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. 2021 Feb 25;53(3):403–411. doi: 10.1038/s41588-021-00790-6

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© The Author(s), under exclusive licence to Springer Nature America, Inc. 2021, corrected publication 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Extended Data Fig. 2 — a, Schematic describing the individual benchmarking steps compared across ArchR (blue), Signac (purple), and SnapATAC (orange) for (1) Data Import, (2) Dimensionality Reduction and Clustering, and (3) Gene Score Matrix Creation. b-i, Comparison of ArchR, Signac, and SnapATAC for run time and peak memory usage for the analysis of (b) ~20,000 cells from the PBMCs dataset using 128 GB of RAM and 20 cores (plot corresponds to Fig. 1b), (c) ~70,000 cells from the PBMCs dataset using 32 GB of RAM and 8 cores (plot corresponds to Fig. 1c), (d-e) ~10,000 cells from the PBMCs dataset using (d) 32 GB of RAM and 8 cores or (e) 128 GB of RAM and 20 cores, (f-g) ~30,000 cells from the PBMCs dataset using (f) 32 GB of RAM and 8 cores or (g) 128 GB of RAM and 20 cores, and (h-i) ~30,000 cells from the bone marrow dataset using (h) 32 GB of RAM and 8 cores or (i) 128 GB of RAM and 20 cores. Dots represent individual replicates of benchmarking analysis (N = 3). OoM corresponds to out of memory. j, Benchmarks from ArchR for run time and peak memory usage for the analysis of ~70,000 cells from the sci-ATAC-seq mouse atlas dataset (N = 13 tissues) for (left) 32 GB of RAM with 8 cores and (right) 128 GB of RAM with 20 cores. Dots represent individual replicates of benchmarking analysis. k, t-SNE of mouse atlas scATAC-seq data (N = 64,286 cells) colored by individual samples.