DDA - Label-Free¶

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

Data Preparation¶

Original dataset is from PXD012986 (Uszkoreit et al., 2022) and search was performed with Sage v0.14.7.

For demonstration purposes, the example dataset was reduced to six samples and a total of 5,000 PSMs.

In [1]:

base_dir = "https://raw.githubusercontent.com/bertis-informatics/msmu/refs/heads/dev/data/sage_lfq"
sage_idents = f"{base_dir}/sage/results.sage.tsv"
sage_quants = f"{base_dir}/sage/lfq.tsv"
sdrf = f"{base_dir}/meta.sdrf.tsv"

base_dir = "https://raw.githubusercontent.com/bertis-informatics/msmu/refs/heads/dev/data/sage_lfq" sage_idents = f"{base_dir}/sage/results.sage.tsv" sage_quants = f"{base_dir}/sage/lfq.tsv" 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 [2]:

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 Sage, MaxQuant, and FragPipe formats.

For this tutorial, we will use the Sage output as an example.

read_sage() function reads the Sage output files (lfq.tsv, results.sage.tsv) and creates modalities at MuData object.

In [3]:

# Sage format
mdata = mm.read_sage(
    identification_file=sage_idents,
    quantification_file=sage_quants,
    label="label_free",
    sdrf_file=sdrf,
)

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

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

# Sage format mdata = mm.read_sage( identification_file=sage_idents, quantification_file=sage_quants, label="label_free", sdrf_file=sdrf, ) # # MaxQuant format # mdata = mm.read_maxquant( # identification_file="path_to_maxquant_output", # sdrf_file=sdrf, # label="label_free", # acquisition="dda", # ) # # FragPipe format # mdata = mm.read_fragpipe( # identification_file="path_to_fragpipe_output", # sdrf_file=sdrf, # label="label_free", # acquisition="dda", # )

In [4]:

mdata

mdata

Out[4]:

MuData object with n_obs × n_vars = 6 × 8233
  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_data_file', 'comment_fraction_identifier', 'comment_technical_replicate', 'comment_label', 'comment_instrument', 'comment_cleavage_agent_details', 'factor_value_condition'
  uns:	'sdrf', '_cmd'
  2 modalities
    psm:	6 x 4655
      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_data_file', 'comment_fraction_identifier', 'comment_technical_replicate', 'comment_label', 'comment_instrument', 'comment_cleavage_agent_details', 'factor_value_condition'
      var:	'proteins', 'peptide', 'stripped_peptide', 'filename', 'scan_num', 'charge', 'peptide_length', 'expmass', 'calcmass', 'rt', 'missed_cleavages', 'semi_enzymatic', 'contaminant', 'PEP', 'score', 'q_value', 'decoy'
      uns:	'level', 'search_engine', 'quantification', 'label', 'acquisition', 'identification_file', 'quantification_file', 'decoy', 'sdrf'
      varm:	'search_result'
    peptide:	6 x 3578
      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_data_file', 'comment_fraction_identifier', 'comment_technical_replicate', 'comment_label', 'comment_instrument', 'comment_cleavage_agent_details', 'factor_value_condition'
      uns:	'level', 'sdrf'

Saving Raw MuData Object¶

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

In [5]:

mdata["psm"].var

mdata["psm"].var

Out[5]:

