spec2function 0.2.0

Deep learning model for MS2 spectrum annotation and metabolite set analysis

Spec2Function

Spec2Function performs MS2 spectrum annotation and metabolite-set functional analysis using the MS2BioText model. It exposes two entry points:

• run_single(...) — annotate a single MS2 spectrum and retrieve similar fragments from the bundled HMDB-derived database.
• run_set(...) — cluster a set of metabolite spectra (CSV/DataFrame), generate per-cluster functional summaries, and produce a t-SNE layout.

Large model and reference-database files are hosted on Hugging Face Hub (cgxjdzz/ms2function-assets) and downloaded automatically on first use.

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1. System requirements

Software dependencies (pinned minimums; tested versions in parentheses)

Package	Minimum	Tested with
Python	3.10	3.10.13
torch	2.0	2.2.2
transformers	4.35	4.41.2
numpy	1.24	1.26.4
pandas	2.0	2.2.2
scikit-learn	1.3	1.5.0
huggingface_hub	0.20	0.23.4
openai	1.30	1.35.7
python-dotenv	1.0	1.0.1
tqdm	4.65	4.66.4

All dependencies are installed automatically by pip install spec2function.

Operating systems

Tested on:

• Windows 11 (build 26100)
• Ubuntu 22.04 LTS

The code is OS-agnostic and should also work on macOS 13+.

Hardware

• Any x86_64 desktop with ≥ 16 GB RAM and ≥ 5 GB free disk (for the cached model + reference embeddings).
• A CUDA-capable GPU is optional. If detected (torch.cuda.is_available()), the model is moved to GPU automatically; otherwise it runs on CPU.
• Demo and single-spectrum inference run comfortably on CPU. For large set analyses (>500 spectra), an NVIDIA GPU with ≥ 6 GB VRAM is recommended.

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2. Installation guide

From PyPI

pip install spec2function

From source

git clone https://github.com/<your-org>/Spec2Function.git
cd Spec2Function
pip install -e .

Typical install time

On a "normal" desktop (4-core CPU, 100 Mbps internet, fresh virtualenv):

• pip install spec2function: 2–4 minutes (most of the time is downloading torch, ~750 MB).
• First run additionally downloads ~1.2 GB of model + embedding assets from Hugging Face Hub: 1–3 minutes depending on bandwidth. Subsequent runs reuse the cached assets and take no extra download time.

Asset cache and environment overrides

Variable	Purpose
MS2FUNCTION_ASSET_DIR	Use a local directory of assets (skip HF download).
MS2FUNCTION_ASSET_REPO	Override the HF repo id (default: cgxjdzz/ms2function-assets).
HF_TOKEN / HUGGINGFACE_HUB_TOKEN	For private asset repos.

Optional LLM / PubMed (only needed for the annotation and story fields):

Variable	Purpose
SILICONFLOW_API_KEY (or OPENAI_API_KEY, LLM_API_KEY)	LLM credential.
LLM_PROVIDER	openai, siliconflow, or gemini (default: siliconflow).
LLM_MODEL	Model name override.
LLM_BASE_URL	API base URL override.
PUBMED_EMAIL	Contact email for PubMed E-utilities (NCBI requirement).

If no LLM key is configured, the workflow still runs end-to-end; LLM-derived fields fall back to placeholder strings.

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3. Demo

A small bundled demo lives under examples/:

• examples/demo_single.json — one MS2 spectrum (precursor m/z 184.07, choline-like).
• examples/demo_set.csv — 10 features with logFC / pval / partial annotations.
• examples/run_demo.py — runs both entry points and prints a summary.

How to run

From the repo root:

python examples/run_demo.py

The script disables LLM calls (enable_gpt_pubmed=False) so it works without any API key. To enable LLM annotation, set SILICONFLOW_API_KEY (or another supported key) and remove that flag.

Expected output

============================================================
Demo 1: single-spectrum annotation
============================================================
Top metabolites:        ['Phosphocholine', 'Choline', 'Glycerophosphocholine', ...]
Retrieved fragments:    5 hits
Best hit:               Phosphocholine (similarity=0.87x)
Elapsed:                3.x s

============================================================
Demo 2: metabolite set analysis
============================================================
Clusters discovered:    2
Features in plot_data:  8
  cluster  1: Metabolic Cluster 1                      (known=2, unknown=2)
  cluster  2: Metabolic Cluster 2                      (known=2, unknown=2)
Elapsed:                4.x s

The exact metabolite names, similarity values, cluster count, and elapsed times depend on the asset-pack version, your hardware, and whether assets are already cached. Variations of ±20% on timing and ±0.05 on similarity scores are normal.

