DIA - Label-Free¶

This tutorial demonstrates how to analyze label-free quantification (LFQ) data from data-independent acquisition (DIA) using the msmu package.

Data Preparation¶

Original dataset is from PXD012988 (Uszkoreit et al., 2022) and search was performed with DIA-NN v2.1.0.

For demonstration purposes, the example dataset was reduced to six samples and a total of 1,000 peptides.

In [2]:

base_dir = "https://raw.githubusercontent.com/bertis-informatics/msmu/refs/heads/dev/data/diann_dia"
diann_idents = f"{base_dir}/diann/report.parquet"
sdrf = f"{base_dir}/meta.sdrf.tsv"

base_dir = "https://raw.githubusercontent.com/bertis-informatics/msmu/refs/heads/dev/data/diann_dia" diann_idents = f"{base_dir}/diann/report.parquet" sdrf = f"{base_dir}/meta.sdrf.tsv"

Load Required Packages¶

If you haven't installed the msmu package yet, please follow the installation guide.

In [3]:

import msmu as mm
import plotly.io as pio

pio.renderers.default = "png"

import msmu as mm import plotly.io as pio pio.renderers.default = "png"

Read Data¶

You can read data from various proteomics software outputs. Below are examples for DIA-NN, MaxQuant, and FragPipe formats.

For this tutorial, we will use DIA-NN output as an example.

read_diann() function reads DIA-NN output file (report.parquet or report.tsv) and creates modalities at MuData object.

In [6]:

# DIA-NN format
mdata = mm.read_diann(identification_file=diann_idents)

# MaxQuant format
# mdata = mm.read_maxquant(identification_file="path_to_maxquant_output", label="label_free", acquisition="dia")

# FragPipe format
# mdata = mm.read_fragpipe(identification_file="path_to_fragpipe_output", label="label_free", acquisition="dia")

# Sample metadata from the SDRF: keep the table on the container, then project it onto obs
mdata = mm.pp.attach_sdrf(mdata, sdrf)
mdata = mm.pp.apply_sdrf_to_obs(mdata)

mdata.obs
# Without an SDRF, add_meta() joins any table (DataFrame, csv, tsv, parquet) onto obs instead:
# mdata = mm.pp.add_meta(mdata, sdrf, format="sdrf", metadata_on="assay name")

# DIA-NN format mdata = mm.read_diann(identification_file=diann_idents) # MaxQuant format # mdata = mm.read_maxquant(identification_file="path_to_maxquant_output", label="label_free", acquisition="dia") # FragPipe format # mdata = mm.read_fragpipe(identification_file="path_to_fragpipe_output", label="label_free", acquisition="dia") # Sample metadata from the SDRF: keep the table on the container, then project it onto obs mdata = mm.pp.attach_sdrf(mdata, sdrf) mdata = mm.pp.apply_sdrf_to_obs(mdata) mdata.obs # Without an SDRF, add_meta() joins any table (DataFrame, csv, tsv, parquet) onto obs instead: # mdata = mm.pp.add_meta(mdata, sdrf, format="sdrf", metadata_on="assay name")

INFO - Validating SDRF metadata for https://raw.githubusercontent.com/bertis-informatics/msmu/refs/heads/dev/data/diann_dia/meta.sdrf.tsv.
INFO - SDRF validation succeeded for https://raw.githubusercontent.com/bertis-informatics/msmu/refs/heads/dev/data/diann_dia/meta.sdrf.tsv.

