mpactr-data-object-lifecycle-tracking
Summary
Track whether mpactr filter operations mutate the original data object in-place or return independent copies by inspecting the copy_object parameter and verifying row count changes in the peak table. This skill is essential for understanding memory efficiency, chaining filters, and avoiding unintended data loss in metabolomics preprocessing workflows.
When to use
When chaining multiple mpactr filters on a peak table and you need to decide whether to preserve intermediate filtered objects or accept in-place mutation for memory efficiency. Specifically: (1) if your workflow requires independent snapshots of data at each filtering stage, you must use copy_object=TRUE; (2) if you want to minimize memory overhead while applying sequential filters (mispicked, group, CV, insource), use copy_object=FALSE with the understanding that the original object will be mutated.
When NOT to use
- If you are working with a single filter operation and do not care about memory efficiency; the lifecycle choice becomes irrelevant.
- If your downstream analysis requires statistical comparison of pre- vs. post-filter peak abundances without explicit manual bookkeeping; in this case, always use copy_object=TRUE to preserve the pre-filter object.
- If you have already imported data in a non-Progenesis format or as a pre-built feature table; lifecycle tracking is specific to mpactr's data object abstraction.
Inputs
- mpactr data object (imported via import_data with Progenesis format)
- peak table (accessible via get_peak_table())
- metadata table (required for group-based filtering)
Outputs
- mutated or copied mpactr data object with updated peak table
- integer row count of peak table (before and after filtering)
- boolean confirmation of in-place mutation vs. independent copy
How to apply
Before calling a filter function like filter_mispicked_ions, decide on your lifecycle strategy: if you need the pre-filter object preserved, either set copy_object=TRUE or assign the filtered result to a new variable. If using copy_object=FALSE (the default), understand that calling filter_mispicked_ions with parameters ringwin=0.5, isowin=0.01, trwin=0.005, max_iso_shift=3, merge_peaks=TRUE, merge_method='sum' will mutate the input object in-place, reducing its peak table row count by removing mispicked ions and merging isotopic patterns. Verify the mutation occurred by comparing get_peak_table(data_object) before and after the call; if row counts match between the input and output, the original was modified in-place. For memory-constrained workflows with large peak tables, use copy_object=FALSE and chain filters sequentially; for exploratory workflows, use copy_object=TRUE to preserve intermediate states for comparison.
Related tools
- mpactr (R package providing filter functions (filter_mispicked_ions, filter_group, filter_cv, filter_insource_ions) with copy_object parameter to control data object lifecycle; import_data and get_peak_table are used to instantiate and inspect objects respectively) — https://github.com/mums2/mpactr
- R (Programming language and runtime for executing mpactr workflows, tracking object identity, and comparing row counts via data.table and base functions)
Examples
data2 <- import_data('cultures_peak_table.csv', 'cultures_metadata.csv', format='Progenesis'); rows_before <- nrow(get_peak_table(data2)); data2_filtered <- filter_mispicked_ions(data2, ringwin=0.5, isowin=0.01, trwin=0.005, max_iso_shift=3, merge_peaks=TRUE, merge_method='sum', copy_object=FALSE); rows_after <- nrow(get_peak_table(data2)); identical(rows_after, nrow(get_peak_table(data2_filtered)))
Evaluation signals
- Peak table row count from get_peak_table(data_object) after filtering is strictly less than the pre-filter row count (confirming mispicked ions were removed and isotopic patterns were merged).
- When copy_object=FALSE, get_peak_table(original_object) row count equals get_peak_table(filtered_result) row count, confirming in-place mutation of the original.
- When copy_object=TRUE, get_peak_table(original_object) row count remains unchanged from pre-filter state, confirming the original was preserved and only the returned object was modified.
- Sequential chaining of filters with copy_object=FALSE produces cumulative row count reduction consistent with the union of filtering effects (mispicked removal + group removal + CV threshold + insource removal).
