Natasha_r - Bioinformatics Pathway

Certificates June 2020 Certificates Bioinformatics Workflow Self-Assessments
1

Self-assessment for the week of 6/9 (week two):

  • 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

  • Tools

    • Explored R/R Studio to conduct data visualization and analysis tasks
    • Used Jupyter Notebook to run and develop Python code

  • 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)

  • 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

  • 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

  • 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

  • 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
2

Self-assessment for the week of 6/15 (week three):

  • Technical Areas

    • Performed quality control using simpleaffy package in R
    • Learned the importance of normalizing microarray data as well as performed normalization with GCRMA
    • Learned how to perform principal component analysis, graph and interpret findings

  • Tools

    • Utilized R/R Studio to work on deliverables
    • Used Asana and Slack to coordinate with team members

  • Soft skills

    • Reached out to team to begin deliverables early on
    • Became comfortable with working in a small group
    • Strengthened communication (Slack, STEM-Away forum, etc) among team members and members from another team to work on a joint presentation

  • Tasks Completed

    • Week 3 deliverables completed
    • Reading through technical paper again to help solidify understanding

  • Achievements :

    • Completed the week 3 deliverables with team on time
    • Created a comprehensive presentation with another team to compare findings between normalization methods

  • Meetings/Trainings attended :

    • 6/15 Gene Team Meeting, 6/16 Asana training, 6/16 Python and Pandas webinar, 6/17 Technical webinar, 6/17 meeting with small team, 6/18 Gene Team meeting

  • Goals

    • Gaining more command over R and learning how to perform annotation and filtering of gene matrices
    • Learning more about limma and using it to find differentially expressed genes
    • Finishing deliverables on/ahead of time with team
3

Self-assessment for the week of 6/22 (week four):

  • Technical Areas
    • Performed data filtering of GSE dataset
    • Learned how to use limma in order to find differentially expressed genes
    • Learned microarray analysis works
  • Tools
    • Utilized R/R Studio to work on deliverables
    • Used Asana and Slack to coordinate with team members
  • Soft skills
    • Reached out to team to begin deliverables early on, as usual
    • Became more comfortable with Team 8
    • Communicated difficulties with leads
  • Tasks Completed
    • Filtered our genes that were not significantly expressed
    • Mapped probe set IDs to genes
    • Created a model matrix and performed limma analysis
  • Achievements :
    • Debugged problems with loading libraries in R
    • Found out how to create the correct expression matrix from probe set IDs to genes after trial and error
  • Meetings/Trainings attended :
    • 6/22 Gene Team Meeting, 6/23 Python webinar, 6/24 office hours with leads, 6/25 GitHub training webinar, 6/25 Introduction to Bioinformatics webinar, 6/25 Gene Team meeting, 6/26 R training
  • Goals
    • Learning more about KEGG pathways
    • Researching network and pathway analysis
4

Self-assessment for the week of 6/29 (week five):

  • Technical Areas
    • Performed limma analysis to find differentially expressed genes
    • Learned more about KEGG pathways and pathway analysis
    • Became familiar with gene set enrichment
  • Tools
    • R/R Studio
    • Used Asana and Slack to coordinate with team members
  • Soft skills
    • Reached out to team to begin deliverables early on
    • Communicated problems with leads/went to office hours from clarification
  • Tasks Completed
    • Conducted limma analysis, created volcano plot to show differentially expressed genes
    • Python exercises
  • Achievements :
    • Debugged problems with limma in R
    • Learned how to use more of Github
  • Meetings/Trainings attended :
    • 6/29 Gene Team Meeting, 7/1 Github webinar, 7/2 Gene Team meeting
  • Goals
    • Finishing up internship
    • Becoming skilled at R
5

Final self-assessment:

  • Technical Areas
    • Became skilled in data visualization using ggplot2 and Seaborn
    • Learned how to plot data from principal component analysis (PCA) as well as hierarchical clustering to analyze differences between biological samples
    • Learned how to use the simpleaffy, GCRMA, and limma packages in R in order to perform quality control, microarray normalization, and find differentially expressed genes
  • Tools
    • Utilized R/R Studio
    • Used Asana and Slack to coordinate with team members
    • Github to collaborate with team and submit deliverables
  • Soft skills
    • Became with working/collaborating in a team as well as delegating task roles
    • Communicated problems with leads
    • Utilized office hours and resources to deliver material
  • Tasks Completed
    • Created detailed diagrams of common figures such as volcano plots and MA plots
    • Performed quality control, microarray normalization, and differential gene analysis
    • Performed principal component analysis to discern patterns in the data
  • Achievements :
    • Became very comfortable working with R and R libraries
    • Became familiar with biological concepts presented in the research paper such as ceRNA networks, KEGG pathways, and PPI networks
    • Worked with another team to create a presentation to compare findings between normalization methods
  • Meetings/Trainings attended :
    • 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, 6/15 Gene Team Meeting, 6/16 Asana training, 6/16 Python and Pandas webinar, 6/17 Technical webinar, 6/17 meeting with small team, 6/18 Gene Team meeting, 6/22 Gene Team Meeting, 6/23 Python webinar, 6/24 office hours with leads, 6/25 GitHub training webinar, 6/25 Introduction to Bioinformatics webinar, 6/25 Gene Team meeting, 6/26 R training, 6/29 Gene Team Meeting, 7/1 Github webinar, 7/2 Gene Team meeting
  • Goals
    • Learning more about using data analysis in the bioinformatics field