flex_multiplets.bib

@ARTICLE{Brown2024,
	title    = "A risk-reward examination of sample multiplexing reagents for
	single cell {RNA-Seq}",
	author   = "Brown, Daniel V and Anttila, Casey J A and Ling, Ling and Grave,
	Patrick and Baldwin, Tracey M and Munnings, Ryan and Farchione,
	Anthony J and Bryant, Vanessa L and Dunstone, Amelia and Biben,
	Christine and Taoudi, Samir and Weber, Tom S and Naik, Shalin H
	and Hadla, Anthony and Barker, Holly E and Vandenberg, Cassandra
	J and Dall, Genevieve and Scott, Clare L and Moore, Zachery and
	Whittle, James R and Freytag, Saskia and Best, Sarah A and
	Papenfuss, Anthony T and Olechnowicz, Sam W Z and MacRaild, Sarah
	E and Wilcox, Stephen and Hickey, Peter F and Amann-Zalcenstein,
	Daniela and Bowden, Rory",
	journal  = "Genomics",
	volume   =  116,
	number   =  2,
	pages    = "110793",
	month    =  mar,
	year     =  2024,
	keywords = "CRISPRclean; Fixed; RNA-seq; Sample multiplexing; Single-cell",
	language = "en"
}


@article{Howitt2023,
	author = {Howitt, George and Feng, Yuzhou and Tobar, Lucas and Vassiliadis, Dane and Hickey, Peter and Dawson, Mark A and Ranganathan, Sarath and Shanthikumar, Shivanthan and Neeland, Melanie and Maksimovic, Jovana and Oshlack, Alicia},
	title = "{Benchmarking single-cell hashtag oligo demultiplexing methods}",
	journal = {NAR Genomics and Bioinformatics},
	volume = {5},
	number = {4},
	pages = {lqad086},
	year = {2023},
	month = {10},
	issn = {2631-9268},
	doi = {10.1093/nargab/lqad086},
	url = {https://doi.org/10.1093/nargab/lqad086},
	eprint = {https://academic.oup.com/nargab/article-pdf/5/4/lqad086/52009497/lqad086.pdf},
}



@ARTICLE{Yang2020,
	title    = "Decontamination of ambient {RNA} in single-cell {RNA-seq} with
	{DecontX}",
	author   = "Yang, Shiyi and Corbett, Sean E and Koga, Yusuke and Wang, Zhe
	and Johnson, W Evan and Yajima, Masanao and Campbell, Joshua D",
	journal  = "Genome Biol.",
	volume   =  21,
	number   =  1,
	pages    = "57",
	month    =  mar,
	year     =  2020,
	keywords = "Bayesian mixture model; Decontamination; Single cell; scRNA-seq",
	language = "en"
}


@ARTICLE{Lun2019,
	title    = "{EmptyDrops}: distinguishing cells from empty droplets in
	droplet-based single-cell {RNA} sequencing data",
	author   = "Lun, Aaron T L and Riesenfeld, Samantha and Andrews, Tallulah and
	Dao, The Phuong and Gomes, Tomas and {participants in the 1st
	Human Cell Atlas Jamboree} and Marioni, John C",
	journal  = "Genome Biol.",
	volume   =  20,
	number   =  1,
	pages    = "63",
	month    =  mar,
	year     =  2019,
	keywords = "Cell detection; Droplet-based protocols; Empty droplets;
	Single-cell transcriptomics",
	language = "en"
}


@ARTICLE{Griffiths2018,
	title    = "Detection and removal of barcode swapping in single-cell
	{RNA-seq} data",
	author   = "Griffiths, Jonathan A and Richard, Arianne C and Bach, Karsten
	and Lun, Aaron T L and Marioni, John C",
	journal  = "Nat. Commun.",
	volume   =  9,
	number   =  1,
	pages    = "2667",
	month    =  jul,
	year     =  2018,
	language = "en"
}

@ARTICLE{Neavin2024,
	title    = "Demuxafy: improvement in droplet assignment by integrating
	multiple single-cell demultiplexing and doublet detection methods",
	author   = "Neavin, Drew and Senabouth, Anne and Arora, Himanshi and Lee,
	Jimmy Tsz Hang and Ripoll-Cladellas, Aida and {sc-eQTLGen
	Consortium} and Franke, Lude and Prabhakar, Shyam and Ye, Chun
	Jimmie and McCarthy, Davis J and Mel{\'e}, Marta and Hemberg,
	Martin and Powell, Joseph E",
	journal  = "Genome Biol.",
	volume   =  25,
	number   =  1,
	pages    = "94",
	month    =  apr,
	year     =  2024,
	keywords = "Doublet detecting; Genetic demultiplexing; Single-cell analysis",
	language = "en"
}