Out[6]:

	source name	characteristics[organism]	characteristics[organism part]	characteristics[cell line]	characteristics[cell type]	characteristics[cellosaurus accession]	characteristics[cellosaurus name]	characteristics[disease]	characteristics[biological replicate]	assay name	technology type	comment[proteomexchange accession number]	comment[proteomics data acquisition method]	comment[fraction identifier]	comment[technical replicate]	comment[label]	comment[instrument]	comment[cleavage agent details]	factor value[condition]
QExHF03751	G1-1	Mus musculus	not available	C2C12	myoblast	CVCL_0188	C2C12	normal	1	QExHF03751	proteomic profiling by mass spectrometry	PXD012988	data-independent acquisition	1	1	label free sample	Q Exactive HF	NT=Trypsin;AC=MS:1001251	G1
QExHF03753	G2-1	Mus musculus	not available	C2C12	myoblast	CVCL_0188	C2C12	normal	1	QExHF03753	proteomic profiling by mass spectrometry	PXD012988	data-independent acquisition	1	1	label free sample	Q Exactive HF	NT=Trypsin;AC=MS:1001251	G2
QExHF03761	G1-2	Mus musculus	not available	C2C12	myoblast	CVCL_0188	C2C12	normal	2	QExHF03761	proteomic profiling by mass spectrometry	PXD012988	data-independent acquisition	1	1	label free sample	Q Exactive HF	NT=Trypsin;AC=MS:1001251	G1
QExHF03763	G2-2	Mus musculus	not available	C2C12	myoblast	CVCL_0188	C2C12	normal	2	QExHF03763	proteomic profiling by mass spectrometry	PXD012988	data-independent acquisition	1	1	label free sample	Q Exactive HF	NT=Trypsin;AC=MS:1001251	G2
QExHF03771	G1-3	Mus musculus	not available	C2C12	myoblast	CVCL_0188	C2C12	normal	3	QExHF03771	proteomic profiling by mass spectrometry	PXD012988	data-independent acquisition	1	1	label free sample	Q Exactive HF	NT=Trypsin;AC=MS:1001251	G1
QExHF03773	G2-3	Mus musculus	not available	C2C12	myoblast	CVCL_0188	C2C12	normal	3	QExHF03773	proteomic profiling by mass spectrometry	PXD012988	data-independent acquisition	1	1	label free sample	Q Exactive HF	NT=Trypsin;AC=MS:1001251	G2

In [5]:

mdata

mdata

Out[5]:

MuData object with n_obs × n_vars = 6 × 5038
  obs:	'source name', 'characteristics[organism]', 'characteristics[organism part]', 'characteristics[cell line]', 'characteristics[cell type]', 'characteristics[cellosaurus accession]', 'characteristics[cellosaurus name]', 'characteristics[disease]', 'characteristics[biological replicate]', 'assay name', 'technology type', 'comment[proteomexchange accession number]', 'comment[proteomics data acquisition method]', 'comment[fraction identifier]', 'comment[technical replicate]', 'comment[label]', 'comment[instrument]', 'comment[cleavage agent details]', 'factor value[condition]'
  uns:	'_cmd', 'sdrf'
  1 modality
    psm:	6 × 5038
      obs:	'source name', 'characteristics[organism]', 'characteristics[organism part]', 'characteristics[cell line]', 'characteristics[cell type]', 'characteristics[cellosaurus accession]', 'characteristics[cellosaurus name]', 'characteristics[disease]', 'characteristics[biological replicate]', 'assay name', 'technology type', 'comment[proteomexchange accession number]', 'comment[proteomics data acquisition method]', 'comment[fraction identifier]', 'comment[technical replicate]', 'comment[label]', 'comment[instrument]', 'comment[cleavage agent details]', 'factor value[condition]'
      var:	'proteins', 'peptide', 'stripped_peptide', 'filename', 'charge', 'peptide_length', 'rt', 'decoy', 'contaminant', 'PEP', 'q_value'
      uns:	'level', 'search_engine', 'quantification', 'label', 'acquisition', 'identification_file', 'quantification_file', 'decoy'
      varm:	'search_result'
      layers:	None

Saving Raw MuData Object¶

After reading the search output, saving the MuData object as an h5mu file is recommended for future use.

