Webinar: A Python Pipeline for Single-Cell RNA-Seq Analysis
Friday, March 25, 2022 | Virtual
10 am Pacific ♦ 11 am Mountain ♦ 12 pm Central ♦ 1 pm Eastern
About the Webinar
The cool (and often maddening!) thing about computer programming languages is that there are so many! So if you just want to do a single-cell RNA-Seq analysis and your favorite programming language is Python, this is the webinar for you. Dr. Bonnie LaFleur's team will present and demonstrate the biostatistic optimizations they've built in to ShunPykeR, a Python-based pipeline app for scRNA-Seq analyses. ShunPykeR runs in a Jupyter Notebook app in CyVerse and so anyone with single-cell RNA-Seq data, a scientific question about those data, basic knowledge of Python, and a CyVerse account can benefit from this webinar. Please join us!
What You'll Learn
- What the Python-based ShunPykeR app is and does for scRNA-Seq analyses
- Some of the app's 'blackbox' optimizations for key steps like clustering and normalizing data
- Where and how to find and use the app in CyVerse
About the Presenters
Joel Parker is a Ph.D candidate in Biostatistics at the University of Arizona and builds statistical pipelines in Dr. Bonnie LeFleur's lab at the UArizona's BIO5 Institute. His research interests include Bayesian modeling, scRNA-seq data and non-parametric modeling. While attending Arizona State University for his undergraduate degree, Joel worked for Circle K as a data analyst using Python to automate tasks and R to run regression analysis.
Anastasia Kousa is a Bioinformatics Research Associate at The Sloan Kettering Institute. Dr. Kousa's extensive computational experience lies in the analysis and interpretation of -omics datasets, with particular expertise in mechanisms that underlie endogenous regeneration of the thymus after acute injury (such as infection, stress, and common cancer therapies), and the diminished regenerative capacity of the aging vs. young thymus.
Xiaoxiao Sun is an Assistant Professor in the Department of Epidemiology and Biostatistics at the University of Arizona. His research focus is developing theoretically justifiable and computationally efficient methods for complex and big data in medical imaging and genomics. He is particularly interested in nonparametric modeling, computational biology, statistical computing, and big data analytics.
Bonnie LaFleur, PhD, is a Research Professor of Biostatistics at the University of Arizona's BIO5 Institute and an Associate Professor with the Mel and Enid Zuckerman College of Public Health. Her research areas include cancer, aging, pediatrics, and methods leveraging observational study designs (health services and real-world evidence designs). Most of her methodologic work is in statistical methods for precision healthcare, specifically biomarkers. Bonnie is currently the Core Director and Project Lead on several program project grants at the University of Arizona.