marvel-py

A pure-Python reimplementation of MARVEL for single-cell alternative splicing analysis across plate-based and 10x droplet workflows.

AnnData-native workflow controller for Scanpy-style pipelines, with MARVEL inputs in adata.uns and results written back to adata.uns / adata.obsm
No rpy2; flat TSV / Matrix Market inputs remain supported as the backend compatibility layer
Implements PSI quantification, modality summaries, differential gene / splicing analysis, variable splicing event selection, PCA helpers, isoform switching, and rMATS / cryptic-splice-site utilities
R MARVEL parity is tracked with committed reference fixtures and full external replay benchmarks

This package is a standalone Python port of the public user-facing MARVEL workflow surface. The R package remains the reference implementation; this repo keeps Python behavior close to R MARVEL through R-vs-Python tests, audit fixtures, and external benchmark reports.

Install

Install the released package from PyPI:

pip install marvel-python

For local development from this repository:

pip install -e .

For benchmark report generation, also install matplotlib, or use the uv run --with ... commands shown below.

The bundled tutorial notebooks are expected to run in the ove micromamba environment and import the package normally:

import marvel_py as mp

Quick Start

Plate workflow

import marvel_py as mp

marvel = mp.create_marvel_object(
    splice_pheno="splice_pheno.tsv",
    splice_junction="splice_junction.tsv",
    intron_counts="intron_counts.tsv",
    gene_feature="gene_feature.tsv",
    exp="exp.tsv",
    gtf="annotation.gtf",
    splice_feature={
        "SE": "SE_feature.tsv",
        "MXE": "MXE_feature.tsv",
        "RI": "RI_feature.tsv",
        "A5SS": "A5SS_feature.tsv",
        "A3SS": "A3SS_feature.tsv",
    },
)

marvel = mp.check_alignment(marvel, level="SJ")
marvel = mp.compute_psi(marvel, event_type="SE", coverage_threshold=10.0)

# Results stay on the MARVEL-style object.
marvel.psi["SE"]

pass_ids = marvel.splice_pheno.loc[marvel.splice_pheno["qc.seq"] == "pass", "sample.id"]
marvel = mp.subset_samples(marvel, sample_ids=pass_ids.tolist())
marvel = mp.transform_exp_values(marvel, offset=1.0, transformation="log2", threshold_lower=1.0)

g1 = marvel.splice_pheno.loc[marvel.splice_pheno["cell.type"] == "iPSC", "sample.id"].tolist()
g2 = marvel.splice_pheno.loc[marvel.splice_pheno["cell.type"] == "Endoderm", "sample.id"].tolist()
marvel = mp.compare_values(
    marvel,
    cell_group_g1=g1,
    cell_group_g2=g2,
    level="gene",
    method="wilcox",
    min_cells=25,
)

The same downstream function names also accept AnnData objects. See the API comparison below for AnnData-native usage.

10x droplet workflow

import marvel_py as mp

marvel = mp.create_marvel_object_10x(
    gene_norm_matrix="gene_norm_matrix.mtx",
    gene_norm_pheno="gene_norm_pheno.tsv",
    gene_norm_feature="gene_norm_feature.tsv",
    gene_count_matrix="gene_count_matrix.mtx",
    gene_count_pheno="gene_count_pheno.tsv",
    gene_count_feature="gene_count_feature.tsv",
    sj_count_matrix="sj_count_matrix.mtx",
    sj_count_pheno="sj_count_pheno.tsv",
    sj_count_feature="sj_count_feature.tsv",
    gtf="annotation.gtf",
)

marvel = mp.annotate_genes_10x(marvel)
marvel = mp.annotate_sj_10x(marvel)
marvel = mp.validate_sj_10x(marvel)
marvel = mp.filter_genes_10x(marvel)
marvel = mp.check_alignment_10x(marvel)

