Sample Group Injection Aggregation

sample-group-injection-aggregation

Summary

Consolidate multiple injections of the same sample and merge features across sample groups defined in the sequence file to enable group-level statistical analysis and comparative metabolomics. This skill is essential in high-throughput untargeted metabolomics workflows where replicate injections must be unified before group-wise comparisons.

When to use

When you have picked and annotated MS1 features from replicate injections of the same sample and need to produce a unified feature matrix indexed by sample (not injection). Use this skill after background filtering, adduct merging, and feature annotation are complete—particularly in FIA-MS FullScan Unknowns or semi-targeted workflows where the sequence file defines sample groupings and you plan to perform downstream group comparisons or statistical tests.

When NOT to use

• Input is already a sample-level feature table (no injection replicates to merge).
• You need to preserve injection-level variance for QC trend analysis or time-series monitoring across a sample batch.
• Sample groupings in the sequence file are malformed, missing, or do not reflect the biological design (validate sequence metadata first).

Inputs

• picked and annotated MS1 feature list (mzTab or feature table format with intensity values per injection)
• sequence file (.csv) with sample identifiers, group assignments, and injection replicates
• feature annotation results (e.g., from SEARCH_ACCURATE_MASS and MERGE_FEATURES)

Outputs

• sample-level feature matrix (rows = samples, columns = m/z × RT features, values = aggregated intensities)
• group-annotated feature table with sample-group metadata preserved
• feature list with sample-level quality metrics (e.g., coefficient of variation across replicates)

How to apply

After filtering features by blank signal intensity and merging adducts of the same compound, apply MERGE_INJECTIONS to consolidate all injections belonging to the same sample into a single row, typically by summing or averaging feature intensities across replicates. Then apply STORE_FEATURES_SAMPLE_GROUP to merge features across the sample groups (e.g., case vs. control cohorts) specified in the sequence file metadata. The rationale is to reduce the injection dimension while preserving sample-level biological variability; grouping enforces consistent handling of the sample structure defined in the input sequence configuration, ensuring that downstream statistical tests operate on the intended sample-group contrasts rather than on individual injections.

Related tools

• SmartPeak (orchestrates the full workflow including MERGE_INJECTIONS and STORE_FEATURES_SAMPLE_GROUP steps) — https://github.com/AutoFlowResearch/SmartPeak
• SmartPeakGUI (provides interactive configuration and monitoring of injection/sample merging and group storage operations) — https://github.com/AutoFlowResearch/SmartPeak
• SmartPeakCLI (enables command-line execution of injection and group merging workflows without GUI) — https://github.com/AutoFlowResearch/SmartPeak
• OpenMS (provides underlying feature merging and intensity aggregation algorithms)
• BFAIR (post-processing and statistical analysis of aggregated sample-group features) — https://github.com/AutoFlowResearch/BFAIR

Evaluation signals

• Verify that the number of rows in the output feature table equals the number of unique samples (not injections) in the sequence file.
• Check that sample-level feature intensities are non-negative and within expected range (e.g., no NaN or negative values after aggregation).
• Confirm that group assignments are correctly propagated: all samples in a group should share the same group label in the output metadata.
• Compute coefficient of variation (CV) for aggregated intensities across replicate injections; high CV (>50%) may indicate injection-level variability worth investigating.
• Validate that the number of features per sample is consistent after merging (no unexpected loss or gain of features due to missing values or aggregation errors).

Limitations

• Injection merging assumes all replicates of a sample use the same or compatible MS parameters; mismatched acquisition settings may produce misleading aggregate intensities.
• If the sequence file lacks explicit group labels or sample metadata, STORE_FEATURES_SAMPLE_GROUP may fail or create uninformative groupings; preprocessing and validation of the sequence file is mandatory.
• Aggregation (e.g., sum or mean) of intensities across injections can mask outlier injections or instrumental drift; no robust outlier detection is described in the workflow.
• The workflow does not explicitly handle unbalanced designs (e.g., differing numbers of replicates per sample or missing injections); such cases may require manual sequence file curation or post-hoc filtering.

Evidence

• [methods] Merge injections belonging to the same sample using MERGE_INJECTIONS: "Merge injections belonging to the same sample using MERGE_INJECTIONS."
• [methods] Merge features across sample groups specified in the sequence file using STORE_FEATURES_SAMPLE_GROUP: "Merge features across sample groups specified in the sequence file using STORE_FEATURES_SAMPLE_GROUP."
• [intro] SmartPeak automates all steps from peak detection and integration over calibration curve optimization, to quality control reporting: "The workflow automates all steps from peak detection and integration over calibration curve optimization, to quality control reporting."
• [readme] The collection of examples is located at src/example/data directory of the SmartPeak source code. The directory contains examples of different kinds of data in .mzML format and their corresponding configuration files.: "The collection of examples is located at src/example/data directory of the SmartPeak source code."