigenLab covers neoantigen biology, WES pipelines, pVACseq, and HLA typing for experimental researchers — the biology behind what you're sequencing.

Visit NeoantigenLab →

Beyond Ligand-Receptor Scores: A Combined Decision Framework for Spatial Cell Communication

Spatial transcriptomics · ligand-receptor · cell communication · Squidpy

Why Spatial Context Matters for Cell Communication

Single-cell RNA-seq can tell you which ligands and receptors are expressed. It cannot tell you whether the cells expressing them are actually close enough to interact. Spatial transcriptomics closes that gap.

Two cells 500 µm apart in a tumor section are unlikely to exchange short-range signals regardless of what their transcriptomes predict. Squidpy's gr.ligrec and gr.nhood_enrichment functions let you ask both questions at once: what is being expressed, and are the right cells adjacent?

Figure 1. Left: ligand-receptor interaction scores between cell type pairs (* = p<0.05). Right: spatial co-occurrence z-scores — positive values indicate cell types that cluster together in tissue.

Setting Up the Interaction Database

Squidpy ships with a curated ligand-receptor database derived from CellChatDB and CellPhoneDB. You load it once and pass it to gr.ligrec:

import squidpy as sq
import scanpy as sc

# Load preprocessed Visium AnnData with cell type annotation in adata.obs["cell_type"]
adata = sc.read_h5ad("visium_annotated.h5ad")

# Build spatial graph first (required for proximity-aware analysis)
sq.gr.spatial_neighbors(adata, coord_type="grid", n_rings=2)

# Run ligand-receptor permutation test
sq.gr.ligrec(
    adata,
    n_perms=1000,
    cluster_key="cell_type",
    copy=False,
    use_raw=False,
    transmitter_params={"categories": "ligand"},
    receiver_params={"categories": "receptor"},
)
# Results stored in adata.uns["cell_type_ligrec"]

The permutation test shuffles cell type labels 1,000 times and asks: how often would this interaction score arise by chance given the spatial arrangement of cells? This is stricter than expression-only approaches because it accounts for the actual tissue layout.

Visualizing the Results

The raw output is a dictionary with means and pvalues DataFrames. Squidpy provides a dedicated dotplot:

sq.pl.ligrec(
    adata,
    cluster_key="cell_type",
    source_groups="Macrophages",
    target_groups=["T cells", "Cancer"],
    means_range=(0.3, float("inf")),  # filter weak interactions
    alpha=0.05,                        # significance threshold
    swap_axes=False,
    figsize=(10, 6),
)

Two practical filters here: means_range removes interactions with low absolute expression, and alpha applies the permutation p-value cutoff. Without both, the plot becomes unreadable with hundreds of LR pairs.

Neighborhood Enrichment: Are These Cells Actually Neighbors?

Before trusting any LR interaction, check whether the source and target cell types are spatially co-localized. gr.nhood_enrichment runs a permutation test on the spatial graph:

sq.gr.nhood_enrichment(adata, cluster_key="cell_type")
sq.pl.nhood_enrichment(
    adata,
    cluster_key="cell_type",
    method="average",
    cmap="RdBu_r",
    vmin=-3, vmax=3,
    figsize=(7, 6),
)

Z-scores above +2 indicate strong spatial co-occurrence; below −2 indicates mutual exclusion. From the figure: Macrophages and Cancer cells co-occur strongly (z = 3.1) — a known feature of immunosuppressive tumor microenvironments where TAMs cluster around tumor nests. T cells and Fibroblasts are mutually exclusive (z = −0.9), consistent with fibroblast-mediated immune exclusion.

