Pre Course Questionnaire

Leon Eyrich Jessen

Is there any specific area of bio-research you would like to see covered?

General Bioinformatics & Data Analysis:

  • General interest in bioinformatics.
  • Comfortable handling different types of biological data in R.
  • How to work with data and make it easier to analyze.
  • Visualizations of big data.

Genetics & Genomics:

  • Genetics, Genomics, and Evolution.
  • Transcriptomics, Metagenomics, Genomic data analysis, and RNA-seq.
  • Single cell omics, Bulk RNA-seq data manipulation.
  • Gene expression data analysis in R.

Disease & Medical Research:

  • Personalized medicine and precision medicine.
  • Research related to specific diseases: obesity, cancer, autoimmune diseases, infectious diseases.
  • Drug design, clinical drug trials, and drug trial data analysis.
  • Analysis of complex cancer patient data.

Immunology & Microbiology:

  • Immunology, especially related to MHC, neoantigens, antibodies, and antigens.
  • Clinical research in R related to immunology.
  • Gut microbiome, Microbiome studies on cancer, and Microbiologic studies.
  • Immune response bio-research and immune system or stem cells.

Advanced Computational Techniques:

  • Predictive modeling and visualizations of complex networks/pathways.
  • Deep learning in R and Artificial Intelligence.
  • Analysis of peptide sequencing via mass spec (TD-search and de novo).

Specific Bio-research Topics:

  • Food-related research.
  • Ecology.
  • Plastic degradation by microorganisms or enzymes.
  • CO2 capture by microorganisms.
  • Mass screening and patient profiling, especially for cancer.
  • LC-MS data, peptide prediction from proteins.

Others:

  • Some students have expressed that they are open to any topic or aren’t particularly focused on a specific area.
  • A few are excited about the course in general and don’t have specific preferences.
  • There’s interest in the integration of bioinformatics with scientific articles and hospital data.

Briefly, what are your general expectations to this course?

R Proficiency:

  • Many students wish to gain or improve proficiency in using R for data analysis.
  • There’s an emphasis on understanding the R environment, syntax, and packages.
  • Some students are already familiar with R and wish to polish and expand their skills, while others are complete beginners hoping to grasp the basics.

Bioinformatics and Biology Application:

  • Many students want to learn how to apply R specifically to biological and bioinformatic datasets.
  • They expect to work with real-life bio data and learn how to handle and analyze data relevant to their bio studies.

Data Visualization & Manipulation:

  • Students are keen to learn about data visualization and manipulation in R.
  • They are interested in using R for creating plots, visualizations, and handling various data types.

Practical Skills for Future Application:

  • Several students hope the course will prepare them for future projects, research, or roles that require data analysis.
  • Some students are interested in using the skills they gain in this course for their thesis or future studies.
  • A few want to be able to transfer the knowledge they gain to other programming languages, like Python.

Data Analysis Techniques:

  • Students wish to learn about different data analysis methods, including statistics, RNA sequencing data processing, etc.
  • They hope to understand how to organize, clean, and interpret data.

Tool and Package Familiarity:

  • Some students want to familiarize themselves with specific R packages, like tidyverse.
  • There’s an interest in learning how to utilize GitHub for shared programming.
  • Several mention wanting to know how to use specific tools for data analysis, such as data wrangling and lab notebook style coding.

Learning Environment:

  • A few students mentioned hoping for a structured or gradual introduction, especially for those without prior knowledge.
  • Some have heard from past students and have expectations based on word-of-mouth.
  • A couple of students are concerned about the timing and structure of the exam.

Miscellaneous:

  • There are mentions of topics like the application of R in various biological datasets, multiomics, and genetics.
  • Some are looking forward to learning how to plan scientific studies or adjust chosen methods.
  • A few students don’t have specific expectations, while others hope for a challenging but rewarding experience.

Summary

Alignment of Expectations

  • Key criteria for succes is that we agree on the content of this course
  • Luckily, much of what is mentioned is part of this course
  • However, if we were to cover all of the mentioned aspects, that would basically be an entire bioinformatics education

This course

  • Practical Application: Equip students with hands-on bio data science skills, emphasizing on transforming messy datasets to clean, structured ones, and performing insightful data analyses.
  • Tools & Platforms: Utilize Tidyverse R, RStudio IDE, Quarto reporting, git/GitHub, and other specific tools such as dplyr, ggplot, and shinyapps.io for comprehensive bio data science tasks.
  • Reproducibility & Collaboration: Stress on the importance of reproducible data analysis and effective collaboration in bio data science projects using Tidyverse R and git/GitHub.
  • Broad Skill Set: Train students to perform basic statistical tests, create R packages, design Shiny apps, employ Large-Language-Model (LLM) technology like chatGPT, and more.
  • Project Analysis & Presentation: Guide students to assess and critique bio data science projects on method choices and data communication, and organize & present their own results in dynamic Quarto reports.

This course - In other words

  • Creates the foundation for you to explore the multitude of bioinformatics subjects
  • Gives you concrete skills to handle (almost) any kind of bio data
  • Trains your collaborative and communicative meta skills

Tag along and you’ll have fun and learn a ton!

R for Bio Data Science