🔬 Proteomics Quality Control: Beyond the Basics

## Peptide Identification Rate Analysis

#pl.read_csv??

'''
path ='/Volumes/dgh-lab/PROTEOMICS_DATA_DUMP/020_2025_DUN_DH/DIA-NN/020_2025_DUN_DH/'
fname = '020_2025_DUN_DH-report.parquet'
tmp = pl.read_parquet(os.path.join(path,fname)).to_pandas()
tmp['RT.Diff']=tmp['RT.Stop']-tmp['RT.Start']
tmp['RT.Bin']=tmp['RT'].astype(int)
rt_dataset = tmp[['Run','RT','RT.Start','RT.Stop','RT.Diff','RT.Bin','Q.Value','Ms1.Area','Precursor.Quantity']]
rt_dataset = rt_dataset.sort_values(['Run','RT'])
rt_dataset['Significant']=(rt_dataset['Q.Value']<0.01).astype(int)
rt_dataset.head()
rt_dataset['Significant'].value_counts()
rt_dataset[rt_dataset['Run']=='020_2025-DUN_DH-GB-2T1-A'].plot(kind='scatter',x='RT',y='RT.Diff')
# evantually track
RT vs Precursor.Quantity / Ms1.Area
RT vs RT.Diff
'''
print(1)
# working in progress

1

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prepare_data_MaxQuant

 prepare_data_MaxQuant (df)

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prepare_data_Spectronaut

 prepare_data_Spectronaut (df)

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prepare_data_DiaNN

 prepare_data_DiaNN (df)

*Process DIA-NN output data to prepare it for plotting retention time bin analysis.

This function takes DIA-NN report output and: 1. Creates retention time bins from the RT column 2. Groups data by Run and RT bin 3. Counts identified and non-identified peptides in each bin 4. Calculates the identification ratio*

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plot_proteomics_run

 plot_proteomics_run (df, run_name=None, figsize=(12, 6),
                      identified_color='blue', notidentified_color='red',
                      alpha=1.0, add_labels=True,
                      use_different_colors=False)

Plot RT_bin vs Identified and RT_bin vs NotIdentified for runs.

Example

path ='/Volumes/dgh-lab/PROTEOMICS_DATA_DUMP/020_2025_DUN_DH/DIA-NN/020_2025_DUN_DH/'
fname = '020_2025_DUN_DH-report.parquet'
tmp = pl.read_parquet(os.path.join(path,fname))
count_data= prepare_data_DiaNN(tmp)
count_data=count_data.to_pandas()
count_data.head()
plot_proteomics_run(count_data)