Bioinformatics Training & Education Program

What are common statistical analyses for binary data? What is the distribution of your binary dependent variable? What is the difference from normally distributed data? How do you model the binary outcome with multiple predictors in a regression?

This is a two-hour lecture intended for those doing basic data analysis using R. Basic R programming is a pre-requisite for this course, as is knowledge of basic statistical concepts, such as, mean and p-value from statistical hypothesis testing. This class will be taught by the Clinical Center's Biostatistics and Clinical Epidemiology Service (CC/BCES).

 The learning outcomes include:

• calculating and displaying descriptive statistics, such as rates, proportions, and barplots 
• recognizing the binomial probability density function as distinct from the normal density function
• estimating proportion and confidence intervals
• hypothesis testing for one-sample and two-sample 
• logistic regression and checking model assumptions 
• model diagnostics checking and results interpretation

R code snippets will be shared during the lecture and within lecture notes. The class will be recorded, so you can go back to the material as you begin to do your own modeling. During the class, time will be devoted to explaining the concepts, and code snippets and output and references will be provided for in-depth material. 

Preclass Requirements: You must take the basic R programming and statistical inference – Part I classes as pre-requisite through the NIH Library or have acquired the equivalent knowledge elsewhere prior to registering for this class.

Statistical Software: We will be using R and RStudio for our statistical analysis. R is open source and free. There are versions for Mac OSX, Windows, and Linux. You can download it from https://cran.r-project.org/. Additionally, we will be using RStudio as a graphical interface for R. RStudio is free for everyone to download from https://posit.co/download/rstudio-desktop/. See above for pre-requisites in R programming.

The Elemento lab combines Big Data analytics with experimentation to develop entirely new ways to help prevent, diagnose, understand, treat and ultimately cure disease. Our research involves routine use of ultrafast DNA sequencing, proteomics, high-performance computing, mathematical modeling, and artificial intelligence/machine learning. We’re revolutionizing healthcare by developing innovative approaches to better predict, diagnose, treat, and prevent disease to improve clinical care for every patient.  

We cordially invite you to attend the upcoming Data Sharing and Reuse Seminar featuring Dr. Robert Schuler and Dr. Jifan Feng. Dr. Schuler, a Senior Computer Scientist and Lead Scientist at the University of Southern California's Information Sciences Institute, will be joined by Dr. Feng, a Research Associate at the Center for Craniofacial Molecular Biology, Herman Ostrow School of Dentistry of USC. They will be presenting on "FaceBase: Empowering Dental, Oral, and Craniofacial Research Through Data Sharing and Reuse." This event is generously supported by the National Institute of Dental and Craniofacial Research to highlight the importance of data sharing in advancing dental and craniofacial research.

The seminar will introduce FaceBase, a trusted data resource for research and education on craniofacial and dental development and malformations/diseases across human and animal models. Dr. Schuler will present FaceBase as a community-building platform offering a cloud-based repository of high-quality FAIR data resources. Dr. Feng will then showcase examples of FaceBase data reuse in dental, oral, and craniofacial research. This presentation will demonstrate how FaceBase facilitates data sharing, analysis, and interpretation to accelerate discoveries in the field. We look forward to your participation in this informative session!

Dr. Mardis is an internationally recognized expert in cancer genomics, with ongoing interests in the integrated characterization of cancer genomes, defining DNA-based somatic and germline interactions and RNA-based pathways, and immune microenvironments that lead to cancer onset and progression, specifically involving pediatric cancers. Most recently, her research has been oriented toward translational aspects of cancer genomics, specifically identifying how the cancer genome changes with treatment, including acquired resistance, the use of genomics in understanding immune therapy response, and the clinical benefit of cancer molecular profiling in the pediatric setting.

Dr. O'Neill's research programs employ molecular genetics, genomics and computational approaches to study the mechanisms that maintain, and disrupt, genome stability with a particular focus on repetitive elements. Projects include studying: retroelement transcription and centromere function; novel small RNA biogenesis pathways; and global chromosome and genome changes during instability (such as in cancer and hybrid dysgenesis). In addition, we use a diverse set of rapidly evolving next generation sequencing (NGS) technologies and novel library preparation and computational methodologies for drafting and characterizing genome sequences in efforts to establish broad eukaryotic species as models for studying genome biology. Recently, Dr. O'Neill's lab has expanded their efforts towards applying broad NGS techniques to both model and non-model systems to understand the dynamic response of the genome to environmental queues, such as global warming.

The goal of Artificial Intelligence Resource (AIR) is to make AI tools available to Center for Cancer Research (CCR) investigators. The strength of AI is that algorithms can be trained to seek specific information that may be scientifically or clinically important.  AIR will mainly focus on “Computer Vision” which analyzes medical images, such as radiologic, digital pathology, video/endoscopy, and optical imaging among others.  Examples of potential projects include developing better screening, detection methods or predictive markers, or improving procedures among many others. Both clinical and laboratory-based imaging projects will be considered. Please refer to our ongoing projects and prior publications for more information.