AMULET: a novel read count-based method for effective multiplet detection from single nucleus ATAC-seq data

Asa Thibodeau; Alper Eroglu; Christopher S McGinnis; Nathan Lawlor; Djamel Nehar-Belaid; Romy Kursawe; Radu Marches; Daniel N Conrad; George A Kuchel; Zev J Gartner; Jacques Banchereau; Michael L Stitzel; A Ercument Cicek; Duygu Ucar

doi:10.1186/s13059-021-02469-x

AMULET: a novel read count-based method for effective multiplet detection from single nucleus ATAC-seq data

Genome Biol. 2021 Sep 1;22(1):252. doi: 10.1186/s13059-021-02469-x.

Authors

Asa Thibodeau^#¹, Alper Eroglu^#¹, Christopher S McGinnis², Nathan Lawlor¹, Djamel Nehar-Belaid¹, Romy Kursawe¹, Radu Marches¹, Daniel N Conrad², George A Kuchel³, Zev J Gartner^{2

4

5}, Jacques Banchereau¹, Michael L Stitzel^{1

6

7}, A Ercument Cicek^{8

9}, Duygu Ucar^{10

11

12}

Affiliations

¹ The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA.
² Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, 94158, USA.
³ University of Connecticut Center on Aging, UConn Health Center, Farmington, CT, 06030, USA.
⁴ Chan-Zuckerberg Biohub, San Francisco, CA, 94158, USA.
⁵ NSF Center for Cellular Construction, San Francisco, CA, 94158, USA.
⁶ Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT, 06030, USA.
⁷ Institute for Systems Genomics, University of Connecticut Health Center, Farmington, CT, 06030, USA.
⁸ Computer Engineering Department, Bilkent University, 06800, Ankara, Turkey.
⁹ Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
¹⁰ The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA. duygu.ucar@jax.org.
¹¹ Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT, 06030, USA. duygu.ucar@jax.org.
¹² Institute for Systems Genomics, University of Connecticut Health Center, Farmington, CT, 06030, USA. duygu.ucar@jax.org.

^# Contributed equally.

Abstract

Detecting multiplets in single nucleus (sn)ATAC-seq data is challenging due to data sparsity and limited dynamic range. AMULET (ATAC-seq MULtiplet Estimation Tool) enumerates regions with greater than two uniquely aligned reads across the genome to effectively detect multiplets. We evaluate the method by generating snATAC-seq data in the human blood and pancreatic islet samples. AMULET has high precision, estimated via donor-based multiplexing, and high recall, estimated via simulated multiplets, compared to alternatives and identifies multiplets most effectively when a certain read depth of 25K median valid reads per nucleus is achieved.

Keywords: Doublets; Multiplets; Single nucleus ATAC-seq; snATAC-seq.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Aged
Chromatin Immunoprecipitation Sequencing*
DNA / genetics
Humans
Leukocytes, Mononuclear / metabolism
Likelihood Functions
Software*
Transposases / metabolism

Substances

DNA
Transposases

Abstract

Publication types

MeSH terms

Substances

Grants and funding