scDiagnostics: diagnostic functions to assess the quality of cell type annotations in single-cell RNA-seq data

Overview

The accurate annotation of cell types is a critical step in single-cell RNA-sequencing (scRNA-seq) analysis. While annotation transfer from a reference dataset offers a convenient and automated approach, it can also introduce biases and errors if not performed carefully.

scDiagnostics is an R package designed to address this challenge by providing a comprehensive set of diagnostic tools for evaluating the quality of cell type annotations in scRNA-seq data. With scDiagnostics, researchers can systematically assess the compatibility and accuracy of annotations, ensuring reliable and reproducible results in their scRNA-seq analysis workflow.

Workshop Materials

  • This repository contains the workshop materials. You may find the PDF slides in inst/slides which contain an overview of the role of the scDiagnostics package in cell type annotation.
  • You may find the fully documented workshop code examples in the vignettes folder.
  • There is also a pkgdown website for the workshop with the code materials.
  • There is also a publicly available Docker image to run the vignette in a container with all dependencies already installed.

Installation

To install the development version of the scDiagnostics from GitHub use the following command:

devtools::install_github("ccb-hms/scDiagnostics")

NOTE: you will need the remotes package to install from GitHub.

To build the scDiagnostics package vignettes upon installation use:

devtools::install_github("ccb-hms/scDiagnostics",
                         build_vignettes = TRUE,
                         dependencies = TRUE)

Usage

To get a complete overview of the functionality of the package, refer to the pkgdown website for code examples. The complete documentation of each available function in scDiagnostics, which includes implementation details and working examples, is available in the reference tab.

scDiagnostics is designed to be user-friendly and integrates seamlessly into any scRNA-seq analysis workflow. By providing robust diagnostic tools, the package helps ensure the accuracy and reliability of cell type annotations, leading to more meaningful and reproducible results.