- Memory profiling or object size inspection via object.size() in R shows smaller total memory footprint when copy_object=FALSE is used on large peak tables.
Limitations
- The copy_object parameter only controls deep vs. shallow copying at the R object level; it does not affect the underlying data.table structure's internal reference semantics, so users must rely on explicit row count comparisons rather than language-level object identity checks.
- In-place mutation with copy_object=FALSE is irreversible within a single R session; if the filter produces unexpected results, the pre-filter data cannot be recovered without re-importing.
- The speed and memory efficiency gains from copy_object=FALSE are most pronounced for large peak tables (e.g., tens of thousands of features); for small datasets (<1000 features), the overhead difference is negligible.
- Lifecycle tracking requires manual bookkeeping of variable names and pre/post row counts; there is no built-in audit trail or versioning system within mpactr to track which filters were applied and in what order.
Evidence
- [abstract] in-place mutation confirmation: "We recommend using the default
copy_object = FALSE as this makes for an extremely fast and memory-efficient way to chain mpactr filters together"
- [methods] research question and finding on reference semantics: "Running filter_mispicked_ions with copy_object=FALSE reduces the original data2 object's peak table from the initial row count to a smaller row count, demonstrating in-place mutation rather than"
- [methods] filter_mispicked_ions parameters and behavior: "Call filter_mispicked_ions on data2 with parameters ringwin=0.5, isowin=0.01, trwin=0.005, max_iso_shift=3, merge_peaks=TRUE, merge_method='sum', and copy_object=FALSE"
- [methods] verification workflow for lifecycle tracking: "Verify that get_peak_table(data2) row count matches get_peak_table(data2_mispicked) row count, confirming in-place mutation of the original object"
- [readme] filter independence in mpactr design: "All filters are independent, meaning they can be used to create a project-specific workflow"
1---2name: mpactr-data-object-lifecycle-tracking3description: Use when when chaining multiple mpactr filters on a peak table and you need to decide whether to preserve intermediate filtered objects or accept in-place mutation for memory efficiency.4license: CC-BY-4.05---67# mpactr-data-object-lifecycle-tracking89## Summary1011Track whether mpactr filter operations mutate the original data object in-place or return independent copies by inspecting the copy_object parameter and verifying row count changes in the peak table. This skill is essential for understanding memory efficiency, chaining filters, and avoiding unintended data loss in metabolomics preprocessing workflows.1213## When to use1415When chaining multiple mpactr filters on a peak table and you need to decide whether to preserve intermediate filtered objects or accept in-place mutation for memory efficiency. Specifically: (1) if your workflow requires independent snapshots of data at each filtering stage, you must use copy_object=TRUE; (2) if you want to minimize memory overhead while applying sequential filters (mispicked, group, CV, insource), use copy_object=FALSE with the understanding that the original object will be mutated.1617## When NOT to use1819- If you are working with a single filter operation and do not care about memory efficiency; the lifecycle choice becomes irrelevant.20- If your downstream analysis requires statistical comparison of pre- vs. post-filter peak abundances without explicit manual bookkeeping; in this case, always use copy_object=TRUE to preserve the pre-filter object.21- If you have already imported data in a non-Progenesis format or as a pre-built feature table; lifecycle tracking is specific to mpactr's data object abstraction.2223## Inputs2425- mpactr data object (imported via import_data with Progenesis format)26- peak table (accessible via get_peak_table())27- metadata table (required for group-based filtering)2829## Outputs3031- mutated or copied mpactr data object with updated peak table32- integer row count of peak table (before and after filtering)33- boolean confirmation of in-place mutation vs. independent copy3435## How to apply3637Before calling a filter function like filter_mispicked_ions, decide on your lifecycle strategy: if you need the pre-filter object preserved, either set copy_object=TRUE or assign the filtered result to a new variable. If using copy_object=FALSE (the