G Tonthat - Bioinformatics Pathway - Self Assessment

Self-assessment 8/4

Overview of things learned:

  • Technical: Using Limma to find differentially expressed genes, visualization of results using packages in R, writing well-commented code in R
  • Tools: R Studio and associated packages
  • Soft skills: Pacing, communication with teammates. After not being able to work with my team on Week 1’s deliverables due to time issues, I was behind and felt like I was lacking in initiative and contributions. I made sure my group was aware of my intention to catch up, and then I took most of the week to make up on lost ground - pacing myself especially over the weekend to complete the Week 2 deliverables on my own before comparing notes with them on Monday.

Three achievement highlights:

  • One thing I’m very proud of after this past week was my growth in using R. For the longest time, I’ve disliked languages that weren’t Python, which led to some hesitation in learning others such as Java/R. I’m even taking a statistics in R course at my university, but I’ve written most of my code in Python (which the professor allowed, funnily enough). With the week’s deliverables, however, I actually sat down and forced myself to understand R syntax over sessions that probably amounted to 12+ hours. The fact that I finished about 100 lines of R code is something I’m pretty happy about.
  • Consulting R documentation. When facing challenges with certain packages, I looked up documentation to understand function parameters and usage. This is something I normally avoid in Python because of the plethora of Stack Overflow posts available, but R seems to have a lot less of those.
  • Initialized a Git repository for my project folder. Definitely going to look into making use of this/keeping it updated and potentially uploading to Github as well.

List of meetings attended:

  • Team meeting 7/27

Goals for the upcoming week:

  • More engagement with the Stem-Away team. I’m intending to go to Thursday’s Office Hours and Friday’s Happy Hours (for once), so hopefully this isn’t too late of a step towards engagement.
  • Pacing on deliverables. I’m nearing finals week for my university courses, so time will definitely be hard-pressed once again. To address this, I’ll be increasing my time management awareness and incrementally chip away at the project through the week rather than putting it off.
  • Stronger intuition and understanding of the project. I find that I can be easily lose track of the big picture and main idea behind projects when focusing on code and debugging. I’d like to increase my awareness of the biological concepts and tools that propel this week’s deliverables.
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Self-assessment 8/10

Overview of things learned:

  • Technical: Functional analysis (KEGG, String DB, enrichment, gene ontology, GSEA) and associated visualizations
  • Tools: R Studio and associated packages
  • Soft skills: Communication, time management

Three achievement highlights:

  • A reason why this self-assessment is so late is because I had finals last week for my summer classes. My last self-assessment addressed this and proposed that I better manage my time to finish it by Thursday. I actually accomplished this and was able to discuss my progress with my team in our meeting. :slight_smile:
  • Consulting R documentation to look up parameters and function descriptions was something that I became more efficient at.
  • The intuition behind the meanings of plots was something I couldn’t grasp at first. However, I set aside some time after finals to go through some articles and blog posts on functional analysis to better understand what information I could extract from plots.

List of meetings attended:

  • Team meeting 8/4
  • Office hours 8/7

Goals for post-Stem-Away:

  • Posting my code to Github. First, I’ll have to edit my code to make it more legible and insert sufficient comments. Hopefully, this will help me finalize my progress and understanding of this whole series.
  • Improving my resume. I’m currently applying to jobs in biotech and comp bio, so I hope to make use of my experience with Stem-Away effectively.
  • Continuous learning. I’m not sure if I’ll have time to do another computational biology project like this, but I hope to! Bioinformatics and the literature behind it is something that’s def exciting, and I hope I keep on getting more experience in this field (esp since I hope to get a career in it as well).
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Self-assessment 8/17

Overview of things learned:

  • Technical: Different quality control packages for non-CEL datasets, streamlining the entire microarray analysis pipeline
  • Tools: R Studio and associated packages
  • Soft skills: Stress management, time management

Three achievement/challenge highlights:

  • For some reason, I decided to pile a lot more work onto myself by choosing the Alzheimer’s dataset. :sweat_smile: Right off the bat, I probably burned at least 75% of my time on figuring out how to work with the data. The dataset itself was unable to be used with the ReadAffy function, so I looked up several resources on how to deal with Illumina microarray data. Recommended packages such as beadarray didn’t work for me for some reason, and unfortunately, there isn’t an in-depth debugging resource like there is for Python on Stack Overflow. Therefore, I had to manually QC the data through visualization tools. This is still something I have yet to figure out.
  • Consolidated all my progress from previous weeks into one big R file. I felt like I finally achieved a complete workflow on microarray analysis. :slight_smile:
  • In an effort to reach my goal of gaining more intuition into plots, I read a lot of literature on Alzheimer’s and previous research on genes responsible for the disease, as well as mechanisms behind them. As a result, I was able to connect general ideas behind binding and kinases with the results from analysis! This was a main focus in my presentation.

List of meetings attended:

  • Team meeting 8/17
  • Webinar 8/18

Goals for post-Stem-Away: (Mentioned in previous post on accident)

Detailed list of tasks done

  • Quality control/gene filtering
  • Normalization
  • Metadata retrieval
  • Differential gene expression analysis: limma, heatmap, DEG .csv file
  • Enrichment analysis/GSEA/String DB
  • Researched literature on past DGE analysis of Alzheimer’s to have something to compare to
  • Compiling insights and visualizations into a succinct presentation

LINK TO PRESENTATION