Combining Both Analyses: A Decision Framework

Use neighborhood enrichment as a gate before interpreting LR results:

High enrichment + significant LR → strong candidate interaction worth validating (e.g., SPP1-CD44 between Macrophages and Cancer)

Low enrichment + significant LR → likely expression artifact; cells aren't actually proximal enough to interact

High enrichment + no significant LR → cells co-localize for structural reasons (e.g., Fibroblasts around vessels) rather than active signaling

Key LR Pairs in the Tumor Microenvironment

These consistently appear in Visium TME datasets:

• SPP1 (OPN) → CD44: Macrophage-to-cancer signaling, promotes invasion and immunosuppression
• CXCL12 → CXCR4: T cell and cancer cell chemoattraction; CXCR4 inhibition is a clinical target
• VEGFA → KDR (VEGFR2): Tumor-to-endothelial angiogenic signaling; highest scores near vessel-rich regions
• TGFB1 → TGFBR2: Fibroblast-mediated immunosuppression and ECM remodeling
• PDGFB → PDGFRB: Pericyte recruitment by endothelial cells; marks active neovascularization zones

Performance Notes

gr.ligrec with 1,000 permutations on a full Visium slide (~3,500 spots, 10+ cell types) takes 5–15 minutes on a standard laptop. Use n_jobs=-1 to parallelize across CPU cores. For exploratory work, drop to n_perms=200 first to check the output structure, then rerun with 1,000 for publication figures.

# Fast exploratory run
sq.gr.ligrec(adata, n_perms=200, cluster_key="cell_type", n_jobs=-1)

# Extract top interactions programmatically
import pandas as pd
means = adata.uns["cell_type_ligrec"]["means"]
pvals = adata.uns["cell_type_ligrec"]["pvalues"]

# Filter: mean > 0.5, p < 0.05 for Macrophage->Cancer axis
mac_cancer = means.loc[:, ("Macrophages", "Cancer")]
sig = mac_cancer[pvals.loc[:, ("Macrophages", "Cancer")] < 0.05]
print(sig.sort_values(ascending=False).head(10))

What's Next

Ligand-receptor analysis identifies candidate interactions. The next step is spatial trajectory analysis — reconstructing how gene expression changes as you move from one tissue region to another, which reveals which signals are active at each point along the tumor-stroma interface.

Working in tumor microenvironment biology? The companion blog NeoantigenLab covers the immunotherapy side — checkpoint inhibitor mechanisms, neoantigen prediction pipelines, and CAR-T biology for the same cell types you're mapping spatially.

Get the complete pack

SpatiaBio Squidpy Pack 1 — 16 Notebooks ($19)

All 16 notebooks — neighborhood enrichment, co-occurrence, ligand-receptor, spatial autocorrelation, publication figures, and the complete workflow. Includes memory tips for Visium HD. Ready to run on your own data.

Get it for $19 →

---

From the NeoantigenLab sister blog

Working with tumor transcriptomics for neoantigen research?

NeoantigenLab covers neoantigen biology, WES pipelines, pVACseq, and HLA typing for experimental researchers — the biology behind what you're sequencing.

Visit NeoantigenLab →

 

SPATIAL TRANSCRIPTOMICS · TUTORIAL

BANKSY: Using Neighborhood Context to Find Spatial Domains

Why adding just 20% neighbor signal outperforms pure expression clustering for tissue layer detection

July 2026  ·  9 min read  ·  SpatiaBio

Standard Leiden clustering on spatial data ignores one obvious fact: cells that sit next to each other tend to be in the same tissue layer. BANKSY (Building Aggregates with a Neighborhood Kernel for Yeoman Segmentation) fixes this by mixing each cell's own expression with a smoothed average of its neighbors — then clustering on the combined signal. The result is cleaner, more spatially coherent domains without requiring any graph-based post-processing.

Figure 1. BANKSY spatial domain map (left) and performance comparison vs. other methods by ARI and NMI (right).

The Core Idea: Lambda Controls the Mix

BANKSY introduces one key parameter: lambda (0 to 1), which controls how much neighbor context to blend in:

lambda = 0

Pure cell-intrinsic expression. Identical to standard Leiden.

lambda = 0.2

Sweet spot. Adds spatial coherence while preserving cell identity.

lambda = 0.8

Strong spatial smoothing. Best for laminar structures (brain cortex).

The embedding is constructed as: z = (1 - lambda) * x_self + lambda * x_neighbors, where x_neighbors is a weighted mean of expression in the spatial neighborhood (typically k=15-30 nearest neighbors in physical space).

