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  • Analyzing single-cell regulatory chromatin in R. • ArchR
    ArchR is a full-featured R package for processing and analyzing single-cell ATAC-seq data ArchR provides the most extensive suite of scATAC-seq analysis tools of any software available Additionally, ArchR excels in both speed and resource usage, making it possible to analyze 1 million cells in 8 hours on a MacBook Pro laptop
  • ArchR: Robust and scaleable analysis of single-cell chromatin . . .
    ArchR is a full-featured software suite for the analysis of single-cell chromatin accessibility data It is designed to handle hundreds of thousands of single cells without large memory or computational requirements, keeping pace with the experimental scale that is achievable with commercial platforms such as the 10x Genomics Chromium system
  • Package index • ArchR
    Add a globally-applied number of threads to use for parallel computing
  • Chapter 3 Getting Started with ArchR | ArchR: Robust and scaleable . . .
    A guide to ArchR This chapter will introduce you to how to import data into ArchR and how to create ArrowFiles, the base unit of ArchR analysis
  • 9. 1 Calculating Gene Scores in ArchR | ArchR: Robust and scaleable . . .
    A guide to ArchR 9 1 Calculating Gene Scores in ArchR In our publication, we tested over 50 different gene score models and identified a class of models that consistently outperformed the rest across a variety of test conditions This class of model, which is implemented as the default in ArchR, has three major components: Accessibility within the entire gene body contributes to the gene score
  • Chapter 17 Integrative Analysis with ArchR | ArchR: Robust and . . .
    A guide to ArchR Chapter 17 Integrative Analysis with ArchR One of the main strengths of ArchR is its ability to integrate multiple levels of information to provide novel insights This can take the form of ATAC-seq-only analyses such as identifying peak co-accessibility to predict regulatory interactions, or analyses that integrate scRNA-seq data such as prediction of enhancer activity
  • 3. 5 Getting Set Up | ArchR: Robust and scaleable analysis of single . . .
    3 5 Getting Set Up The first thing we do is change to our desired our working directory, set the number of threads we would like to use, and load our gene and genome annotations Depending on the configuration of your local environment, you may need to modify the number of threads used below in addArchRThreads() By default ArchR uses half of the total number of threads available but you can
  • 17. 3 Peak2GeneLinkage with ArchR | ArchR: Robust and scaleable analysis . . .
    17 3 Peak2GeneLinkage with ArchR Similar to co-accessibility, ArchR can also identify so-called “peak-to-gene links” The primary differences between peak-to-gene links and co-accessibility is that co-accessibility is an ATAC-seq-only analysis that looks for correlations in accessibility between two peaks while peak-to-gene linkage leverages integrated scRNA-seq data to look for


















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