Feature: pp.var_consistency() and pp.filter_by_var_consistency()

HISTORY.md

@@ -28,12 +28,21 @@ and this project adheres to
   that overlap in the protein sequence, keeping the most abundant
   member of each group. Reimplements CCprofiler's
   `summarizeAlternativePeptideSequences(topN = 1)`
-- **Tools** (`pr.tl`): `infer_proteoform_groups()` wraps the
+- `var_consistency()`: per-variable Pearson correlation of the
+  profiles across `group_by` groups between every pair of batches
+  (`batch_key`, e.g. replicates), summarized as median, min and mean
+  in `.var`; per-pair values in `.varm`
+- `filter_by_var_consistency()`: keeps variables whose consistency
+  score (e.g. `replicate_corr_min`) reaches a threshold.
+
+**Tools** (`pr.tl`)
+
+- `infer_proteoform_groups()` wraps the
   currently recommended proteoform inference approach behind one call.
   `method` defaults to `"copf"` (correlations, dendrograms, clusters,
   scores), but the default may change in future releases; pass it
   explicitly for reproducibility
-- **Tools** (`pr.tl`): `peptide_proximity()` tests whether the peptides
+- `peptide_proximity()` tests whether the peptides
   of a proteoform cluster sit closer together in the protein sequence
   than a random grouping of the same size. A reimplementation of
   CCprofiler's `evaluateProteoformLocation`, which is the

docs/sphinx/source/api/pp.rst

@@ -21,6 +21,7 @@ filtering, normalization, and imputation of proteomics data.
    proteopy.pp.filter_samples_by_category_count
    proteopy.pp.remove_zero_variance_vars
    proteopy.pp.remove_contaminants
+   proteopy.pp.filter_by_var_consistency
 
 .. rubric:: Normalization
 
@@ -58,3 +59,4 @@ filtering, normalization, and imputation of proteomics data.
    :nosignatures:
 
    proteopy.pp.calculate_cv
+   proteopy.pp.var_consistency

proteopy/pp/__init__.py

@@ -31,3 +31,8 @@
 )
 
 from .stats import calculate_cv
+
+from .var_consistency import (
+    var_consistency,
+    filter_by_var_consistency,
+)