Self-assessment for the week of 6/9 (week two):
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Technical Areas
- Learned how to use R packages for visualization (ex. ggplot2) to create detailed diagrams of common figures such as volcano plots and MA plots
- Gained familiarity with Bioconductor and using the DESeq2 package to understand differential gene analysis
- Learned how to plot data from principal component analysis (PCA) as well as hierarchical clustering to analyze differences between samples
- Gained familiarity with biological concepts presented in the research paper such as ceRNA networks, KEGG pathways, and PPI networks
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Tools
- Explored R/R Studio to conduct data visualization and analysis tasks
- Used Jupyter Notebook to run and develop Python code
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Soft skills
- Reached out to leads and team members for guidance/introductions
- Become comfortable speaking in a group and contributing to discussions
- Strengthened communication channels (Slack, STEM-Away forum, etc)
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Tasks Completed
- R/Python exercises completed
- Reading/annotating the research paper and watching supplementary material
- Strengthening biological knowledge specifically in the context of techniques and methods used in the paper
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Achievements:
- Joined the internship in the second week but have caught up to speed
- All of the R exercises and Python exercises have been completed ahead of time
- Without a strong background in molecular biology, gained familiarity with the research paper by taking detailed notes and studying outside resources
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Meetings/Trainings attended:
- (Joined internship on week 2) 6/10 logistical webinar, 6/10 technical training webinar, 6/11 office hours with leads for Gene Team, 6/11 Gene Team meeting, 6/12 R training workshop, 6/12 Gene Team happy hour, 6/15 Gene Team meeting
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Goals
- Gaining more familiarity with R in the context of the dataset
- Learning more about microarray analysis/quality control
- Making strong progress towards actionables due next week as well as getting to know my team