	proteins	peptide	stripped_peptide	filename	scan_num	charge	peptide_length	expmass	calcmass	rt	missed_cleavages	semi_enzymatic	contaminant	PEP	score	q_value	decoy
QExHF04048.35738	D3Z2H2;D3Z2H7;D3Z7H6;E9Q8Z4;E9Q8Z5;E9Q8Z6;E9Q8...	AAALVLQTIWGYK	AAALVLQTIWGYK	QExHF04048	35738	3	13	1432.80710	1432.80790	96.601020	0	0	0	4.910910e-11	25.524469	0.000003	0
QExHF04028.32557	A0A286YDI8;G3X972	GQVPPLVTTNFLVK	GQVPPLVTTNFLVK	QExHF04028	32557	2	14	1511.87290	1511.87110	88.938370	0	0	0	1.174559e-12	39.376776	0.000003	0
QExHF04036.26748	Q61166	NIELIC[+57.0215]QENEGENDPVLQR	NIELICQENEGENDPVLQR	QExHF04036	26748	2	19	2269.07100	2269.06450	74.547970	0	0	0	2.329968e-69	71.549478	0.000003	0
QExHF04028.17723	A0A0A6YY12;Q3U9G9	LGAFDLK	LGAFDLK	QExHF04028	17723	2	7	762.42847	762.42755	52.822567	0	0	0	3.912462e-03	30.037241	0.000177	0
QExHF04036.42576	Q8BMJ2	FSEAEHWLDYFPPLAVQDLK	FSEAEHWLDYFPPLAVQDLK	QExHF04036	42576	3	20	2404.17940	2404.17360	113.637380	0	0	0	1.198105e-33	51.200146	0.000003	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
QExHF04038.39796	P20108	DYGVLLESAGIALR	DYGVLLESAGIALR	QExHF04038	39796	2	14	1475.80030	1475.79850	106.520210	0	0	0	2.429070e-71	53.174612	0.000003	0
QExHF04048.44300	Q9JHU4	GIFEALRPLETLPVEGLIR	GIFEALRPLETLPVEGLIR	QExHF04048	44300	3	19	2122.21920	2122.21500	117.822130	0	0	0	3.890935e-17	69.300551	0.000003	0
QExHF04048.17559	P56480	IPVGPETLGR	IPVGPETLGR	QExHF04048	17559	2	10	1037.58740	1037.58690	52.281140	0	0	0	4.175938e-30	43.566094	0.000003	0
QExHF04026.48861	D3YZ68;D3Z3I8;P10126;P62631	NM[+15.9949]ITGTSQADC[+57.0215]AVLIVAAGVGEFEAGISK	NMITGTSQADCAVLIVAAGVGEFEAGISK	QExHF04026	48861	3	29	2925.46500	2924.42550	130.408460	0	0	0	9.746395e-24	54.283167	0.000003	0
QExHF04048.7284	Q9Z1Z2	GHFGPIHC[+57.0215]VR	GHFGPIHCVR	QExHF04048	7284	3	10	1178.57760	1178.57680	26.950920	0	0	0	3.588228e-81	43.212689	0.000003	0

4655 rows × 17 columns

In [6]:

mdata.write_h5mu("dda_lfq_PXD012986_raw.h5mu")

mdata.write_h5mu("dda_lfq_PXD012986_raw.h5mu")

Handling PSM level¶

Filtering - PSM¶

You can filter the data based on the column values, such as q-value. You can also filter the data based on string containment, which can be useful for removing contaminants or decoys.

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 and remove contaminants (protein IDs containing "contam_").

In [7]:

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

mdata

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

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

Out[7]:

MuData object with n_obs × n_vars = 6 × 7837
  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_data_file', 'comment_fraction_identifier', 'comment_technical_replicate', 'comment_label', 'comment_instrument', 'comment_cleavage_agent_details', 'factor_value_condition'
  uns:	'sdrf', '_cmd'
  2 modalities
    psm:	6 x 4259
      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_data_file', 'comment_fraction_identifier', 'comment_technical_replicate', 'comment_label', 'comment_instrument', 'comment_cleavage_agent_details', 'factor_value_condition'
      var:	'proteins', 'peptide', 'stripped_peptide', 'filename', 'scan_num', 'charge', 'peptide_length', 'expmass', 'calcmass', 'rt', 'missed_cleavages', 'semi_enzymatic', 'contaminant', 'PEP', 'score', 'q_value', 'decoy'
      uns:	'level', 'search_engine', 'quantification', 'label', 'acquisition', 'identification_file', 'quantification_file', 'decoy', 'sdrf', 'filter', 'decoy_filter'
      varm:	'search_result', 'filter'
    peptide:	6 x 3578
      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_data_file', 'comment_fraction_identifier', 'comment_technical_replicate', 'comment_label', 'comment_instrument', 'comment_cleavage_agent_details', 'factor_value_condition'
      uns:	'level', 'sdrf'

Handling peptide level¶

Summarization - peptide¶

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

In [8]:

mdata = mm.pp.to_peptide(mdata)

mdata = mm.pp.to_peptide(mdata)

INFO - Peptide-level identifications: 3634 (3615 at 1% FDR)
INFO - Using existing peptide quantification data.

