config.toml

# This is the config file for running the decryptM pipeline
# Please add the parameters here. Don't introduce linebreaks.
# Please modify this file in a text editor. Excel is NOT a text editor !!
# 
# TOC:
#
# Meta Data => Additional information about the data set
# PTMs => Specify the expected PTMs in the data set
# TMT => Interpretation of the channels
# Processing => Control of the analysis
# Path => Relative paths for files to this toml file
# MaxQuant => MaxQuant related info
# Plot => Plot parameters
# CurveFilter => Filter criteria for curves to remove garbage
#


['Meta Data']
# Add the name of the data set. A dataset can consist of multiple experiments and PTM batches.
dataset_name = 'Drug X'

# Add your name:
who = 'Kusterlab'

# Add experiment type: dd -> dose-dependent, td -> time dependent
experiment_type = 'dd' 

# Add a short description of the experiment:
experiment_description = 'Drug X in cell Y for Z min'

# Add the drug type:
drug_type = 'Alpha Inhibitor'

# Add the cell types. Multiple is possible ['Cell 1', 'Cell 2', ...]:
cells = ['Cell Y']

# Add the treatment dose in td experiments:
treatment_dose = ''

# Add the treatment time in dd experiments:
treatment_time = 'Z min'


['PTMs']
# Add with <true> or <false>, if a PTM is present in the data set:
Fullproteome = false
Phosphorylation = true
Ubiquitinylation = false
Acetylation = false


['TMT']
# The TMT plex number e.g. 6, 10, 11, 16, 18, ... :
plex = 10

# The TMT quantification type. It could be lfq, ms2, or ms3.
quantification = 'ms3'

# The correction factor lot number e.g. 'UF291262_UE277617'. This is relevant for TMT data:
correction_lot = 'TE268169'

# TMT-channel list: 1:126, 2:127N, 3:127C, ... :
# The length must match with the doses or time_points array 
channels = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]  

# Write the drug concentrations in the same order as the channels. DMSO control as 0.0:
# The length must match with channels array
doses = [100.0, 30.0, 10.0, 3.0, 1.0, 0.3, 0.1, 0.03, 0.01, 0.0]

# Add here the concentration scale factor eg. 1 nM = '1e-9' in the channels array:
dose_scale = '1e-9'

# Concentration unit after applying dose scaling factor e.g. '1e-9': 'M' or '1': 'nM'
dose_label = 'M'

# Write the time points in the same order as the channels.:
# The length must match with channels array
time_points = []  # List of time points

# The units of the time array e.g. 'h':
time_label = 'h' 


['Processing']
# TMT control channel to which the ratios should be calculated:
control_channel = 10

# Normalization based on a subproteome in the evidence e.g. 'Phosphoproteome':
# Put '' for using all peptides for normalization:
subproteome_normalization = 'Phosphoproteome'

# Should the data points be fitted by a logistic model?
fit_logistic_model = true

# Should the simple fitting approach be applied?
# interpolation boundaries are only relevant for the optimized and not the simple fit. Default low=1, high=0
simple_fit = false
interpolation_low_exclude = 1
interpolation_high_exclude = 0


['Paths']
# Output paths. Add the relative paths to a folder where the curves file and the pdfs will be stored (from the perspective of this toml file).
table_out_folder = ''
output_file = 'curves.txt'
pdf_out_folder = 'pdfs'


['MaxQuant']
# MaxQuant meta data and file paths
MQ_version = '1.6.12.0'
txt_foler = 'combined\txt'
input_file = 'evidence.txt'

# The column names from the MaxQuant output that should be copied and aggregated in the curves file. 
# Depending on the data in the columns, different aggregation strategies must be applied.
# info cols store information that is the same for each curve
# max cols will use the maximum value for duplicates when aggregated
# min cols will use the minimum value for duplicates when aggregated
# concat cols will concatenate all values of each duplicate when aggregated
evd_info_cols = ['Sequence', 'Length', 'Missed cleavages', 'Proteins', 'Leading proteins', 'Gene names', 'Protein names']
evd_max_cols = ['Score']
evd_min_cols = ['PEP']
evd_concat_cols = ['Phospho (STY) Probabilities']


['Plot']
# Modulating the plot
up_down_pdfs = true
curve_x_extrapolate = [-1.0, 0.5]
plot_helper_line = [0.55, 2.00]
colormap_color = 'viridis_r'
colormap_lower = 13
colormap_upper = 18


['CurveFilter']
active = false
signal_min = 12.5
min_R2 = 0.2