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/main/data/sage_lfq"
sage_idents = f"{base_dir}/sage/results.sage.tsv"
sage_quants = f"{base_dir}/sage/lfq.tsv"
meta = f"{base_dir}/meta.csv"

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

Load Required Packages¶

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

In [2]:

import msmu as mm
import pandas as pd
import plotly.io as pio

pio.renderers.default = "png"

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

Determination of memory status is not supported on this 
 platform, measuring for memoryleaks will never fail

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")

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

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

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

INFO - Identification file loaded: (5000, 40)
INFO - Quantification file loaded: (3578, 12)
INFO - Decoy entries separated: (345, 15)

In [4]:

mdata

mdata

Out[4]:

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

Adding Metadata¶

Optionally, you can add metadata for samples to the mdata.obs dataframe. Make sure that the index of the metadata dataframe matches the sample names in mdata.obs.

In [5]:

meta_df = pd.read_csv(
    "https://raw.githubusercontent.com/bertis-informatics/msmu/refs/heads/main/data/sage_lfq/meta.csv"
)
meta_df = meta_df.set_index("sample_id")  # set the index to match sample id in mdata.obs

mdata.obs = mdata.obs.join(meta_df)
mdata.push_obs()  # update all modalities with the new obs data
mdata.obs

meta_df = pd.read_csv( "https://raw.githubusercontent.com/bertis-informatics/msmu/refs/heads/main/data/sage_lfq/meta.csv" ) meta_df = meta_df.set_index("sample_id") # set the index to match sample id in mdata.obs mdata.obs = mdata.obs.join(meta_df) mdata.push_obs() # update all modalities with the new obs data mdata.obs

Out[5]:

	set	sample_name	condition	replicate
QExHF04026	S1	G1-1	G1	1
QExHF04028	S1	G2-1	G2	1
QExHF04036	S1	G1-2	G1	2
QExHF04038	S1	G2-2	G2	2
QExHF04046	S1	G1-3	G1	3
QExHF04048	S1	G2-3	G2	3

Saving Raw MuData Object¶

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

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

[Filter] Applying var filters: ['q_value_lt_0.01', 'proteins_not_contains_contam_']

Out[7]:

MuData object with n_obs × n_vars = 6 × 7837
  obs:	'set', 'sample_name', 'condition', 'replicate'
  uns:	'_cmd'
  2 modalities
    psm:	6 x 4259
      obs:	'set', 'sample_name', 'condition', 'replicate'
      var:	'proteins', 'peptide', 'stripped_peptide', 'filename', 'scan_num', 'charge', 'peptide_length', 'missed_cleavages', 'semi_enzymatic', 'contaminant', 'PEP', 'q_value', 'rt', 'calcmass'
      uns:	'level', 'search_engine', 'quantification', 'label', 'acquisition', 'identification_file', 'quantification_file', 'decoy', 'filter', 'decoy_filter'
      varm:	'search_result', 'filter'
    peptide:	6 x 3578
      obs:	'set', 'sample_name', 'condition', 'replicate'
      uns:	'level'

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)
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

[Filter] Applying var filters: ['q_value_lt_0.01']

Out[9]:

MuData object with n_obs × n_vars = 6 × 7874
  obs:	'set', 'sample_name', 'condition', 'replicate'
  uns:	'_cmd'
  2 modalities
    psm:	6 x 4259
      obs:	'set', 'sample_name', 'condition', 'replicate'
      var:	'proteins', 'peptide', 'stripped_peptide', 'filename', 'scan_num', 'charge', 'peptide_length', 'missed_cleavages', 'semi_enzymatic', 'contaminant', 'PEP', 'q_value', 'rt', 'calcmass'
      uns:	'level', 'search_engine', 'quantification', 'label', 'acquisition', 'identification_file', 'quantification_file', 'decoy', '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")

[Filter] Applying var filters: ['q_value_lt_0.01', 'peptide_type_eq_unique']
INFO - Ranking features by 'median_intensity' to select top 3 features.
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

[Filter] Applying var filters: ['q_value_lt_0.01']

Out[13]:

MuData object with n_obs × n_vars = 6 × 9337
  obs:	'set', 'sample_name', 'condition', 'replicate'
  uns:	'_cmd'
  3 modalities
    psm:	6 x 4259
      obs:	'set', 'sample_name', 'condition', 'replicate'
      var:	'proteins', 'peptide', 'stripped_peptide', 'filename', 'scan_num', 'charge', 'peptide_length', 'missed_cleavages', 'semi_enzymatic', 'contaminant', 'PEP', 'q_value', 'rt', 'calcmass'
      uns:	'level', 'search_engine', 'quantification', 'label', 'acquisition', 'identification_file', 'quantification_file', 'decoy', 'filter', 'decoy_filter'
      varm:	'search_result', 'filter'
    peptide:	6 x 3615
      obs:	'set', 'sample_name', 'condition', 'replicate'
      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:	'set', 'sample_name', 'condition', 'replicate'
      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="condition", obs_column="sample_name")

mm.pl.plot_id(mdata, modality="protein", colorby="condition", obs_column="sample_name")

Intensity distribution plot¶

In [15]:

mm.pl.plot_intensity(mdata, modality="protein", groupby="condition", obs_column="sample_name")

mm.pl.plot_intensity(mdata, modality="protein", groupby="condition", obs_column="sample_name")

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.