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@article{Crowell2019-muscat,
	Author = {Crowell, Helena L and Soneson, Charlotte and Germain, Pierre-Luc and Calini, Daniela and Collin, Ludovic and Raposo, Catarina and Malhotra, Dheeraj and Robinson, Mark D},
	Date-Added = {2020-03-10 11:36:58 -0400},
	Date-Modified = {2020-03-10 11:36:58 -0400},
	Journal = {bioRxiv},
	Title = {On the discovery of population-specific state transitions from multi-sample multi-condition single-cell {RNA} sequencing data},
	Volume = 713412,
	Year = 2019}

@article{Sala2019,
	Abstract = {Across the animal kingdom, gastrulation represents a key developmental event during which embryonic pluripotent cells diversify into lineage-specific precursors that will generate the adult organism. Here we report the transcriptional profiles of 116,312 single cells from mouse embryos collected at nine sequential time points ranging from 6.5 to 8.5 days post-fertilization. We construct a molecular map of cellular differentiation from pluripotency towards all major embryonic lineages, and explore the complex events involved in the convergence of visceral and primitive streak-derived endoderm. Furthermore, we use single-cell profiling to show that Tal1−/−chimeric embryos display defects in early mesoderm diversification, and we thus demonstrate how combining temporal and transcriptional information can illuminate gene function. Together, this comprehensive delineation of mammalian cell differentiation trajectories in vivo represents a baseline for understanding the effects of gene mutations during development, as well as a roadmap for the optimization of in vitro differentiation protocols for regenerative medicine.},
	Author = {Pijuan-Sala, Blanca and Griffiths, Jonathan A. and Guibentif, Carolina and Hiscock, Tom W. and Jawaid, Wajid and Calero-Nieto, Fernando J. and Mulas, Carla and Ibarra-Soria, Ximena and Tyser, Richard C. V. and Ho, Debbie Lee Lian and Reik, Wolf and Srinivas, Shankar and Simons, Benjamin D. and Nichols, Jennifer and Marioni, John C. and G{\"o}ttgens, Berthold},
	Da = {2019/02/01},
	Date-Added = {2019-04-23 14:25:44 +0200},
	Date-Modified = {2019-04-23 14:26:05 +0200},
	Doi = {10.1038/s41586-019-0933-9},
	Id = {Pijuan-Sala2019},
	Isbn = {1476-4687},
	Journal = {Nature},
	Number = {7745},
	Pages = {490--495},
	Title = {A single-cell molecular map of mouse gastrulation and early organogenesis},
	Ty = {JOUR},
	Url = {https://doi.org/10.1038/s41586-019-0933-9},
	Volume = {566},
	Year = {2019},
	Bdsk-Url-1 = {https://doi.org/10.1038/s41586-019-0933-9}}

@article{Ovchinnikova2018,
	Abstract = {Dimension-reduction methods, such as t-SNE or UMAP, are widely used when exploring high-dimensional data describing many entities, e.g., RNA-Seq data for many single cells. However, dimension reduction is unavoidably prone to introducing artefacts, and we hence need means to see where a dimension-reduced embedding is a faithful representation of the local neighbourhood and where it is not.We present Sleepwalk, a simple but powerful tool that allows the user to interactively explore an embedding, using colour to depict {\textquotedblleft}true{\textquotedblright} similarities of all points to the cell under the mouse cursor. We show how this approach not only highlights distortions, but also reveals otherwise hidden characteristics of the data, and how Sleepwalk{\textquoteright}s comparative modes help integrate multi-sample data and understand differences between embedding and preprocessing methods. Sleepwalk is a versatile and intuitive tool that unlocks the full power of dimension reduction and will be of value not only in single-cell RNA-Seq but also in any other area with matrix-shaped big data.},
	Author = {Ovchinnikova, Svetlana and Anders, Simon},
	Date-Added = {2019-04-23 08:11:43 +0200},
	Date-Modified = {2019-04-23 08:30:14 +0200},
	Doi = {10.1101/603589},
	Elocation-Id = {603589},
	Eprint = {https://www.biorxiv.org/content/early/2019/04/12/603589.full.pdf},
	Journal = {bioRxiv},
	Keywords = {sleepwalk},
	Publisher = {Cold Spring Harbor Laboratory},
	Title = {Exploring dimension-reduced embeddings with {S}leepwalk},
	Url = {https://www.biorxiv.org/content/early/2019/04/12/603589},
	Year = {2019},
	Bdsk-Url-1 = {https://www.biorxiv.org/content/early/2019/04/12/603589},
	Bdsk-Url-2 = {https://doi.org/10.1101/603589}}

