Getting help

Table of Contents

Getting help from the TA

Camila Amorim and Elise English are the Teaching Assistants for the course. Please don’t hesitate to reach out to them via the course Slack page

Finding help online

  • Bioconductor’s help site - full of great tutorials, links to videos, and message board for more more complicated questions.
  • Bioinformatics questions - bioinformatics questions not pertaining to a specific R package are best addressed on Biostars
  • Programming questions - try StackOverflow

Getting help without ever leaving RStudio

  • Help within R - R has a comprehensive help system built right in! Type ‘?FunctionName’ right in the R terminal (no quotes and replace FunctionName with the name of the function you need help with)
  • sample datasets - type ‘data()’ to see all available sample datasets for the packages you have loaded. These can be extremely useful when learning to use new packages.

  • learn R, in R - download the Swirl package for R and follow their interactive tutorital to learn R and data science at your own pace. Install interactive Swirl courses through the Swirl course repository. This will be the basis for all homework assignments in class.

Take advantage of your local UPenn and Philly environment

Below are a few examples of some of the great local resources.

  • Use R! group at UPenn - A groups of Penn staff, students and researchers who get together to improve their R knowledge and skills. The group, which is open to R users of all levels, meets weekly on Wednesdays, 11am-noon, in the Weigle Informations Commons (WIC) seminar room of the Van Pelt-Dietrich Library Center.

  • Weekly ‘Code Review’ - Led by Kyle Bittinger, a Computational Biologist and faculty at CHOP. Each week one person presents some programming code they are working on for their research. Attendees get a chance to hear how code was put together to achieve a goal, while the presenter gets feedback, collective troubleshooting, etc. Less experienced programmers will have a chance to learn about design options when programming. Group meets one Wednesdays @ 9am in Johnson 207. Contact Kyle by email at to be added to the mailing list.

  • Python Bootcamp - learn Python programming for bioinformatics in a casual and friendly environment with the guidance of Sarah Middleton, UPenn Grad Student in Junhyong Kim’s lab, with assistance from several student TAs. Course usually meets from June 2nd-26th, from 9am-12pm.

  • Cytomic Data Analysis Workshop - Led by Wade Rogers, the Director of Computational Biology and Research Informatics for the Path BioResource at the Perelman School of Medicine. Given the pervasive nature of flow cytometry in all aspects of biomedical research, the mutiparameter nature of flow experiments, and the increasing abundance of clinical metadata, it only makes sense to leverage the power of R for analyzing the rich datasets produced by flow cytometry. Wade is an expert user of R/bioconductor for flow analysis, and has been very active in tool development in this area, including the development of a ‘fingerprinting’ method for analysing flow data. Contact Wade by email at for more information on the workshop.

  • Girl Develop It - Philadelphia has a local chapter of GDI that hosts speakers, workshops and social events aimed at bringing together and empowering women to learn web and software development in a judgment-free environment.

  • FemmeHacks is a all-women collegiate hackathon held in Philadelphia at UPenn’s Huntsman Hall, and is hosted by the Penn Women in Computer Science (WiCS). This year’s hackathon will take place in February. Friday, Feb 24th will consist of social mixers, workshops, and teams will be formed at Penn Engineering (220 South 33rd St), with the hackthon taking place the following day on Saturday the 25th at the Pennovation Center (3401 Grays Ferry Ave)

Take an online course to hone and expand your data science skills

  • Bioconductor for Genomic Data Science - This is the fifth course in the Genomic Data Science Specialization from Johns Hopkins University. (see below). Learn to use tools from the Bioconductor project to perform analysis of genomic data.

  • Coursera’s specialization in Genomic Data Science. Takes you through 7 courses, each lasting 1 month, that cover topics relevant to the analysis of data generated by biomedical research approaches.

  • Coursera’s specialization in Data Science - Brought to you by Johns Hopkins and Coursera, this 10 course ‘degree’ spans nearly a year, and covers takes a much more broad approach to understanding data than the genomic specialization described above. This specialization offers more on statistics, modeling, deploying data to the web, etc. In a way, I feel that my course on transcriptomics is a blend of the Coursera Genomic Data Science and Data Science Specializations.

  • Coursera’s specialization in Systems Biology - Yet another specialization offered through Coursera, this time in partnership with the Icahn School of Medicine at Mount Sinai and Coursera, this 5 course ‘degree’ covers the basics of network analysis and integrating diverse data types.

  • HarvardX’s course of Biomedical Data Science - and excellent and free online course taught by the talented Rafael Irizarry

  • Datacamp tutorials - learn R through your web browser. Several free courses, and additional subscription-based access to other courses.