The scpcaTools package contains a set of tools for working with single-cell and single-nuclei RNA-seq counts data.
Mainly, this package can work with data that has been produced using Alevin, Alevin-Fry, Cell Ranger, or Kallisto.
Counts matrices from either of these pre-processing tools can be imported into R using the import_quant_data function to return a SingleCellExperiment object to be used for downstream analysis.
Currently, by default the import_quant_data function will return a SingleCellExperiment object with two assays, the counts assay will contain the total counts from both unspliced and spliced reads and the spliced assay will contain only reads from the spliced reads.
Input data must be aligned to a reference containing both spliced and unspliced transcripts, otherwise the include_unspliced=FALSE option can be used to return just the spliced reads only as the main counts assay.
Note that this option is only for reading in data from Alevin, Alevin-fry, and Kallisto and is not used for Cell Ranger.
The package can be installed from GitHub with:
remotes::install_github("AlexsLemonade/scpcaTools")To test the import functions in this package, you must also have installed the scpcaData package.
This can be done by running remotes::install_github("AlexsLemonade/scpcaData")
This repository also includes a Dockerfile and associated scripts for building a Docker image that includes the scpcaTools package and some additional packages used in other ScPCA repositories, notably scpca-nf.
To support this, we use renv to track the versioned set of packages that will be installed in that Docker image.
Packages that are required for the main scpcaTools package should be included in the renv.lock file as they are installed and used.
To keep this up to date, renv::snapshot() should be run periodically during development (before submitting PRs), which should add any packages that are used in scpcaTools scripts and notebooks to renv.lock.
When checking out a branch, renv::restore() can be used to keep local package installations in sync.
Packages that are not required for the scpcaTools scripts directly, but that should be installed in the Docker image can be added to docker/dependencies.R to be sure they are part of the image.
Conversely, if there are packages that should not be added to the docker image (such as scpcaData), they should be set as ignored in renv/settings.dcf.
Note that renv is not used when testing the package in CI, so package versions may be different from those specified in renv.lock.
Python packages are also tracked with renv.
These are present mostly for use in the Docker image, as the scpcaTools package itself does not require any Python packages beyond those installed via basilisk-based packages.
It is important to note that some of the Python functionality may not be exposed in the RStudio GUI, so you will need to use functions in the console and/or terminal to manage these packages.
To get started, you will want to open an R session in the project directory and run renv::restore().
You will first be prompted to update any R packages (if required), then for the Python environment updates.
This will create a .venv directory and populate it with the required Python packages, as specified in requirements.txt.
To use the python environment from the terminal, you may need to activate it.
This activation is done automatically by RStudio, but for other terminals you will need to run source .venv/bin/activate.
Once the environment is activated, you can run python to start a Python session with the required packages available.
To add new Python packages, you can use pip install <package> from the terminal.
This will not add the packages to requirements.txt; to record packages to requirements.txt, you should again run renv::snapshot() from the R console, which will update both renv.lock and requirements.txt as required.
Note that renv::status() may not report that the requirements.txt file is out of date, even if you have installed new packages.
Any packages in requirements.txt will automatically be added to the Docker image when it is built, so you should not need to do anything special beyond the above steps to add them to the docker image.