
@article{love_moderated_2014,
	title = {Moderated estimation of fold change and dispersion for {RNA}-seq data with {DESeq2}},
	volume = {15},
	issn = {1465-6906},
	url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4302049/},
	doi = {10.1186/s13059-014-0550-8},
	abstract = {In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html.},
	number = {12},
	urldate = {2016-02-19},
	journal = {Genome Biology},
	author = {Love, Michael I and Huber, Wolfgang and Anders, Simon},
	year = {2014},
	pmid = {25516281},
	pmcid = {PMC4302049},
	file = {PubMed Central Full Text PDF:C\:\\Users\\cclass\\Zotero\\storage\\UISPM7GK\\Love et al. - 2014 - Moderated estimation of fold change and dispersion.pdf:application/pdf}
}

@article{sergushichev_algorithm_2016,
	title = {An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation},
	url = {http://biorxiv.org/content/early/2016/06/20/060012},
	doi = {10.1101/060012},
	journal = {bioRxiv},
	author = {Sergushichev, Alexey},
	year = {2016}
}

@book{nanostring_technologies_ncounter_2011,
	title = {{nCounter} {Expression} {Data} {Analysis} {Guide}},
	url = {https://www.genetics.pitt.edu/sites/default/files/pdfs/nCounter_Gene_Expression_Data_Analysis_Guidelines.pdf},
	urldate = {2019-02-14},
	publisher = {NanoString Technologies, Inc.},
	author = {NanoString Technologies, Inc.},
	year = {2011}
}

@article{wang_nanostringdiff_2016,
	title = {{NanoStringDiff}: a novel statistical method for differential expression analysis based on {NanoString} {nCounter} data},
	volume = {44},
	issn = {0305-1048},
	shorttitle = {{NanoStringDiff}},
	url = {https://academic.oup.com/nar/article/44/20/e151/2607975},
	doi = {10.1093/nar/gkw677},
	abstract = {Abstract.  The advanced medium-throughput NanoString nCounter technology has been increasingly used for mRNA or miRNA differential expression (DE) studies due t},
	language = {en},
	number = {20},
	urldate = {2020-07-09},
	journal = {Nucleic Acids Research},
	author = {Wang, Hong and Horbinski, Craig and Wu, Hao and Liu, Yinxing and Sheng, Shaoyi and Liu, Jinpeng and Weiss, Heidi and Stromberg, Arnold J. and Wang, Chi},
	month = nov,
	year = {2016},
	note = {Publisher: Oxford Academic},
	pages = {e151--e151},
	file = {Full Text PDF:C\:\\Users\\cclass\\Zotero\\storage\\9CWWZGST\\Wang et al. - 2016 - NanoStringDiff a novel statistical method for dif.pdf:application/pdf;Snapshot:C\:\\Users\\cclass\\Zotero\\storage\\RYMZCMGZ\\2607975.html:text/html}
}

@article{lundy_t_2018,
	title = {T {Helper} 1 {Cellular} {Immunity} {Toward} {Recoverin} {Is} {Enhanced} in {Patients} {With} {Active} {Autoimmune} {Retinopathy}},
	volume = {5},
	issn = {2296-858X},
	doi = {10.3389/fmed.2018.00249},
	abstract = {Autoimmune retinopathy (AIR) causes rapidly progressive vision loss that is treatable but often is confused with other forms of retinal degeneration including retinitis pigmentosa (RP). Measurement of anti-retinal antibodies (ARA) by Western blot is a commonly used laboratory assay that supports the diagnosis yet does not reflect current disease activity. To search for better diagnostic indicators, this study was designed to compare immune biomarkers and responses toward the retinal protein, recoverin, between newly diagnosed AIR patients, slow progressing RP patients and healthy controls. All individuals had measurable anti-recoverin IgG and IgM antibodies by ELISA regardless of disease status or Western blot results. Many AIR patients had elevated anti-recoverin IgG1 levels and a strong cellular response toward recoverin dominated by IFNγ. RP patients and controls responded to recoverin with a lower IFNγ response that was balanced by IL-10 production. Both AIR and RP patients displayed lower levels of total peripheral blood mononuclear cells that were due to reductions of CD4+ TH cells. A comparison of messenger RNA (mRNA) for immune-related genes in whole blood of AIR patients versus RP patients or controls indicated lower expression of ATG5 and PTPN22 and higher expression of several genes involved in TH cell signaling/transcription and adhesion. These data indicate that an immune response toward recoverin is normal in humans, but that in AIR patients the balance shifts dramatically toward higher IFNγ production and cellular activation.},
	language = {eng},
	journal = {Frontiers in Medicine},
	author = {Lundy, Steven K. and Nikoopour, Enayat and Karoukis, Athanasios J. and Ohara, Ray and Othman, Mohammad I. and Tagett, Rebecca and Jayasundera, K. Thiran and Heckenlively, John R.},
	year = {2018},
	pmid = {30271775},
	pmcid = {PMC6146138},
	keywords = {autoimmunity, cancer immunotherapy, cell mediated immunity, interferon-gamma, interleukin 10, Nanostring technologies, recoverin, retinal diseases},
	pages = {249},
	file = {Full Text:C\:\\Users\\cclass\\Zotero\\storage\\6XXXFGB7\\Lundy et al. - 2018 - T Helper 1 Cellular Immunity Toward Recoverin Is E.pdf:application/pdf}
}

