Increasing the reproducibility and rigor of single-cell RNA-seq through the use of statistics and data science

Fri, April 29, 2022, 09:00 – 10:00AM (PDT), 12:00 – 1:00PM (EST)

Abstract: 

In this seminar, I will introduce tools for reproducibility and demonstrated how to make single-cell RNA-seq analyses more reproducible with these tools. I will give a brief overview of single-cell RNA-seq data. Next, I will introduce best practices around reproducibility in single-cell data science including the Markdown language, RStudio, and git/GitHub. Finally, I will give examples of making my own single-cell data analyses more reproducible.In this seminar, I will introduce tools for reproducibility and demonstrated how to make single-cell RNA-seq analyses more reproducible with these tools.

Speaker:

Stephanie Hicks is an Assistant Professor in the [Department of Biostatistics](https://www.jhsph.edu/departments/biostatistics/) at [Johns Hopkins Bloomberg School of Public Health](https://www.jhsph.edu). Her research interests focus around developing statistical methods, tools and software for the analysis of genomics data. Specifically, her research addresses statistical challenges in epigenomics, single-cell genomics and spatial transcriptomics such as the pre-processing, normalization, analysis of noisy high-throughput data leading to an improved quantification and understanding of biological variability. She actively contributes software packages to [Bioconductor](https://bioconductor.org) and is involved in teaching courses for data science and the analysis of genomics data. She is also a faculty member of the [Johns Hopkins Data Science Lab](https://jhudatascience.org), co-host of [The Corresponding Author](https://twitter.com/CorrespondAuth) podcast and co-founder of [R-Ladies Baltimore](https://rladies-baltimore.github.io). For more information, please see http://www.stephaniehicks.com.

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