In [7]:

mdata.write_h5mu("dia_lfq_PXD012988_raw.h5mu")

mdata.write_h5mu("dia_lfq_PXD012988_raw.h5mu")

Handling PSM level¶

If you are using DIA-NN version under 2.0, or the data doesn't contain decoy features, please jump to this section.

Filtering - PSM¶

You can filter the data based on the column values, such as q-value. You can also filter on the flags the reader records, such as contaminant, or on string containment.

Filtering is split into two steps: first, you mark a filter condition using mm.pp.add_filter(), and then you apply the filter using mm.pp.apply_filter().

Here, we keep protein groups with q-value < 0.01.

In [8]:

mdata = mm.pp.add_filter(mdata, modality="psm", column="q_value", keep="lt", value=0.01)
mdata = mm.pp.apply_filter(mdata, modality="psm")

mdata

mdata = mm.pp.add_filter(mdata, modality="psm", column="q_value", keep="lt", value=0.01) mdata = mm.pp.apply_filter(mdata, modality="psm") mdata

INFO - Applying var filters for psm: ['q_value_lt_0.01']

Out[8]:

MuData object with n_obs × n_vars = 6 × 5038
  obs:	'source name', 'characteristics[organism]', 'characteristics[organism part]', 'characteristics[cell line]', 'characteristics[cell type]', 'characteristics[cellosaurus accession]', 'characteristics[cellosaurus name]', 'characteristics[disease]', 'characteristics[biological replicate]', 'assay name', 'technology type', 'comment[proteomexchange accession number]', 'comment[proteomics data acquisition method]', 'comment[fraction identifier]', 'comment[technical replicate]', 'comment[label]', 'comment[instrument]', 'comment[cleavage agent details]', 'factor value[condition]'
  uns:	'_cmd', 'sdrf'
  1 modality
    psm:	6 × 5038
      obs:	'source name', 'characteristics[organism]', 'characteristics[organism part]', 'characteristics[cell line]', 'characteristics[cell type]', 'characteristics[cellosaurus accession]', 'characteristics[cellosaurus name]', 'characteristics[disease]', 'characteristics[biological replicate]', 'assay name', 'technology type', 'comment[proteomexchange accession number]', 'comment[proteomics data acquisition method]', 'comment[fraction identifier]', 'comment[technical replicate]', 'comment[label]', 'comment[instrument]', 'comment[cleavage agent details]', 'factor value[condition]'
      var:	'proteins', 'peptide', 'stripped_peptide', 'filename', 'charge', 'peptide_length', 'rt', 'decoy', 'contaminant', 'PEP', 'q_value'
      uns:	'level', 'search_engine', 'quantification', 'label', 'acquisition', 'identification_file', 'quantification_file', 'decoy', 'filter', 'decoy_filter'
      varm:	'search_result', 'filter'
      layers:	None

Normalization¶

Here, we log2 transform and normalize the data at the PSM level.

Median centering normalization is applied using mm.pp.normalize() function.

In [12]:

mdata = mm.pp.log2_transform(mdata, modality="psm")
mdata = mm.pp.normalize(mdata, modality="psm", method="median")

mdata = mm.pp.log2_transform(mdata, modality="psm") mdata = mm.pp.normalize(mdata, modality="psm", method="median")

Handling peptide level¶

Summarization - peptide¶

You can summarize psm-level data to peptide-level data using the mm.pp.to_peptide() function.

In [13]:

mdata = mm.pp.to_peptide(mdata)

mdata = mm.pp.to_peptide(mdata)

INFO - Peptide-level identifications: 982 (966 at 1% FDR)
INFO - Building new peptide quantification data.

