Built site for prolfqua@1.6.3: 1dcdf9c

Author: wolski

CLAUDE.html

@@ -1,263 +1,3 @@
-# CLAUDE.md
+# NA
 
-This file provides guidance to Claude Code (claude.ai/code) when working
-with code in this repository.
-
-## What is prolfqua
-
-An R package for mass spectrometry-based label-free quantification (LFQ)
-proteomics analysis. It provides a complete workflow: QC, normalization,
-protein aggregation, statistical modelling, hypothesis testing, and
-sample size estimation. Data is always in long (tidy) format. Branch
-`main` is the active development branch.
-
-## Build & Test Commands
-
-``` bash
-make test            # Run testthat suite (runs document first)
-make check-fast      # R CMD check without vignettes (quick validation)
-make check           # Full R CMD check (document → build → check)
-make document        # Generate roxygen2 docs (NAMESPACE + man/)
-make install         # Install package locally
-make lint            # Run lintr static analysis
-make format          # Format with air
-make build-vignettes # Build vignettes into inst/doc
-make site            # Build pkgdown site locally
-```
-
-Single test file:
-
-``` bash
-Rscript -e "testthat::test_file('tests/testthat/test-LFQData.R')"
-```
-
-Library setup:
-
-``` bash
-Rscript -e ".libPaths()"
-```
-
-Use the normal user / system R libraries for this workspace; `renv`
-autoload is disabled.
-
-## Code Style
-
-- **Line length:** 120 chars, **indentation:** 2 spaces (`.lintr`)
-- `object_name_linter` is disabled — the codebase uses camelCase for R6
-  classes and snake_case/mixed for functions
-- NAMESPACE is **auto-generated** by roxygen2 — never edit directly; run
-  `make document`
-- Roxygen is configured with `r6 = TRUE` for R6 class documentation
-- **Never use `\dontrun{}` or `\donttest{}` in `@examples`** — all
-  examples must run during R CMD check. If an example is too slow,
-  optimize it instead of skipping it.
-
-## Architecture
-
-### Core Data Flow
-
-    Raw Data + AnalysisConfiguration → LFQData
-        ├── get_Transformer() → LFQDataTransformer  (log2, robscale, normalize)
-        ├── get_Aggregator()  → LFQDataAggregator   (peptide → protein rollup)
-        ├── get_Stats()       → LFQDataStats         (CV, variance per group)
-        ├── get_Plotter()     → LFQDataPlotter       (heatmaps, PCA, boxplots)
-        ├── get_Summariser()  → LFQDataSummariser    (missingness, hierarchy counts)
-        └── get_Imputer()     → LFQDataImp           (missing value imputation)
-
-    LFQData → build_contrast_analysis(lfqdata, modelstr, contrasts, method)
-        └── Returns a Facade with uniform API:
-            $get_contrasts(), $get_missing(), $get_Plotter(), $to_wide()
-
-### Facade Pattern (`ContrastsFacades.R`, `ContrastsChildToParentFacades.R`, `build_contrast_analysis.R`)
-
-[`build_contrast_analysis()`](https://wolski.github.io/prolfqua/reference/build_contrast_analysis.md)
-is the recommended entry point. Each method dispatches to a Facade class
-that wires strategy → model → contrasts → moderation internally.
-
-Facades split by input/output hierarchy shape.
-`lookup_facade(name)$needs` returns one of two values:
-
-**`needs = "same"`** — facade emits contrasts at the same hierarchy
-level as its input (protein → protein FC, or peptide/precursor →
-peptide/precursor FC; `subject_Id == hierarchy_keys`). Lives in
-`R/ContrastsFacades.R`: `lm`, `rlm`, `lm_missing`, `lm_impute`, `limma`,
-`limma_impute`, `limma_voom`, `limma_voom_impute`, `deqms`,
-`deqms_voom`, `firth`, `limpa`
-
-**`needs = "nested"`** — facade takes child-level input
-(peptide/precursor) and emits parent-level (protein) contrasts;
-`subject_Id` is a strict subset of `hierarchy_keys`. Lives in
-`R/ContrastsChildToParentFacades.R`: `lmer_nested`, `ropeca_nested`,
-`firth_nested`, `limpa_nested`
-
-Downstream dispatch convention: protein-level readers pair with `"same"`
-facades only; peptide-level readers pair with either.
-
-### Weights & `nr_children`
-
-`config$nr_children` names the column tracking child-feature counts
-(e.g. peptides per protein). After `get_Aggregator()` rollup, each
-protein×sample row gets its own count — **`nr_children` is
-sample-wise**. For peptide/precursor-level data it is typically 1.
-
-**Two distinct uses:**
-
-1.  **Fitting weights** (sample-wise): Aggregated facades (`lm`,
-    `limma`, `lm_missing`, `lm_impute`, `deqms`) pass `nr_children` as
