Package: triqler
Triqler: TRansparent Identification-Quantification-Linked Error RatesTriqler is a probabilistic graphical model that propagates error
information through all steps from MS1 feature to protein level,
employing distributions in favor of point estimates, most notably for
missing value imputation. The model outputs posterior probabilities for
fold changes between treatment groups, highlighting uncertainty rather
than hiding it.For a detailed explanation of how to install and run Triqler
(stand-alone or in combination with MaxQuant, Quandenser or Dinosaur) as
well as how to interpret the results, please read ourTriqler user
manual.Brief instructions for installing and running Triqler as well as
descriptions of the input and output formats can be found below.
Instructions for running the converters to the Triqler input format are
available in ourwiki.Method description / CitationThe, M. & Käll, L. (2019). Integrated identification and quantification
error probabilities for shotgun proteomics.Molecular & Cellular
Proteomics, 18(3), 561-570.https://doi.org/10.1074/mcp.RA118.001018Truong, P., The, M., & Käll, L. (2023). Triqler for Protein
Summarization of Data from Data-Independent Acquisition Mass
Spectrometry.Journal of Proteome Research, 22(4), 1359-1366.https://doi.org/10.1021/acs.jproteome.2c00607Installation viapippip install triqlerInstallation from sourcegit clone https://github.com/statisticalbiotechnology/triqler.git
cd triqler
pip install .Usageusage: triqler [-h] [--out_file OUT] [--fold_change_eval F]
             [--decoy_pattern P] [--missing_value_prior D] [--min_samples N]
             [--num_threads N] [--ttest] [--write_spectrum_quants]
             [--write_protein_posteriors P_OUT]
             [--write_group_posteriors G_OUT]
             [--write_fold_change_posteriors F_OUT]
             [--csv-field-size-limit CSV_FIELD_SIZE_LIMIT]
             IN_FILE

positional arguments:
  IN_FILE               List of PSMs with abundances (not log transformed!)
                        and search engine score. See README for a detailed
                        description of the columns.

optional arguments:
  -h, --help            show this help message and exit
  --out_file OUT        Path to output file (writing in TSV format). N.B. if
                        more than 2 treatment groups are present, suffixes
                        will be added before the file extension. (default:
                        proteins.tsv)
  --fold_change_eval F  log2 fold change evaluation threshold. (default: 1.0)
  --decoy_pattern P     Prefix for decoy proteins. (default: decoy_)
  --missing_value_prior D
                        Distribution to fit for missing value prior. Use "DIA"
                        for using means of NaNs to fit the censored normal
                        distribution. The "default" option fits the censored
                        normal distribution with all observed XIC values.
                        (default: default)
  --min_samples N       Minimum number of samples a peptide needed to be
                        quantified in. (default: 2)
  --num_threads N       Number of threads, by default this is equal to the
                        number of CPU cores available on the device. (default:
                        6)
  --ttest               Use t-test for evaluating differential expression
                        instead of posterior probabilities. (default: False)
  --write_spectrum_quants
                        Write quantifications for consensus spectra. Only
                        works if consensus spectrum index are given in input.
                        (default: False)
  --write_protein_posteriors P_OUT
                        Write raw data of protein posteriors to the specified
                        file in TSV format. (default: )
  --write_group_posteriors G_OUT
                        Write raw data of treatment group posteriors to the
                        specified file in TSV format. (default: )
  --write_fold_change_posteriors F_OUT
                        Write raw data of fold change posteriors to the
                        specified file in TSV format. (default: )
  --csv-field-size-limit CSV_FIELD_SIZE_LIMIT
                        Set a new maximum CSV field size (default: None)ExampleA sample fileiPRG2016.tsvis provided in theexamplefolder. You
can run Triqler on this file by running the following command:python -m triqler --fold_change_eval 0.8 example/iPRG2016.tsvA detailed example of the different levels of Triqler output can be
found inSupplementary Note
2of the
Quandenser publication.InterfaceThe simplest input format is a tab-separated file consisting of a header
line followed by one PSM per line in the following format:run <tab> condition <tab> charge <tab> searchScore <tab> intensity <tab> peptide     <tab> proteins
r1  <tab> 1         <tab> 2      <tab> 1.345       <tab> 21359.123 <tab> A.PEPTIDE.A <tab> proteinA <tab> proteinB 
r2  <tab> 1         <tab> 2      <tab> 1.945       <tab> 24837.398 <tab> A.PEPTIDE.A <tab> proteinA <tab> proteinB 
r3  <tab> 2         <tab> 2      <tab> 1.684       <tab> 25498.869 <tab> A.PEPTIDE.A <tab> proteinA <tab> proteinB
...