@manual{10X_flex_protocol,
	title        = {Chromium Fixed RNA Profiling Reagent Kits for Multiplexed Samples},
	author       = {10X Genomics},
	year         = 2023,
	month        = {September},
	address      = {Pleasanton, CA},
	note         = {Revision E, available at \url{https://www.10xgenomics.com/support/single-cell-gene-expression-flex/documentation/steps/library-prep/chromium-single-cell-gene-expression-flex-reagent-kits-for-multiplexed-samples}},
	organization = {10X Genomics},
}

@manual{10X_3'_protocol,
	title        = {Chromium Next GEM Single Cell 3' Reagent Kits v3.1	User Guide},
	author       = {10X Genomics},
	year         = 2019,
	month        = {November},
	address      = {Pleasanton, CA},
	note         = {Revision D, available at \url{https://www.10xgenomics.com/support/single-cell-gene-expression/documentation/steps/library-prep/chromium-single-cell-3-reagent-kits-user-guide-v-3-1-chemistry}},
	organization = {10X Genomics},
}


@ARTICLE{Germain2021,
	title    = "Doublet identification in single-cell sequencing data using
	{\textit{scDblFinder}}",
	author   = "Germain, Pierre-Luc and Lun, Aaron and Garcia Meixide, Carlos and
	Macnair, Will and Robinson, Mark D",
	journal  = "F1000Res.",
	volume   =  10,
	pages    = "979",
	month    =  sep,
	year     =  2021,
	keywords = "doublets; filtering; multiplets; single-cell sequencing",
	language = "en"
}


@ARTICLE{Bais2020,
	title    = "scds: computational annotation of doublets in single-cell {RNA}
	sequencing data",
	author   = "Bais, Abha S and Kostka, Dennis",
	journal  = "Bioinformatics",
	volume   =  36,
	number   =  4,
	pages    = "1150--1158",
	month    =  feb,
	year     =  2020,
	language = "en"
}

@ARTICLE{Xiong2022,
	title    = "Chord: an ensemble machine learning algorithm to identify
	doublets in single-cell {RNA} sequencing data",
	author   = "Xiong, Ke-Xu and Zhou, Han-Lin and Lin, Cong and Yin, Jian-Hua
	and Kristiansen, Karsten and Yang, Huan-Ming and Li, Gui-Bo",
	journal  = "Commun Biol",
	volume   =  5,
	number   =  1,
	pages    = "510",
	month    =  may,
	year     =  2022,
	language = "en"
}


@ARTICLE{Hao2021,
	title    = "Integrated analysis of multimodal single-cell data",
	author   = "Hao, Yuhan and Hao, Stephanie and Andersen-Nissen, Erica and
	Mauck, 3rd, William M and Zheng, Shiwei and Butler, Andrew and
	Lee, Maddie J and Wilk, Aaron J and Darby, Charlotte and Zager,
	Michael and Hoffman, Paul and Stoeckius, Marlon and Papalexi,
	Efthymia and Mimitou, Eleni P and Jain, Jaison and Srivastava,
	Avi and Stuart, Tim and Fleming, Lamar M and Yeung, Bertrand and
	Rogers, Angela J and McElrath, Juliana M and Blish, Catherine A
	and Gottardo, Raphael and Smibert, Peter and Satija, Rahul",
	journal  = "Cell",
	volume   =  184,
	number   =  13,
	pages    = "3573--3587.e29",
	month    =  jun,
	year     =  2021,
	keywords = "CITE-seq; COVID-19; T cell; immune system; multimodal analysis;
	reference mapping; single cell genomics",
	language = "en"
}


@ARTICLE{Xi2021,
	title    = "Benchmarking Computational {Doublet-Detection} Methods for
	{Single-Cell} {RNA} Sequencing Data",
	author   = "Xi, Nan Miles and Li, Jingyi Jessica",
	journal  = "Cell Syst",
	volume   =  12,
	number   =  2,
	pages    = "176--194.e6",
	month    =  feb,
	year     =  2021,
	keywords = "cell clustering; differential gene expression; doublet detection;
	parallel computing; reproducibility; scRNA-seq; software
	implementation; trajectory inference",
	language = "en"
}