-    `weights` by default to [`lm()`](https://rdrr.io/r/stats/lm.html) or
-    [`limma::lmFit()`](https://rdrr.io/pkg/limma/man/lmFit.html). This
-    down-weights protein intensities derived from fewer peptides in a
-    given sample. Disable with `weights = NULL`.
-
-2.  **DEqMS variance moderation** (experiment-wide):
-    `ContrastsDEqMSFacade` additionally aggregates `nr_children` via
-    [`max()`](https://rdrr.io/r/base/Extremes.html) per protein across
-    all samples for count-dependent variance shrinkage. This is separate
-    from the fitting weights.
-
-**Protein-level input must carry `nr_children`.** If the column is
-missing,
-[`setup_analysis()`](https://wolski.github.io/prolfqua/reference/setup_analysis.md)
-adds it set to 1 with a warning — but this defeats the purpose for
-aggregated data where the actual peptide count matters.
-
-### Key Design Patterns
-
-**Decorator/Composition**: LFQData factory methods (`get_Transformer()`,
-`get_Plotter()`, etc.) return decorator objects that wrap the LFQData.
-Decorators hold a reference in their `lfq` field.
-
-**Method chaining**: Transformer methods return `self` for chaining,
-access result via `$lfq`:
-
-``` r
-lfqdata <- lfqdata$get_Transformer()$log2()$robscale()$lfq
-```
-
-**Strategy pattern for models**: **Strategy R6 classes for models**:
-`StrategyLM`, `StrategyRLM`, `StrategyLmer`, `StrategyLogistf` — each
-with `model_fun`, `isSingular`, `contrast_fun`, `df_residual`, `sigma`
-methods. Wrapper functions
-[`strategy_lm()`](https://wolski.github.io/prolfqua/reference/strategy.md),
-[`strategy_rlm()`](https://wolski.github.io/prolfqua/reference/strategy.md),
-[`strategy_lmer()`](https://wolski.github.io/prolfqua/reference/strategy.md),
-[`strategy_logistf()`](https://wolski.github.io/prolfqua/reference/strategy.md)
-create instances.
-[`strategy_limma()`](https://wolski.github.io/prolfqua/reference/strategy_limma.md)
-returns a plain list (formula, trend, robust, weights) consumed by
-[`build_model_limma()`](https://wolski.github.io/prolfqua/reference/build_model_limma.md).
-
-**Config immutability**: AnalysisConfiguration is always deep-cloned
-when passed to new LFQData instances. Never modify config in-place on an
-existing LFQData.
-
-### R6 Classes (22 classes across R/)
-
-| Category            | Classes                                                                                                                                              | Files                                                   |
-|---------------------|------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------|
-| Core data           | `LFQData`, `AnalysisConfiguration`                                                                                                                   | LFQData.R, AnalysisConfiguration.R                      |
-| Decorators          | `LFQDataTransformer`, `LFQDataAggregator`, `LFQDataStats`, `LFQDataPlotter`, `LFQDataSummariser`, `LFQDataImp`                                       | LFQData\*.R                                             |
-| Model interfaces    | `ModelInterface`, `Model`, `ModelFirth`, `ModelLimma`                                                                                                | Model\*.R, ContrastsLimma.R                             |
-| Contrast interfaces | `ContrastsInterface`, `Contrasts`, `ContrastsModerated`, `ContrastsLimma`, `ContrastsROPECA`, `ContrastsMissing`, `ContrastsFirth`, `ContrastsTable` | Contrasts\*.R, ContrastFirth.R, ContrastsSimpleImpute.R |
-| Visualization       | `ContrastsPlotter`                                                                                                                                   | ContrastsPlotter.R                                      |
-| Utilities           | `MissingHelpers`                                                                                                                                     | tidyMS_missingness_imputation.R                         |
-
-### AnalysisConfiguration
-
-Flat R6 class that maps column roles in the data: - **hierarchy**:
-ordered measurement levels (protein_Id → peptide_Id → precursor_Id →
-fragment_Id). `hierarchy_depth` controls which level is modelled. -
-**factors**: explanatory variables (group, treatment). `factor_depth`
-controls interaction depth. - **work_intensity**: response column. Uses
