HoloViz+Bokeh for Neuroscience

Warning

This is a development space and changing rapidly.

Why does Neuroscience need HoloViz+Bokeh?

We hypothesize the process of science stands to benefit from having the option to suddenly become interactive and shareable - allowing for the poking or plucking, pushing or pulling, drilling in or out, grouping or separating, and sending or receiving of what would otherwise be a static snapshot of the data. The combined use of HoloViz and Bokeh tools could provide the interactivity, shareability, and scalability needed to support research as a collective action rather than a collection of solitary observations.

What is the purpose of this GitHub repository?

One of our overall goals is to facilitate the creation of fully open, reproducible, OS-independent, browser-based workflows for biomedical research primarily using sustainable, domain-independent visualization tools. In support of this goal, this repository is the development ground for optimization of HoloViz and Bokeh tools within the realm of neuroscience.

Specific repo objectives:

• Workflow Development: Host the development versions of workflows, facilitating consistency and code sharing across them.
• Collaboration Hub: Foster collaborative efforts between the developer teams and scientific collaborators outside these groups - aiming to effectively tailor development to specific requirements of the neuroscience community.
• Project Management: Track ideas, feedback, requirements, specifications, issues, requests, topic research, and progress in the associated Project Board and Meeting Notes.
• Host Domain-Specific Scripts: For instance, simulated data generators.
• Temporarily Host Benchmark Tooling: Eventually, to be be migrated to a dedicated, domain-independent repository.

What are workflows?

This repository contains developmental versions of workflows, which can be loosely categorized into two types: generalized and specialized. Generalized workflows aim to be broadly applicable and primarily utilize domain-independent Pandata tools such as Numpy, Pandas, Xarray, SciPy, etc. These generalized workflows serve as the foundational building blocks for specialized workflows. Specialized workflows are designed to cater to specific contexts and have no limitations on the use of domain-specific tools.

Generalized Workflows in Development:

Title	Example Modality	Thumbnail	Info & Links	Description
Multi-Channel Timeseries	eeg, ephys		⚠️ workflow	Synchronized examination of stacked time-series with large data handling, scale bar, annotations, minimap, and signal grouping.
Deep Image Stack	miniscope imaging		⚠️ workflow	Efficient visualization of deep 2D calcium imaging movies with, playback controls, 2D annotation, scale bar, time views, intensity histogram, and summary statistics.
Waveform	ephys		⚠️ workflow	Oscilloscope-style display of action potential waveform snippets
Spike Raster	ephys		⚠️ workflow	Efficient visualization of large-scale neuronal spike time data, with a simple API, aggregate views of spike counts, and spike-level metadata management

• Streaming data - extend the ephys, eeg, and/or video viewer workflows to display live streaming data.
• Multimodal - visualizing and aligning ca-imaging with simultaneously recorded (but differently sampled) timeseries like EEG, EMG, and/or behavior. Alternatively, visualizing behavioral video (eye tracking, maze running) with timeseries data.
• Linked electrode-array layout

Specialized Workflows in Development:

Title	Example Modality	Thumbnail	Info & Links	Description
Neuroglancer notebook	electron microscopy, histology		⚠️ workflow	Notebook-based workflow for visualizing 3D volumetric data in a Neuroglancer application

• Spike Motif
• MNE integration
• Minian CNMF Temporal update parameter exploration app long timeseries improvement workflows/neuroglancer_notebook/assets/20240612_neuroglancerNB.png

Dissemination

• Workflows will be shared with the broader scientific community as they are ready. The target date for a first round of workflows is the end of 2024. Completed workflows will be listed on examples.holoviz.org, while select aspects will also go into the relevant Bokeh and HoloViz documentation pages.
• Workflow progress will be presented at the CZI open science conference in Boston, MA in June 2024.
• If you have ideas for where our workflows might be cross-linked of hosted, please reach out! We would love it if there was also a central place for bioscience workflows, like the Geoscience community has with Project Pythia.

Get Involved

• We are actively looking for opportunities to deliver tutorials, workshops, or other educational resources to help researchers in underrepresented communities effectively utilize our tools. Reach out on Discord if you want to brainstorm some ideas!
• Visit the Community page on HoloViz.org for more ways to join the conversation.
• If you want to contribute to the workflows or underlying libraries, read on for installation and contribution instructions.

Who is behind this effort?

This work is a collaboration between developers and scientists, and some developer-scientists. While some contributions are visible through the GitHub repo, many other contributions are less visible yet equally important.

Funding:

• 2023 - 2024: Chan Zuckerberg Initiative. Learn more in the grant announcement.

Need to contact us?

• Project Lead: Dr. Demetris Roumis (@droumis on Discord)
• HoloViz Director: Dr. James (Jim) Bednar (@jbednar on Discord)
• Bokeh Director: Bryan Van de Ven ([email protected])

---

Contributors

Installation for individual workflows with Conda

Prerequisites

Before installing the workflow environments, make sure you have Miniconda installed. If not, you can download and install it from the official site.

Initial Installation Steps

1. Clone the Repository: Clone the neuro repository to your local machine.

   git clone https://github.com/holoviz-topics/neuro.git

2. Navigate to Workflow: Change to the directory of the workflow you're interested in.

   cd neuro/workflows/<workflow>

3. Create Environment: Use conda to create a new environment from the environment.yml file.

   conda env create -f environment.yml

4. Activate Environment: After the environment is created, activate it.

   conda activate <environment>

Updating Workflow Environments

If you've already installed a workflow environment and the environment.yml file has been updated, follow these steps to update the environment:

1. Update Repository: Pull the latest changes from the repository.

   git pull

2. Navigate to Workflow: Go to the directory of the workflow you're interested in.

   cd neuro/workflows/<workflow>

3. Update Environment: Update the existing Conda environment based on the latest environment.yml file.

   conda env update -f environment.yml --prune

The --prune option will remove packages from the environment not present in the updated environment.yml file.

---

Resources for Contributing

• Task Management: As workflows are developed and honed, performance and UI bottlenecks will be identified and addressed. Some improvements for the workflows themselves will be within this repo, but many improvements will be in the appropriate underlying libraries within the HoloViz, Bokeh, or other GitHub Organizations. We will do our best to track the disparate tasks related to these efforts into this project board.
• Communication:
  • Meeting minutes: Logged in the Wiki > Meeting Notes whenever possible.
  • HoloViz Discord #neuro channel for real-time chat (if archived, post on the General HoloViz Discord channel)
  • holoviz-topics/neuro GitHub repo issue tracker
• Specifications and Research: The Wiki has some data specifications and modality notes.
• Data Generation: To assist the development using real data, some workflows utilize simple data generators to help benchmark across data and parameter space. As the data generators/simulators can be useful to multiple workflows, they are kept as a separate module (/src/neurodatagen).
• Visualization source code: If there is visualization code or utilities that we want to live separate from the individual workflows, we can store them in /src/hvneuro for now. However, this should be considered a temporary space until the code can be incorporated into existing libraries, or live in particular workflows.
• Repo Structure and dev patterns:

  /workflows
    /example1
      readme_example1.md
      workflow_example1.ipynb
      environment.yml
      /dev
          date_example-workflow_task.ipynb
  

  • Use readme_<workflow>.md for any essential workflow-specific info or links.
  • Maintain workflow_<workflow>.ipynb as the latest version of the workflow.
  • Each workflow should have an environment.yml with which to create a conda env
  • The dev dir in each workflow is scratch space. There is no expectation that anything here is maintained.