fix(diann): Add q-value filtering to DIANN big clean function

[tonywu1999]

Summary by CodeRabbit

• New Features

  • Added three new optional quality value cutoff parameters to the DIANN converter function (global_qvalue_cutoff, qvalue_cutoff, pg_qvalue_cutoff), each with a default value of 0.01 for backward compatibility.

• Documentation

  • Updated documentation to describe the new quality filtering parameters and their defaults.

[coderabbitai]

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📝 Walkthrough

Walkthrough

This pull request extends the DIANN converter pipeline to support three new q-value cutoff parameters (global_qvalue_cutoff, qvalue_cutoff, pg_qvalue_cutoff), propagating them through bigDIANNtoMSstatsFormat to downstream functions. Function signatures are updated and parameters are explicitly forwarded, with documentation added.

Changes

Cohort / File(s)	Summary
DIANN Converter Pipeline R/clean_DIANN.R, R/converters.R	Added three new q-value cutoff parameters to bigDIANNtoMSstatsFormat and forwarded them through reduceBigDIANN to cleanDIANNChunk. Replaced positional arguments with explicit named parameters in MSstatsClean call.
Function Documentation man/bigDIANNtoMSstatsFormat.Rd	Updated documentation to describe the three new cutoff parameters (global_qvalue_cutoff, qvalue_cutoff, pg_qvalue_cutoff), their defaults, and expanded quantificationColumn description for DIANN 2.x behavior.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

Possibly related PRs

• Implementing and Testing DIANN converter for MSstatsBIG. #9: Modifies the same DIANN converter files to add and forward identical q-value cutoff parameters through the reduction pipeline.
• refactor(diann): Integrate MSstatsImport and MSstatsClean functions into DIANN converter #10: Related changes that integrate MSstatsImport/MSstatsClean and switch to explicit named arguments in the same DIANN cleaning pipeline.

Suggested reviewers

• Rudhik1904

Poem

🐰 Three cutoffs hop along the way,
Through converter tunnels, they gently sway,
From big to small, they journey far,
Each parameter a guiding star,
The pipeline flows, the workflow's tight! ✨

🚥 Pre-merge checks | ✅ 3

✅ Passed checks (3 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The PR title accurately describes the main change: adding q-value filtering parameters (global_qvalue_cutoff, qvalue_cutoff, pg_qvalue_cutoff) to the DIANN conversion function, which is clearly evident across all modified files.
Docstring Coverage	✅ Passed	No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.

_{✏️ Tip: You can configure your own custom pre-merge checks in the settings.}

✨ Finishing touches

🧪 Generate unit tests (beta)

• Create PR with unit tests
• Post copyable unit tests in a comment
• Commit unit tests in branch fix-diann

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[coderabbitai]

Actionable comments posted: 1

🤖 Fix all issues with AI agents

In `@R/converters.R`:
- Around line 186-190: The intermediate output path is built by concatenating
strings which breaks when output_file_name contains directories; update the
calls around reduceBigDIANN to construct the path with
file.path(dirname(output_file_name), paste0("reduce_output_",
basename(output_file_name))) (and the analogous construct used at the other
occurrence on line ~194) so the intermediate file is created in the same
directory as output_file_name; change both references to use dirname(...) and
basename(...) wrapped with file.path(...) instead of paste0("reduce_output_",
output_file_name).

🧹 Nitpick comments (2)

R/clean_DIANN.R (1)

25-26: Consider using named arguments in the closure as well.

The cutoff parameters are passed positionally to cleanDIANNChunk. For consistency with the named-argument style adopted at the MSstatsClean call site (line 53-60), consider using named arguments here too to guard against future parameter reordering.

♻️ Suggested change

-  diann_chunk <- function(x, pos) cleanDIANNChunk(x, output_path, MBR, quantificationColumn, pos,
-                     global_qvalue_cutoff, qvalue_cutoff, pg_qvalue_cutoff)
+  diann_chunk <- function(x, pos) cleanDIANNChunk(
+    input = x, output_path = output_path, MBR = MBR,
+    quantificationColumn = quantificationColumn, pos = pos,
+    global_qvalue_cutoff = global_qvalue_cutoff,
+    qvalue_cutoff = qvalue_cutoff,
+    pg_qvalue_cutoff = pg_qvalue_cutoff
+  )

R/converters.R (1)

189-190: Consider using named arguments for the reduceBigDIANN call.

The q-value cutoff parameters are passed positionally. Named arguments would make the call more readable and resilient to parameter reordering.

♻️ Suggested change

   reduceBigDIANN(input_file, 
-                 paste0("reduce_output_", output_file_name),
-                 MBR,
-                 quantificationColumn,
-                 global_qvalue_cutoff, qvalue_cutoff, pg_qvalue_cutoff)
+                 intermediate_file,
+                 MBR = MBR,
+                 quantificationColumn = quantificationColumn,
+                 global_qvalue_cutoff = global_qvalue_cutoff,
+                 qvalue_cutoff = qvalue_cutoff,
+                 pg_qvalue_cutoff = pg_qvalue_cutoff)

[coderabbitai]

⚠️ Potential issue | 🟠 Major

Use file.path() for intermediate file path construction.

paste0("reduce_output_", output_file_name) will produce a broken path when output_file_name contains directory components (e.g., "results/output.csv" → "reduce_output_results/output.csv"). Both references (lines 187 and 194) should be fixed.

🐛 Proposed fix

-  reduceBigDIANN(input_file, 
-                 paste0("reduce_output_", output_file_name),
+  intermediate_file <- file.path(dirname(output_file_name),
+                                 paste0("reduce_output_", basename(output_file_name)))
+  reduceBigDIANN(input_file, 
+                 intermediate_file,
                  MBR,
                  quantificationColumn,
                  global_qvalue_cutoff, qvalue_cutoff, pg_qvalue_cutoff)
  
   # Preprocess the cleaned data (feature selection, etc.)
   msstats_data <- MSstatsPreprocessBig(
-    paste0("reduce_output_", output_file_name),
+    intermediate_file,
     output_file_name, backend, max_feature_count,

Based on learnings: "In R converters across the MSstatsBig package, replace direct concatenation with file names by constructing intermediate file paths using file.path(dirname(output_file_name), paste0("prefix_", basename(output_file_name))). This ensures the intermediate file is created in the correct directory even when output_file_name contains directories."

📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change

	reduceBigDIANN(input_file,
	paste0("reduce_output_", output_file_name),
	MBR,
	quantificationColumn)
	quantificationColumn,
	global_qvalue_cutoff, qvalue_cutoff, pg_qvalue_cutoff)
	intermediate_file <- file.path(dirname(output_file_name),
	paste0("reduce_output_", basename(output_file_name)))
	reduceBigDIANN(input_file,
	intermediate_file,
	MBR,
	quantificationColumn,
	global_qvalue_cutoff, qvalue_cutoff, pg_qvalue_cutoff)

🤖 Prompt for AI Agents

In `@R/converters.R` around lines 186 - 190, The intermediate output path is built
by concatenating strings which breaks when output_file_name contains
directories; update the calls around reduceBigDIANN to construct the path with
file.path(dirname(output_file_name), paste0("reduce_output_",
basename(output_file_name))) (and the analogous construct used at the other
occurrence on line ~194) so the intermediate file is created in the same
directory as output_file_name; change both references to use dirname(...) and
basename(...) wrapped with file.path(...) instead of paste0("reduce_output_",
output_file_name).