-a stack (`set_response()` / `pop_response()` / `get_response()`) for
-working with multiple intensity columns. - **file_name**: sample
-identifier column.
-
-Concrete config factories (e.g. `create_config_Skyline()`,
-`create_config_Spectronaut_Peptide()`) were in
-`tidyMS_R6_ConcreteConfigurations.R` (now removed —
-`create_config_MQ_peptide()` was dead code). Remaining factories are in
-downstream packages.
-
-### Key Functions (not in classes)
-
-- `build_contrast_analysis(lfqdata, modelstr, contrasts, method)` — main
-  entry point, returns a Facade (in build_contrast_analysis.R)
-- `setup_analysis(data, config)` — prepare data for analysis (in
-  tidyMS_data_setup.R)
-- `build_model(data, strategy, subject_Id)` — fit per-protein models (in
-  tidyMS_build_model.R)
-- `build_model_impute(lfqdata, strategy)` — fit with LOD imputation +
-  borrowed covariance for missing groups (in tidyMS_build_model.R)
-- `build_model_limma(lfqdata, strategy)` — fit limma matrix model (in
-  ContrastsLimma.R)
-- `StrategyLM`, `StrategyRLM`, `StrategyLmer` R6 classes +
-  `strategy_lm/rlm/lmer()` wrappers (tidyMS_R6_Modelling.R);
-  `StrategyLogistf` +
-  [`strategy_logistf()`](https://wolski.github.io/prolfqua/reference/strategy.md)
-  (logistf.R)
-- [`strategy_limma()`](https://wolski.github.io/prolfqua/reference/strategy_limma.md)
-  — limma matrix model strategy (in ContrastsLimma.R)
-- [`sim_lfq_data_peptide_config()`](https://wolski.github.io/prolfqua/reference/sim_lfq_data_peptide_config.md)
-  — simulate test data (in simulate_LFQ_data.R)
-
-### File Naming Convention
-
-- `R/LFQData*.R` — Core data container and its decorator classes
-- `R/Model*.R`, `R/Contrasts*.R` — Modelling and hypothesis testing
-- `R/AnalysisConfiguration.R` — Configuration (column role mapping +
-  serialization)
-- `R/tidyMS_data_setup.R` — `setup_analysis`, `complete_cases`,
-  `sample_subset`
-- `R/tidyMS_summarize_hierarchy.R` — `table_factors`,
-  `hierarchy_counts`, etc.
-- `R/tidyMS_R6_Modelling.R` — Strategy R6 classes (`StrategyLM`,
-  `StrategyRLM`, `StrategyLmer`)
-- `R/tidyMS_build_model.R` — `build_model`, `model_analyse`, imputation
-  internals
-- `R/tidyMS_contrasts.R` — `linfct_*` family, `compute_contrast`,
-  `contrasts_linfct`, `pivot_model_contrasts_to_wide`
-- `R/tidyMS_moderation.R` — `moderated_p_limma*`, `adjust_p_values`,
-  ROPECA, Fisher
-- `R/tidyMS_*.R` — Other utility functions (plotting, stats,
-  aggregation, missingness)
-- `R/utilities.R` — Shared helpers (`make_interaction_column`,
-  `.error_handler`)
-
-### Vectorized mode
-
-`options(prolfqua.vectorize = TRUE)` activates vectorized
-implementations of `compute_contrast` and `linfct_matrix_contrasts`
-(matrix multiplication instead of per-row loops). Affects all Wald test
-facades (lm, rlm, firth, firth_nested, lmer_nested) and limma’s linfct
-path. Results are numerically identical. Default is `FALSE`.
-
-## Testing
-
-- **When fixing a bug, first add a test that reproduces it**, then fix.
-  This ensures regressions are caught.
-
-11 test files in `tests/testthat/`: - `test-LFQData.R` — Core data
-container and decorators - `test-Model.R` — Model fitting and
-coefficient extraction - `test-Contrasts.R` — Contrast computation (Wald
-test path) - `test-ContrastsFacades.R` — All facade classes and
-[`build_contrast_analysis()`](https://wolski.github.io/prolfqua/reference/build_contrast_analysis.md) -
-`test-ContrastsLimma.R` — Limma backend (ModelLimma, ContrastsLimma,
-merge, 2-factor) - `test-ContrastsModeratedDEqMS.R` — DEqMS moderation
-and facade - `test-ContrastsPlotter.R` — Contrast visualization -
-`test-ImputeModel.R` — LOD imputation with borrowed covariance -
-`test-plotting_functions.R` — Low-level plots -
-`test-tidyconfig_functions.R` — Configuration and utilities -
-`test-vectorize-contrasts.R` — Side-by-side original vs vectorized
-contrast functions
-
-## Cross-Package Context
-
-prolfqua is part of the prolfqua ecosystem (see `../CLAUDE.md`).
-Downstream packages depend on its R6 classes and exported API: -
-**prolfquapp** — CLI wrapper for core facility workflows -
-**prophosqua** — Phosphoproteomics analysis - **prolfquabenchmark** —
-Benchmarking vignettes
-
-Renaming R6 methods, changing exported function signatures, or modifying
-AnalysisConfiguration fields can silently break these packages.
+@AGENTS.md