Filtering - peptide¶

In [14]:

mdata = mm.pp.add_filter(mdata, modality="peptide", column="q_value", keep="lt", value=0.01)
mdata = mm.pp.apply_filter(mdata, modality="peptide", on="var")

mdata

mdata = mm.pp.add_filter(mdata, modality="peptide", column="q_value", keep="lt", value=0.01) mdata = mm.pp.apply_filter(mdata, modality="peptide", on="var") mdata

INFO - Applying var filters for peptide: ['q_value_lt_0.01']

Out[14]:

MuData object with n_obs × n_vars = 6 × 6004
  obs:	'source name', 'characteristics[organism]', 'characteristics[organism part]', 'characteristics[cell line]', 'characteristics[cell type]', 'characteristics[cellosaurus accession]', 'characteristics[cellosaurus name]', 'characteristics[disease]', 'characteristics[biological replicate]', 'assay name', 'technology type', 'comment[proteomexchange accession number]', 'comment[proteomics data acquisition method]', 'comment[fraction identifier]', 'comment[technical replicate]', 'comment[label]', 'comment[instrument]', 'comment[cleavage agent details]', 'factor value[condition]'
  uns:	'_cmd', 'sdrf'
  2 modalities
    psm:	6 × 5038
      obs:	'source name', 'characteristics[organism]', 'characteristics[organism part]', 'characteristics[cell line]', 'characteristics[cell type]', 'characteristics[cellosaurus accession]', 'characteristics[cellosaurus name]', 'characteristics[disease]', 'characteristics[biological replicate]', 'assay name', 'technology type', 'comment[proteomexchange accession number]', 'comment[proteomics data acquisition method]', 'comment[fraction identifier]', 'comment[technical replicate]', 'comment[label]', 'comment[instrument]', 'comment[cleavage agent details]', 'factor value[condition]'
      var:	'proteins', 'peptide', 'stripped_peptide', 'filename', 'charge', 'peptide_length', 'rt', 'decoy', 'contaminant', 'PEP', 'q_value'
      uns:	'level', 'search_engine', 'quantification', 'label', 'acquisition', 'identification_file', 'quantification_file', 'decoy', 'filter', 'decoy_filter'
      varm:	'search_result', 'filter'
      layers:	None
    peptide:	6 × 966
      obs:	'source name', 'characteristics[organism]', 'characteristics[organism part]', 'characteristics[cell line]', 'characteristics[cell type]', 'characteristics[cellosaurus accession]', 'characteristics[cellosaurus name]', 'characteristics[disease]', 'characteristics[biological replicate]', 'assay name', 'technology type', 'comment[proteomexchange accession number]', 'comment[proteomics data acquisition method]', 'comment[fraction identifier]', 'comment[technical replicate]', 'comment[label]', 'comment[instrument]', 'comment[cleavage agent details]', 'factor value[condition]'
      var:	'peptide', 'proteins', 'stripped_peptide', 'count_psm', 'PEP', 'q_value'
      uns:	'level', 'decoy', 'filter', 'decoy_filter'
      varm:	'filter'
      layers:	None

Protein inference¶

You can infer protein-level data from peptide-level data using the mm.pp.infer_protein() function.

In [15]:

mdata = mm.pp.infer_protein(mdata)

mdata = mm.pp.infer_protein(mdata)

INFO - Starting protein inference
INFO - Initial proteins: 1672
INFO - Removed indistinguishable: 808
INFO - Removed subsettable: 60
INFO - Removed subsumable: 0
INFO - Total protein groups: 804

Handling protein level¶

Summarization - protein¶

You can summarize peptide-level data to protein-level data using the mm.pp.to_protein() function.

As default, top 3 peptides within protein group can be used for protein group quantification aggregation. If top_n is None, all peptides will be used.

