comparative-omics-report-generation
Summary
Generate structured comparison reports that harmonize and cross-tabulate feature lists from multiple mass spectrometry acquisition methods or processing software, documenting overlap statistics and source-specific identifiers. This skill enables multi-method validation and systematic comparison of omics results across different analytical pipelines.
When to use
When you have feature lists (in CSV format) from two or more different MS acquisition methods (e.g., LC-MS vs. LC-IMS-MS), different processing software (e.g., vendor-specific peak detection vs. open-source alternatives), or different fragmentation strategies (DDA vs. DIA), and need to identify which features are reproducibly detected across methods, which are method-specific, and how identifiers and metadata map across pipelines.
When NOT to use
- Input is already a unified feature table or has been pre-harmonized across methods; use this skill only when raw, separate feature lists require integration.
- Feature lists lack sufficient metadata (m/z, RT) to enable reliable matching across methods; harmonization requires at least one common numerical field.
- Analysis goal is method-specific optimization (e.g., tuning a single processing pipeline); this skill is for cross-method comparison, not within-method refinement.
Inputs
- CSV files containing feature lists (rows=features, columns including feature ID, m/z, retention time, abundance or intensity, and metadata)
- Multiple feature list files from different acquisition methods or processing software
- Metadata file (optional) describing source method, instrument type, or processing parameters for each input
Outputs
- Harmonized feature comparison report (CSV or tabular format)
- Feature overlap statistics (e.g., count and percentage of features shared across all methods, pairwise overlaps)
- Structured table with harmonized feature IDs, source method tags, and cross-method metadata mapping
- Analysis results documenting unique vs. shared features per method
How to apply
Load multiple feature list CSV files using pandas, each containing columns such as feature identifiers, retention time (RT), m/z values, and acquisition method metadata. Apply harmonization procedures by matching features across lists on common fields (m/z and RT) within specified tolerances, resolving naming conflicts by tagging each feature with its source method. Identify overlapping features using cross-list comparison logic (e.g., m/z tolerance and RT window matching) and flag unique features present in only one method. Generate a structured output report (CSV or tabular format) that includes a harmonized feature ID space, overlap counts, source method tags, and per-method abundance or detection metrics. Validate the report by spot-checking a sample of matched features and verifying that overlap statistics sum correctly across all input lists.
Related tools
- Comparador (Primary tool to ingest feature lists in CSV format from different acquisition methods or processing software, apply harmonization procedures, and perform comparative analysis on the results.) — https://github.com/pnnl/IonToolPack
- IonToolPack (Host software suite providing the Comparador tool; reads data from multiple instrument formats and provides omics-agnostic GUI for feature comparison workflows.) — https://github.com/pnnl/IonToolPack
- pandas (Python library used to load and manipulate multiple feature list CSV files.)
Evaluation signals
- Output CSV report contains all expected columns: harmonized feature ID, source method tags, m/z, RT, and overlap count (number of methods detecting each feature).
- Overlap statistics are self-consistent: sum of features unique to each method plus features shared by all methods equals total unique features across all inputs.
- Spot-check: manually verify 5–10 matched features by confirming m/z and RT differences fall within specified tolerance thresholds across input methods.
- All input CSV files are represented in the source method tags with non-zero feature counts; no input file is silently dropped.
- Harmonized feature IDs are globally unique and traceable back to original feature identifiers in each input file via an audit trail or mapping column.
Limitations
- Harmonization accuracy depends on the choice of m/z and RT tolerances; overly loose tolerances may merge features that are actually distinct, while overly strict tolerances may fragment true duplicates across methods.
- Feature lists must contain at least m/z and retention time (or equivalent temporal metadata) to enable reliable matching; lists lacking these fields cannot be harmonized.
- The skill does not perform statistical testing on overlap significance; high overlap may reflect genuine agreement or convergent artifacts depending on the methods' input data and parameters.
- No changelog or versioning information available for Comparador; reproducibility and version tracking should be documented manually.
Evidence
- [other] Comparador ingests feature lists in CSV format from different acquisition methods or processing software, applies harmonization procedures, and performs comparative analysis on the results.: "Comparador ingests feature lists in CSV format from different acquisition methods or processing software, applies harmonization procedures, and performs comparative analysis on the results."
- [other] Load multiple feature list CSV files from different acquisition methods or processing software using pandas.: "Load multiple feature list CSV files from different acquisition methods or processing software using pandas."
- [other] Harmonize feature identifiers, retention time, m/z, and other metadata across lists by matching on common fields and resolving naming conflicts.: "Harmonize feature identifiers, retention time, m/z, and other metadata across lists by matching on common fields and resolving naming conflicts."
- [other] Identify overlapping and unique features across all input lists using cross-list comparison logic.: "Identify overlapping and unique features across all input lists using cross-list comparison logic."
- [other] Generate a structured comparison report (CSV or tabular format) documenting feature overlap statistics, harmonized feature IDs, source method tags, and analysis results.: "Generate a structured comparison report (CSV or tabular format) documenting feature overlap statistics, harmonized feature IDs, source method tags, and analysis results."
- [readme] Tool to compare lists of features (CSV files) from different acquisition methods or processing software, by harmonizing and analyzing results.: "Tool to compare lists of features (CSV files) from different acquisition methods or processing software, by harmonizing and analyzing results."
