imspy 0.2.24

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imspy - Python package for working with timsTOF raw data

Welcome to the imspy short introduction. This package is designed to work with timsTOF raw data files, which are generated by the Bruker timsTOF mass spectrometers. The package provides a high-level API for accessing raw data, as well as a chemistry module for working with peptide sequences. The package also includes algorithms for ion mobility and retention time prediction, as well as machine learning algorithms for data analysis. Want to see how to build full data processing pipelines with imspy? Check out the imspy_dda and timsim cmd tools as well as our publication pre-print on bioRxiv.

Raw data access

Establish a connection to a timsTOF raw file and access data

import numpy as np
from imspy.timstof import TimsDataset

# you can use in-memory mode for faster access, but it requires more memory
tdf = TimsDataset("path/to/rawfolder.d", in_memory=False)

# show global meta data table
print(tdf.global_meta_data)

# show frame meta data
print(tdf.meta_data)

# get the first frame (bruker frame indices start at 1)
frame = tdf.get_tims_frame(1)

# you can also use indexing
frame = tdf[1]

# print data as pandas dataframe
frame.df()

# get all spectra in a tims frame (sorted by scan = ion mobility)
spectra = frame.to_tims_spectra()

# get a slice of multiple frames
frames = tdf.get_tims_slice(np.array([1, 2, 3]))

# or, by using slicing
frames = tdf[1:4]

DDA data

from imspy.timstof import TimsDatasetDDA
# read a DDA dataset
tdf = TimsDatasetDDA("path/to/rawfolder.d", in_memory=False)

# get raw data of precursors together with their fragment ions
dda_fragments = tdf.get_pasef_fragments()

# the timsTOF re-fragments precursors below a certain intensity threshold,
# you can aggregate the data for increased sensitivity like so:
dda_fragments_grouped = dda_fragments.groupby('precursor_id').agg({
    'frame_id': 'first',
    'time': 'first',
    'precursor_id': 'first',
    # this will sum up the raw data of all fragments with the same precursor_id
    'raw_data': 'sum',
    'scan_begin': 'first',
    'scan_end': 'first',
    'isolation_mz': 'first',
    'isolation_width': 'first',
    'collision_energy': 'first',
    'largest_peak_mz': 'first',
    'average_mz': 'first',
    'monoisotopic_mz': 'first',
    'charge': 'first',
    'average_scan': 'first',
    'intensity': 'first',
    'parent_id': 'first',
})

# for convenience, you can calculate the inverse mobility 
# of the precursor ion by finding the maximum intensity along the scan dimension
mobility = dda_fragments_grouped.apply(
    lambda r: r.raw_data.get_inverse_mobility_along_scan_marginal(), axis=1
)

# add the inverse mobility to the grouped data as a new column
dda_fragments_grouped['mobility'] = mobility

DIA data

from imspy.timstof import TimsDatasetDIA
# read a DIA dataset
tdf = TimsDatasetDIA("path/to/rawfolder.d", in_memory=False)

The chemistry module

Basic usage

</code></pre>
<h3>Working with peptide sequences</h3>
<pre lang="python"><code>

Algorithms and machine learning

ion mobility and retention time prediction

</code></pre>
<h3>Locality sensitive hashing</h3>
<pre lang="python"><code>

Mixture models

</code></pre>
<h2>Pipeline: DDA data analysis (imspy_dda)</h2>
<pre lang="python"><code>

Pipeline: Synthetic raw data generation (timsim)