In [16]:

mdata = mm.pp.to_protein(mdata, top_n=3, rank_method="total_intensity")

mdata = mm.pp.to_protein(mdata, top_n=3, rank_method="total_intensity")

INFO - Applying var filters for peptide: ['q_value_lt_0.01', 'peptide_type_eq_unique']
INFO - Protein-level identifications: 799 (797 at 1% FDR)

Filtering - protein¶

In [17]:

mdata = mm.pp.add_filter(mdata, modality="protein", column="q_value", keep="lt", value=0.01)
mdata = mm.pp.apply_filter(mdata, modality="protein", on="var")

mdata

mdata = mm.pp.add_filter(mdata, modality="protein", column="q_value", keep="lt", value=0.01) mdata = mm.pp.apply_filter(mdata, modality="protein", on="var") mdata

INFO - Applying var filters for protein: ['q_value_lt_0.01']

Out[17]:

MuData object with n_obs × n_vars = 6 × 6801
  obs:	'source name', 'characteristics[organism]', 'characteristics[organism part]', 'characteristics[cell line]', 'characteristics[cell type]', 'characteristics[cellosaurus accession]', 'characteristics[cellosaurus name]', 'characteristics[disease]', 'characteristics[biological replicate]', 'assay name', 'technology type', 'comment[proteomexchange accession number]', 'comment[proteomics data acquisition method]', 'comment[fraction identifier]', 'comment[technical replicate]', 'comment[label]', 'comment[instrument]', 'comment[cleavage agent details]', 'factor value[condition]'
  uns:	'_cmd', 'sdrf'
  3 modalities
    psm:	6 × 5038
      obs:	'source name', 'characteristics[organism]', 'characteristics[organism part]', 'characteristics[cell line]', 'characteristics[cell type]', 'characteristics[cellosaurus accession]', 'characteristics[cellosaurus name]', 'characteristics[disease]', 'characteristics[biological replicate]', 'assay name', 'technology type', 'comment[proteomexchange accession number]', 'comment[proteomics data acquisition method]', 'comment[fraction identifier]', 'comment[technical replicate]', 'comment[label]', 'comment[instrument]', 'comment[cleavage agent details]', 'factor value[condition]'
      var:	'proteins', 'peptide', 'stripped_peptide', 'filename', 'charge', 'peptide_length', 'rt', 'decoy', 'contaminant', 'PEP', 'q_value'
      uns:	'level', 'search_engine', 'quantification', 'label', 'acquisition', 'identification_file', 'quantification_file', 'decoy', 'filter', 'decoy_filter'
      varm:	'search_result', 'filter'
      layers:	None
    peptide:	6 × 966
      obs:	'source name', 'characteristics[organism]', 'characteristics[organism part]', 'characteristics[cell line]', 'characteristics[cell type]', 'characteristics[cellosaurus accession]', 'characteristics[cellosaurus name]', 'characteristics[disease]', 'characteristics[biological replicate]', 'assay name', 'technology type', 'comment[proteomexchange accession number]', 'comment[proteomics data acquisition method]', 'comment[fraction identifier]', 'comment[technical replicate]', 'comment[label]', 'comment[instrument]', 'comment[cleavage agent details]', 'factor value[condition]'
      var:	'peptide', 'proteins', 'stripped_peptide', 'count_psm', 'PEP', 'q_value', 'protein_group', 'peptide_type'
      uns:	'level', 'decoy', 'filter', 'decoy_filter'
      varm:	'filter'
      layers:	None
    protein:	6 × 797
      obs:	'source name', 'characteristics[organism]', 'characteristics[organism part]', 'characteristics[cell line]', 'characteristics[cell type]', 'characteristics[cellosaurus accession]', 'characteristics[cellosaurus name]', 'characteristics[disease]', 'characteristics[biological replicate]', 'assay name', 'technology type', 'comment[proteomexchange accession number]', 'comment[proteomics data acquisition method]', 'comment[fraction identifier]', 'comment[technical replicate]', 'comment[label]', 'comment[instrument]', 'comment[cleavage agent details]', 'factor value[condition]'
      var:	'count_psm', 'count_stripped_peptide', 'PEP', 'q_value'
      uns:	'level', 'decoy', 'filter', 'decoy_filter'
      varm:	'filter'
      layers:	None

Visualization¶

ID plot¶

In [18]:

mm.pl.plot_id(mdata, modality="protein", colorby="factor value[condition]")

mm.pl.plot_id(mdata, modality="protein", colorby="factor value[condition]")

Intensity distribution plot¶

In [19]:

mm.pl.plot_intensity(mdata, modality="protein", groupby="factor value[condition]")

mm.pl.plot_intensity(mdata, modality="protein", groupby="factor value[condition]")

Saving Processed MuData Object¶

You can save MuData object into an h5mu file. This allows you to easily reload the processed data in future sessions without repeating the entire analysis pipeline.