default), understand that calling filter_mispicked_ions with parameters ringwin=0.5, isowin=0.01, trwin=0.005, max_iso_shift=3, merge_peaks=TRUE, merge_method='sum' will mutate the input object in-place, reducing its peak table row count by removing mispicked ions and merging isotopic patterns. Verify the mutation occurred by comparing get_peak_table(data_object) before and after the call; if row counts match between the input and output, the original was modified in-place. For memory-constrained workflows with large peak tables, use copy_object=FALSE and chain filters sequentially; for exploratory workflows, use copy_object=TRUE to preserve intermediate states for comparison.3839## Related tools4041- **mpactr** (R package providing filter functions (filter_mispicked_ions, filter_group, filter_cv, filter_insource_ions) with copy_object parameter to control data object lifecycle; import_data and get_peak_table are used to instantiate and inspect objects respectively) — https://github.com/mums2/mpactr42- **R** (Programming language and runtime for executing mpactr workflows, tracking object identity, and comparing row counts via data.table and base functions)4344## Examples4546```47data2 <- import_data('cultures_peak_table.csv', 'cultures_metadata.csv', format='Progenesis'); rows_before <- nrow(get_peak_table(data2)); data2_filtered <- filter_mispicked_ions(data2, ringwin=0.5, isowin=0.01, trwin=0.005, max_iso_shift=3, merge_peaks=TRUE, merge_method='sum', copy_object=FALSE); rows_after <- nrow(get_peak_table(data2)); identical(rows_after, nrow(get_peak_table(data2_filtered)))48```4950## Evaluation signals5152- Peak table row count from get_peak_table(data_object) after filtering is strictly less than the pre-filter row count (confirming mispicked ions were removed and isotopic patterns were merged).53- When copy_object=FALSE, get_peak_table(original_object) row count equals get_peak_table(filtered_result) row count, confirming in-place mutation of the original.54- When copy_object=TRUE, get_peak_table(original_object) row count remains unchanged from pre-filter state, confirming the original was preserved and only the returned object was modified.55- Sequential chaining of filters with copy_object=FALSE produces cumulative row count reduction consistent with the union of filtering effects (mispicked removal + group removal + CV threshold + insource removal).56- Memory profiling or object size inspection via object.size() in R shows smaller total memory footprint when copy_object=FALSE is used on large peak tables.5758## Limitations5960- The copy_object parameter only controls deep vs. shallow copying at the R object level; it does not affect the underlying data.table structure's internal reference semantics, so users must rely on explicit row count comparisons rather than language-level object identity checks.61- In-place mutation with copy_object=FALSE is irreversible within a single R session; if the filter produces unexpected results, the pre-filter data cannot be recovered without re-importing.62- The speed and memory efficiency gains from copy_object=FALSE are most pronounced for large peak tables (e.g., tens of thousands of features); for small datasets (<1000 features), the overhead difference is negligible.63- Lifecycle tracking requires manual bookkeeping of variable names and pre/post row counts; there is no built-in audit trail or versioning system within mpactr to track which filters were applied and in what order.6465## Evidence6667- [abstract] in-place mutation confirmation: "We recommend using the default `copy_object = FALSE` as this makes for an extremely fast and memory-efficient way to chain mpactr filters together"68- [methods] research question and finding on reference semantics: "Running filter_mispicked_ions with copy_object=FALSE reduces the original data2 object's peak table from the initial row count to a smaller row count, demonstrating in-place mutation rather than"69- [methods] filter_mispicked_ions parameters and behavior: "Call filter_mispicked_ions on data2 with parameters ringwin=0.5, isowin=0.01, trwin=0.005, max_iso_shift=3, merge_peaks=TRUE, merge_method='sum', and copy_object=FALSE"70- [methods] verification workflow for lifecycle tracking: "Verify that get_peak_table(data2) row count matches get_peak_table(data2_mispicked) row count, confirming in-place mutation of the original object"71- [readme] filter independence in mpactr design: "All filters are independent, meaning they can be used to create a project-specific workflow"