Expected runtime

On a "normal" desktop (4-core CPU, 16 GB RAM, no GPU), with assets already cached:

• Demo 1 (single spectrum): 2–6 seconds (dominated by the first model load on a cold process; <1 s for repeat calls in the same process).
• Demo 2 (10-feature set): 3–8 seconds.
• First-ever run on a fresh machine adds ~60–180 s for asset download.

GPU runs are typically 2–3× faster after the first call.

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4. Instructions for use

4.1 Single spectrum

Input is a JSON-serializable dict with peaks (list of [mz, intensity]) and optional precursor_mz:

from Spec2Function import run_single

payload = {
    "precursor_mz": 184.0733,
    "peaks": [[60.08, 12.4], [86.10, 38.7], [104.11, 65.1], [184.07, 100.0]],
}

result = run_single(payload, top_k=10, user_focus="lipid metabolism")

print(result["top_metabolites"])      # de-duplicated metabolite names
print(result["retrieved_fragments"])  # list of DB hits with `similarity`
print(result["annotation"])           # LLM summary (if enabled)
print(result["papers"])               # PubMed hits (if enabled)

4.2 Metabolite set

Input is a CSV path or a pandas.DataFrame. The set analysis matches columns case-insensitively by substring:

Role	Matches columns containing	Required
Spectrum string	spectrum	Yes
log fold change	logfc / log2fc	No (used for filtering & cluster summaries)
p-value	pval / p.value	No (used for filtering)
Annotation	annotator / annotation_name	No (marks known metabolites)
Precursor m/z	precursor	No
Feature ID	exact column variable_id	No (falls back to row index)

Spectrum-string format: mz:intensity|mz:intensity|... (both | and ; work as separators).

from Spec2Function import run_set

result = run_set(
    "examples/demo_set.csv",
    background_info="case vs control in liver tissue",
    min_abs_logfc=0.5,
    max_pvalue=0.05,
    min_features=5,
)

print(result["story"])         # global functional summary (LLM)
print(result["clusters"])      # per-cluster reports + top metabolites
print(result["plot_data"])     # t-SNE coordinates with cluster labels
print(result["filter"])        # filter info: total/kept/columns used

If too few features pass the filters you get {"error": "Too few features selected (N)", "filter": {...}}.

4.3 Reusing a workflow (faster for many calls)

If you plan to call inference repeatedly, build the analyzer once:

from Spec2Function import MS2BioTextWorkflow

workflow = MS2BioTextWorkflow.from_spec2function_root(
    project_root=None,           # use the auto-resolved asset cache
    enable_gpt_pubmed=False,
)
single_result = workflow.run_single(payload, top_k=5, include_annotation=False)
set_result = workflow.run_set("examples/demo_set.csv")

4.4 Visualizing t-SNE

import pandas as pd
import matplotlib.pyplot as plt

plot_df = pd.DataFrame(result["plot_data"])
plt.scatter(plot_df["tsne_x"], plot_df["tsne_y"], c=plot_df["cluster_id"], cmap="tab20")
plt.xlabel("t-SNE 1"); plt.ylabel("t-SNE 2")
plt.show()

4.5 Troubleshooting

• Assets not found. Set MS2FUNCTION_ASSET_DIR to a local directory that contains models/best_model.pth, models/config.json, data/hmdb_subsections_WITH_NAME.jsonl, and data/all_jsonl_embeddings.pt, or check that huggingface_hub is installed and reachable.
• annotation / story is a placeholder. No LLM key was found — set SILICONFLOW_API_KEY (or another supported key) and rerun.
• CSV parsing errors. Make sure your spectrum column contains mz:intensity pairs separated by | or ;, and the column header includes the substring spectrum.
• Too few features. Relax min_abs_logfc / max_pvalue or supply more rows; the set analysis requires at least 5 features after filtering.

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License

Spec2Function is licensed under the PolyForm Noncommercial License 1.0.0 (starting from version 0.2.0). You may use, modify, and redistribute the code for any noncommercial purpose — academic research, education, personal study, and use by nonprofit / public-research / government organizations all qualify.

Commercial use (any use that is for, or directed toward, commercial advantage or monetary compensation) requires a separate license. Contact [e-mail] to negotiate commercial terms.

Versions 0.1.x and earlier were released under the MIT License and remain under MIT — this license change applies only to 0.2.0 and later releases.

See LICENSE for the full license text.