In [20]:

mdata.write_h5mu("dia_lfq_PXD012988.h5mu")

mdata.write_h5mu("dia_lfq_PXD012988.h5mu")

For DIA-NN without decoy¶

DIA-NN versions earlier than 2.0 do not include decoy features in the final report.tsv.

Also, even in DIA-NN version higher than 2.0, if the search settings in DIA-NN does not contain --report-decoys, the final will still lack decoy features.

As a result, step-wise q-value estimation cannot be performed and mm.pp.infer_protein() cannot be applied to DIA-NN version under 2.0.

Therefore, the protein groups reported directly by DIA-NN should be used without re-inferring them in msmu.

Filtering - without decoy¶

For protein group–level q-values, use Lib.PG.Q.Value (when MBR is enabled) or Global.PG.Q.Value (when MBR is disabled) from the DIA-NN search results.

In [21]:

# Removing decoy features from DIA-NN output for mimicking data without decoy

mdata = mm.read_diann(identification_file=diann_idents)
del mdata["psm"].uns["decoy"]

mdata

# Removing decoy features from DIA-NN output for mimicking data without decoy mdata = mm.read_diann(identification_file=diann_idents) del mdata["psm"].uns["decoy"] mdata

Out[21]:

MuData object with n_obs × n_vars = 6 × 5038
  uns:	'_cmd'
  1 modality
    psm:	6 × 5038
      var:	'proteins', 'peptide', 'stripped_peptide', 'filename', 'charge', 'peptide_length', 'rt', 'decoy', 'contaminant', 'PEP', 'q_value'
      uns:	'level', 'search_engine', 'quantification', 'label', 'acquisition', 'identification_file', 'quantification_file'
      varm:	'search_result'
      layers:	None

In [22]:

# Adding PG Q-value column for filtering
mdata = mm.pp.add_filter(mdata, modality="psm", column="q_value", keep="lt", value=0.01)
mdata = mm.pp.add_filter(
    mdata, modality="psm", on="varm", key="search_result", column="Lib.PG.Q.Value", keep="lt", value=0.01
)
mdata = mm.pp.apply_filter(mdata, modality="psm", on="var")

mdata

# Adding PG Q-value column for filtering mdata = mm.pp.add_filter(mdata, modality="psm", column="q_value", keep="lt", value=0.01) mdata = mm.pp.add_filter( mdata, modality="psm", on="varm", key="search_result", column="Lib.PG.Q.Value", keep="lt", value=0.01 ) mdata = mm.pp.apply_filter(mdata, modality="psm", on="var") mdata

INFO - Applying var filters for psm: ['q_value_lt_0.01', 'Lib.PG.Q.Value_lt_0.01']

Out[22]:

MuData object with n_obs × n_vars = 6 × 4999
  uns:	'_cmd'
  1 modality
    psm:	6 × 4999
      var:	'proteins', 'peptide', 'stripped_peptide', 'filename', 'charge', 'peptide_length', 'rt', 'decoy', 'contaminant', 'PEP', 'q_value'
      uns:	'level', 'search_engine', 'quantification', 'label', 'acquisition', 'identification_file', 'quantification_file', 'filter'
      varm:	'search_result', 'filter'
      layers:	None

Normalization - without decoy¶

Once filtering is done, you can process and summarize the data to peptide and protein levels as shown below.

