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

Thibodeau, Asa; Eroğlu, Alper; McGinnis, Christopher S.; Lawlor, Nathan; Nehar-Belaid, Djamel; Kursawe, Romy; Marcheş, Radu; Conrad, Daniel N.; Kuchel, George A.; Gartner, Zev J.; Banchereau, Jacques; Stitzel, Michael L.; Çiçek, A. Ercüment; Ucar, Duygu

doi:10.1186/s13059-021-02469-x

Cited by 51 publications

(63 citation statements)

References 26 publications

(52 reference statements)

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“…We next investigated whether scDblFinder could be applied to other types of single-cell data prone to doublets, such as single-cell Assay for Transposase-Accessible Chromatin sequencing (ATACseq). We compared scDblFinder to two methods specifically designed to scATACseq: the ArchR package ( Granja et al 2021 ), which implements a doublet detection method that is also based on the comparison to artificial doublets, and the AMULET method ( Thibodeau et al 2021 ). AMULET is based on the assumption that, in a diploid cell, any given genomic region should be captured at most twice, and therefore interprets a larger number of loci with more than two reads as indicative of the droplet being a doublet.…”

Section: Resultsmentioning

confidence: 99%

“…The methods were compared across three datasets where a genotype-based annotation was available as ground truth: two obtained from Granja et al (2021 ; GSM4957261 and GSM4957262), which by design do not have homotypic doublets, and the dataset published along AMULET (GSM5457171; Thibodeau et al 2021 ). The latter contains homotypic doublets, but its doublet annotation is highly incomplete: due to the low number of individuals multiplexed, we expected to have approximately 35% of the doublets within-individual, and hence mislabeled as singlets.…”

Section: Resultsmentioning

confidence: 99%

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Doublet identification in single-cell sequencing data using scDblFinder

Lun²,

et al. 2022

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Doublets are prevalent in single-cell sequencing data and can lead to artifactual findings. A number of strategies have therefore been proposed to detect them. Building on the strengths of existing approaches, we developed scDblFinder, a fast, flexible and accurate Bioconductor-based doublet detection method. Here we present the method, justify its design choices, demonstrate its performance on both single-cell RNA and accessibility (ATAC) sequencing data, and provide some observations on doublet formation, detection, and enrichment analysis. Even in complex datasets, scDblFinder can accurately identify most heterotypic doublets, and was already found by an independent benchmark to outcompete alternatives.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Doublet identification in single-cell sequencing data using scDblFinder

Lun²,

et al. 2022

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“…We removed potential doublet cells by the number of regions with per-base coverage greater than 3 (Ref. 16 ). We also removed fragments with interval length smaller than 10 that are likely to be misalignment.…”

Section: Methodsmentioning

confidence: 99%

Is single nucleus ATAC-seq accessibility a qualitative or quantitative measurement?

Miao

Kim

2022

Preprint

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Single nucleus ATAC-seq is a key assay for gene regulation analysis. Existing approaches to scoring feature matrices from sequencing reads are inconsistent with each other, creating differences in downstream analysis, and displaying artifacts. We show that even with sparse single cell data, quantitative counts are informative for estimating a cell’s regulatory state, which calls for consistent treatment. We propose Paired-Insertion-Counting (PIC) as a uniform method for snATAC-seq feature characterization.

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“…Pseudo bulk peak positions for each cell type were identified using MACS2 ( Zhang et al, 2008 ). In addition, we used Amulet ( Thibodeau et al, 2021 )to detect multiplets, which identified 532 nuclei (∼7.5%) as multiplets. These nuclei were removed to visualize the read coverage and TSR enrichment plots.…”

Section: Methodsmentioning

confidence: 99%

Glucocorticoid Receptor-Regulated Enhancers Play a Central Role in the Gene Regulatory Networks Underlying Drug Addiction

et al. 2022

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Substance abuse and addiction represent a significant public health problem that impacts multiple dimensions of society, including healthcare, the economy, and the workforce. In 2021, over 100,000 drug overdose deaths were reported in the US, with an alarming increase in fatalities related to opioids and psychostimulants. Understanding the fundamental gene regulatory mechanisms underlying addiction and related behaviors could facilitate more effective treatments. To explore how repeated drug exposure alters gene regulatory networks in the brain, we combined capped small (cs)RNA-seq, which accurately captures nascent-like initiating transcripts from total RNA, with Hi-C and single nuclei (sn)ATAC-seq. We profiled initiating transcripts in two addiction-related brain regions, the prefrontal cortex (PFC) and the nucleus accumbens (NAc), from rats that were never exposed to drugs or were subjected to prolonged abstinence after oxycodone or cocaine intravenous self-administration (IVSA). Interrogating over 100,000 active transcription start regions (TSRs) revealed that most TSRs had hallmarks of bonafide enhancers and highlighted the KLF/SP1, RFX, and AP1 transcription factors families as central to establishing brain-specific gene regulatory programs. Analysis of rats with addiction-like behaviors versus controls identified addiction-associated repression of transcription at regulatory enhancers recognized by nuclear receptor subfamily 3 group C (NR3C) factors, including glucocorticoid receptors. Cell-type deconvolution analysis using snATAC-seq uncovered a potential role of glial cells in driving the gene regulatory programs associated with addiction-related phenotypes. These findings highlight the power of advanced transcriptomics methods to provide insight into how addiction perturbs gene regulatory programs in the brain.

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

Cited by 51 publications

References 26 publications

Doublet identification in single-cell sequencing data using scDblFinder

Doublet identification in single-cell sequencing data using scDblFinder

Is single nucleus ATAC-seq accessibility a qualitative or quantitative measurement?

Glucocorticoid Receptor-Regulated Enhancers Play a Central Role in the Gene Regulatory Networks Underlying Drug Addiction

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