Filtering - peptide¶

In [9]:

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[9]:

MuData object with n_obs × n_vars = 6 × 7874
  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_data_file', 'comment_fraction_identifier', 'comment_technical_replicate', 'comment_label', 'comment_instrument', 'comment_cleavage_agent_details', 'factor_value_condition'
  uns:	'sdrf', '_cmd'
  2 modalities
    psm:	6 x 4259
      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_data_file', 'comment_fraction_identifier', 'comment_technical_replicate', 'comment_label', 'comment_instrument', 'comment_cleavage_agent_details', 'factor_value_condition'
      var:	'proteins', 'peptide', 'stripped_peptide', 'filename', 'scan_num', 'charge', 'peptide_length', 'expmass', 'calcmass', 'rt', 'missed_cleavages', 'semi_enzymatic', 'contaminant', 'PEP', 'score', 'q_value', 'decoy'
      uns:	'level', 'search_engine', 'quantification', 'label', 'acquisition', 'identification_file', 'quantification_file', 'decoy', 'sdrf', 'filter', 'decoy_filter'
      varm:	'search_result', 'filter'
    peptide:	6 x 3615
      var:	'peptide', 'proteins', 'stripped_peptide', 'count_psm', 'PEP', 'q_value'
      uns:	'level', 'decoy', 'filter', 'decoy_filter'
      varm:	'filter'

Normalization¶

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

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

In [10]:

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

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

Protein inference¶

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

In [11]:

mdata = mm.pp.infer_protein(mdata)

mdata = mm.pp.infer_protein(mdata)

INFO - Starting protein inference
INFO - Initial proteins: 3651
INFO - Removed indistinguishable: 1586
INFO - Removed subsettable: 539
INFO - Removed subsumable: 2
INFO - Total protein groups: 1524

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 [12]:

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

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

INFO - Applying var filters for peptide: ['q_value_lt_0.01', 'peptide_type_eq_unique']
INFO - Protein-level identifications: 1489 (1463 at 1% FDR)

Filtering - protein¶

In [13]:

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[13]:

MuData object with n_obs × n_vars = 6 × 9337
  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_data_file', 'comment_fraction_identifier', 'comment_technical_replicate', 'comment_label', 'comment_instrument', 'comment_cleavage_agent_details', 'factor_value_condition'
  uns:	'sdrf', '_cmd'
  3 modalities
    psm:	6 x 4259
      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_data_file', 'comment_fraction_identifier', 'comment_technical_replicate', 'comment_label', 'comment_instrument', 'comment_cleavage_agent_details', 'factor_value_condition'
      var:	'proteins', 'peptide', 'stripped_peptide', 'filename', 'scan_num', 'charge', 'peptide_length', 'expmass', 'calcmass', 'rt', 'missed_cleavages', 'semi_enzymatic', 'contaminant', 'PEP', 'score', 'q_value', 'decoy'
      uns:	'level', 'search_engine', 'quantification', 'label', 'acquisition', 'identification_file', 'quantification_file', 'decoy', 'sdrf', 'filter', 'decoy_filter'
      varm:	'search_result', 'filter'
    peptide:	6 x 3615
      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_data_file', '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'
    protein:	6 x 1463
      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_data_file', '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'

Visualization¶

ID plot¶

In [14]:

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 [15]:

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 [16]:

mdata.write_h5mu("dda_lfq_PXD012986.h5mu")

mdata.write_h5mu("dda_lfq_PXD012986.h5mu")

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.

Lazear, M. R. (2023). Sage: an open-source tool for fast proteomics searching and quantification at scale. Journal of Proteome Research, 22(11), 3652-3659.