massSight

massSight implements probabilistic drift-aware optimal transport for cross-study LC–MS feature matching.

Install

Install using uv:

uv add mass-sight

Or using pip:

pip install mass-sight

Core dependencies include numpy, pandas, scikit-learn, and scipy.

Quickstart

Input feature tables

massSight expects two feature tables with at least:

• MZ (m/z)
• RT (retention time in minutes)
• Intensity (a per-feature intensity summary; optional)

MassSightConfig and column names

massSight is configured via MassSightConfig. By default it expects canonical column names (MZ, RT, Intensity).

If your tables use different column names, either rename your columns or pass a schema override:

from mass_sight import MassSightConfig

cfg = MassSightConfig(mz_col="mz", rt_col="rt_min", intensity_col="area")

If study_a and study_b use different schemas, use study-specific overrides:

cfg = MassSightConfig(
    mz_col_study_a="mz",
    rt_col_study_a="rt_min",
    mz_col_study_b="m_over_z",
    rt_col_study_b="rt",
)

For the CLI, use --mz-col, --rt-col, and --intensity-col.

Python usage

import pandas as pd
from mass_sight import MassSightConfig, match_features

study_a = pd.read_csv("study_a.csv")
study_b = pd.read_csv("study_b.csv")

cfg = MassSightConfig()
res = match_features(study_a, study_b, cfg)

top1 = res.top1 # id1, id2, decision, margin, prob_raw, ...
candidates = res.candidates  # residuals, log-likelihoods, OT weights, etc.

Command-line usage

mass_sight match study_a.csv study_b.csv \
  --out-candidates candidates.csv \
  --out-top1 top1.csv

Citation

• See CITATION.cff.

License

MIT.