g1 = marvel.sample_metadata.loc[marvel.sample_metadata["cell.type"] == "iPSC", "cell.id"].tolist()
g2 = marvel.sample_metadata.loc[marvel.sample_metadata["cell.type"] == "Cardio day 10", "cell.id"].tolist()
marvel = mp.plot_pct_expr_cells_genes_10x(marvel, cell_group_g1=g1, cell_group_g2=g2, min_pct_cells=5.0)
marvel = mp.plot_pct_expr_cells_sj_10x(
    marvel,
    cell_group_g1=g1,
    cell_group_g2=g2,
    min_pct_cells_genes=5.0,
    min_pct_cells_sj=5.0,
)
marvel = mp.compare_values_sj_10x(
    marvel,
    cell_group_g1=g1,
    cell_group_g2=g2,
    min_pct_cells_genes=10.0,
    min_pct_cells_sj=10.0,
    min_gene_norm=1.0,
    n_iterations=10,
)
marvel = mp.compare_values_genes_10x(marvel)

Pass pca="pca.tsv" only when you want to use coordinate-based helpers such as mp.plot_values_pca_cell_group_10x(...), mp.plot_values_pca_gene_10x(...), or mp.plot_values_pca_psi_10x(...).

Pass gtf="annotation.gtf" when you want py-MARVEL to annotate genes/SJs with mp.annotate_genes_10x(...), mp.annotate_sj_10x(...), or gene-structure plotting helpers. If your feature tables are already annotated (gene_type in gene features and gene_short_name.start, gene_short_name.end, sj.type in SJ features), 10x object creation, AnnData setup, and downstream SJ filtering/alignment can run without gtf.

Both MARVEL-style object calls and AnnData function calls are supported. The old nested api facade and legacy py_marvel mirror are not the supported interface.

R API vs py-MARVEL R-style vs AnnData-native API

There are three related ways to run MARVEL workflows. R MARVEL is the reference implementation. py-MARVEL keeps an R-style object API for parity and migration, and adds an AnnData-native API for Scanpy / omicverse workflows.

Topic	R MARVEL API	py-MARVEL R-style API	py-MARVEL AnnData-native API
Import	`library(MARVEL)`	`import marvel_py as mp`	`import marvel_py as mp`
Primary object	R S3 `MarvelObject`	`MarvelPlate` or `Marvel10x`	`AnnData`
Plate setup	`marvel <- CreateMarvelObject(...)`	`marvel = mp.create_marvel_object(...)`	`adata = mp.setup_plate_anndata(...)`
10x setup	`marvel <- CreateMarvelObject.10x(...)`	`marvel = mp.create_marvel_object_10x(...)`	`adata = mp.setup_10x_anndata(...)`
Function naming	R-style names, e.g. `ComputePSI(...)`	Python `snake_case`, e.g. `mp.compute_psi(...)`	Same Python `snake_case`, e.g. `mp.compute_psi(...)`
Workflow call target	`MarvelObject=marvel`	First argument is `marvel`	First argument is `adata`
Cell metadata	R object slots such as `$SplicePheno` or `$sample.metadata`	`marvel.splice_pheno` or `marvel.sample_metadata`	`adata.obs`
Gene metadata	R slots such as `$GeneFeature` or `$gene.metadata`	`marvel.gene_feature` or `marvel.gene_metadata`	`adata.var` plus MARVEL tables in `adata.uns`
PSI / SJ / DE results	R slots such as `$PSI`, `$DE`, `$N.Events`	Python attributes such as `marvel.psi`, `marvel.de_gene`, `marvel.n_events`	`adata.uns["marvel"]["tables"]` and `adata.obsm[...]`
Scanpy integration	No	No	Yes, shares `obs`, `var`, `obsm`, and `layers` conventions
Serialization	R serialization / exported tables	Python pickle or exported tables	AnnData `.h5ad` workflows, subject to AnnData-supported stored values
Best use case	Exact reference behavior and R-only workflows	R parity benchmarks, direct porting of MARVEL scripts	New Python, Scanpy, and omicverse workflows

The py-MARVEL R-style and AnnData-native APIs intentionally share downstream function names. Migration usually changes the setup call and result access pattern, while analysis verbs remain the same.

