iBAQ (intensity Based Absolute Quantification) determines the abundance of a protein by dividing the total precursor intensities by the number of theoretically observable peptides of the protein. The TPA (Total Protein Approach) value is determined by summing peptide intensities of each protein and then dividing by the molecular mass to determine the relative concentration of each protein. By using ProteomicRuler, it is possible to calculate the protein copy number and absolute concentration. ibaqpy compute IBAQ values, TPA values, copy numbers and concentration for proteins starting from a feature parquet from quantmsio and a SDRF file. In addition, it supports the merging of iBAQ results from multiple datasets and the elimination of outliers and batch effects. This package provides multiple functions:
Assemble features as peptides: In features, the differences between different runs and different fractions in the sample were reduced, and then normalized after combining into peptides. See details in commands/features2peptides.
Assemble peptides as proteins: In the sample, the protein was combined from its unique peptides, then the iBAQ value was calculated as the Intensity of the protein devided by the theoretical number of unique peptides cut by the enzyme. This command also normalized iBAQ as riBAQ. See details in commands/peptides2proteins.
Compute TPA: Compute TPA values, protein copy numbers and concentration from the output file from script commands/features2peptides. See details in commands/compute_tpa.py.
Merge projects: Merge ibaq results from multiple datasets. It consists of three steps: missing value imputation, outlier removal, and batch effect removal. See details in commands/datasets_merger.py.
NOTE: In all scripts and result files, uniprot accession is used as the protein identifier.
Ibaqpy is available in PyPI and can be installed using pip:
pip install ibaqpyYou can install the package from code:
- Clone the repository:
>$ git clone https://github.com/bigbio/ibaqpy
>$ cd ibaqpy- Install conda environment:
>$ mamba env create -f conda-environment.yaml- Install ibaqpy:
>$ python setup.py installAbsolute quantification files has been store in the following url:
http://ftp.pride.ebi.ac.uk/pub/databases/pride/resources/proteomes/absolute-expression/quantms-data/
Inside each project reanalysis folder, the folder proteomicslfq contains the msstats input file with the structure {Name of the project}.{Random uuid}.feature.parquet .
python commands/features2peptides.py -p tests/PXD003947/PXD003947-featrue.parquet -s tests/PXD003947/PXD003947.sdrf.tsv --remove_ids data/contaminants_ids.tsv --remove_decoy_contaminants --remove_low_frequency_peptides --output tests/PXD003947/PXD003947-peptides-norm.csv --log2 --save_parquetUsage: features2peptides.py [OPTIONS]
Options:
-p, --parquet TEXT Parquet file import generated by quantms.io
-s, --sdrf TEXT SDRF file import generated by quantms
--min_aa INTEGER Minimum number of amino acids to filter
peptides
--min_unique INTEGER Minimum number of unique peptides to filter
proteins
--remove_ids TEXT Remove specific protein ids from the
analysis using a file with one id per line
--remove_decoy_contaminants Remove decoy and contaminants proteins from
the analysis
--remove_low_frequency_peptides
Remove peptides that are present in less
than 20% of the samples
--output TEXT Peptide intensity file including other all
properties for normalization
--skip_normalization Skip normalization step
--nmethod TEXT Normalization method used to normalize
feature intensities for all samples
(options: mean, median, iqr, none)
--pnmethod TEXT Normalization method used to normalize
peptides intensities for all samples
(options: mean, median, max, global,
max_min, none)
--log2 Transform to log2 the peptide intensity
values before normalization
--save_parquet Save normalized peptides to parquet
--help Show this message and exit.Peptide normalization starts from the peptides dataframe extracted from feature parquet. It contains the following columns:
- ProteinName: Protein name
- PeptideSequence: Peptide sequence including post-translation modifications
(e.g. .(Acetyl)ASPDWGYDDKN(Deamidated)GPEQWSK) - PrecursorCharge: Precursor charge
- FragmentIon: Fragment ion
- ProductCharge: Product charge
- IsotopeLabelType: Isotope label type
- Condition: Condition label
(e.g. heart) - BioReplicate: Biological replicate index
(e.g. 1) - Run: Run index
(e.g. 1) - Fraction: Fraction index
(e.g. 1) - Intensity: Peptide intensity
- Reference: Name of the RAW file containing the peptide intensity
(e.g. Adult_Heart_Gel_Elite_54_f16) - SampleID: Sample ID
(e.g. PXD003947-Sample-3) - StudyID: Study ID
(e.g. PXD003947). In most of the cases the study ID is the same as the ProteomeXchange ID.
