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Merged
ammarcsj merged 7 commits into from
Jul 31, 2026
Merged

Add residual design-effect correction and aggregation calibration parameters #146

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330ca98
Add explicit median_normalization option
ammarcsj Jul 31, 2026
cbe7b95
Make per-protein peptide cap configurable
ammarcsj Jul 31, 2026
5069a90
Extend residual decorrelation cutoff grid and expose it as a parameter
ammarcsj Jul 31, 2026
b94b133
Cap samples used for the sibling-correlation estimate
ammarcsj Jul 31, 2026
a2c6d38
Aggregate via median when pruning collapses a parent to one child
ammarcsj Jul 31, 2026
c7bd007
Add residual design-effect correction at the between-peptide level
ammarcsj Jul 31, 2026
632e92b
Save residual decorrelation diagnostic plots when runtime_plots is set
ammarcsj Jul 31, 2026
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3 changes: 2 additions & 1 deletion alphaquant/diffquant/condpair_analysis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -67,7 +67,8 @@ def analyze_condpair(*,runconfig, condpair):
return

df_c1_normed, df_c2_normed = aqnorm.normalize_if_specified(df_c1 = df_c1, df_c2 = df_c2, c1_samples = c1_samples, c2_samples = c2_samples, normalize_within_conds = runconfig.normalize, normalize_between_conds = runconfig.normalize,
runtime_plots = runconfig.runtime_plots, protein_subset_for_normalization_file=runconfig.protein_subset_for_normalization_file, pep2prot = pep2prot)#, "./test_data/normed_intensities.tsv")
runtime_plots = runconfig.runtime_plots, protein_subset_for_normalization_file=runconfig.protein_subset_for_normalization_file, pep2prot = pep2prot,
median_normalization = getattr(runconfig, 'median_normalization', False))#, "./test_data/normed_intensities.tsv")
Comment on lines 69 to +71

@mschwoer mschwoer Aug 4, 2026

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can you please simplify this by splitting into several statements?

@mschwoer mschwoer Aug 4, 2026

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nevermind, now I got it..
ping me once it is a good time to format the codebase ;-)


summarization_nodes = getattr(runconfig, 'summarization_nodes', [])
if summarization_nodes:
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18 changes: 9 additions & 9 deletions alphaquant/norm/normalization.py
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Expand Up @@ -257,7 +257,7 @@ def mode_normalization(x):
import numpy as np
from scipy import stats

def get_betweencond_shift(df_c1_normed, df_c2_normed, enfore_median = False):
def get_betweencond_shift(df_c1_normed, df_c2_normed, median_normalization = False):

both_idx = df_c1_normed.index.intersection(df_c2_normed.index)
df1 = df_c1_normed.loc[both_idx]
Expand All @@ -269,7 +269,7 @@ def get_betweencond_shift(df_c1_normed, df_c2_normed, enfore_median = False):

diff_fcs = df1[col1].to_numpy() - df2[col2].to_numpy()
median = np.nanmedian(diff_fcs)
if enfore_median:
if median_normalization:
return -median

if len(diff_fcs)<100:
Expand All @@ -288,7 +288,7 @@ def get_betweencond_shift(df_c1_normed, df_c2_normed, enfore_median = False):
# Cell
import pandas as pd

def normalize_if_specified(df_c1, df_c2, c1_samples, c2_samples, normalize_within_conds = True, normalize_between_conds = True, runtime_plots = True, protein_subset_for_normalization_file = None, pep2prot =None):
def normalize_if_specified(df_c1, df_c2, c1_samples, c2_samples, normalize_within_conds = True, normalize_between_conds = True, runtime_plots = True, protein_subset_for_normalization_file = None, pep2prot =None, median_normalization = False):

if normalize_within_conds:
df_c1 = normalize_within_cond(df_c=df_c1, samples_c= c1_samples)
Expand All @@ -299,15 +299,15 @@ def normalize_if_specified(df_c1, df_c2, c1_samples, c2_samples, normalize_withi
aq_plot_pairwise.plot_withincond_normalization(df_c1, df_c2)