Plate example:

# R MARVEL
marvel <- CreateMarvelObject(...)
marvel <- CheckAlignment(MarvelObject=marvel, level="SJ")
marvel <- ComputePSI(MarvelObject=marvel, EventType="SE", CoverageThreshold=10)
psi_se <- marvel$PSI$SE

# py-MARVEL R-style object API
marvel = mp.create_marvel_object(...)
marvel = mp.check_alignment(marvel, level="SJ")
marvel = mp.compute_psi(marvel, event_type="SE", coverage_threshold=10)
psi_se = marvel.psi["SE"]

# py-MARVEL AnnData-native API
adata = mp.setup_plate_anndata(...)
adata = mp.check_alignment(adata, level="SJ")
adata = mp.compute_psi(adata, event_type="SE", coverage_threshold=10)
psi_se = adata.uns["marvel"]["tables"]["psi"]["SE"]

10x droplet example:

# R MARVEL
marvel <- CreateMarvelObject.10x(...)
marvel <- AnnotateGenes.10x(MarvelObject=marvel)
marvel <- CompareValues.Genes.10x(MarvelObject=marvel)
de_gene <- marvel$DE$Gene$Table

# py-MARVEL R-style object API
marvel = mp.create_marvel_object_10x(...)
marvel = mp.annotate_genes_10x(marvel)
marvel = mp.compare_values_genes_10x(marvel)
de_gene = marvel.de_gene

# py-MARVEL AnnData-native API
adata = mp.setup_10x_anndata(...)
adata = mp.annotate_genes_10x(adata)
adata = mp.compare_values_genes_10x(adata)
de_gene = adata.uns["marvel"]["tables"]["de_gene"]

R MARVEL API Mapping

The Python API follows the R MARVEL workflow names but uses Python snake_case. The preferred Python entry point is AnnData-native: setup functions create an AnnData object, and downstream functions update that object in place while returning it. Low-level MarvelPlate and Marvel10x objects remain available for parity tests and legacy scripts.

Core object model

R MARVEL	py-MARVEL	Notes
`CreateMarvelObject(...)`	`mp.setup_plate_anndata(...)`	Preferred plate entry point; stores inputs in `adata.uns["marvel_input"]`.
`CreateMarvelObject(...)`	`mp.create_marvel_object(...)`	Legacy low-level `MarvelPlate` constructor, kept for R parity and benchmarks.
`CreateMarvelObject.10x(...)`	`mp.setup_10x_anndata(...)`	Preferred 10x entry point; Matrix Market inputs are lazy by default.
`CreateMarvelObject.10x(...)`	`mp.create_marvel_object_10x(...)`	Legacy low-level `Marvel10x` constructor.
R S3 `MarvelObject` slots	`adata.uns["marvel"]["tables"]`, `adata.obsm[...]`	Tabular results are mirrored into AnnData; the runtime backend object is cached outside `adata.uns`.