Running BANKSY with Squidpy

import squidpy as sq
import scanpy as sc
import numpy as np
from banksy import Banksy

# Load and preprocess
adata = sc.read_visium('path/to/visium/')
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata, n_top_genes=2000)
adata = adata[:, adata.var.highly_variable]

# Build spatial graph (required for BANKSY)
sq.gr.spatial_neighbors(adata, coord_type='generic', n_neighs=15)

# Run BANKSY
banksy = Banksy(
    adata,
    banksy_lambda=0.2,   # neighbor mix ratio
    resolution=0.8,       # Leiden resolution
    pca_dims=20,
    n_neighbors=15,
)
banksy.fit()
banksy.plot_domains(adata, color='banksy_labels')

Installation: pip install banksy-py — works with AnnData/Squidpy natively.

BANKSY vs. Other Spatial Domain Methods

	BANKSY	BayesSpace	STAGATE	Leiden only
Approach	Neighbor smoothing + clustering	Bayesian HMRF	Graph autoencoder	Expression only
Speed	Fast	Slow	Slow (GPU)	Fastest
Scalability	Visium HD / Xenium OK	Visium only	Large datasets OK	Any
Interpretability	High (lambda is intuitive)	Low	Low (latent space)	High
Best for	General use, layered tissues	Small datasets, fine structure	Complex, non-laminar	Quick baseline

Practical Tips

• Start with lambda=0.2 for most tissues. Go higher (0.5-0.8) only for clearly laminar structures like brain cortex or retina.
• Run both BANKSY and Leiden and compare — BANKSY domains should look spatially cleaner. If they don't, your spatial graph might be poorly constructed.
• Use sq.gr.spatial_neighbors with coord_type='generic' for Visium. The default 'grid' mode can misrepresent actual tissue distances.
• BANKSY domains are not cell types — they are tissue microenvironments. Combine with cell type deconvolution (cell2location or Tangram) to get the full picture.

Key Takeaways

• BANKSY adds spatial coherence with a single parameter (lambda) on top of standard clustering
• lambda=0.2 is a reliable default; increase for laminar tissues
• Faster and more interpretable than deep learning alternatives for most use cases
• Best used alongside cell type deconvolution, not as a replacement

Interested in the immunology behind those spatial domains?

See NeoantigenLab for checkpoint inhibitors, CAR-T, and neoantigen biology.

Explore NeoantigenLab

---

From the NeoantigenLab sister blog

Working with tumor transcriptomics for neoantigen research?

NeoantigenLab covers neoantigen biology, WES pipelines, pVACseq, and HLA typing for experimental researchers — the biology behind what you're sequencing.

Visit NeoantigenLab →

SPATIAL TRANSCRIPTOMICS · TUTORIAL

Tangram: Mapping Single-Cell Annotations onto Spatial Coordinates

Transfer cell type labels, gene programs, and custom scores from scRNA-seq directly onto your Visium or Xenium data

July 2026  ·  10 min read  ·  SpatiaBio

Cell2location (covered in Post 12) infers cell type abundances per spot using a Bayesian model. Tangram takes a complementary approach: it learns a mapping from single-cell space to spatial space by aligning gene expression profiles, then transfers any annotation — cell types, transcription factor activity, pseudotime, custom gene scores — onto spatial coordinates. If you have a well-annotated scRNA-seq atlas and want to ask "where do these cells sit in tissue?", Tangram is your tool.

Figure 1. Tangram-mapped dominant cell type per spatial location (left) and deconvolution method comparison by accuracy and compute speed (right).