In this section, ibaqpy will do:
- Remove lines where intensity or study condition is empty
- Parse the identifier of proteins and retain only unique peptides
- Low confidence proteins were removed according to the threshold of unique peptides (optional)
- Filter decoy, contaminants, entrapments, and user-specified proteins (optional)
- Remove peptides with low frequency if
sample number > 1(optional) - Data logization (optional)
In this section, ibaqpy corrects the intensity of each MS runs in the sample, eliminating the effect between runs (including technique repetitions). It will do:
- When
MS runs > 1in the sample, themeanof all average(mean,medianoriqr) in each MS run is calculated(SampleMean) - The ratio between SampleMean and the average MS run is used as reference to scale the original intensity
A peptidoform is a combination of a PeptideSequence(Modifications) + Charge + BioReplicate + Fraction, and a peptide is a combination of a PeptideSequence(Canonical) + BioReplicate. ibaqpy will do:
- Select peptidoforms with the highest intensity across different modifications, fractions, and technical repeats
- Merge peptidoforms across different charges and combined into peptides
Finally, the intensity of the peptide was normalized globally by median, mean, max, sum, or max_min.
IBAQ is an absolute quantitative method based on strength that can be used to estimate the relative abundance of proteins in a sample. IBAQ value is the total intensity of a protein divided by the number of theoretical peptides.
python commands/peptides2proteins.py --fasta tests/PXD003947/Homo-sapiens-uniprot-reviewed-contaminants-decoy-202210.fasta --peptides tests/PXD003947/PXD003947-peptides-norm.csv --enzyme Trypsin --output tests/PXD003947/PXD003947-ibaq-norm.csv --normalize --verboseThe command provides an additional flag for normalize IBAQ values.
Usage: peptides2proteins.py [OPTIONS]
Options:
-f, --fasta TEXT Protein database to compute IBAQ values
-p, --peptides TEXT Peptide identifications with intensities following the
peptide intensity output
-e, --enzyme TEXT Enzyme used during the analysis of the dataset
(default: Trypsin)
-n, --normalize Normalize IBAQ values using by using the total IBAQ of
the experiment
--min_aa INTEGER Minimum number of amino acids to consider a peptide
--max_aa INTEGER Maximum number of amino acids to consider a peptide
-o, --output TEXT Output file with the proteins and ibaq values
--verbose Print addition information about the distributions of
the intensities, number of peptides remove after
normalization, etc.
--qc_report TEXT PDF file to store multiple QC images
--help Show this message and exit.The current version of this tool uses OpenMS method to load fasta file, and use ProteaseDigestion to enzyme digestion of protein sequences, and finally get the theoretical peptide number of each protein.
First, peptide intensity dataframe was grouped according to protein name, sample name and condition. The protein intensity of each group was summed. Finally, the sum of the intensity of the protein is divided by the number of theoretical peptides.
If protein-group exists in the peptide intensity dataframe, the intensity of all proteins in the protein-group is summed based on the above steps, and then multiplied by the number of proteins in the protein-group.
Normalize the ibaq values using the total ibaq of the sample. The resulted ibaq values are then multiplied by 100'000'000 (PRIDE database noramalization), for the ibaq ppb and log10 shifted by 10 (ProteomicsDB).
The total protein approach (TPA) is a label- and standard-free method for absolute protein quantitation of proteins using large-scale proteomic data. In the current version of the tool, the TPA value is the total intensity of the protein divided by its theoretical molecular mass.
ProteomicRuler is a method for protein copy number and concentration estimation that does not require the use of isotope labeled standards. It uses the mass spectrum signal of histones as a "proteomic ruler" because it is proportional to the amount of DNA in the sample, which in turn depends on cell count. Thus, this approach can add an absolute scale to the mass spectrometry readout and allow estimates of the copy number of individual proteins in each cell.
python commands/compute_tpa.py --fasta Homo-sapiens-uniprot-reviewed-contaminants-decoy-202210.fasta --organism 'human' --peptides PXD003947-peptides.csv --ruler --ploidy 2 --cpc 200 --output PXD003947-tpa.tsv --verbosepython compute_tpa.py --help
Usage: compute_tpa.py [OPTIONS]
Compute the protein copy numbers and concentrations according to a file output of peptides with the
format described in peptide_normalization.py.
:param fasta: Fasta file used to perform the peptide identification
:param peptides: Peptide intensity file
:param organism: Organism source of the data
:param ruler: Whether to compute protein copy number, weight and concentration.
:param ploidy: Ploidy number
:param cpc: Cellular protein concentration(g/L)
:param output: Output format containing the TPA values, protein copy numbers and concentrations
:param verbose: Print addition information about the distributions of the intensities,
number of peptides remove after normalization, etc.