@article{ritchie_limma_2015,
	title = {limma powers differential expression analyses for {RNA}-sequencing and microarray studies},
	volume = {43},
	number = {7},
	journal = {Nucleic Acids Research},
	author = {Ritchie, Matthew E. and Phipson, Belinda and Wu, Di and Hu, Yifang and Law, Charity W. and Shi, Wei and Smyth, Gordon K.},
	year = {2015},
	pages = {e47}
}


@article{gandolfo_rle_2018,
	title = {{RLE} plots: {Visualizing} unwanted variation in high dimensional data},
	volume = {13},
	issn = {1932-6203},
	shorttitle = {{RLE} plots},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0191629},
	doi = {10.1371/journal.pone.0191629},
	abstract = {Unwanted variation can be highly problematic and so its detection is often crucial. Relative log expression (RLE) plots are a powerful tool for visualizing such variation in high dimensional data. We provide a detailed examination of these plots, with the aid of examples and simulation, explaining what they are and what they can reveal. RLE plots are particularly useful for assessing whether a procedure aimed at removing unwanted variation, i.e. a normalization procedure, has been successful. These plots, while originally devised for gene expression data from microarrays, can also be used to reveal unwanted variation in many other kinds of high dimensional data, where such variation can be problematic.},
	language = {en},
	number = {2},
	urldate = {2022-09-22},
	journal = {PLOS ONE},
	author = {Gandolfo, Luke C. and Speed, Terence P.},
	month = feb,
	year = {2018},
	note = {Publisher: Public Library of Science},
	keywords = {Data visualization, Gene expression, Metabolomics, Microarrays, Proteomic databases, Simulation and modeling, Singular value decomposition, University laboratories},
	pages = {e0191629},
}


@book{gagnon-bartsch_ruv_2019,
	title = {ruv: {Detect} and {Remove} {Unwanted} {Variation} using {Negative} {Controls}},
	url = {https://CRAN.R-project.org/package=ruv},
	author = {Gagnon-Bartsch, Johann},
	year = {2019}
}

@article{martens_wikipathways_2021,
	title = {{WikiPathways}: connecting communities},
	volume = {49},
	issn = {0305-1048},
	shorttitle = {{WikiPathways}},
	url = {https://doi.org/10.1093/nar/gkaa1024},
	doi = {10.1093/nar/gkaa1024},
	abstract = {WikiPathways (https://www.wikipathways.org) is a biological pathway database known for its collaborative nature and open science approaches. With the core idea of the scientific community developing and curating biological knowledge in pathway models, WikiPathways lowers all barriers for accessing and using its content. Increasingly more content creators, initiatives, projects and tools have started using WikiPathways. Central in this growth and increased use of WikiPathways are the various communities that focus on particular subsets of molecular pathways such as for rare diseases and lipid metabolism. Knowledge from published pathway figures helps prioritize pathway development, using optical character and named entity recognition. We show the growth of WikiPathways over the last three years, highlight the new communities and collaborations of pathway authors and curators, and describe various technologies to connect to external resources and initiatives. The road toward a sustainable, community-driven pathway database goes through integration with other resources such as Wikidata and allowing more use, curation and redistribution of WikiPathways content.},
	number = {D1},
	urldate = {2021-02-23},
	journal = {Nucleic Acids Research},
	author = {Martens, Marvin and Ammar, Ammar and Riutta, Anders and Waagmeester, Andra and Slenter, Denise N and Hanspers, Kristina and A. Miller, Ryan and Digles, Daniela and Lopes, Elisson N and Ehrhart, Friederike and Dupuis, Lauren J and Winckers, Laurent A and Coort, Susan L and Willighagen, Egon L and Evelo, Chris T and Pico, Alexander R and Kutmon, Martina},
	month = jan,
	year = {2021},
	pages = {D613--D621},
}


@article{bhattacharya_approach_2020,
	title = {An approach for normalization and quality control for {NanoString} {RNA} expression data},
	volume = {22},
	issn = {1467-5463},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8138885/},
	doi = {10.1093/bib/bbaa163},
	number = {3},
	urldate = {2022-09-23},
	journal = {Brief Bioinform},
	author = {Bhattacharya, Arjun and Hamilton, Alina M and Furberg, Helena and Pietzak, Eugene and Purdue, Mark P and Troester, Melissa A and Hoadley, Katherine A and Love, Michael I},
	month = aug,
	year = {2020},
	pmid = {32789507},
	pmcid = {PMC8138885},
	pages = {bbaa163},
}

@article{molania_removing_2022,
	title = {Removing unwanted variation from large-scale {RNA} sequencing data with {PRPS}},
	copyright = {2022 The Author(s)},
	issn = {1546-1696},
	url = {https://www.nature.com/articles/s41587-022-01440-w},
	doi = {10.1038/s41587-022-01440-w},
	language = {en},
	urldate = {2022-09-23},
	journal = {Nat Biotechnol},
	author = {Molania, Ramyar and Foroutan, Momeneh and Gagnon-Bartsch, Johann A. and Gandolfo, Luke C. and Jain, Aryan and Sinha, Abhishek and Olshansky, Gavriel and Dobrovic, Alexander and Papenfuss, Anthony T. and Speed, Terence P.},
	month = sep,
	year = {2022},
	note = {Publisher: Nature Publishing Group},
	keywords = {Cancer genomics, Statistical methods},
	pages = {1--14},
}