@ARTICLE{Luecken2019,
	title    = "Current best practices in single-cell {RNA-seq} analysis: a
	tutorial",
	author   = "Luecken, Malte D and Theis, Fabian J",
	journal  = "Mol. Syst. Biol.",
	volume   =  15,
	number   =  6,
	pages    = "e8746",
	month    =  jun,
	year     =  2019,
	keywords = "analysis pipeline development; computational biology; data
	analysis tutorial; single‐cell RNA‐seq",
	language = "en"
}

@ARTICLE{Zhang2024,
	title    = "Synthetic {DNA} barcodes identify singlets in {scRNA-seq}
	datasets and evaluate doublet algorithms",
	author   = "Zhang, Ziyang and Melzer, Madeline E and Arun, Keerthana M and
	Sun, Hanxiao and Eriksson, Carl-Johan and Fabian, Itai and
	Shaashua, Sagi and Kiani, Karun and Oren, Yaara and Goyal, Yogesh",
	abstract = "Single-cell RNA sequencing (scRNA-seq) datasets contain true
	single cells, or singlets, in addition to cells that coalesce
	during the protocol, or doublets. Identifying singlets with high
	fidelity in scRNA-seq is necessary to avoid false negative and
	false positive discoveries. Although several methodologies have
	been proposed, they are typically tested on highly heterogeneous
	datasets and lack a priori knowledge of true singlets. Here, we
	leveraged datasets with synthetically introduced DNA barcodes for
	a hitherto unexplored application: to extract ground-truth
	singlets. We demonstrated the feasibility of our framework,
	``singletCode,'' to evaluate existing doublet detection methods
	across a range of contexts. We also leveraged our ground-truth
	singlets to train a proof-of-concept machine learning classifier,
	which outperformed other doublet detection algorithms. Our
	integrative framework can identify ground-truth singlets and
	enable robust doublet detection in non-barcoded datasets.",
	journal  = "Cell Genom",
	volume   =  4,
	number   =  7,
	pages    = "100592",
	month    =  jul,
	year     =  2024,
	keywords = "barcoding; benchmarking; doublet detection; lineage tracing;
	machine learning; scRNA-seq; single-cell genomics; singletCode;
	singlets",
	language = "en"
}

@ARTICLE{Curion2024,
	title     = "Hadge: A comprehensive pipeline for donor deconvolution in
	single-cell studies",
	author    = "Curion, Fabiola and Wu, Xichen and Heumos, Lukas and André,
	Mylene Mariana Gonzales and Halle, Lennard and Ozols, Matiss and
	Grant-Peters, Melissa and Rich-Griffin, Charlotte and Yeung,
	Hing-Yuen and Dendrou, Calliope A and Schiller, Herbert B and
	Theis, Fabian J",
	journal   = "Genome Biol.",
	publisher = "BioMed Central",
	volume    =  25,
	number    =  1,
	pages     =  109,
	abstract  = "Single-cell multiplexing techniques (cell hashing and genetic
	multiplexing) combine multiple samples, optimizing sample
	processing and reducing costs. Cell hashing conjugates
	antibody-tags or chemical-oligonucleotides to cell membranes,
	while genetic multiplexing allows to mix genetically diverse
	samples and relies on aggregation of RNA reads at known genomic
	coordinates. We develop hadge (hashing deconvolution combined
	with genotype information), a Nextflow pipeline that combines 12
	methods to perform both hashing- and genotype-based
	deconvolution. We propose a joint deconvolution strategy
	combining best-performing methods and demonstrate how this
	approach leads to the recovery of previously discarded cells in a
	nuclei hashing of fresh-frozen brain tissue.",
	month     =  apr,
	year      =  2024,
	keywords  = "Donor deconvolution; Genetic; Hashing; Nextflow; Single-cell",
	language  = "en"
}

@book{Andrews1998,
	title={The theory of partitions},
	author={Andrews, George E},
	number={2},
	year={1998},
	publisher={Cambridge university press}
}

