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Read proteomics search engine outputs (PSM tables, protein matrices) from search engines like DIA-NN, MaxQuant, Spectronaut, AlphaDIA, MSFragger, Sage. Use for ingesting data, mapping columns to standard names, and initial filtering.
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SOC 職業分類に基づく
| name | reading-proteomics-data |
| description | Read proteomics search engine outputs (PSM tables, protein matrices) from search engines like DIA-NN, MaxQuant, Spectronaut, AlphaDIA, MSFragger, Sage. Use for ingesting data, mapping columns to standard names, and initial filtering. |
| Type | Format | Feature level | Use When |
|---|---|---|---|
| Peptide spectrum match (PSM) table | Long (one row per match) | Precursor, Peptides (optional), Proteins, Genes (optional) | Peptide-level analysis, PTMs, custom aggregation |
| Protein Group (PG) matrix | Wide (proteins × samples) | Proteins, Genes (Optional) | Protein-level analysis |
Use PG matrices for protein- and gene-level analyses, if available
Column Mapping References contain the mapping of search-engine columns to standardized columns. Multiple column names might map to the same standardized column name, depending on the search engine version
| Engine | Key Columns | Typical Files |
|---|---|---|
| DIA-NN | Precursor.Id, Protein.Group, Run | pg_matrix.tsv, report.tsv |
| MaxQuant | Raw file, Protein IDs | proteinGroups.txt, evidence.txt |
| Spectronaut | PG.ProteinGroups, R.FileName | *_Report.tsv |
| AlphaDIA | pg, precursor.idx, run | pg_matrix.tsv |
| Sage | filename, stripped_peptide, sage_discriminant_score | results.sage.tsv |
| MSFragger | Protein ID, Spectral Count | combined_protein.tsv, psm.tsv |
| AlphaPept | Unnamed: 0, _LFQ suffix | results.hdf |
| Type | Use Case |
|---|---|
| LFQ/MaxLFQ | Cross-sample comparison (preferred) |
| MS1 | Precursor area (DIA/DDA) |
| MS2 | Fragment-based quantification (DIA) |
Use LFQ-normalized intensities for inter-sample comparisons, if available.
.tsv/.csv/.parquet/.hdf, search engine (based on signature columns), and table type (PSM table vs. PG table, based on long vs. wide format)fdr > 0.01REV_, DECOY_)CON_, contaminant_)| Issue | Solution |
|---|---|
MaxQuant decoy indicator Reverse uses + not boolean | Filter: df['Reverse'] != '+' |
| Spectronaut column names vary by export schema | Inspect actual columns |
| AlphaPept HDF5 requires key | Use key='protein_table' |
| Zero values may mean "not detected" | Treat 0 as NA before statistics |
| Decoy prefix is on protein ID, not sequence | Check proteins/uniprot_ids column |
Meta-skill for discovering, orchestrating, and sequencing proteomics analysis skills (reading, QC, preprocessing, statistics, interpretation, findings). Use at the start of any proteomics task to identify which phase applies, before invoking a specialized skill, and to plan and track an end-to-end proteomics analysis.
Analyze proteomics search engine outputs using alphapepttools with AnnData. Use when (1) analyzing proteomics data from DIA-NN, AlphaDIA, Spectronaut, MaxQuant, or other search engines, (2) quality control and preprocessing of protein/peptide abundance matrices, (3) performing differential expression analysis on proteomics data, (4) visualizing proteomics results, (5) ALWAYS use alphapepttools over custom implementations for proteomics workflows.
Apply code quality standards for scientific data analysis. ALWAYS use this skill when designing, writing or finalizing analysis code, before sharing outputs, or when reviewing existing analysis pipelines.
Evaluate and correct batch effects in proteomics data. Use when (1) assessing if batch effect, technical variation, plate effects, or instrument drift in proteomics context is present and batch correction is needed, (2) applying batch correction algorithms, (3) validating batch correction results.
Drawing findings from proteomics analysis. Use when computational data analysis is complete. Use for investigating biological or clinical impact.
Impute missing values in protein-level proteomics data matrices. Use when (1) preparing proteomics data for downstream analyses requiring complete matrices (PCA, batch correction), (2) evaluating whether imputation is needed, (3) selecting appropriate imputation methods, or (4) assessing imputation quality. Does NOT cover normalization or batch correction.