Package: deepof
A suite for postprocessing time-series extracted from videos of freely moving rodents usingDeepLabCutandSLEAP.You can use this package to either extract pre-defined motifs from the time series (such as time-in-zone, climbing,
basic social interactions) or to embed your data into a sequence-aware latent space to extract meaningful motifs in an
unsupervised way! Both of these can be used within the package, for example, to automatically
compare user-defined experimental groups. The package is compatible with single and multi-animal DLC 2.X, and SLEAP projects.How do I start?Installation:The easiest way to install DeepOF is to usepip. Create and activate a virtual environment with Python >=3.9 and <3.11, for example using conda:condacreate-ndeepofpython=3.9Then, activate the environment and install DeepOF:condaactivatedeepof
pipinstalldeepofAlternatively, you can download our pre-builtDocker image,
which contains all compatible dependencies:# download the latest available imagedockerpulllucasmiranda42/deepof:latest# run the image in interactive mode, enabling you to open python and import deepofdockerrun-itlucasmiranda42/deepofOr usepoetry:# after installing poetry and clonning the DeepOF repository, just runpoetryinstall# from the main directoryNOTE: installation via pip is at the moment not compatible with Apple Silicon. If you'd like to install DeepOF on such machines,
please use either poetry or Docker. You should also install hdf5 usinghomebrew, as described inthisissue.Before we delve in:DeepOF relies heavily on DeepLabCut and SLEAP output. Thorough tutorials on how to get started with pose estimation using DLC can be foundhere, and for SLEAPhere.
Once your videos are processed and tagged, you can use DeepOF to extract and annotate your motion-tracking time-series. While many features in DeepOF can work regardless of the set of labels used, we currently recommend using videos from a top-down perspective, and follow our recommended
set of labels (which can be found in the full documentation page). Pre-trained models following this scheme, and capable of recognizing eitherC57Bl6mice alone, orC57Bl6andCD1mice can be downloaded fromour repository.Basic usage:The main module with which you'll interact is calleddeepof.data. Let's import it and create a project:importdeepof.datamy_deepof_project=deepof.data.Project(project_path=".",# Path where to create project filesvideo_path="/path/to/videos",# Path to DLC or SLEAP tracked videostable_path="/path/to/tables",# Path to DLC or SLEAP outputproject_name="my_deepof_project",# Name of the current projectexp_conditions={exp_ID:exp_condition}# Dictionary containing one or more experimental conditions per provided video)This command will create adeepof.data.Projectobject storing all the necessary information to start. There are
many parameters that we can set here, but let's stick to the basics for now.One you have this, you can run you project using the.create()method, which will do quite a lot of computing under
the hood (load your data, smooth your trajectories, compute distances, angles, and areas between body parts, and save all
results to disk). The returned object belongs to thedeepof.data.Coordinatesclass.my_project=my_project.create(verbose=True)Once you have this, you can do several things! But let's first explore how the results of those computations mentioned
are stored. To extract trajectories, distances, angles and/or areas, you can respectively type:my_project_coords=my_project.get_coords(center="Center",polar=False,align="Nose",speed=0)my_project_dists=my_project.get_distances(speed=0)my_project_angles=my_project.get_angles(speed=0)my_project_areas=my_project.get_areas(speed=0)Here, the data are stored asdeepof.data.table_dictinstances. These are very similar to python dictionaries
with experiment IDs as keys and pandas.DataFrame objects as values, with a few extra methods for convenience. Peeping
into the parameters you see in the code block above,centercenters your data (it can be either a boolean or
one of the body parts in your model! in which case the coordinate origin will be fixed to the position of that point);polarmakes the.get_coords()method return polar instead of Cartesian coordinates, andspeedindicates the derivation level to apply (0 is position-based, 1 speed, 2 acceleration, 3 jerk, etc). Regardingalignandalign-inplace, they take care of aligning the animal position to the y Cartesian axis: if we
center the data to "Center" and setalign="Nose", align_inplace=True, all frames in the video will be aligned in a
way that will keep the Center-Nose axis fixed. This is useful to constrain the set of movements that one can extract
with our unsupervised methods.As mentioned above, the two main analyses that you can run are supervised and unsupervised. They are executed by
the.supervised_annotation()method, and the.deep_unsupervised_embedding()methods of thedeepof.data.Coordinatesclass, respectively.supervised_annot=my_project.supervised_annotation()gmvae_embedding=my_project.deep_unsupervised_embedding()The former returns adeepof.data.TableDictobject, with a pandas.DataFrame per experiment containing a series of
annotations. The latter is a bit more complicated: it returns a series of objects that depend on the model selected (we
offer three flavours of deep clustering models), and allow for further analysis comparing cluster expression and dynamics.That's it for this (very basic) introduction. Check out the tutorials andfull documentationfor details!Cite us!If you use DeepOF for your research, please consider citing the following:@article{DeepOF:JOSS,title={{DeepOF: a Python package for supervised and unsupervised pattern recognition in mice motion tracking data}},year={2023},journal={Journal of Open Source Software},author={Miranda, Lucas and Bordes, Joeri and P{\"{u}}tz, Benno and Schmidt, Mathias V. and M{\"{u}}ller-Myhsok, Bertram},number={86},month={6},pages={5394},volume={8},url={https://joss.theoj.org/papers/10.21105/joss.05394},doi={10.21105/JOSS.05394},issn={2475-9066}}@article{DeepOF:NCOMMS,doi={10.1038/s41467-023-40040-3},url={https://doi.org/10.1038/s41467-023-40040-3},year={2023},month=jul,publisher={Springer Science and Business Media {LLC}},volume={14},number={1},author={Joeri Bordes and Lucas Miranda and Maya Reinhardt and Sowmya Narayan and Jakob Hartmann and Emily L. Newman and Lea Maria Brix and Lotte van Doeselaar and Clara Engelhardt and Larissa Dillmann and Shiladitya Mitra and Kerry J. Ressler and Benno P\"{u}tz and Felix Agakov and Bertram M\"{u}ller-Myhsok and Mathias V. Schmidt},title={Automatically annotated motion tracking identifies a distinct social behavioral profile following chronic social defeat stress},journal={Nature Communications}}All data and code used to generate the results in the NCOMMS paper are availablehere(password:DeepOF2023).IssuesIf you encounter any problems while using this package, please open an issue in theissue tracker.ContributionsWe welcome contributions from the community! If you want to contribute to this project, please check out ourcontribution guidelines.This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No.  813533