@ARTICLE{Mulqueen2021,
	title    = "High-content single-cell combinatorial indexing",
	author   = "Mulqueen, Ryan M and Pokholok, Dmitry and O'Connell, Brendan L and
	Thornton, Casey A and Zhang, Fan and O'Roak, Brian J and Link,
	Jason and Yardımcı, Galip Gürkan and Sears, Rosalie C and
	Steemers, Frank J and Adey, Andrew C",
	journal  = "Nat. Biotechnol.",
	volume   =  39,
	number   =  12,
	pages    = "1574--1580",
	abstract = "Single-cell combinatorial indexing (sci) with transposase-based
	library construction increases the throughput of single-cell
	genomics assays but produces sparse coverage in terms of usable
	reads per cell. We develop symmetrical strand sci ('s3'), a
	uracil-based adapter switching approach that improves the rate of
	conversion of source DNA into viable sequencing library fragments
	following tagmentation. We apply this chemistry to assay chromatin
	accessibility (s3-assay for transposase-accessible chromatin,
	s3-ATAC) in human cortical and mouse whole-brain tissues, with
	mouse datasets demonstrating a six- to 13-fold improvement in
	usable reads per cell compared with other available methods.
	Application of s3 to single-cell whole-genome sequencing (s3-WGS)
	and to whole-genome plus chromatin conformation (s3-GCC) yields
	148- and 14.8-fold improvements, respectively, in usable reads per
	cell compared with sci-DNA-sequencing and sci-HiC. We show that
	s3-WGS and s3-GCC resolve subclonal genomic alterations in
	patient-derived pancreatic cancer cell lines. We expect that the
	s3 platform will be compatible with other transposase-based
	techniques, including sci-MET or CUT\&Tag.",
	month    =  dec,
	year     =  2021,
	language = "en"
}

@ARTICLE{Stoeckius2018,
	title    = "Cell Hashing with barcoded antibodies enables multiplexing and
	doublet detection for single cell genomics",
	author   = "Stoeckius, Marlon and Zheng, Shiwei and Houck-Loomis, Brian and
	Hao, Stephanie and Yeung, Bertrand Z and Mauck, 3rd, William M and
	Smibert, Peter and Satija, Rahul",
	journal  = "Genome Biol.",
	volume   =  19,
	number   =  1,
	pages    =  224,
	abstract = "Despite rapid developments in single cell sequencing,
	sample-specific batch effects, detection of cell multiplets, and
	experimental costs remain outstanding challenges. Here, we
	introduce Cell Hashing, where oligo-tagged antibodies against
	ubiquitously expressed surface proteins uniquely label cells from
	distinct samples, which can be subsequently pooled. By sequencing
	these tags alongside the cellular transcriptome, we can assign
	each cell to its original sample, robustly identify cross-sample
	multiplets, and ``super-load'' commercial droplet-based systems
	for significant cost reduction. We validate our approach using a
	complementary genetic approach and demonstrate how hashing can
	generalize the benefits of single cell multiplexing to diverse
	samples and experimental designs.",
	month    =  dec,
	year     =  2018,
	language = "en"
}

@ARTICLE{McGinnis2019,
	title    = "{MULTI}-seq: sample multiplexing for single-cell {RNA} sequencing
	using lipid-tagged indices",
	author   = "McGinnis, Christopher S and Patterson, David M and Winkler,
	Juliane and Conrad, Daniel N and Hein, Marco Y and Srivastava,
	Vasudha and Hu, Jennifer L and Murrow, Lyndsay M and Weissman,
	Jonathan S and Werb, Zena and Chow, Eric D and Gartner, Zev J",
	journal  = "Nat. Methods",
	volume   =  16,
	number   =  7,
	pages    = "619--626",
	abstract = "Sample multiplexing facilitates scRNA-seq by reducing costs and
	identifying artifacts such as cell doublets. However, universal
	and scalable sample barcoding strategies have not been described.
	We therefore developed MULTI-seq: multiplexing using lipid-tagged
	indices for single-cell and single-nucleus RNA sequencing.
	MULTI-seq reagents can barcode any cell type or nucleus from any
	species with an accessible plasma membrane. The method involves
	minimal sample processing, thereby preserving cell viability and
	endogenous gene expression patterns. When cells are classified
	into sample groups using MULTI-seq barcode abundances, data
	quality is improved through doublet identification and recovery of
	cells with low RNA content that would otherwise be discarded by
	standard quality-control workflows. We use MULTI-seq to track the
	dynamics of T-cell activation, perform a 96-plex perturbation
	experiment with primary human mammary epithelial cells and
	multiplex cryopreserved tumors and metastatic sites isolated from
	a patient-derived xenograft mouse model of triple-negative breast
	cancer.",
	month    =  jul,
	year     =  2019,
	language = "en"
}