CLAUDE.md

@@ -396,10 +396,11 @@ myProteinIDS <- c("sp|P0AC33|FUMA_ECOLI",  "sp|P28635|METQ_ECOLI",  "sp|Q14C86|G
 dplyr::filter(contrdf, protein_Id %in% myProteinIDS)
 ```
 
-    ## # A tibble: 0 × 13
-    ## # ℹ 13 variables: modelName <chr>, protein_Id <chr>, contrast <chr>,
-    ## #   diff <dbl>, std.error <dbl>, avgAbd <dbl>, statistic <dbl>, df <dbl>,
-    ## #   p.value <dbl>, conf.low <dbl>, conf.high <dbl>, sigma <dbl>, FDR <dbl>
+    ## # A tibble: 0 × 14
+    ## # ℹ 14 variables: modelName <chr>, estimate_type <chr>, protein_Id <chr>,
+    ## #   contrast <chr>, diff <dbl>, std.error <dbl>, avgAbd <dbl>, statistic <dbl>,
+    ## #   df <dbl>, p.value <dbl>, conf.low <dbl>, conf.high <dbl>, sigma <dbl>,
+    ## #   FDR <dbl>
 
 ## Contrasts with missing value imputation
 
@@ -414,16 +415,17 @@ mC <- prolfqua::ContrastsMissing$new(lfqdata = transformed, contrasts = contr_sp
 colnames(mC$get_contrasts())
 ```
 
-    ##  [1] "modelName"                "protein_Id"              
-    ##  [3] "meanAbundanceImp_group_1" "meanAbundanceImp_group_2"
-    ##  [5] "diff"                     "group_1_name"            
-    ##  [7] "group_2_name"             "contrast"                
-    ##  [9] "avgAbd"                   "indic"                   
-    ## [11] "nrMeasured_group_1"       "nrMeasured_group_2"      
-    ## [13] "df"                       "sigma"                   
-    ## [15] "std.error"                "statistic"               
-    ## [17] "p.value"                  "conf.low"                
-    ## [19] "conf.high"                "FDR"
+    ##  [1] "modelName"                "estimate_type"           
+    ##  [3] "protein_Id"               "meanAbundanceImp_group_1"
+    ##  [5] "meanAbundanceImp_group_2" "diff"                    
+    ##  [7] "group_1_name"             "group_2_name"            
+    ##  [9] "contrast"                 "avgAbd"                  
+    ## [11] "indic"                    "nrMeasured_group_1"      
+    ## [13] "nrMeasured_group_2"       "df"                      
+    ## [15] "sigma"                    "std.error"               
+    ## [17] "statistic"                "p.value"                 
+    ## [19] "conf.low"                 "conf.high"               
+    ## [21] "FDR"
 
 Finally we are merging the results and give priority to the results
 where we do not have missing values in one group.