Plate workflow

R MARVEL	py-MARVEL
`CheckAlignment(...)`	`mp.check_alignment(...)`
`SubsetSamples(...)`	`mp.subset_samples(...)`
`TransformExpValues(...)`	`mp.transform_exp_values(...)`
`DetectEvents(...)`, `DetectEvents.AFE(...)`, `DetectEvents.ALE(...)`	`mp.detect_events(...)`
`ComputePSI(...)`, `ComputePSI.SE(...)`, `ComputePSI.MXE(...)`, `ComputePSI.RI(...)`, `ComputePSI.A5SS(...)`, `ComputePSI.A3SS(...)`, `ComputePSI.AFE(...)`, `ComputePSI.ALE(...)`	`mp.compute_psi(..., event_type="SE")`
`ComputePSI.Posterior(...)`	`mp.compute_psi_posterior(...)`
`AssignModality(...)`	`mp.assign_modality(...)`
`CountEvents(...)`	`mp.count_events(...)`
`PropModality(...)`, `PropModality.Bar(...)`, `PropModality.Doughnut(...)`	`mp.prop_modality(...)`, `mp.prop_modality_bar(...)`, `mp.prop_modality_doughnut(...)`
`CompareValues(...)`, `CompareValues.PSI(...)`, `CompareValues.Exp(...)`, `CompareValues.Exp.Spliced(...)`	`mp.compare_values(...)`
`RunPCA(...)`	`mp.run_pca(...)`
`PlotValues(...)`	`mp.plot_values(...)`
`PlotDEValues(...)`	`mp.plot_de_values(...)`
`ModalityChange(...)`	`mp.modality_change(...)`
`IsoSwitch(...)`, `IsoSwitch.PlotExpr(...)`	`mp.iso_switch(...)`, `mp.iso_switch_plot_expr(...)`
`IdentifyVariableEvents(...)`	`mp.identify_variable_events(...)`
`PctASE(...)`	`mp.pct_ase(...)`
`ParseGTF(...)`	`mp.parse_gtf(...)`
`PrepareBedFile.RI(...)`	`mp.prepare_bed_file_ri(...)`
`Preprocess_rMATS(...)`, `Preprocess_rMATS.SE(...)`, `Preprocess_rMATS.MXE(...)`, `Preprocess_rMATS.RI(...)`, `Preprocess_rMATS.A5SS(...)`, `Preprocess_rMATS.A3SS(...)`	`mp.preprocess_rmats(...)`, `mp.preprocess_rmats_se(...)`, `mp.preprocess_rmats_mxe(...)`, `mp.preprocess_rmats_ri(...)`, `mp.preprocess_rmats_a5ss(...)`, `mp.preprocess_rmats_a3ss(...)`
`RemoveCrypticSS(...)`, `RemoveCrypticSS.AFE(...)`, `RemoveCrypticSS.ALE(...)`	`mp.remove_cryptic_ss(...)`, `mp.remove_cryptic_ss_afe(...)`, `mp.remove_cryptic_ss_ale(...)`
`SubsetCrypticSS(...)`, `SubsetCrypticSS.A5SS(...)`, `SubsetCrypticSS.A3SS(...)`, `SubsetCrypticA3SS(...)`	`mp.subset_cryptic_ss(...)`, `mp.subset_cryptic_ss_a5ss(...)`, `mp.subset_cryptic_ss_a3ss(...)`, `mp.subset_cryptic_a3ss(...)`

10x droplet workflow

R MARVEL	py-MARVEL
`AnnotateGenes.10x(...)`	`mp.annotate_genes_10x(...)`
`AnnotateSJ.10x(...)`	`mp.annotate_sj_10x(...)`
`ValidateSJ.10x(...)`	`mp.validate_sj_10x(...)`
`FilterGenes.10x(...)`	`mp.filter_genes_10x(...)`
`CheckAlignment.10x(...)`	`mp.check_alignment_10x(...)`
`PlotPctExprCells.Genes.10x(...)`	`mp.plot_pct_expr_cells_genes_10x(...)`
`PlotPctExprCells.SJ.10x(...)`	`mp.plot_pct_expr_cells_sj_10x(...)`
`CompareValues.SJ.10x(...)`	`mp.compare_values_sj_10x(...)`
`CompareValues.Genes.10x(...)`	`mp.compare_values_genes_10x(...)`
`CompareValues.SJ.DonorLevel.10x(...)`	`mp.compare_values_sj_donor_level_10x(...)`
`PlotDEValues.SJ.10x(...)`	`mp.plot_de_values_sj_10x(...)`
`PlotDEValues.Genes.10x(...)`	`mp.plot_de_values_genes_10x(...)`
`PlotValues.Gene.Pseudobulk.10x(...)`	`mp.plot_values_gene_pseudobulk_10x(...)`
`PlotValues.Gene.SingleCell.10x(...)`	`mp.plot_values_gene_single_cell_10x(...)`
`PlotValues.PCA.CellGroup.10x(...)`	`mp.plot_values_pca_cell_group_10x(...)`
`PlotValues.PCA.Gene.10x(...)`	`mp.plot_values_pca_gene_10x(...)`
`PlotValues.PCA.PSI.10x(...)`	`mp.plot_values_pca_psi_10x(...)`
`PlotValues.PSI.Pseudobulk.10x(...)`	`mp.plot_values_psi_pseudobulk_10x(...)`
`PlotValues.PSI.Pseudobulk.Heatmap.10x(...)`	`mp.plot_values_psi_pseudobulk_heatmap_10x(...)`
`IsoSwitch.10x(...)`	`mp.iso_switch_10x(...)`
`adhocGene.TabulateExpression.Gene.10x(...)`	`mp.adhoc_gene_tabulate_expression_gene_10x(...)`
`adhocGene.TabulateExpression.PSI.10x(...)`	`mp.adhoc_gene_tabulate_expression_psi_10x(...)`
`adhocGene.DE.Gene.10x(...)`	`mp.adhoc_gene_de_gene_10x(...)`
`adhocGene.DE.PSI.10x(...)`	`mp.adhoc_gene_de_psi_10x(...)`
`adhocGene.PlotDEValues.10x(...)`	`mp.adhoc_gene_plot_de_values_10x(...)`
`adhocGene.PlotSJPosition.10x(...)`	`mp.adhoc_gene_plot_sj_position_10x(...)`

