scrattch

Single-cell RNA-seq analysis for transcriptomic type characterization

This 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. You can use scrattch to automatically install or update any of the underlying packages.

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Scrattch packages

Scrattch includes several packages for clustering, mapping, and data formatting and visualization, along with example data for demos. These include:

Data preparation: file formats and schema

• scrattch.taxonomy - Taxonomy building scripts for RNA-seq based taxonomies following the Allen Institute (AIT) schema TO BE MOVED SOON.
• scrattch.io - [deprecated]. Library for file handling and data formatting, replaced by scrattch.taxonomy in 2024

Data analysis: cell clustering and mapping (also called label transfer)

• scrattch.hicat - Hierarchical, iterative clustering for analysis of transcriptomics
• scrattch.bigcat - Clustering analysis for extremely large single cell dataset
• scrattch.mapping - Generalized mapping scripts for RNA-seq, Patch-seq or any gene expression data
• scrattch.patchseq - Functions for generating additional QC metrics and output files for patch-seq analysis

Data visualization

• scrattch.vis - Plotting functions for visualization of RNA-seq data

Example data: small RNA-seq data sets

• tasic2016data - Data from Tasic, et al. (2016), which is used for demos
• hodge2019data - Data from Hodge, et al. (2019), which is used for demos

If you're interested in only one of these modules, you can install them separately. That said, we recommend using the installation instructions below to install combinations of scrattch packages to ensure they interact properly.

Related content

Several related websites and R and python libraries are outside of the scrattch suite, but are either used as part of scrattch libraries or directly work with scrattch outputs. These include (but are not limited to):

• bmark - Standardized strategies for benchmarking clustering and mapping results
• transcriptomic_clustering - Python implementation of scrattch.hicat clustering
• cell_type_mapper - Python implementation of hierarchical mapping algorithm used in scrattch.mapping and MapMyCells
• ACE - R Shiny and web-based app for comparison of annotations, including clustering and mapping results
• mfishtools - Functions for gene selection and analysis of spatial transcriptomics data

Installation

We strongly encourage the use of docker to the scrattch suite. In particular, several functions in scrattch.taxonomy and scrattch.mapping have known issues in certain R environments. That said, we provide options for installing and running R in both a docker environment and through standard R approaches.

Using docker

The current docker version is accessible through docker hub via: ('jeremyinseattle/scrattch')[https://hub.docker.com/r/jeremyinseattle/scrattch]. As of 20 December 2024 the version is jeremyinseattle/scrattch:0.7.1.

Docker can be run on some HPC environments that use singularity as follows:

• Non-interactive: singularity shell --cleanenv docker://jeremyinseattle/scrattch:0.7.1 Rscript YOUR_CODE.R
• Interactive: singularity shell --cleanenv docker://jeremyinseattle/scrattch:0.7.1
• To create a sif file for use in other environments: singularity pull scrattch:0.7.1.sif docker://jeremyinseattle/scrattch:0.7.1

Instructions for using Docker in other environments will be posted soon; in the meantime, please post an issue if you can't figure it out.

Running scrattch in R

While we advise using the provided docker, you can install all scrattch packages along with their GitHub and BioConductor dependencies, as follows:

devtools::install_github("AllenInstitute/scrattch")
scrattch::install_scrattch()

Note that doMC may need to be installed manually from the download link at https://r-forge.r-project.org/R/?group_id=947 if you use Windows.

Installing previous versions

Two historical versions of scrattch are included in this package. These can be safely run without using docker, but are missing several recent components of the scrattch suite.

• scrattch_2023 is the stable version of the package prior to the release of scrattch.mapping, scrattch.taxonomy, scrattch.patchseq, and hodge2019data THIS NEEDS TO BE ARCHIVED PROPERLY
• archive is the original package from ~2018, and should not be used for most folks

Should you need one of these previous versions, they can still be installed using:

devtools::install_github("AllenInstitute/scrattch", ref = "scrattch_2023") # -OR-
devtools::install_github("AllenInstitute/scrattch", ref = "archive")

Documentation

THIS NEEDS TO BE UPDATED

There are now a lot of functions available in scrattch packages. To assist in finding what package a function is stored in, you can check this CSV file stored in the scrattch umbrella package:
scrattch function list (OUT OF DATE)

You can find a detail description of all scrattch.taxonomy functions here: .

License

The license for this package is available on Github at: https://github.com/AllenInstitute/scrattch/blob/master/LICENSE

Contribution Agreement

If you contribute code to this repository through pull requests or other mechanisms, you are subject to the Allen Institute Contribution Agreement, which is available in full at: https://github.com/AllenInstitute/scrattch/blob/master/CONTRIBUTION

Level of Support

We are planning on occasional updating this tool with no fixed schedule. Community involvement is encouraged through both issues and pull requests. We encourage community involvement in child packages directly, rather than through the scrattch umbrella package, when appropriate.