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%% This BibTeX bibliography file was created using BibDesk.
%% https://bibdesk.sourceforge.io/
%% Created for Ava Hoffman at 2022-03-29 13:52:24 -0400
%% Saved with string encoding Unicode (UTF-8)
@article{Himes2014,
abstract = {Asthma is a chronic inflammatory respiratory disease that affects over 300 million people worldwide. Glucocorticoids are a mainstay therapy for asthma because they exert anti-inflammatory effects in multiple lung tissues, including the airway smooth muscle (ASM). However, the mechanism by which glucocorticoids suppress inflammation in ASM remains poorly understood. Using RNA-Seq, a high-throughput sequencing method, we characterized transcriptomic changes in four primary human ASM cell lines that were treated with dexamethasone---a potent synthetic glucocorticoid (1 µM for 18 hours). Based on a Benjamini-Hochberg corrected p-value <0.05, we identified 316 differentially expressed genes, including both well known (DUSP1, KLF15, PER1, TSC22D3) and less investigated (C7, CCDC69, CRISPLD2) glucocorticoid-responsive genes. CRISPLD2, which encodes a secreted protein previously implicated in lung development and endotoxin regulation, was found to have SNPs that were moderately associated with inhaled corticosteroid resistance and bronchodilator response among asthma patients in two previously conducted genome-wide association studies. Quantitative RT-PCR and Western blotting showed that dexamethasone treatment significantly increased CRISPLD2 mRNA and protein expression in ASM cells. CRISPLD2 expression was also induced by the inflammatory cytokine IL1β, and small interfering RNA-mediated knockdown of CRISPLD2 further increased IL1β-induced expression of IL6 and IL8. Our findings offer a comprehensive view of the effect of a glucocorticoid on the ASM transcriptome and identify CRISPLD2 as an asthma pharmacogenetics candidate gene that regulates anti-inflammatory effects of glucocorticoids in the ASM.},
author = {Himes, Blanca E. AND Jiang, Xiaofeng AND Wagner, Peter AND Hu, Ruoxi AND Wang, Qiyu AND Klanderman, Barbara AND Whitaker, Reid M. AND Duan, Qingling AND Lasky-Su, Jessica AND Nikolos, Christina AND Jester, William AND Johnson, Martin AND Panettieri, Jr, Reynold A. AND Tantisira, Kelan G. AND Weiss, Scott T. AND Lu, Quan},
doi = {10.1371/journal.pone.0099625},
journal = {PLOS ONE},
month = {06},
number = {6},
pages = {1-13},
publisher = {Public Library of Science},
title = {RNA-Seq Transcriptome Profiling Identifies CRISPLD2 as a Glucocorticoid Responsive Gene that Modulates Cytokine Function in Airway Smooth Muscle Cells},
url = {https://doi.org/10.1371/journal.pone.0099625},
volume = {9},
year = {2014},
bdsk-url-1 = {https://doi.org/10.1371/journal.pone.0099625}}
@Article{Law2020,
AUTHOR = { Law, CW and Zeglinski, K and Dong, X and Alhamdoosh, M and Smyth, GK and Ritchie, ME},
TITLE = {A guide to creating design matrices for gene expression experiments},
JOURNAL = {F1000Research},
VOLUME = {9},
YEAR = {2020},
NUMBER = {1444},
DOI = {10.12688/f1000research.27893.1},
url = {https://bioconductor.org/packages/release/workflows/vignettes/RNAseq123/inst/doc/designmatrices.html#regression-model-for-covariates}
}
@article{Sheridan2015,
abstract = {The molecular regulators that orchestrate stem cell renewal, proliferation and differentiation along the mammary epithelial hierarchy remain poorly understood. Here we have performed a large-scale pooled RNAi screen in primary mouse mammary stem cell (MaSC)-enriched basal cells using 1295 shRNAs against genes principally involved in transcriptional regulation.},
author = {Sheridan, Julie M. and Ritchie, Matthew E. and Best, Sarah A. and Jiang, Kun and Beck, Tamara J. and Vaillant, Fran{\c c}ois and Liu, Kevin and Dickins, Ross A. and Smyth, Gordon K. and Lindeman, Geoffrey J. and Visvader, Jane E.},
date = {2015/04/03},
date-added = {2022-03-22 17:14:42 -0400},
date-modified = {2022-03-22 17:14:42 -0400},
doi = {10.1186/s12885-015-1187-z},
id = {Sheridan2015},
isbn = {1471-2407},
journal = {BMC Cancer},
number = {1},
pages = {221},
title = {A pooled shRNA screen for regulators of primary mammary stem and progenitor cells identifies roles for Asap1 and Prox1},
url = {https://doi.org/10.1186/s12885-015-1187-z},
volume = {15},
year = {2015},
bdsk-url-1 = {https://doi.org/10.1186/s12885-015-1187-z}}
@article{Ritchie2015,
abstract = {{limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.}},
author = {Ritchie, Matthew E. and Phipson, Belinda and Wu, Di and Hu, Yifang and Law, Charity W. and Shi, Wei and Smyth, Gordon K.},
doi = {10.1093/nar/gkv007},
eprint = {https://academic.oup.com/nar/article-pdf/43/7/e47/7207289/gkv007.pdf},
issn = {0305-1048},
journal = {Nucleic Acids Research},
