Year 2022

Instruction

• You will set up a question you want to address from the dataset. If you have one, please send it TA via Slack DM. It will be one of your assignment. This should be no later than 12th Oct 10pm. You can change your question after your initial submission.

• The question should not be overlapped with other students or previous years. So if you made a question, please send it as soon as possible. First come first serve.

• Your assignment has to be written in the R notebook (as your daily assignment), including plot(s), the codes for data munging and plot, and description about your question, aim, finding. It would be great if you add some explanations on terms you have learnt over the assignment.

• The number of plots in your assignment is limited to two figures. However, you can use multi-panel plots (like facet_wrap or cowplot).

• Language: English + R.

• Submission has to be made via Slack DM to TA.

• Document file format: PDF (if you cannot knit PDF directly from your Rstudio, you can knit html and convert it to PDF)

• Filename should be "<student number><yourname in English>.pdf". For example, "1812345_JoonYongAn.pdf". If you do not submit this format, you will get 10% penalty.

• Please submit your assignment by 24th Nov, 10pm. You can have only 70% score for the late submission

Evaluation

• Accuracy (20%)

  • How well you describe the dataset related to your question in the R notebook?

  • Whether you understand the aim and methods of the dataset and utilize the dataset accurately

• Flowing Data (20%)

  • Whether your code is based on tidyverse functions for data munging and analysis?

  • Whether the code is briefly and efficiently written for your task?

• Storytelling with Data (20%)

  • Readability of your analysis and text

  • Whether you fully utilize the dataset to demonstrate your question.

  • Novelty of your question. Please do not set up a simple question for your analysis (e.g. "number of mutations or genes per lung cancer patients).

• Readability of your plot (20%)

  • How much information you summarize into your plot?

• Right visual format (20%)

  • Whether you properly use color, scale, and data type for your data visualization.

  • Whether you coherently make up visualization for your story.

Topic

Satpathy et al. (2021), Cell, A proteogenomic portrait of lung squamous cell carcinoma

Please read this article and understand the dataset provided in the supplementary table. Set up your question from the dataset and visualize information for your question.

MISC.

Examples from previous studies

Here are some examples, which made me impressed.

• [Year 2020] Temporal expression changes of transcription factors associated with cell type specific differentially expressed genes in autism spectrum disorder

• [Year 2019] Distribution of amino acid position per protein domain in SCN2A mutation variants

• [Year 2019] Expression pattern of genes requlating the onset, progression, and termination of neurogenesis