Short Courses and Workshops

Hopkins faculty, staff, fellows and graduate students, as well as those outside the Hopkins community, are encouraged to join.

Workshops are being scheduled for Fall 2014; please check the list below for dates and times.

To register for courses, please send an email to containing your name, interests and experience pertaining to the course(s) you are registering.

Statistics and Data Analysis using R -Course: 510.707
10 sessions, on Wednesday and Friday
10AM to Noon
Nov. 8, 10, 15, 17, 29 & Dec. 1, 6, 8, 13, 15
Location: PCTB G19
Instructors: Leslie Cope, Luigi Marchionni, and Chenguang Wang
Requirements: Laptop
Cost: JHU SOM tuition (Fall Semester)

Statistics and Data Analysis Using R is a hands-on introduction to the R statistical software suite for biomedical scientists. It is assumed that the student is familiar with the plots and statistical summaries that are most commonly used in biomedical papers, but no formal background in statistics or programming is necessary. The primary objective is learning to use R, but the course also emphasizes the standards of practice that programmers and data analysts have implemented to ensure transparency, accuracy and accountability. Students are required to have a laptop.
Please Contact the Johns Hopkins University School of Medicine Registrar's office to Register.

Genomics Research Seminars
These six presentations cover principles of genomics research and their application, including practical guidance on key statistical and quantitative concepts involved in designing, executing, interpreting and communicating genomics data. The seminars are intended to be complementary but are self-contained, so attendance at previous seminars is not required.

September 12: Principles of Making Genomic Measurements
Srinivasan Yegnasubramanian, MD, PhD, Department of Oncology
10:00 am, CRB2 Room 111

September 19: Data Visualization
Sarah Wheelan, MD, PhD, Department of Oncology
10:00 am, CRB2 Room 111

September 28: Dealing with uncertainty (principles of statistics)
Leslie Cope, PhD, Department of Oncology, Division of Biostatistics and Bioinformatics
11:00 am, CRB2 Room 111

October 3: Statistical Evidence: how we know what we know
Ravi Varadhan, Ph.D., Department of Oncology, Division of Biostatistics and Bioinformatics
10:00 am, CRB2 Room 111

October 10: Hypothesis generation and testing with public resources
Luigi Marchionni, MD, PhD, Department of Oncology
10:00 am, CRB2 Room 111

October 17: Reproducibility
Rob Scharpf, PhD and Chenguang Wang, PhD, Department of Oncology, Division of Biostatistics and Bioinformatics
10:00 am, CRB2 Owens Auditorium

Please contact Lauren Ciotti ( to register. Registration is not mandatory, however, it is very much appreciated.

Subclonal Deconvolution and Phylogeny
April 10, 12, and 14 3PM to 4PM
Cost: $30
Locations: CRB1 3M42

Instructor: Rumen Kostadinov
Requirements: Laptopfor the hands-on session

In this course, Dr. Kostadinov will demonstrate clonal evolutionary dynamics within tumors using computer simulations [1] (see my example videos at I will discuss how growth, mutation, selection, and tissue structure affect the evolution of subclones within a tumor. Next, I will discuss existing subclonal deconvolution methods and apply some of them on simulated data above. I will then discuss phylogeny inference (the so-called field of PhyloOncology [2]) in cancer data sets [3] and briefly demonstrate phylogeny inference using Phylip and PAUP.
1. Kostadinov, R., Maley, C. C., & Kuhner, M. K. (2016). Bulk Genotyping of Biopsies Can Create Spurious Evidence for Hetereogeneity in Mutation Content. PLoS Computational Biology, 12(4), e1004413.
2. Somarelli, J. A., Ware, K. E., Kostadinov, et. al. (2016). PhyloOncology: Understanding cancer through phylogenetic analysis. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer.
3. Kostadinov, R., et. al. (2013). NSAIDs modulate clonal evolution in Barrett’s esophagus. PLoS Genetics, 9(6), e1003553.

To register, email:

Introduction to Unix
Cost: $40
Instructor: Frederick Tan
Requirements: Laptop
**Prior to attending the course, please download VirtualBox and our pre-built Ubuntu instance onto your laptop.

Understanding the Unix environment and interface is critical to using modern bioinformatics programs. This course will cover the basics of using Unix, including how to find help with any Unix command.

sed, AWK, and Bash Scripting
Prerequisite: Working experience with all the material covered in the 'Introduction to Unix' workshop"
One, 3 hour session
Cost: $30

An introduction to the classic and essential Unix tools.
Requirements: Laptop
**Prior to attending the course, please download VirtualBox and our pre-built Ubuntu instance onto your laptop.


Computational analysis of sequencing data
Cost: $20
Instructor: Ben Langmead

This two-hour lecture is an introduction to the array of computational methods, many new but some old, that underlie popular software used today. We will cover the computational ideas behind these tools, describe what makes them different from or similar to each other, and address questions on how to interpret their output.

Gene Expression Analysis
Requirements: Laptop and successful completion of the "Statistics and Data Analysis Using R" course Package.
Cost: $60

This course will cover the basic concepts of genomic
analysis, and is designed for students with a background in biology and/or biostatistics, and interest in basic or clinical/translational research. The goal is to provide a general orientation and pointers to simple and effective methodologies for analyzing genomic data in these

Specific topics will include:
Part 1: Read and explore gene expression data
a) measurement technologies, preprocessing, and quality control;
Part 2: Differential gene expression analysis
a) gene annotation;
b) identification of features associated with phenotypes;
c) analysis by gene sets and pathway

High Throughput Biology
Instructors: Sarah Wheelan & Srinivasan (Vasan) Yegnasubramanian
Cost: $20

As high throughput technologies (sequencing, microarrays, and more) grow in popularity, researchers are increasingly interested in what is available and how they can utilize these technologies in their own work. The class will briefly discuss the available technologies and some typical experimental designs, and will open the class to questions about the way technologies are used, how to design experiments, and how the technology may be used to address particular experimental questions.