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Computational Analysis Toolbox

List of computational analysis tools

This list captures a selection of public computational analysis tools available from Allen Institute scientists. They're presented in alphabetical order with basic metadata about each tool. 

Simply browse the list or Ctrl + F search this page for keywords, e.g. mapping, to find the right tool for your use cases.

Please review the documentation in the GitHub repositories for more details. Questions about these tools can be submitted in the Allen Brain Map Community Forum.

Access the general Allen Institute GitHub repository for more tools and resources.

Allen Brain Cell Atlas - Data Access

Description: Jupyter notebooks that provide tutorials and examples for downloading and analysing data from the Allen Brain Cell (ABC) Atlas.

GitHub repository: https://alleninstitute.github.io/abc_atlas_access/intro.html

Language: Jupyter Notebook

Keywords: data access, visualization

Cell Type Mapper

Description: A python package for mapping single sell RNA sequencing data onto a cell type taxonomy such as that provided by the Allen Institute for Brain Science.

GitHub repository: https://github.com/AllenInstitute/cell_type_mapper

Language: Python

Keywords: mapping


Description: Package for gene selection and analysis of spatial transcriptomics data, including MERFISH. Gene panels for several published studies using smFISH and MERFISH from the Allen Institute used these scripts.

GitHub repository: https://github.com/AllenInstitute/mfishtools

Language: R

Keywords: spatial transcriptomics


Description: Companion GitHub page to scrattch.taxonomy which shows mapping predictions in determining cluster labels in a self-projection evaluation.

Website: https://allenbenchmark.org/taxonomies

Keywords: standards


Description: Single-cell RNA-seq analysis for transcriptomic type characterization (or "scrattch") is the umbrella package for the scrattch suite of R packages from the Allen Institute for Brain Science. It is modeled after the tidyverse package.

GitHub repository: https://github.com/AllenInstitute/scrattch 

Language: R

Keywords: clustering, visualization, mapping, standards


Description: Package for clustering analysis for extremely large single cell dataset. This library performs the same clustering as scrattch.hicat, but can work with millions of cells.

GitHub repository: https://github.com/AllenInstitute/scrattch.bigcat

Language: R

Keywords: clustering, visualization


Description: Package for Hierarchical, iterative clustering for analysis of transcriptomics

GitHub repository: https://github.com/AllenInstitute/scrattch.hicat 

Language: R

Keywords: clustering, visualization


Description: Package with several cell type mapping methods for application to single cell/nucleus RNA-seq data. Used for assigning cell types to Patch-seq data at the Allen Institute.

GitHub repository: https://github.com/AllenInstitute/scrattch.mapping

Language: R, Python

Keywords: mapping


Description: Generalized scripts for converting any single cell RNA-seq-based study into a standard taxonomy format. It also lists multiple published single-cell RNAseq taxonomies from the Allen Institute and other groups.

GitHub repository: https://github.com/AllenInstitute/scrattch.taxonomy

Language: R

Keywords: standards, data access


Description: Plotting functions for visualization of RNA-seq data. Many manuscript figures visualizing single cell/nucleus RNA-seq data from the Allen Institute were created using this package.

GitHub repository: https://github.com/AllenInstitute/scrattch.vis

Language: R

Keywords: visualization


Description: Python implementation of clustering method detailed in scrattch.hicat. Additional development for HMBA to (1) handle clustering human/NHP data with donor effects and (2) implement more computationally efficient methods utilizing GPUs for faster computations.

GitHub repository: https://github.com/AllenInstitute/transcriptomic_clustering/tree/hmba/dev

Language: Python

Keywords: clustering


Description: R package for adding quality metrics to cells in single cell RNA sequencing data

GitHub repository: https://github.com/AllenInstitute/QCR

Language: R

Keywords: shiny app, enhancers