Table 2.
List of recommended code bases and limitations to address each of the data modality specific and general challenges enumerated in the text
| Step | Code (source) | Current hurdles |
|---|---|---|
| Physiology | ||
| Micro (ephys) | Open Ephys (https://open-ephys.org/) | Limited analysis |
| Micro (opto) | CaImAn (https://github.com/flatironinstitute/CaImAn) | Requires manual fine-tuning |
| Meso (fMRI) | C-PAC (https://fcp-indi.github.io) | Single institution developing |
| Macro (behavior) | None | NA |
| Anatomy | ||
| Nano (EM) | NeuroData Cloud (https://neurodata.io/nd_cloud/) | Centralized |
| Micro (LM) | TeraSticher (https://abria.github.io/TeraStitcher/) | Only does linear registration |
| Meso (sMRI & dMRI) | DiPy (https://dipy.org) | No pipelines |
| Genetics | ||
| Nano (in situ) | None | NA |
| Micro (scRNAseq) | None | NA |
| Meso (Tissue) | None | NA |
| Systems | ||
| Storage | CloudVolume (https://github.com/seung-lab/cloud-volume) | Not yet widely adopted |
| Compression | Brotli (https://github.com/google/brotli) | Not yet widely adopted |
| Pipelines | Docker (https://www.docker.com) | Complex to set up |
| Visualization | NeuroGlancer (https://github.com/google/neuroglancer) | Lacks annotation support |
| Statistics | ||
| Tabular | Scikit-Learn (https://scikit-learn.org/stable/) | Parallel execution is weak |
| Images | Scikit-Image (https://scikit-image.org/) | Lacks sophisticated methods |
| Time series | StatsModels (https://www.statsmodels.org/stable/index.html) | Lacks sophisticated methods |
| Networks | NetworkX (https://networkx.github.io) | Lacks sophisticated methods |
Abbreviations: C-PAC, configurable pipelines for the analysis of connectomes; CaImAn, calcium imaging analysis; dMRI, diffusion MRI; EM, electron microscopy; ephys, electrophysiology; fMRI, functional MRI; LM, light microscopy; NA, not applicable; opto, optical microscopy; scRNAseq, single-cell RNA sequencing; sMRI, structural MRI.