if normalize_between_conds:
df_c1, df_c2 = get_normalized_dfs_between_conditions(df_c1, df_c2, protein_subset_for_normalization_file, pep2prot,runtime_plots = runtime_plots)
df_c1, df_c2 = get_normalized_dfs_between_conditions(df_c1, df_c2, protein_subset_for_normalization_file, pep2prot,runtime_plots = runtime_plots, median_normalization = median_normalization)
LOGGER.info("normalized between conditions")

return df_c1, df_c2



def get_normalized_dfs_between_conditions(df_c1, df_c2, protein_subset_for_normalization_file, pep2prot,runtime_plots):
shift_between_cond = prepare_tables_and_get_betweencond_shift(df_c1, df_c2, protein_subset_for_normalization_file, pep2prot)
def get_normalized_dfs_between_conditions(df_c1, df_c2, protein_subset_for_normalization_file, pep2prot,runtime_plots, median_normalization = False):
shift_between_cond = prepare_tables_and_get_betweencond_shift(df_c1, df_c2, protein_subset_for_normalization_file, pep2prot, median_normalization = median_normalization)

LOGGER.info(f"shift comparison by {shift_between_cond}")
df_c2 = df_c2-shift_between_cond
Expand All @@ -322,12 +322,12 @@ def normalize_within_cond(df_c, samples_c):
df_c_normed = pd.DataFrame(apply_sampleshifts(df_c.to_numpy().T, sample2shift).T, index = df_c.index, columns = samples_c)
return df_c_normed

def prepare_tables_and_get_betweencond_shift(df_c1, df_c2, protein_subset_for_normalization_file, pep2prot):
def prepare_tables_and_get_betweencond_shift(df_c1, df_c2, protein_subset_for_normalization_file, pep2prot, median_normalization = False):
specified_protein_subset = read_specified_protein_subset_if_given(protein_subset_for_normalization_file)
prepared1 = prepare_table_for_betweencond_shift(df_c1, specified_protein_subset, pep2prot)
prepared2 = prepare_table_for_betweencond_shift(df_c2, specified_protein_subset, pep2prot)
enforce_median = protein_subset_for_normalization_file is not None
return get_betweencond_shift(prepared1, prepared2, enforce_median)
median_normalization = median_normalization or (protein_subset_for_normalization_file is not None)
return get_betweencond_shift(prepared1, prepared2, median_normalization)

def read_specified_protein_subset_if_given(specified_protein_subset_file):
if specified_protein_subset_file is not None:
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2 changes: 2 additions & 0 deletions alphaquant/run_pipeline.py
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Expand Up @@ -77,6 +77,7 @@ def run_pipeline(input_file: str,
volcano_fcthresh: float = 0.5,
annotation_columns: Optional[List[str]] = None,
protein_subset_for_normalization_file: Optional[str] = None,
median_normalization: bool = False,
protnorm_peptides: bool = True,
peptides_to_exclude_file: Optional[str] = None,
reset_progress_folder: bool = False,
Expand Down Expand Up @@ -154,6 +155,7 @@ def run_pipeline(input_file: str,
volcano_fcthresh (float): Fold change threshold for volcano plot significance. Defaults to 0.5.
annotation_columns (list): Additional columns to include in output tables.
protein_subset_for_normalization_file (str): File specifying proteins to use for normalization.
median_normalization (bool): Take the median of the between-condition fold-change distribution as the shift, instead of choosing between its median and its mode. Passing protein_subset_for_normalization_file also implies this. Defaults to False.
protnorm_peptides (bool): Enable protein-level peptide normalization. Defaults to True.
peptides_to_exclude_file (str): File listing peptides to exclude (e.g., shared between species).
reset_progress_folder (bool): Clear and recreate the progress folder. Defaults to False.
Expand Down