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targeted reanalysis dataframe system function and tests implemented
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,3 +1,4 @@ | ||
| from .identifier import Identifier | ||
| from .idpickerFunctions import identify_high_confidence_proteins | ||
| from .scoringFunctions import calculate_fdr_rates_of_decoy_array | ||
| from .targetedReanalysisFunctions import create_mass_spec_input_dataframes_for_targeted_reanalysis_of_identified_peptides |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,106 @@ | ||
| from csodiaq.utils import format_protein_list_to_string | ||
| import pandas as pd | ||
| import numpy as np | ||
|
|
||
| def calculate_mz_of_heavy_version_of_peptide(peptide, lightMz, z): | ||
| lightAndHeavyLysKMassDiff = 8.014199 | ||
| lightAndHeavyArgRMassDiff = 10.00827 | ||
|
|
||
| numLysK = peptide.count('K') | ||
| numArgR = peptide.count('R') | ||
|
|
||
| return (lightMz + | ||
| (numLysK * lightAndHeavyLysKMassDiff) / z + | ||
| (numArgR * lightAndHeavyArgRMassDiff) / z) | ||
|
|
||
| def filter_to_only_keep_peptides_with_possibly_heavy_K_or_R_terminal_residue(fdrDf): | ||
| return fdrDf[ | ||
| fdrDf["peptide"].str.endswith("R") | fdrDf["peptide"].str.endswith("K") | ||
| ].reset_index(drop=True) | ||
|
|
||
| def filter_to_only_keep_top_peptides_unique_to_protein(fdrDf, maxPeptidesPerProtein): | ||
| fdrDf = ( | ||
| fdrDf[fdrDf["uniquePeptide"] == 1] | ||
| .sort_values("ionCount", ascending=False) | ||
| .reset_index(drop=True) | ||
| ) | ||
| return fdrDf.groupby(["leadingProtein"]).head(maxPeptidesPerProtein).reset_index(drop=True) | ||
|
|
||
| def calculate_mz_of_heavy_isotope_of_each_peptide(fdrDf): | ||
| return fdrDf.apply(lambda x: calculate_mz_of_heavy_version_of_peptide(x["peptide"],x["MzLIB"],x["zLIB"]), axis=1) | ||
|
|
||
| def make_bin_assignments_for_mz_values(mzValues, binWidth=0.75): | ||
| bins = np.arange(int(min(mzValues)) - 1, int(max(mzValues) + binWidth*3)+1, binWidth*2) | ||
| values = np.digitize(mzValues, bins) | ||
| mzBinValues = bins[values] | ||
| mzBinValues -= binWidth | ||
| return mzBinValues | ||
|
|
||
| def filter_out_peptides_based_on_user_settings(fdrDf, isIncludeHeavy, maximumPeptidesPerProtein): | ||
| if isIncludeHeavy: | ||
| fdrDf = filter_to_only_keep_peptides_with_possibly_heavy_K_or_R_terminal_residue(fdrDf) | ||
| if maximumPeptidesPerProtein: | ||
| fdrDf = filter_to_only_keep_top_peptides_unique_to_protein(fdrDf, maximumPeptidesPerProtein) | ||
| return fdrDf | ||
|
|
||
| def calculate_binning_information_by_compensation_voltage(fdrDf, isIncludeHeavy): | ||
| dfs = [ | ||
| organize_for_targeted_reanalysis_of_identified_peptides(df, isIncludeHeavy) for _, df in fdrDf.groupby(["CompensationVoltage"]) | ||
| ] | ||
| return pd.concat(dfs) | ||
|
|
||
| def organize_for_targeted_reanalysis_of_identified_peptides(fdrDf, isIncludeHeavy): | ||
| fdrDf["lightMzBin"] = make_bin_assignments_for_mz_values(fdrDf["MzLIB"]) | ||
| if isIncludeHeavy: | ||
| fdrDf["heavyMz"] = calculate_mz_of_heavy_isotope_of_each_peptide(fdrDf) | ||
| fdrDf["heavyMzBin"] = make_bin_assignments_for_mz_values(fdrDf["heavyMz"]) | ||
| return fdrDf | ||
|
|
||
| def make_targeted_reanalysis_line(peptide, mz, id): | ||
| formula = "" | ||
| adduct = "(no adduct)" | ||
| genericCharge = 2 | ||
| return [peptide, formula, adduct, mz, genericCharge, id+1] | ||
|
|
||
| def consolidate_peptides_by_bin_values(df, isIncludeHeavy): | ||
| bins = ["lightMzBin"] | ||
| if isIncludeHeavy: | ||
| bins.append("heavyMzBin") | ||
| return df.groupby(bins).apply(lambda x: format_protein_list_to_string(x["peptide"])).reset_index(name="peptide").sort_values(bins) | ||
|
|
||
| def organize_binned_data_for_targeted_reanalysis(condensedDf, isIncludeHeavy): | ||
| data = [ | ||
| make_targeted_reanalysis_line(condensedDf.loc[i]["peptide"], condensedDf.loc[i]["lightMzBin"], i) for i in range(len(condensedDf.index)) | ||
| ] | ||
| if isIncludeHeavy: | ||
| data.extend( | ||
| [ | ||
| make_targeted_reanalysis_line(condensedDf.loc[i]["peptide"], condensedDf.loc[i]["heavyMzBin"], i) for i | ||
| in range(len(condensedDf.index)) | ||
|
|
||
| ] | ||
| ) | ||
| return data | ||
|
|
||
| def create_targeted_reanalysis_dataframe(df, isIncludeHeavy): | ||
| consolidatedDf = consolidate_peptides_by_bin_values(df, isIncludeHeavy) | ||
| targetedReanalysisData = organize_binned_data_for_targeted_reanalysis(consolidatedDf, isIncludeHeavy) | ||
| return pd.DataFrame(targetedReanalysisData, columns=["Compound","Formula","Adduct","m.z","z","MSXID"]).sort_values(["MSXID","m.z"]).reset_index(drop=True) | ||
|
|
||
| def make_cv_header(cvValue): | ||
| if cvValue: | ||
| return f'CV_{str(abs(int(cvValue)))}' | ||
| else: | ||
| return 'noCV' | ||
|
|
||
| def create_targeted_reanalysis_dataframe_by_compensation_voltage(fdrDf, isIncludeHeavy): | ||
| return { | ||
| make_cv_header(cv[0]): create_targeted_reanalysis_dataframe(df, isIncludeHeavy) for cv, df in fdrDf.groupby(["CompensationVoltage"]) | ||
| } | ||
|
|
||
| def create_mass_spec_input_dataframes_for_targeted_reanalysis_of_identified_peptides(fdrDf, isIncludeHeavy=False, maximumPeptidesPerProtein=0): | ||
| fdrDf = filter_out_peptides_based_on_user_settings(fdrDf, isIncludeHeavy, maximumPeptidesPerProtein) | ||
| fdrDf = organize_for_targeted_reanalysis_of_identified_peptides(fdrDf, isIncludeHeavy) | ||
| outputDfDict = create_targeted_reanalysis_dataframe_by_compensation_voltage(fdrDf, isIncludeHeavy) | ||
| outputDfDict["fullDf"] = fdrDf | ||
| return outputDfDict |
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