:param qc_report: PDF file to store multiple QC images
Options:
-f, --fasta TEXT Protein database to compute IBAQ values [required]
-p, --peptides TEXT Peptide identifications with intensities following the peptide intensity output [required]
-m, --organism Organism source of the data.
-r, --ruler Calculate protein copy number and concentration according to ProteomicRuler
-n, --ploidy Ploidy number (default: 2)
-c, --cpc Cellular protein concentration(g/L) (default: 200)
-o, --output TEXT Output format containing the TPA values, protein copy numbers and concentrations
--verbose Print addition information about the distributions of the intensities,
number of peptides remove after normalization, etc.
--qc_report PDF file to store multiple QC images (default: "TPA-QCprofile.pdf")
--help Show this message and exit.The OpenMS tool was used to calculate the theoretical molecular mass of each protein. Similar to the calculation of IBAQ, the TPA value of protein-group was the sum of its intensity divided by the sum of the theoretical molecular mass.
The protein copy calculation follows the following formula:
protein copies per cell = protein MS-signal * (avogadro / molecular mass) * (DNA mass / histone MS-signal)
For cellular protein copy number calculation, the uniprot accession of histones were obtained from species first, and the molecular mass of DNA was calculated. Then the dataframe was grouped according to different conditions, and the copy number, molar number and mass of proteins were calculated.
In the calculation of protein concentration, the volume is calculated according to the cell protein concentration first, and then the protein mass is divided by the volume to calculate the intracellular protein concentration.
There are batch effects in protein identification and quantitative results between different studies, which may be caused by differences in experimental techniques, conditional methods, data analysis, etc. Here we provide a method to apply batch effect correction. First to impute ibaq data, then remove outliers using hdbscan, and apply batch effect correction using pycombat.
python commands/datasets_merge.py datasets_merge --data_folder ../ibaqpy_test/ --output datasets-merge.csv --verbosepython datasets_merge.py --help
Usage: datasets_merge.py [OPTIONS]
Merge ibaq results from compute_ibaq.py.
:param data_folder: Data dolfer contains SDRFs and ibaq CSVs.
:param output: Output file after batch effect removal.
:param covariate: Indicators included in covariate consideration when datasets are merged.
:param organism: Organism to keep in input data.
:param covariate_to_keep: Keep tissue parts from metadata, e.g. 'LV,RV,LA,RA'.
:param non_missing_percent_to_keep: non-missing values in each group.
:param n_components: Number of principal components to be computed.
:param min_cluster: The minimum size of clusters.
:param min_sample_num: The minimum number of samples in a neighborhood for a point to be considered as a core point.
:param n_iter: Number of iterations to be performed.
:param verbose/quiet: Output debug information.
Options:
Options:
-d, --data_folder TEXT Data dolfer contains SDRFs and ibaq CSVs. [required]
-o, --output TEXT Output file after batch effect removal. [required]
-c, --covariate TEXT Indicators included in covariate consideration when datasets are merged.
--organism TEXT Organism to keep in input data.
--covariate_to_keep TEXT Keep tissue parts from metadata, e.g. 'LV,RV,LA,RA'.
--non_missing_percent_to_keep FLOAT
non-missing values in each group.
--n_components TEXT Number of principal components to be computed.
--min_cluster TEXT The minimum size of clusters.
--min_sample_num TEXT The minimum number of samples in a neighborhood for a point to be considered as a core point.
--n_iter TEXT Number of iterations to be performed.
-v, --verbose / -q, --quiet Output debug information.
--help Show this message and exit.Remove proteins missing more than 30% of all samples. Users can keep tissue parts interested, and data will be converted to a expression matrix (samples as columns, proteins as rows), then missing values will be imputed with KNNImputer.
When outliers are removed, multiple hierarchical clustering is performed using hdbscan.HDBSCAN, where outliers are labeled -1 in the PCA plot. When clustering is performed, the default cluster minimum value and the minimum number of neighbors of the core point are the minimum number of samples in all datasets.
Users can skip this step and do outliers removal manually.
Using pycombat for batch effect correction, and batch is set to datasets (refers specifically to PXD ids) and the covariate should be tissue_part.
Wang H, Dai C, Pfeuffer J, Sachsenberg T, Sanchez A, Bai M, Perez-Riverol Y. Tissue-based absolute quantification using large-scale TMT and LFQ experiments. Proteomics. 2023 Oct;23(20):e2300188. doi: 10.1002/pmic.202300188. Epub 2023 Jul 24. PMID: 37488995.
- Julianus Pfeuffer
- Yasset Perez-Riverol
- Hong Wang