analyzing-proteomics-data

Analyzing Proteomics Data with alphapepttools

alphapepttools is a Python package that provides search engine-agnostic proteomics analysis compatible with the scverse ecosystem.

Structure

alphapepttools contains multiple subpackages:

.io: Read search engine outputs into standardized anndata format (see ./references/io-patterns.md) .pp: Quality control and preprocess proteomics data .tl: Statistical analysis of proteomics data (principal component analysis, differential expression) .pl: Plotting and visualization functionalities .metrics: Assess quality of analysis steps

Philosophy

alphapepttools formalizes best practices efforts. Function docstrings, contain recommended best practices, and code examples.

When considering using a method, ALWAYS check its docstring first. Carefully inspect the provided code snippets in the Examples section.

help(alphapepttools.tl.<function>)

Core Pattern: Layer Checkpointing

Every transformation should be checkpointed to a new layer for debugging, reproducibility, and rollback:

import alphapepttools as at

# Checkpoint raw data
adata.layers["raw"] = adata.X.copy()

# Transform and checkpoint each step
at.pp.nanlog(adata, base=2)  # Modifies X inplace
adata.layers["log2"] = adata.X.copy()

at.pp.normalize(adata, strategy="total_mean")
adata.layers["normalized"] = adata.X.copy()

Workflow

Iterative Workflow with Decision Checkpoints

1. Load Data

adata = at.io.read_pg_table(path, search_engine="diann")
adata = at.pp.add_metadata(adata, metadata_df, axis=0)

2. Subsetting AnnData objects

Sample-level

adata = at.pp.filter_by_metadata(adata, filter_dict={"continuous_column1_in_obs": (0, 0.5), "continuous_column2_in_obs": (0, None), "categorical_column_in_obs": "A"}, action="keep", logic="and", axis=0)
# Keep all samples whose
# Values in continuous_column1_in_obs are in the range (0, 0.5)
# Values in continuous_column2_in_obs are in the range (0, infinity)
# Values in categorical_column_in_obs are category A