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diffcyt-evaluations

This repository contains scripts to reproduce all performance evaluations, comparisons, and figures in our paper introducing the diffcyt framework.

The diffcyt R package implements statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on (i) high-resolution clustering and (ii) empirical Bayes moderated tests adapted from transcriptomics.

A preprint of the paper is available from bioRxiv:

  • Weber L. M. et al. (2018), diffcyt: Differential discovery in high-dimensional cytometry via high-resolution clustering, bioRxiv preprint. Available here.

Contents

The scripts in this repository are organized according to the benchmarking datasets: 'AML-sim', 'BCR-XL-sim', 'Anti-PD-1', and 'BCR-XL'.

Within each dataset, scripts are organized into sub-directories to:

  • prepare data (semi-simulated datasets 'AML-sim' and 'BCR-XL-sim' only)
  • run methods
  • generate plots

Code comments are included to explain the purpose of each script.

Benchmarking datasets

Data files for the benchmarking datasets are available from FlowRepository under accession number FR-FCM-ZYL8.

Installation of diffcyt package

The diffcyt package is freely available from Bioconductor. The stable release version can be installed using the Bioconductor installer as follows. Note that installation requires R version 3.5.0 or later.

# Install Bioconductor installer from CRAN
install.packages("BiocManager")

# Install 'diffcyt' package from Bioconductor
BiocManager::install("diffcyt")

To run the examples in the package vignette, the HDCytoData and CATALYST packages from Bioconductor are also required.

BiocManager::install("HDCytoData")
BiocManager::install("CATALYST")

For details on the development version of the diffcyt package, see the GitHub page.

Tutorial and examples

For a tutorial and examples of usage, see the Bioconductor package vignette (link also available via the main Bioconductor page for the diffcyt package).

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Scripts to reproduce evaluations and figures in our paper introducing the 'diffcyt' framework

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