month = {01},
number = {7},
pages = {e47-e47},
title = {{limma powers differential expression analyses for RNA-sequencing and microarray studies}},
url = {https://doi.org/10.1093/nar/gkv007},
volume = {43},
year = {2015},
bdsk-url-1 = {https://doi.org/10.1093/nar/gkv007}}
@manual{rmarkdown2021,
author = {JJ Allaire and Yihui Xie and Jonathan McPherson and Javier Luraschi and Kevin Ushey and Aron Atkins and Hadley Wickham and Joe Cheng and Winston Chang and Richard Iannone},
note = {R package version 2.10},
title = {rmarkdown: Dynamic Documents for R},
url = {https://github.com/rstudio/rmarkdown},
year = {2021},
bdsk-url-1 = {https://github.com/rstudio/rmarkdown}}
@article{RueAlbrecht2018,
author = {Rue-Albrecht, K and Marini, F and Soneson, C and Lun, ATL},
doi = {10.12688/f1000research.14966.1},
journal = {F1000Research},
number = {741},
title = {iSEE: Interactive SummarizedExperiment Explorer [version 1; peer review: 3 approved]},
volume = {7},
year = {2018},
bdsk-url-1 = {https://doi.org/10.12688/f1000research.14966.1}}
@article{Su2017,
abstract = {{Summary graphics for RNA-sequencing and microarray gene expression analyses may contain upwards of tens of thousands of points. Details about certain genes or samples of interest are easily obscured in such dense summary displays. Incorporating interactivity into summary plots would enable additional information to be displayed on demand and facilitate intuitive data exploration.The open-source Glimma package creates interactive graphics for exploring gene expression analysis with a few simple R commands. It extends popular plots found in the limma package, such as multi-dimensional scaling plots and mean-difference plots, to allow individual data points to be queried and additional annotation information to be displayed upon hovering or selecting particular points. It also offers links between plots so that more information can be revealed on demand. Glimma is widely applicable, supporting data analyses from a number of well-established Bioconductor workflows (limma, edgeR and DESeq2) and uses D3/JavaScript to produce HTML pages with interactive displays that enable more effective data exploration by end-users. Results from Glimma can be easily shared between bioinformaticians and biologists, enhancing reporting capabilities while maintaining reproducibility.The Glimma R package is available from http://bioconductor.org/packages/Glimma/.}},
author = {Su, Shian and Law, Charity W and Ah-Cann, Casey and Asselin-Labat, Marie-Liesse and Blewitt, Marnie E and Ritchie, Matthew E},
doi = {10.1093/bioinformatics/btx094},
eprint = {https://academic.oup.com/bioinformatics/article-pdf/33/13/2050/25155969/btx094.pdf},
issn = {1367-4803},
journal = {Bioinformatics},
month = {02},
number = {13},
pages = {2050-2052},
title = {{Glimma: interactive graphics for gene expression analysis}},
url = {https://doi.org/10.1093/bioinformatics/btx094},
volume = {33},
year = {2017},
bdsk-url-1 = {https://doi.org/10.1093/bioinformatics/btx094}}
@article{Tasic2016,
abstract = {Mammalian cortex comprises a variety of cells, but the extent of this cellular diversity is unknown. The authors defined cell types in the primary visual cortex of adult mice using single-cell transcriptomics. This revealed 49 cell types, including 23 GABAergic, 19 glutamatergic and 7 non-neuronal types.},
author = {Tasic, Bosiljka and Menon, Vilas and Nguyen, Thuc Nghi and Kim, Tae Kyung and Jarsky, Tim and Yao, Zizhen and Levi, Boaz and Gray, Lucas T and Sorensen, Staci A and Dolbeare, Tim and Bertagnolli, Darren and Goldy, Jeff and Shapovalova, Nadiya and Parry, Sheana and Lee, Changkyu and Smith, Kimberly and Bernard, Amy and Madisen, Linda and Sunkin, Susan M and Hawrylycz, Michael and Koch, Christof and Zeng, Hongkui},
doi = {10.1038/nn.4216},
issn = {1546-1726},
journal = {Nature Neuroscience},
number = {2},
pages = {335--346},
title = {{Adult mouse cortical cell taxonomy revealed by single cell transcriptomics}},
url = {https://doi.org/10.1038/nn.4216},
volume = {19},
year = {2016},
bdsk-url-1 = {https://doi.org/10.1038/nn.4216}}
@book{Xie2018,
address = {Boca Raton, Florida},
author = {Yihui Xie and J.J. Allaire and Garrett Grolemund},
note = {ISBN 9781138359338},
publisher = {Chapman and Hall/CRC},
title = {R Markdown: The Definitive Guide},
url = {https://bookdown.org/yihui/rmarkdown},
year = {2018},
bdsk-url-1 = {https://bookdown.org/yihui/rmarkdown}}
@book{Xie2020,
address = {Boca Raton, Florida},
author = {Yihui Xie and Christophe Dervieux and Emily Riederer},
note = {ISBN 9780367563837},
publisher = {Chapman and Hall/CRC},
title = {R Markdown Cookbook},
url = {https://bookdown.org/yihui/rmarkdown-cookbook},
year = {2020},
bdsk-url-1 = {https://bookdown.org/yihui/rmarkdown-cookbook}}