r1  <tab> 1         <tab> 3      <tab> 0.452       <tab> 13642.232 <tab> A.NTPEPTIDE.- <tab> decoy_proteinAAlternatively, if you have match-between-run probabilities, a slightly
more complicated input format can be used as input:run <tab> condition <tab> charge <tab> searchScore <tab> spectrumId <tab> linkPEP <tab> featureClusterId <tab> intensity <tab> peptide     <tab> proteins
r1  <tab> 1         <tab> 2      <tab> 1.345       <tab> 3          <tab> 0.0     <tab> 1                <tab> 21359.123 <tab> A.PEPTIDE.A <tab> proteinA <tab> proteinB 
r2  <tab> 1         <tab> 2      <tab> 1.345       <tab> 3          <tab> 0.021   <tab> 1                <tab> 24837.398 <tab> A.PEPTIDE.A <tab> proteinA <tab> proteinB 
r3  <tab> 2         <tab> 2      <tab> 1.684       <tab> 4          <tab> 0.0     <tab> 1                <tab> 25498.869 <tab> A.PEPTIDE.A <tab> proteinA <tab> proteinB
...
r1  <tab> 1         <tab> 3      <tab> 0.452       <tab> 6568       <tab> 0.15    <tab> 9845             <tab> 13642.232 <tab> A.NTPEPTIDE.- <tab> decoy_proteinASome remarks:For Triqler to work, it also needs decoy PSMs, preferably resulting
from a search engine search with a reversed protein sequence
database concatenated to the target database.The intensities shouldnotbe log transformed, Triqler will do
this transformation for you.The search engine scores should be such that higher scores indicate
a higher confidence in the PSM.We recommend usage of well calibrated search engine scores, e.g. the
SVM scores from Percolator.Donotset --fold_change_eval to 0 or a very low value (<0.2).
The fold change posterior distribution always has a certain width,
reflecting the uncertainty of our estimation. Even if the fold
change is 0, this distribution will necessarily spill over into low
fold change values, without there being any ground for differential
expression.Multiple proteins can be specified at the end of the line, separated
by tabs. However, it should be noted that Triqler currently discards
shared peptides.The output format is a tab-separated file consisting of a header line
followed by one protein per line in the following format:q_value <tab> posterior_error_prob <tab> protein <tab> num_peptides <tab> protein_id_PEP <tab> log2_fold_change <tab> diff_exp_prob_<FC> <tab> <condition1>:<run1> <tab> <condition1>:<run2> <tab> ... <tab> <conditionM>:<runN> <tab> peptidesSome remarks:Theq_valueandposterior_error_probcolumns represent
respectively the FDR and PEP for the hypothesis that the protein was
correctly identified and has a fold change larger than the specified
--fold_change_eval.Theprotein_id_PEPanddiff_exp_prob_<FC>columns are simply
the separate probabilities that make up the above hypothesis test,
i.e. for correct identification and for fold change respectively.The reported fold change is log2 transformed and is the expected
value based on the posterior distribution of the fold change.If more than 2 treatment groups are present, separate files will be
written out for each pairwise comparison with suffixes added before
the file extension, e.g. proteins.1vs3.tsv.The reported protein expressions per run are the expected value of
the protein's expression in that run. They represent relative
values (notlog transformed) to the protein's mean expression
across all runs, which itself would correspond to the value 1.0. For
example, a value of 1.5 means that the expression in this sample is
50% higher than the mean across all runs. A second example comparing
values across samples: if sample1 has a value of 2.0 and sample2 a
value of 1.5, it means that the expression in sample1 is 33% higher
than in sample2 (2.0/1.5=1.33). We don't necessarily recommend
using these values for downstream analysis, as the idea is that the
actual value of interest is the fold change between treatment groups
rather than between samples.