@article{McCarthy2017,
	Abstract = {Single-cell RNA sequencing (scRNA-seq) is increasingly used to study gene expression at the level of individual cells. However, preparing raw sequence data for further analysis is not a straightforward process. Biases, artifacts and other sources of unwanted variation are present in the data, requiring substantial time and effort to be spent on pre-processing, quality control (QC) and normalization.We have developed the R/Bioconductor package scater to facilitate rigorous pre-processing, quality control, normalization and visualization of scRNA-seq data. The package provides a convenient, flexible workflow to process raw sequencing reads into a high-quality expression dataset ready for downstream analysis. scater provides a rich suite of plotting tools for single-cell data and a flexible data structure that is compatible with existing tools and can be used as infrastructure for future software development.The open-source code, along with installation instructions, vignettes and case studies, is available through Bioconductor at http://bioconductor.org/packages/scater.Supplementary data are available at Bioinformatics online.},
	Author = {McCarthy, Davis J and Campbell, Kieran R and Wills, Quin F and Lun, Aaron T L},
	Booktitle = {Bioinformatics},
	Date-Added = {2019-04-22 16:36:50 +0200},
	Date-Modified = {2019-04-22 16:36:50 +0200},
	Doi = {10.1093/bioinformatics/btw777},
	Isbn = {1367-4803},
	Journal = {Bioinformatics},
	Journal1 = {Bioinformatics},
	Keywords = {scater},
	Month = {4/22/2019},
	Number = {8},
	Pages = {1179--1186},
	Title = {{S}cater: pre-processing, quality control, normalization and visualization of single-cell {RNA}-seq data in {R}},
	Ty = {JOUR},
	Volume = {33},
	Year = {2017},
	Bdsk-Url-1 = {https://doi.org/10.1093/bioinformatics/btw777}}

@article{Albrecht2018,
	Abstract = {High dimensional mass and flow cytometry (HDCyto) experiments have become a method of choice for high throughput interrogation and characterization of cell populations.Here, we present an R-based pipeline for differential analyses of HDCyto data, largely based on Bioconductor packages. We computationally define cell populations using FlowSOM clustering, and facilitate an optional but reproducible strategy for manual merging of algorithm-generated clusters. Our workflow offers different analysis paths, including association of cell type abundance with a phenotype or changes in signaling markers within specific subpopulations, or differential analyses of aggregated signals. Importantly, the differential analyses we show are based on regression frameworks where the HDCyto data is the response; thus, we are able to model arbitrary experimental designs, such as those with batch effects, paired designs and so on. In particular, we apply generalized linear mixed models to analyses of cell population abundance or cell-population-specific analyses of signaling markers, allowing overdispersion in cell count or aggregated signals across samples to be appropriately modeled. To support the formal statistical analyses, we encourage exploratory data analysis at every step, including quality control (e.g. multi-dimensional scaling plots), reporting of clustering results (dimensionality reduction, heatmaps with dendrograms) and differential analyses (e.g. plots of aggregated signals).},
	Author = {Rue-Albrecht, K{\'e}vin and Marini, Federico and Soneson, Charlotte and Lun, Aaron T L},
	Date-Added = {2019-04-22 16:36:47 +0200},
	Date-Modified = {2019-04-23 08:39:40 +0200},
	Doi = {10.12688/f1000research.14966.1},
	Journal = {F1000Res},
	Journal-Full = {F1000Research},
	Keywords = {iSEE},
	Pages = {741},
	Title = {i{SEE}: {I}nteractive {S}ummarized{E}xperiment {E}xplorer},
	Volume = {7},
	Year = {2018},
	Bdsk-File-1 = { }}

@article{Kang2018,
	Abstract = {Droplet single-cell RNA-sequencing (dscRNA-seq) has enabled rapid, massively parallel profiling of transcriptomes. However, assessing differential expression across multiple individuals has been hampered by inefficient sample processing and technical batch effects. Here we describe a computational tool, demuxlet, that harnesses natural genetic variation to determine the sample identity of each droplet containing a single cell (singlet) and detect droplets containing two cells (doublets). These capabilities enable multiplexed dscRNA-seq experiments in which cells from unrelated individuals are pooled and captured at higher throughput than in standard workflows. Using simulated data, we show that 50 single-nucleotide polymorphisms (SNPs) per cell are sufficient to assign 97% of singlets and identify 92% of doublets in pools of up to 64 individuals. Given genotyping data for each of eight pooled samples, demuxlet correctly recovers the sample identity of >99% of singlets and identifies doublets at rates consistent with previous estimates. We apply demuxlet to assess cell-type-specific changes in gene expression in 8 pooled lupus patient samples treated with interferon (IFN)-β and perform eQTL analysis on 23 pooled samples.},
	Author = {Kang, Hyun Min and Subramaniam, Meena and Targ, Sasha and Nguyen, Michelle and Maliskova, Lenka and McCarthy, Elizabeth and Wan, Eunice and Wong, Simon and Byrnes, Lauren and Lanata, Cristina M and Gate, Rachel E and Mostafavi, Sara and Marson, Alexander and Zaitlen, Noah and Criswell, Lindsey A and Ye, Chun Jimmie},
	Date-Added = {2019-02-22 10:30:45 +0100},
	Date-Modified = {2019-02-22 10:30:58 +0100},
	Doi = {10.1038/nbt.4042},
	Journal = {Nat Biotechnol},
	Journal-Full = {Nature biotechnology},
	Keywords = {demuxlet},
	Mesh = {Genotype; High-Throughput Nucleotide Sequencing; Humans; Interferons; Lupus Erythematosus, Systemic; Polymorphism, Single Nucleotide; Quantitative Trait Loci; Single-Cell Analysis; Transcriptome},
	Month = {01},
	Number = {1},
	Pages = {89-94},
	Pmc = {PMC5784859},
	Pmid = {29227470},
	Pst = {ppublish},
	Title = {Multiplexed droplet single-cell RNA-sequencing using natural genetic variation},
	Volume = {36},
	Year = {2018},
	Bdsk-File-1 = { }}
