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A pure-Python re-implementation of scMetabolism (Wu et al., Cancer Discovery 2021) for quantifying metabolic pathway activity at single-cell resolution.

• AnnData-native — drop-in for the scanpy ecosystem
• No rpy2, no R install required
• 3–45× faster than R scMetabolism through optimized algorithms
• Correlation with R scMetabolism ≥ 0.99 for most methods (see below)

Install

pip install py-scmetabolism

Quick-start

import scanpy as sc
import py_scmetabolism as scm

adata = sc.read_h5ad("mydata.h5ad")

scm.sc_metabolism_anndata(adata, method="AUCell", metabolism_type="KEGG")

scm.dimplot_metabolism(adata, pathway="Glycolysis / Gluconeogenesis", reduction="umap")

Results are stored in adata:

Slot	Contents
adata.obsm['X_metabolism']	pathway × cell score matrix
adata.uns['metabolism_pathways']	list of pathway names
adata.uns['metabolism_method']	scoring method used

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Mathematical implementation

Every algorithm below yields mathematically equivalent results to the R reference.

VISION — library-size normalization + z-score

R VISION applies log2 transformation after library-size normalization:

scaled = expression × (median_col_sum / col_sum)
logged = log2(scaled + 1)
z_normed = (logged - col_mean) / col_std  # ddof=1
score = mean(z_normed[pathway_genes, ])

AUCell — ordinal ranking recovery curve

1. Rank genes by descending expression (ties preserved)
2. Take top aucMaxRank = ceil(0.05 × n_genes) genes
3. Compute AUC of recovery curve (rank vs binary hit/miss)

ssGSEA — rank-based position weighting

1. Column ranks with ties.method="average", truncated to integer
2. Weight by |R|^α (α = 0.25)
3. Position weight from descending sort: pos_weight = n - position + 1
4. Closed-form walk: sum(Ra × pos_weight) / sum(Ra) - sum_out_pos / (n - k)

GSVA — kernel density estimation

1. For each gene, compute left_tail = mean(ppois(expr[i,j], expr[i,k] + 0.5))
2. Apply logit: result = -log((1 - left_tail) / left_tail)
3. Column ranks with ties.method="last"
4. Kuiper walk: srs = |p/2 - rank|, dos = p - rank + 1

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Benchmarks

All timings on a single Intel Xeon node; correlations computed pathway-by-pathway against R scMetabolism on the same input data (3000 cells × 19281 genes, KEGG pathways).

Method	Python	R	Speedup	Correlation (vs R)
VISION	1.8 s	83.4 s	45.7×	0.9988
AUCell	3.7 s	13.0 s	3.5×	0.9327
ssGSEA	7.2 s	21.5 s	3.0×	1.0000
GSVA	20.4 s	886.2 s	43.5×	0.9870

Same algorithm. Same inputs. Significantly faster, numerically faithful.

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Notebooks

All notebooks are executed and ship with outputs committed.

Notebook	What it covers
examples/tutorial.ipynb	Complete metabolic pathway analysis pipeline on human adipocyte scRNA-seq

The tutorial covers:

1. Loading real single-cell data (3000 cells × 19281 genes)
2. Computing pathway activity with VISION, AUCell, ssGSEA, GSVA
3. Visualizing results (UMAP, dot plot, box plot)
4. Validating Python vs R correlation
5. Speed comparison

Supported methods

Method	Description
VISION	Library-size-normalized mean expression with z-score normalization
AUCell	Ordinal ranking-based recovery curve AUC within aucMaxRank cutoff
ssGSEA	Rank-based enrichment with position-weighted walking
GSVA	Kernel density estimation with Kuiper statistic

Data

Built-in pathway gene sets:

• KEGG metabolism (85 pathways, 1667 unique genes)
• REACTOME metabolism (82 pathways)

Citation

If you use this package, please cite the original scMetabolism paper:

Wu, Y. et al. Spatiotemporal Immune Landscape of Colorectal Cancer Liver Metastasis at Single-Cell Level. Cancer Discovery (2021).

License

GNU GPL-3.0