Not yet ported from R MARVEL

Some R exports are intentionally not part of the current Python public surface: BioPathways*, FindPTC*, PropPTC, CompareExpr, and AnnoVolcanoPlot. Use R MARVEL directly for those workflows until they are ported.

Workflow Coverage

Area	Plate	10x droplet
Object creation and alignment	yes	yes
Gene and splice-junction annotation	via input feature tables / GTF helpers	yes
PSI / splice-junction expression summaries	yes	yes
Differential gene and splicing analysis	yes	yes
Variable splicing event selection	yes	no
PCA / plotting helper tables	yes	yes
Isoform switching helpers	yes	yes
rMATS and cryptic splice-site utilities	yes	no

The implementation targets the public MARVEL/man workflow surface rather than private R internals.

Benchmarks

The external benchmark replays R MARVEL and marvel_py on the full external tutorial datasets:

external_plate_data: iPSC vs Endoderm, qc.seq == pass, with SE/MXE/RI/A5SS/A3SS inputs available
external_droplet_data: iPSC vs Cardio day 10, using 10 permutations for tractable full-data replay
Plate R replay rebuilds from flat files and recomputes PSI. Droplet R replay uses the bundled R MARVEL object and recomputes downstream summaries / DE; Python replay loads the flat Matrix Market / TSV inputs.

Latest validated run:

Run	Artifacts	Numeric metrics	Nonzero row deltas	Max absolute difference	Minimum Pearson r
`benchmark/runs/20260525T070728Z`	10	323	0	`8.91e-4`	`0.9999961188133987`

Runtime from the latest run:

Section	R	Python	Speedup
Plate full replay	`1116.16 s`	`345.39 s`	`3.23x`
Plate variable splicing only	`2.71 s`	`0.86 s`	`3.15x`
Droplet replay	`1412.29 s`	`477.30 s`	`2.96x`

Artifact-level agreement:

Section	Artifact	Rows R	Rows Python	Max abs diff	Min Pearson r
Plate	`psi_se`	20509	20509	`6.66e-16`	`0.9999999999999976`
Plate	`psi_ri`	8295	8295	`6.66e-16`	`0.9999999999999988`
Plate	`de_gene`	21059	21059	`5.15e-14`	`0.9999999999999998`
Plate	`count_events_iPSC`	5	5	`0`	`1.0`
Plate	`count_events_endoderm`	5	5	`0`	`0.9999999999999999`
Plate	`variable_splicing`	13318	13318	`8.91e-4`	`0.9999961188133987`
Droplet	`pct_expr_gene`	20187	20187	`0`	`1.0`
Droplet	`pct_expr_sj`	1861	1861	`0`	`0.9999999999999998`
Droplet	`de_sj`	2937	2937	`8.88e-15`	`0.9999999999999999`
Droplet	`de_gene`	831	831	`7.11e-15`	`0.9999999999999999`

The retained summary figures are in benchmark/results/figures:

benchmark_summary_figure.png
artifact_row_delta.png
top20_metric_max_abs_diff.png
lowest20_metric_pearson_r.png
plate_metric_heatmap.png
droplet_metric_heatmap.png
runtime_r_vs_python.png
per-artifact scatter plots such as plate__psi_se__scatter.png, plate__variable_splicing__scatter.png, and droplet__de_sj__scatter.png

