Quality Control

Autoprot Analysis Functions.

@author: Wignand, Julian, Johannes

@documentation: Julian

autoprot.analysis.qc.SILAC_labeling_efficiency(df_evidence: DataFrame, label: list[Literal['L', 'M', 'H']] | None = None, r_to_p_conversion: Literal['Pro6', 'Pro10'] | None = None)

Parameters:

• df_evidence (MaxQuant evidence table) – DESCRIPTION. clean reverse and contaminant first autoprot.preprocessing.cleaning()

• label (list, optional) – The labels used in the experiment. The default is [“L”, “M”, “H”].

• r_to_p_conversion (variable modifications ["Pro6", "Pro10"] set in MaxQuant.)

Return type:

Fig, table for SILAC label incorporation

autoprot.analysis.qc.dimethyl_labeling_efficieny(df_evidence, label, save=True) → DataFrame

This function calculates the labeling efficiency of dimethyl labeled samples using a MaxQuant evidence table.

Parameters:

• df_evidence (pd.DataFrame) – MaxQuant evidence table as pandas.Dataframe

• label (str) – The label type used in the experiment (‘L’, ‘M’, ‘H’)

• save (bool) – If True table and fig will be saved in active filepath.

Returns:

Results from the analysis

Return type:

pd.DataFrame

autoprot.analysis.qc.enrichment_specifity(df_evidence, mod_col='Phospho (STY)', save=True)

Parameters:

• df_evidence (cleaned pandas DataFrame from Maxquant analysis)

• mod_col (str,) – Give type of enrichment for analysis. The default is ‘Phospho (STY)’. (‘Met–> Phosphonate’, ‘Cys–> Phosphonate’, ‘Met –> Biotin’)

• save (bool,) – While True table and fig will be saved in active filepath.

Return type:

None.

autoprot.analysis.qc.miss_analysis(df, cols, n=None, sort='ascending', text=True, vis=True, extra_vis=False, save_dir=None)

Print missing statistics for a dataframe.

Parameters:

• df (pd.Dataframe) – Input dataframe with missing values.

• cols (list of str) – Columns to perform missing values analysis on.

• n (int, optional) – How many rows of the dataframe to displayed. The default is None (uses all rows).

• sort (str, optional) – “ascending” or “descending”. The default is ‘ascending’.

• text (bool, optional) – Whether to output text summaryMap. The default is True.

• vis (bool, optional) – whether to return barplot showing missingness. The default is True.

• extra_vis (bool, optional) – Whether to return matrix plot showing missingness. The default is False.

• save_dir (str, optional) – Path to folder where the results should be saved. The default is None.

Raises:

ValueError – If n_entries is incorrectly specified.

Return type:

None.

Examples

miss_analysis gives a quick overview of the missingness of the provided dataframe. You can provide the complete or prefiltered dataframe as input. Providing n_entries allows you to specify how many of the entries of the dataframe (sorted by missingness) are displayed (i.e. only display the n_entries columns with most (or least) missing values) With the sort argument you can define whether the dataframe is sorted by least to most missing values or vice versa (using “descending” and “ascending”, respectively). The vis and extra_vis arguments can be used to toggle the graphical output. In case of large data (a lot of columns) those might be better turned off.

phos = pd.read_csv("../data/Phospho (STY)Sites_minimal.zip", sep="\t", low_memory=False)
phos = pp.cleaning(phos, file = "Phospho (STY)")
phosRatio = phos.filter(regex="^Ratio .\/.( | normalized )R.___").columns
phos = pp.log(phos, phosRatio, base=2)
phos = pp.filter_loc_prob(phos, thresh=.75)
phosRatio = phos.filter(regex="log2_Ratio .\/.( | normalized )R.___").columns
phos = pp.remove_non_quant(phos, phosRatio)

phosRatio = phos.filter(regex="log2_Ratio .\/. normalized R.___").columns
phos_expanded = pp.expand_site_table(phos, phosRatio)

twitchVsmild = ['log2_Ratio H/M normalized R1','log2_Ratio M/L normalized R2','log2_Ratio H/M normalized R3',
                'log2_Ratio H/L normalized R4','log2_Ratio H/M normalized R5','log2_Ratio M/L normalized R6']
twitchVsctrl = ["log2_Ratio H/L normalized R1","log2_Ratio H/M normalized R2","log2_Ratio H/L normalized R3",
                "log2_Ratio M/L normalized R4", "log2_Ratio H/L normalized R5","log2_Ratio H/M normalized R6"]
mildVsctrl = ["log2_Ratio M/L normalized R1","log2_Ratio H/L normalized R2","log2_Ratio M/L normalized R3",
              "log2_Ratio H/M normalized R4","log2_Ratio M/L normalized R5","log2_Ratio H/L normalized R6"]
phos = ana.ttest(df=phos_expanded, reps=twitchVsmild, cond="TvM")
phos = ana.ttest(df=phos_expanded, reps=twitchVsctrl, cond="TvC")

ana.miss_analysis(phos_expanded,
                 twitchVsctrl+twitchVsmild+mildVsctrl,
                 text=False,
                 sort="descending",
                 extra_vis = True)

(Source code)

(png, hires.png, pdf)

(png, hires.png, pdf)

autoprot.analysis.qc.missed_cleavages(df_evidence, enzyme='Trypsin/P', save=True)

Parameters:

• df_evidence (cleaned pandas DataFrame from Maxquant analysis)

• enzyme (str,) – Give any chosen Protease from MQ. The default is “Trypsin/P”.

• save (bool,) – While True table and fig will be saved in active filepath.

Return type:

None.

autoprot.analysis.qc.tmt6plex_labeling_efficiency(evidence_under, evidence_sty_over, evidence_h_over)

Calculate TMT6plex labeling efficiency from 3 dedicated MaxQuant searches as described in Zecha et al. 2019. TMT6plex channels should be named in MQ experiments. @author: Johannes Zimmermann

Parameters:

• evidence_under (evidence.txt as pd.DataFrame from under-labeling search,) – label-free search with TMT as variable modification on peptide n-term and lysine

• evidence_sty_over (evidence.txt as pd.DataFrame from over-labeling search,) – MS2-TMT experiment with TMT as variable modification on serine, threonine, tyrosine

• evidence_h_over (evidence.txt as pd.DataFrame from over-labeling search,) – MS2-TMT experiment with TMT as variable modification on histidine

Returns:

• df (pd.DataFrame) – Results from labeling efficiency calculations as absolute and relative numbers.

• fig (Figure of labeling efficiency as stacked bars. Under/Over-labeling as separated axis.)