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1. Introduction to data science : data analysis and prediction algorithms with R.

Author

Irizarry, Rafael A.

Subjects
Computer algorithms, Data mining, Information visualization, Probabilities -- Data processing, Quantitative research, R (Computer program language), Statistics -- Data processing
Abstract

Summary: "The book begins by going over the basics of R and the tidyverse. You learn R throughout the book, but in the first part we go over the building blocks needed to keep learning during the rest of the book"-- Provided by publisher.

Published
2020

4. UBR5 forms ligand-dependent complexes on chromatin to regulate nuclear hormone receptor stability

Author

Tsai, Jonathan M., Aguirre, Jacob D., Li, Yen-Der, Brown, Jared, Focht, Vivian, Kater, Lukas, Kempf, Georg, Sandoval, Brittany, Schmitt, Stefan, Rutter, Justine C., Galli, Pius, Sandate, Colby R., Cutler, Jevon A., Zou, Charles, Donovan, Katherine A., Lumpkin, Ryan J., Cavadini, Simone, Park, Paul M.C., Sievers, Quinlan, Hatton, Charlie, Ener, Elizabeth, Regalado, Brandon D., Sperling, Micah T., Słabicki, Mikołaj, Kim, Jeonghyeon, Zon, Rebecca, Zhang, Zinan, Miller, Peter G., Belizaire, Roger, Sperling, Adam S., Fischer, Eric S., Irizarry, Rafael, Armstrong, Scott A., Thomä, Nicolas H., and Ebert, Benjamin L.

Published
2023
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11. A Guide to Teaching Data Science

Author

Hicks, Stephanie C. and Irizarry, Rafael A.

Subjects
Statistics - Other Statistics, Computer Science - Computers and Society
Abstract

Demand for data science education is surging and traditional courses offered by statistics departments are not meeting the needs of those seeking training. This has led to a number of opinion pieces advocating for an update to the Statistics curriculum. The unifying recommendation is computing should play a more prominent role. We strongly agree with this recommendation, but advocate the main priority is to bring applications to the forefront as proposed by Nolan and Speed (1999). We also argue that the individuals tasked with developing data science courses should not only have statistical training, but also have experience analyzing data with the main objective of solving real-world problems. Here, we share a set of general principles and offer a detailed guide derived from our successful experience developing and teaching a graduate-level, introductory data science course centered entirely on case studies. We argue for the importance of statistical thinking, as defined by Wild and Pfannkuck (1999) and describe how our approach teaches students three key skills needed to succeed in data science, which we refer to as creating, connecting, and computing. This guide can also be used for statisticians wanting to gain more practical knowledge about data science before embarking on teaching an introductory course., Comment: 2 tables, 3 figures, 2 supplemental figures

Published
2016

15. Vaginal microbiome topic modeling of laboring Ugandan women with and without fever

Author

Movassagh, Mercedeh, Bebell, Lisa M., Burgoine, Kathy, Hehnly, Christine, Zhang, Lijun, Moran, Kim, Sheldon, Kathryn, Sinnar, Shamim A., Mbabazi-Kabachelor, Edith, Kumbakumba, Elias, Bazira, Joel, Ochora, Moses, Mulondo, Ronnie, Nsubuga, Brian Kaaya, Weeks, Andrew D., Gladstone, Melissa, Olupot-Olupot, Peter, Ngonzi, Joseph, Roberts, Drucilla J., Meier, Frederick A., Irizarry, Rafael A., Broach, James R., Schiff, Steven J., and Paulson, Joseph N.

Published
2021
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18. Orchestrating high-throughput genomic analysis with Bioconductor.

Author

Huber, Wolfgang, Carey, Vincent, Gentleman, Robert, Anders, Simon, Carlson, Marc, Carvalho, Benilton, Bravo, Hector, Davis, Sean, Gatto, Laurent, Girke, Thomas, Gottardo, Raphael, Hahne, Florian, Hansen, Kasper, Irizarry, Rafael, Lawrence, Michael, Love, Michael, MacDonald, James, Obenchain, Valerie, Oleś, Andrzej, Pagès, Hervé, Reyes, Alejandro, Shannon, Paul, Smyth, Gordon, Tenenbaum, Dan, Waldron, Levi, and Morgan, Martin

Subjects
Computational Biology, Gene Expression Profiling, Genomics, High-Throughput Screening Assays, Programming Languages, Software, User-Computer Interface
Abstract

Bioconductor is an open-source, open-development software project for the analysis and comprehension of high-throughput data in genomics and molecular biology. The project aims to enable interdisciplinary research, collaboration and rapid development of scientific software. Based on the statistical programming language R, Bioconductor comprises 934 interoperable packages contributed by a large, diverse community of scientists. Packages cover a range of bioinformatic and statistical applications. They undergo formal initial review and continuous automated testing. We present an overview for prospective users and contributors.

Published
2015

19. Effects of KRAS Genetic Interactions on Outcomes in Cancers of the Lung, Pancreas, and Colorectum.

Author

Grabski, Isabella N., Heymach, John V., Kehl, Kenneth L., Kopetz, Scott, Lau, Ken S., Riely, Gregory J., Schrag, Deborah, Yaeger, Rona, Irizarry, Rafael A., and Haigis, Kevin M.

Abstract

Background: KRAS is among the most commonly mutated oncogenes in cancer, and previous studies have shown associations with survival in many cancer contexts. Evidence from both clinical observations and mouse experiments further suggests that these associations are allele- and tissue-specific. These findings motivate using clinical data to understand gene interactions and clinical covariates within different alleles and tissues. Methods: We analyze genomic and clinical data from the AACR Project GENIE Biopharma Collaborative for samples from lung, colorectal, and pancreatic cancers. For each of these cancer types, we report epidemiological associations for different KRAS alleles, apply principal component analysis (PCA) to discover groups of genes co-mutated with KRAS, and identify distinct clusters of patient profiles with implications for survival. Results: KRAS mutations were associated with inferior survival in lung, colon, and pancreas, although the specific mutations implicated varied by disease. Tissue- and allele-specific associations with smoking, sex, age, and race were found. Tissue-specific genetic interactions with KRAS were identified by PCA, which were clustered to produce five, four, and two patient profiles in lung, colon, and pancreas. Membership in these profiles was associated with survival in all three cancer types. Conclusions: KRAS mutations have tissue- and allele-specific associations with inferior survival, clinical covariates, and genetic interactions. [ABSTRACT FROM AUTHOR]

Published
2024
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20. A statistical framework for the analysis of microarray probe-level data

Author

Wu, Zhijin and Irizarry, Rafael A.

Subjects
Statistics - Applications
Abstract

In microarray technology, a number of critical steps are required to convert the raw measurements into the data relied upon by biologists and clinicians. These data manipulations, referred to as preprocessing, influence the quality of the ultimate measurements and studies that rely upon them. Standard operating procedure for microarray researchers is to use preprocessed data as the starting point for the statistical analyses that produce reported results. This has prevented many researchers from carefully considering their choice of preprocessing methodology. Furthermore, the fact that the preprocessing step affects the stochastic properties of the final statistical summaries is often ignored. In this paper we propose a statistical framework that permits the integration of preprocessing into the standard statistical analysis flow of microarray data. This general framework is relevant in many microarray platforms and motivates targeted analysis methods for specific applications. We demonstrate its usefulness by applying the idea in three different applications of the technology., Comment: Published in at http://dx.doi.org/10.1214/07-AOAS116 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Published
2007
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