To rerun the external benchmark and archive the results:

# Optional: only needed when Rscript is not already on PATH.
export MARVEL_RSCRIPT=/path/to/Rscript

uv run --with matplotlib \
  python benchmark/scripts/run_external_benchmark_archive.py

uv run --with matplotlib \
  python benchmark/scripts/plot_benchmark_summary_figure.py \
  --run-dir benchmark/runs/<run_id>

R benchmarks require an R environment with MARVEL and its dependencies installed. MARVEL_RSCRIPT can point to any compatible R installation; it is not tied to a local micromamba path.

The archive contains copied results, code snapshots, command logs, metric_summary.tsv, artifact_summary.tsv, and report figures.

Examples

Notebook	What it covers
`examples/plate_data.ipynb`	Plate-based MARVEL workflow using `import marvel_py as mp`
`examples/Droplet_data.ipynb`	10x droplet MARVEL workflow using `import marvel_py as mp`

Scripted demos are also available:

Script	Purpose
`scripts/run_plate_ref_python.py`	Run the core plate tutorial workflow from exported flat files
`scripts/run_ref1_python.py`	Run the core droplet ref1 workflow from Matrix Market / TSV inputs
`scripts/export_plate_demo_inputs.R`	Export R MARVEL plate demo data into Python-friendly inputs
`scripts/export_marvel_demo_inputs.R`	Export R MARVEL droplet demo data into Python-friendly inputs

Relationship to R MARVEL

R MARVEL is the canonical package:

Reference package: MARVEL
Python package: this repo, importing as marvel_py
Benchmark code: benchmark/scripts

If you need exact R package behavior for unsupported private internals, use R MARVEL directly. If you need the implemented public workflows from Python, use marvel_py.

Relationship to omicverse

Developed following the omicverse-to-developer py- conventions (pure-Python, no rpy2 in production code, AnnData-native I/O, Numba only on hot kernels). Upstream integration plan:

Canonical implementation: omicverse.external.copykat_py (pending) Standalone mirror (this repo): same code, same API, without the full omicverse packaging

Citation

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

Wei Xiong Wen, Adam J Mead, Supat Thongjuea, MARVEL: an integrated alternative splicing analysis platform for single-cell RNA sequencing data, Nucleic Acids Research, Volume 51, Issue 5, 21 March 2023, Page e29, https://doi.org/10.1093/nar/gkac1260

and acknowledge omicverse / this repo for the Python port and optimisations.

License

GNU GPLv3.

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This release

0.1.1 This release

May 26, 2026 2 release files Full provenance

0.1.0

May 26, 2026 2 release files Full provenance

marvel-python 0.1.1

marvel-py

Install

Quick Start

Plate workflow

10x droplet workflow

R API vs py-MARVEL R-style vs AnnData-native API

R MARVEL API Mapping

Core object model

Plate workflow

10x droplet workflow

Not yet ported from R MARVEL

Workflow Coverage

Benchmarks

Examples

Relationship to R MARVEL

Relationship to omicverse

Citation

License

Key dates

1 maintainer

Provenance

License

Requires

Provides Extra

Tags

Classifiers

Metadata

Key dates

1 maintainer

Provenance

License

Requires

Provides Extra

Tags

Classifiers

Release files for marvel-python 0.1.1

Source distribution (sdist)

Built distribution (wheel)

Release files / marvel_python-0.1.1.tar.gz

Provenance

PyPI Publish Attestation

Identity

Workflow

Artifact

Release files / marvel_python-0.1.1-py3-none-any.whl

Provenance

PyPI Publish Attestation

Identity

Workflow

Artifact

Security for marvel-python 0.1.1

Provenance

Full provenance

Attestation for marvel_python-0.1.1.tar.gz

PyPI Publish Attestation

Identity

Workflow

Artifact

Attestation for marvel_python-0.1.1-py3-none-any.whl

PyPI Publish Attestation

Identity

Workflow

Artifact

A note on package security

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