fix: handle NA values correctly in meta compare pathways

[fxwiegand]

Summary by CodeRabbit

• Bug Fixes

  • Improved handling of missing values when reading CSV files.

• Documentation

  • Clarified the interpretation of null values in data processing.

[coderabbitai]

Walkthrough

The pull request introduces a minor modification to the compare_pathways.py script, specifically in how CSV files are read. By adding the null_values="NA" parameter to pl.read_csv function calls, the script now explicitly handles missing values marked as "NA" during data loading. This change ensures more consistent and predictable data processing when reading input CSV files, without altering the core logic of the data analysis workflow.

Changes

File	Change Summary
workflow/scripts/compare_pathways.py	Added null_values="NA" parameter to pl.read_csv() function calls for handling missing values

Poem

🐰 In data's realm, where numbers dance and sway,
A rabbit's touch makes "NA" find its way
Through CSV paths, with precision so neat
Missing values now parse, clean and sweet
Code hops along, no value left behind! 📊

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📜 Recent review details

Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between b6cfc3c and c93e76b.

📒 Files selected for processing (1)

• workflow/scripts/compare_pathways.py (1 hunks)

🧰 Additional context used

🪛 Ruff (0.8.2)

workflow/scripts/compare_pathways.py

8-8: Undefined name snakemake

(F821)

---

9-9: Undefined name snakemake

(F821)

🔇 Additional comments (1)

workflow/scripts/compare_pathways.py (1)

8-9: LGTM! Verify NA value handling with test data.

The addition of null_values="NA" parameter to both pl.read_csv calls is a good fix for handling missing values consistently. This aligns with the PR objective.

Let's verify the NA value handling in the input files:

✅ Verification successful

NA value handling implementation is correct and consistent

The implementation is verified to be correct based on the following evidence:

• The script processes pathway analysis results where NA values are expected in the data
• The null_values="NA" parameter is correctly applied to both input files consistently
• The subsequent data processing (joins, filtering, calculations) properly handles the filled null values through:
  • Explicit type casting of numeric columns
  • Using fill_null(0) after the join operation
  • Proper null handling in the visualization code

🏁 Scripts executed

The following scripts were executed for the analysis:

Script:

#!/bin/bash
# Description: Check for NA values in input files to ensure they're being handled correctly

# Look for NA values in TSV files that might be inputs to this script
rg -l "\tNA\t" --type tsv

# Check the format of any found files to understand the NA value context
for f in $(rg -l "\tNA\t" --type tsv); do
  echo "=== First few lines of $f ==="
  head -n 5 "$f"
done

Length of output: 112

---

Script:

#!/bin/bash
# Let's try a different approach to find TSV/CSV files and check their content
# First, find potential input files
fd -e tsv -e csv

# Then let's examine content of any found files for NA patterns
for f in $(fd -e tsv -e csv); do
  echo "=== File: $f ==="
  head -n 5 "$f"
done

# Also check the Snakemake workflow to understand expected input files
fd Snakefile
fd -e smk
cat workflow/Snakefile 2>/dev/null || true

Length of output: 1199

---

Script:

#!/bin/bash
# Let's check the meta_comparisons.smk file since our script is part of meta comparisons
cat workflow/rules/meta_comparisons.smk

# Also let's check for any test data or example files
fd -e tsv -e csv -p 'test|example'

# And examine the script itself to understand the full context
cat workflow/scripts/compare_pathways.py

Length of output: 7370

🧰 Tools

🪛 Ruff (0.8.2)

8-8: Undefined name snakemake

(F821)

---

9-9: Undefined name snakemake

(F821)

---

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