In [23]:

mdata = mm.pp.log2_transform(mdata, modality="psm")
mdata = mm.pp.normalize(mdata, modality="psm", method="median")

mdata = mm.pp.log2_transform(mdata, modality="psm") mdata = mm.pp.normalize(mdata, modality="psm", method="median")

Summarization - without decoy¶

Manually add protein group and peptide-type information from the DIA-NN search results into the peptide var:

• protein_group from DIA-NN's Protein.Group (string value representing the protein group IDs associated with the peptide)
• peptide_type from DIA-NN's Proteotypic (boolean value indicating whether the peptide is unique to a single protein group or shared among multiple protein groups).

No peptide filtering is performed at this stage.

In [24]:

mdata = mm.pp.to_peptide(mdata, calculate_q=False)

map_df = mdata["psm"].varm["search_result"][["Modified.Sequence", "Protein.Group", "Proteotypic"]]
map_df = map_df.drop_duplicates().set_index("Modified.Sequence")

mdata["peptide"].var["protein_group"] = mdata["peptide"].var_names.map(map_df["Protein.Group"])
mdata["peptide"].var["peptide_type"] = mdata["peptide"].var_names.map(map_df["Proteotypic"])
mdata["peptide"].var["peptide_type"] = ["unique" if x else "shared" for x in mdata["peptide"].var["peptide_type"]]

mdata = mm.pp.to_peptide(mdata, calculate_q=False) map_df = mdata["psm"].varm["search_result"][["Modified.Sequence", "Protein.Group", "Proteotypic"]] map_df = map_df.drop_duplicates().set_index("Modified.Sequence") mdata["peptide"].var["protein_group"] = mdata["peptide"].var_names.map(map_df["Protein.Group"]) mdata["peptide"].var["peptide_type"] = mdata["peptide"].var_names.map(map_df["Proteotypic"]) mdata["peptide"].var["peptide_type"] = ["unique" if x else "shared" for x in mdata["peptide"].var["peptide_type"]]

WARNING - Decoy data not found. Skipping decoy aggregation.
INFO - Building new peptide quantification data.

In [25]:

mdata = mm.pp.to_protein(mdata, top_n=3, rank_method="total_intensity", calculate_q=False)

mdata = mm.pp.to_protein(mdata, top_n=3, rank_method="total_intensity", calculate_q=False)

INFO - Applying var filters for peptide: ['peptide_type_eq_unique']
WARNING - Decoy data not found. Skipping decoy aggregation.

In [26]:

mdata

mdata

Out[26]:

MuData object with n_obs × n_vars = 6 × 6725
  uns:	'_cmd'
  3 modalities
    psm:	6 × 4999
      var:	'proteins', 'peptide', 'stripped_peptide', 'filename', 'charge', 'peptide_length', 'rt', 'decoy', 'contaminant', 'PEP', 'q_value'
      uns:	'level', 'search_engine', 'quantification', 'label', 'acquisition', 'identification_file', 'quantification_file', 'filter'
      varm:	'search_result', 'filter'
      layers:	None
    peptide:	6 × 969
      var:	'peptide', 'proteins', 'stripped_peptide', 'count_psm', 'PEP', 'protein_group', 'peptide_type'
      uns:	'level'
      layers:	None
    protein:	6 × 757
      var:	'count_psm', 'count_stripped_peptide', 'PEP'
      uns:	'level'
      layers:	None

Citation¶

Uszkoreit, J., Barkovits, K., Pacharra, S., Pfeiffer, K., Steinbach, S., Marcus, K., & Eisenacher, M. (2022). Dataset containing physiological amounts of spike-in proteins into murine C2C12 background as a ground truth quantitative LC-MS/MS reference. Data in Brief, 43, 108435.

Demichev, V., Messner, C. B., Vernardis, S. I., Lilley, K. S., & Ralser, M. (2020). DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nature methods, 17(1), 41-44.