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@article{Tasic2018,
	Abstract = {The neocortex contains a multitude of cell types that are segregated into layers and functionally distinct areas. To investigate the diversity of cell types across the mouse neocortex, here we analysed 23,822 cells from two areas at distant poles of the mouse neocortex: the primary visual cortex and the anterior lateral motor cortex. We define 133 transcriptomic cell types by deep, single-cell RNA sequencing. Nearly all types of GABA (γ-aminobutyric acid)-containing neurons are shared across both areas, whereas most types of glutamatergic neurons were found in one of the two areas. By combining single-cell RNA sequencing and retrograde labelling, we match transcriptomic types of glutamatergic neurons to their long-range projection specificity. Our study establishes a combined transcriptomic and projectional taxonomy of cortical cell types from functionally distinct areas of the adult mouse cortex.},
	Author = {{Tasic} Bosiljka, Zizhen Yao, Lucas T. Graybuck, Kimberly A. Smith, Thuc Nghi Nguyen, Darren Bertagnolli, Jeff Goldy, Emma Garren, Michael N. Economo, Sarada Viswanathan, Osnat Penn, Trygve Bakken, Vilas Menon, Jeremy Miller, Olivia Fong, Karla E. Hirokawa, Kanan Lathia, Christine Rimorin, Michael Tieu, Rachael Larsen, Tamara Casper, Eliza Barkan, Matthew Kroll, Sheana Parry, Nadiya V. Shapovalova, Daniel Hirschstein, Julie Pendergraft, Heather A. Sullivan, Tae Kyung Kim, Aaron Szafer, Nick Dee, Peter Groblewski, Ian Wickersham, Ali Cetin, Julie A. Harris, Boaz P. Levi, Susan M. Sunkin, Linda Madisen, Tanya L. Daigle, Loren Looger, Amy Bernard, John Phillips, Ed Lein, Michael Hawrylycz, Karel Svoboda, Allan R. Jones, Christof Koch & Hongkui Zeng},
	Doi = {10.1038/s41586-018-0654-5},
	Journal = {Nature},
	Month = {November},
	Pages = {72-78},
	Title = {{Shared and distinct transcriptomic cell types across neocortical areas}},
	Url = {https://www.nature.com/articles/s41586-018-0654-5},
	Year = {2018},
	Bdsk-Url-1 = {https://www.nature.com/articles/s41586-018-0654-5},
	Bdsk-Url-2 = {https://doi.org/10.1038/s41586-018-0654-5}}

@article{SummarizedExperiment,
	Abstract = {The SummarizedExperiment container contains one or more assays, each represented by a matrix-like object of numeric or other mode. The rows typically represent genomic ranges of interest and the columns represent samples.},
	Author = {{Morgan} Martin, Obenchain Valerie, Hester Jim, Pages Herve},
	Title = {{SummarizedExperiment}},
	Url = {https://bioconductor.org/packages/SummarizedExperiment},
	Bdsk-Url-1 = {https://bioconductor.org/packages/SummarizedExperiment}}

@article{Gilis2021,
	Abstract = {Alternative splicing produces multiple functional transcripts from a single gene. Dysregulation of splicing is known to be associated with disease and as a hallmark of cancer. Existing tools for differential transcript usage (DTU) analysis either lack in performance, cannot account for complex experimental designs or do not scale to massive scRNA-seq data. We introduce satuRn, a fast and flexible quasi-binomial generalized linear modelling framework that is on par with the best performing DTU methods from the bulk RNA-seq realm, while providing good false discovery rate control, addressing complex experimental designs and scaling to scRNA-seq applications.},
	Author = {{Gilis} Jeroen, Kristoffer Vitting-Seerup, Koen Van den Berge & Lieven Clement},
	Date-Modified = {2022-07-14 10:25:31 +0000},
	Doi = {https://doi.org/10.12688/f1000research.51749.1},
	Journal = {F1000},
	Month = {May},
	Title = {{satuRn: Scalable Analysis of differential Transcript Usage for bulk and single-cell RNA-sequencing applications}},
	Url = {https://f1000research.com/articles/10-374/v1},
	Year = {2021},
	Bdsk-Url-1 = {https://www.biorxiv.org/content/10.1101/2021.01.14.426636v1},
	Bdsk-Url-2 = {https://doi.org/10.1101/2021.01.14.426636}}

@article{VandenBerge2017,
	Abstract = {RNA sequencing studies with complex designs and transcript-resolution analyses involve multiple hypotheses per gene; however, conventional approaches fail to control the false discovery rate (FDR) at gene level. We propose stageR, a two-stage testing paradigm that leverages the increased power of aggregated gene-level tests and allows post hoc assessment for significant genes. This method provides gene-level FDR control and boosts power for testing interaction effects. In transcript-level analysis, it provides a framework that performs powerful gene-level tests while maintaining biological interpretation at transcript-level resolution. The procedure is applicable whenever individual hypotheses can be aggregated, providing a unified framework for complex high-throughput experiments.},
	Author = {{Van den Berge} Koen, Charlotte Soneson, Mark Robinson & Lieven Clement},
	Doi = {10.1186/s13059-017-1277-0},
	Journal = {Genome Biology},
	Month = {August},
	Title = {{stageR: a general stage-wise method for controlling the gene-level false discovery rate in differential expression and differential transcript usage}},
	Url = {https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1277-0},
	Year = {2017},
	Bdsk-Url-1 = {https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1277-0},
	Bdsk-Url-2 = {https://doi.org/10.1186/s13059-017-1277-0}}