1---2name: comparative-omics-report-generation-23description: Use when when you have feature lists (in CSV format) from two or more different MS acquisition methods (e.g., LC-MS vs. LC-IMS-MS), different processing software (e.4license: CC-BY-4.05---67# comparative-omics-report-generation89## Summary1011Generate structured comparison reports that harmonize and cross-tabulate feature lists from multiple mass spectrometry acquisition methods or processing software, documenting overlap statistics and source-specific identifiers. This skill enables multi-method validation and systematic comparison of omics results across different analytical pipelines.1213## When to use1415When you have feature lists (in CSV format) from two or more different MS acquisition methods (e.g., LC-MS vs. LC-IMS-MS), different processing software (e.g., vendor-specific peak detection vs. open-source alternatives), or different fragmentation strategies (DDA vs. DIA), and need to identify which features are reproducibly detected across methods, which are method-specific, and how identifiers and metadata map across pipelines.1617## When NOT to use1819- Input is already a unified feature table or has been pre-harmonized across methods; use this skill only when raw, separate feature lists require integration.20- Feature lists lack sufficient metadata (m/z, RT) to enable reliable matching across methods; harmonization requires at least one common numerical field.21- Analysis goal is method-specific optimization (e.g., tuning a single processing pipeline); this skill is for cross-method comparison, not within-method refinement.2223## Inputs2425- CSV files containing feature lists (rows=features, columns including feature ID, m/z, retention time, abundance or intensity, and metadata)26- Multiple feature list files from different acquisition methods or processing software27- Metadata file (optional) describing source method, instrument type, or processing parameters for each input2829## Outputs3031- Harmonized feature comparison report (CSV or tabular format)32- Feature overlap statistics (e.g., count and percentage of features shared across all methods, pairwise overlaps)33- Structured table with harmonized feature IDs, source method tags, and cross-method metadata mapping34- Analysis results documenting unique vs. shared features per method3536## How to apply3738Load multiple feature list CSV files using pandas, each containing columns such as feature identifiers, retention time (RT), m/z values, and acquisition method metadata. Apply harmonization procedures by matching features across lists on common fields (m/z and RT) within specified tolerances, resolving naming conflicts by tagging each feature with its source method. Identify overlapping features using cross-list comparison logic (e.g., m/z tolerance and RT window matching) and flag unique features present in only one method. Generate a structured output report (CSV or tabular format) that includes a harmonized feature ID space, overlap counts, source method tags, and per-method abundance or detection metrics. Validate the report by spot-checking a sample of matched features and verifying that overlap statistics sum correctly across all input lists.3940## Related tools4142- **Comparador** (Primary tool to ingest feature lists in CSV format from different acquisition methods or processing software, apply harmonization procedures, and perform comparative analysis on the results.) — https://github.com/pnnl/IonToolPack43- **IonToolPack** (Host software suite providing the Comparador tool; reads data from multiple instrument formats and provides omics-agnostic GUI for feature comparison workflows.) — https://github.com/pnnl/IonToolPack44- **pandas** (Python library used to load and manipulate multiple feature list CSV files.)4546## Evaluation signals4748- Output CSV report contains all expected columns: harmonized feature ID, source method tags, m/z, RT, and overlap count (number of methods detecting each feature).49- Overlap statistics are self-consistent: sum of features unique to each method plus features shared by all methods equals total unique features across all inputs.50- Spot-check: manually verify 5–10 matched features by confirming m/z and RT differences fall within specified tolerance thresholds across input methods.51- All input CSV files are represented in the source method tags with non-zero feature counts; no input file is silently dropped.52- Harmonized feature IDs are globally unique and traceable back to original feature identifiers in each input file via an audit trail or mapping column.5354## Limitations5556- Harmonization accuracy depends on the choice of m/z and RT tolerances; overly loose tolerances may merge features that are actually distinct, while overly strict tolerances may fragment true duplicates across methods.57- Feature lists must contain at least m/z and retention time (or equivalent temporal metadata) to enable reliable matching; lists lacking these fields cannot be harmonized.58- The skill does not perform statistical testing on overlap significance; high overlap may reflect genuine agreement or convergent artifacts depending on the methods' input data and parameters.59- No changelog or versioning information available for Comparador; reproducibility and version tracking should be documented manually.6061## Evidence6263- [other] Comparador ingests feature lists in CSV format from different acquisition methods or processing software, applies harmonization procedures, and performs comparative analysis on the results.: "Comparador ingests feature lists in CSV format from different acquisition methods or processing software, applies harmonization procedures, and performs comparative analysis on the results."64- [other] Load multiple feature list CSV files from different acquisition methods or processing software using pandas.: "Load multiple feature list CSV files from different acquisition methods or processing software using pandas."65- [other] Harmonize feature identifiers, retention time, m/z, and other metadata across lists by matching on common fields and resolving naming conflicts.: "Harmonize feature identifiers, retention time, m/z, and other metadata across lists by matching on common fields and resolving naming conflicts."66- [other] Identify overlapping and unique features across all input lists using cross-list comparison logic.: "Identify overlapping and unique features across all input lists using cross-list comparison logic."67- [other] Generate a structured comparison report (CSV or tabular format) documenting feature overlap statistics, harmonized feature IDs, source method tags, and analysis results.: "Generate a structured comparison report (CSV or tabular format) documenting feature overlap statistics, harmonized feature IDs, source method tags, and analysis results."68- [readme] Tool to compare lists of features (CSV files) from different acquisition methods or processing software, by harmonizing and analyzing results.: "Tool to compare lists of features (CSV files) from different acquisition methods or processing software, by harmonizing and analyzing results."
HolobiomicsLab