How Tangram Works

Tangram frames cell-to-space mapping as an optimal transport problem. Given a scRNA-seq dataset with C cells and a spatial dataset with S spots, it learns a mapping matrix M (C × S) that minimizes the difference between:

• The spatially reconstructed gene expression (M^T × scRNA matrix)
• The observed spatial gene expression

The key insight: you only need a shared set of marker genes to align the two modalities. Tangram then uses the learned mapping to project everything else — including genes not measured in the spatial assay.

import tangram as tg
import scanpy as sc
import squidpy as sq

# Load data
sc_adata  = sc.read_h5ad('sc_annotated.h5ad')   # scRNA-seq with cell_type labels
sp_adata  = sc.read_h5ad('visium_filtered.h5ad') # Visium spatial data

# Identify shared marker genes (top 100 per cell type works well)
sc.tl.rank_genes_groups(sc_adata, groupby='cell_type', method='wilcoxon')
markers = tg.pp.select_genes(sc_adata, n_genes=100)

tg.pp.filter_gene_list(sc_adata, sp_adata, gene_list=markers)

# Train mapping (GPU recommended for large datasets)
tg.mapping.map_cells_to_space(
    sc_adata,
    sp_adata,
    mode='cells',        # or 'clusters' for memory efficiency
    target_count=sp_adata.obs.cell_count.sum(),
    density_prior='rna_count_based',
    num_epochs=500,
    device='cpu',
)

Three Mapping Modes

cells mode

Maps each individual cell to a spot. Best for datasets <50k cells. Gives highest resolution but memory-intensive.

clusters mode

Maps cluster centroids instead of individual cells. Scalable to large atlases. Recommended starting point.

constrained mode

Uses prior cell density estimates (e.g. from DAPI) to constrain the mapping. More accurate when cell counts per spot are known.

Projecting Cell Types and Custom Scores

After mapping, Tangram stores results in sp_adata.obsm['tangram_ct_pred']. You can then project back to the spatial grid:

# Project cell type probabilities onto spatial coords
tg.pl.plot_cell_annotation_sc(sp_adata, annotation='cell_type', perc=0.02)

# Project any custom score from scRNA-seq (e.g. exhaustion score)
sc_adata.obs['exhaustion_score'] = (
    sc_adata[:, ['PDCD1','HAVCR2','LAG3','TIGIT','TOX']].X.mean(axis=1)
)
tg.pp.project_genes(adata_map=sp_adata, adata_sc=sc_adata)

# Now sp_adata has exhaustion_score — plot spatially
import squidpy as sq
sq.pl.spatial_scatter(sp_adata, color='exhaustion_score', cmap='Reds')

Pro tip: Project T cell exhaustion scores, EMT signatures, or pVACseq neoantigen burden per cell — then map them spatially. This links neoantigen-rich tumor zones to immune infiltration patterns.

Tangram vs cell2location: When to Use Which

	Tangram	cell2location
Approach	Optimal transport mapping	Bayesian deconvolution
Output	Cell-to-spot probability matrix	Cell type abundance per spot
Custom score transfer	✓ Any obs/obsm column	✗ Cell types only
Gene imputation	✓ Projects unmeasured genes	✗ No
Scalability	Moderate (<200k cells)	Large atlases OK
Best for	Transferring rich annotations	Quantifying cell composition

Common Pitfalls

• Too few marker genes — use at least 50-100 per cell type. With <20, mapping is unreliable.
• Batch effects between sc and spatial — if both datasets are from different labs or platforms, normalize carefully before running Tangram. Harmony or scVI integration first helps.
• Overinterpretation of imputed genes — genes projected via Tangram are predicted, not measured. Don't use imputed values for differential expression.
• Using cells mode on >100k cells — switch to clusters mode. The mapping matrix becomes O(C × S) and will OOM on most machines.

Key Takeaways

• Tangram maps any scRNA-seq annotation onto spatial coordinates via optimal transport
• Use clusters mode for large atlases, cells mode for maximum resolution
• Combine with Squidpy for spatial statistics on projected scores
• Complements cell2location — use both for a complete picture

Working on neoantigen biology or cancer immunotherapy?

The spatial exhaustion score workflow above connects directly to neoantigen research — see NeoantigenLab for the immunology side.

Explore NeoantigenLab →

---

From the NeoantigenLab sister blog

Working with tumor transcriptomics for neoantigen research?

NeoantigenLab covers neoantigen biology, WES pipelines, pVACseq, and HLA typing for experimental researchers — the biology behind what you're sequencing.

Visit NeoantigenLab →