Object state mutation detection

object-state-mutation-detection

Summary

A technique to determine whether function calls operating on R6 reference objects modify the original data in-place or create independent deep copies. This is critical for metabolomics workflows where unintended mutation of peak tables can silently corrupt downstream analyses.

When to use

When calling filter functions (e.g., filter_mispicked_ions(), filter_group(), filter_cv()) on R6-based metabolomics data objects in the mpactr package and you need to verify whether the original object's state is preserved. Specifically, apply this skill when: (1) a function accepts a copy_object parameter or similar semantics flag, (2) you assign the result to a new variable but suspect the original may also be modified, or (3) you need to establish baseline row counts before and after filtering to detect in-place mutations.

When NOT to use

• When working with immutable data structures (e.g., base R data.frames with copy-by-value semantics) where mutation is not possible by design.
• When filter functions do not expose a copy_object or equivalent parameter; the skill requires explicit control over copy semantics.
• When the original data object is not needed for downstream analysis and in-place mutation is intentional and acceptable.

Inputs

• R6 metabolomics data object (e.g., from import_data() with format='Progenesis')
• Peak table (CSV format, e.g., cultures_peak_table.csv)
• Metadata table (CSV format, e.g., cultures_metadata.csv)
• Function call with copy_object parameter (boolean)

Outputs

• Comparison table with columns: copy_object setting, original_object_rowcount, assigned_object_rowcount, object_identity_match (boolean)
• Validated mutation behavior (in-place vs. deep-copy confirmation)

How to apply

Load the same data object twice (or make explicit copies before and after each operation). Call the filter function on the first copy with copy_object=FALSE; record the row count of both the original and assigned objects. Reload or re-copy the data and call the same filter function with copy_object=TRUE; again record row counts for both objects. Construct a comparison table with columns: copy_object setting, original_object_rowcount, assigned_object_rowcount, and object_identity_match (TRUE if row counts diverge in-place, FALSE if they remain equal and independent). The rationale is that R6 reference semantics by default mutate objects in-place unless explicit deep-copy parameters are set; comparing row counts before/after and checking whether the original object was modified reveals the actual mutation behavior, which is essential for ensuring data integrity in automated metabolomics pipelines.

Related tools

• mpactr (R6-based filtering library providing filter_mispicked_ions(), filter_group(), filter_cv(), and filter_insource_ions() with copy_object parameter control) — https://github.com/mums2/mpactr
• R (Runtime environment for loading data, calling filter functions, recording object state, and constructing comparison tables)
• data.table (Data structure for efficient row count comparison and construction of mutation detection results table)

Examples

data <- import_data(example_path('cultures_peak_table.csv'), example_path('cultures_metadata.csv'), format='Progenesis'); data_filtered <- filter_mispicked_ions(data, merge_peaks=TRUE, merge_method='sum', copy_object=FALSE); cat('Original rows:', nrow(data), 'Assigned rows:', nrow(data_filtered), '\n')

Evaluation signals

• Original object row count remains unchanged (copy_object=TRUE) or matches assigned object row count (copy_object=FALSE), confirming behavior matches parameter intent.
• Assigned object row count equals expected filtered result (e.g., 7269 → 6625 for mispicked ion removal) in both copy_object=TRUE and FALSE cases.
• object_identity_match column is FALSE when copy_object=TRUE (independent objects with potentially different row counts) and TRUE when copy_object=FALSE (original and assigned point to same mutated state).
• Row count divergence between original_object_rowcount and assigned_object_rowcount occurs only when copy_object=FALSE, never when copy_object=TRUE.
• Workflow is reproducible: reloading data and re-running with opposite copy_object settings yields consistent, inverse mutation patterns.

Limitations

• This detection method requires explicit parameter control; not all filter functions may expose copy_object or similar flags, limiting applicability.
• In-place mutation detection is sensitive to R6 object implementation details; changes to the underlying class structure could alter mutation behavior unpredictably.
• Row count comparison alone does not detect mutations in non-row-count properties (e.g., annotation columns, metadata attributes); more granular object introspection may be needed for complete state validation.
• Reference semantics in R6 classes mean that shared references to the same object will all reflect mutations, potentially obscuring the source of unexpected data changes in complex workflows.

Evidence

• [other] When filter_mispicked_ions() is called with copy_object=FALSE, the original data object is updated in-place; the raw data object with 7269 ions was reduced to 6625 ions, and the original data2 object was also updated despite assigning the result to a new variable data2_mispicked.: "When filter_mispicked_ions() is called with copy_object=FALSE, the original data object is updated in-place; the raw data object with 7269 ions was reduced to 6625 ions, and the original data2 object"
• [abstract] operates on reference semantics in which data is updated in-place. Compared to a shallow copy, where only data pointers are copied, or a deep copy, where the entire data object is copied in memory: "operates on reference semantics in which data is updated in-place. Compared to a shallow copy, where only data pointers are copied, or a deep copy, where the entire data object is copied in memory"
• [other] Construct a comparison table with columns: copy_object setting, original_object_rowcount, assigned_object_rowcount, and object_identity_match (TRUE if both point to same data, FALSE if independent).: "Construct a comparison table with columns: copy_object setting, original_object_rowcount, assigned_object_rowcount, and object_identity_match (TRUE if both point to same data, FALSE if independent)."
• [other] Call filter_mispicked_ions() with merge_peaks=TRUE, merge_method='sum', and copy_object=FALSE on the first copy; record row count of original and assigned objects.: "Call filter_mispicked_ions() with merge_peaks=TRUE, merge_method='sum', and copy_object=FALSE on the first copy; record row count of original and assigned objects."
• [other] Validate that copy_object=FALSE shows matching row counts (in-place mutation) and copy_object=TRUE shows independent row counts (deep copy).: "Validate that copy_object=FALSE shows matching row counts (in-place mutation) and copy_object=TRUE shows independent row counts (deep copy)."