element-interface 0.3.0

Loaders of neurophysiological data into the DataJoint Elements

DataJoint Elements Interface for external analysis packages

• This repository serves a few purposes:

  • Load neurophysiological data into the DataJoint Elements.
  • Trigger packages used for neurophysiological data processing.
  • Functions common to the DataJoint Elements (e.g. search directory tree for data files).

• See DataJoint Elements for descriptions of the elements and workflows developed as part of this initiative.

Architecture

• The functions for each acquisition and analysis package are stored within a separate module.

• Acquisition packages

  • scanimage_utils.py

• Analysis packages

  • suite2p_loader.py
  • caiman_loader.py
  • run_caiman.py

Installation

• Install element-interface:

  pip install element-interface
  

• This package is to be used in combination with the other DataJoint Elements (e.g. element-calcium-imaging). The installation of packages used for data processing (e.g. Suite2p) will be included within the respective DataJoint Element (e.g. element-calcium-imaging).

Usage

• See the workflow-calcium-imaging and element-calcium-imaging repositories for example usage of element-interface.

• ScanImage

  import scanreader
  from element_interface import scanimage_utils
  
  # ScanImage file path
  scan_filepath = '<imaging_root_data_dir>/subject1/session0/<scan_filename>.tif'
  
  loaded_scan = scanreader.read_scan(scan_filepath)
  
  recording_time = scanimage_utils.get_scanimage_acq_time(loaded_scan)
  header = scanimage_utils.parse_scanimage_header(loaded_scan)
  

• Suite2p

  from element_interface import suite2p_loader
  
  # Directory containing Suite2p output
  output_dir = '<imaging_root_data_dir>/subject1/session0/suite2p'
  
  loaded_dataset = suite2p_loader.Suite2p(output_dir)
  

• Suite2p wrapper functions for triggering analysis

  • Functions to independently run Suite2p's motion correction, segmentation, and deconvolution steps. These functions currently work for single plane tiff files. If running all Suite2p pre-processing steps concurrently, these functions are not required and one can run suite2p.run_s2p().

  • These wrapper functions were developed primarily because run_s2p cannot individually run deconvolution using the spikedetect flag (Suite2p Issue #718).

  • Requirements

    • ops dictionary
    • db dictionary

  • Note that the ops dictionary returned from the motion_correction_suite2p and segmentation_suite2p functions is only a subset of the keys generated with the suite2p.default_ops() function.

    import element_interface
    import suite2p
    
    ops = dict(suite2p.default_ops(), nonrigid=False, two_step_registration=False)
    
    db = {
         'h5py': [], # single h5 file path
         'h5py_key': 'data',
         'look_one_level_down': False, # search for TIFFs in all subfolders 
         'data_path': ['/test_data'], # list of folders with tiffs                                    
         'subfolders': [], # choose subfolders of 'data_path'
         'fast-disk': '/test_data' # string path for storing binary file 
         }
    
    ops.update(do_registration=1, roidetect=False, spikedetect=False)
    motion_correction_ops = element_interface.suite2p_trigger.motion_correction_suite2p(ops, db)
    
    motion_correction_ops.update(do_registration=0, roidetect=True, spikedetect=False)
    segmentation_ops = element_interface.suite2p_trigger.segmentation_suite2p(motion_correction_ops, db)
    
    segmentation_ops.update(do_registration=0, roidetect=False, spikedetect=True)
    spikes = element_interface.suite2p_trigger.deconvolution_suite2p(segmentation_ops, db)
    

• CaImAn

  from element_interface import caiman_loader
  
  # Directory containing CaImAn output
  output_dir = '<imaging_root_data_dir>/subject1/session0/caiman'
  
  loaded_dataset = caiman_loader.CaImAn(output_dir)