Roman_Ramirez - Bioinformatics (Level 2) Pathway

Certificates Summer 2021 Certificates R Shiny Transcriptomics App 2021
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My self-assessments are posted as replies to this thread.

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BIOINFORMATICS: LEVEL 1 MODULE 1

Overview:

Technical Area

  • Learning how to install packages in R, and learning R syntax.

Tools

  • RStudio, R, STEM-Away

Soft Skills

  • Problem-Solving, Independence, Time Management

Achievement Highlights

  • I installed the correct version of R: v.4.0.0
  • I was able to install the correct packages that were listed in the assignment: ggplot2 and Bioconductor
  • I was able to understand R syntax from R Basics. Host: Yves Gaetan, Resident R Expert at STEM-Away

Details of Tasks and Hurdles

  • I had difficulty with installing R v.4.0.0. By default, I was installing the latest version of R, R v.4.1.0.
  • At times I found navigating the STEM-Away site confusing, and I had trouble pinpointing the deliverables for Level 1: Module 1 - Self Assessment and Preparation.
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BIOINFORMATICS: LEVEL 1 MODULE 2

1. Overview

  • Technical Area
    • Accessed metadata from GEODatabase
    • Imported metadata into R
  • Tools
    • R
    • GEODatabase
    • Google Slides and Slack as resources for help.
  • Soft Skills
    • Time Management
    • Troubleshooting
    • Communication

2. Achievement Highlights

  • Successfully browsed the GEO Database for chip metadata.
  • Organized my local file system according to R customs.
  • Successfully imported metadata into R.

3. Details of Tasks and Hurdles

  • In the beginning, I found it difficult to determine the deliverables for this module. However, after consulting with @ivanlam27 and @veyssi, I finished my code and prepared my deliverable.

4. Goals for the Upcoming Week

  • Participate and contribute in daily check-in meetings.
  • Work with @veyssi on our portion of Level 2: Module 3.
  • Contribute to Groups B2 and D in the combined Bioinformatics project.
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BIOINFORMATICS: LEVEL 1 MODULE 3

1. Overview

  • Technical Area
    • Created a Normalized and Background-Corrected Boxplot using the dataset, GSE19084.
    • Explored differences between MAS(5) and RMA.
  • Tools
    • R: affy, simpleaffy
    • GEODatabase
    • GitHub
    • Google Slides, Slack, Zoom as resources for help.
  • Soft Skills
    • Teamwork
    • Team Management
    • Communication

2. Achievement Highlights

  • Used information learned from @anya’s meeting to normalize and background-correct a dataset and to create a boxplot.
  • Worked with @veyssi to normalize and background-correct a dataset and to create a boxplot in R.
  • Identify outliers from our modified dataset.
  • Successfully committed a branch to the team GitHub repository.

3. Details of Tasks and Hurdles

  • In the beginning, I struggled to understand what normalization, principal-component analysis, and background correction were and how they were significant to dataset, GSE19084. However, after attending @anya’s meeting and my group’s Tuesday check in, I was able to better understand these concepts.

4. Goals for the Upcoming Week

  • Explore the leading ideas to prepare for meetings in Groups B2 and D.
  • Work with @ivanlam27 on our portion of Module 4.
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BIOINFORMATICS: LEVEL 1 MODULE 4

1. Overview

  • Technical Area
    • Manipulated data to be used to generate plots.
    • Created a Volcano Plot using the dataset, GSE19084.
    • Analyzed the top 10 DEGs of the data.]
  • Tools
    • R: EnhancedVolcano, ggplot2, simpleaffy, tidyverse, limma, Biobase, dplyr
    • GEODatabase and GEO2R
    • GitHub
    • Google Slides, Slack, Zoom as resources for help.
  • Soft Skills
    • Problem-Solving
    • Teamwork
    • Team Management
    • Communication

2. Achievement Highlights

  • Used information learned from @anya’s meeting and from my module partner, @ivanlam27, to generate the code for the volcano plot.
  • Compare results to that of the paper and from GEO2R.
  • Identify outliers from our modified dataset.

3. Details of Tasks and Hurdles

  • In the beginning, I struggled to understand R syntax and the data preprocessing needed to generate the volcano plot. However, after working with @ivanlam27, I was able to better understand these topics.

4. Goals for the Upcoming Week

  • Work as @veyssi as Group B2 Project Managers to plan our first group meeting.
    • Provide a weekly timeline and expectations in the group.
  • Work with @KellyZhang on our portion of Module 5.
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BIOINFORMATICS: LEVEL 1 MODULE 5

1. Overview

  • Technical Area
    • Manipulated data to be used to generate plots.
    • Created a GSEA Plot using the dataset, GSE19084.
    • Analyze a GSEA Plot.
  • Tools
    • R: clusterProfiler, GSEA, AnnotationDbi
    • GEODatabase and GEO2R
    • GitHub
    • Google Slides, Slack, Zoom as resources for help.
  • Soft Skills
    • Problem-Solving
    • Teamwork
    • Team Management
    • Communication

2. Achievement Highlights

  • Used information learned from @anya’s meeting and from my module partner, @KellyZhang, to generate the code for our portion of the functional analysis.
  • Compare results to that of the paper and from @anya’s Guided Module.
  • Identify significant gene sets from the generated GSEA plots.

3. Details of Tasks and Hurdles

  • In the beginning, I found it difficult to organize the data from limma analysis into a proper data frame and vector. However, I was able to reform the data and perform the expected GSEA analysis.
  • We noticed that at a certain p-value cutoff in the GSEA function, some categories were omitted from the generated tables. After trying various p-value cutoffs, we found that a p-value of 0.15 was sufficient.

4. Goals for the Upcoming Week

  • Work as @veyssi as Group B2 Project Managers to organize the rest of group with coding tasks.
  • Work with @sanisetti on our portion of Module 6.
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BIOINFORMATICS: LEVEL 1 MODULE 6

1. Overview

  • Technical Area
    • Uploading gene sets to web-based Functional Analysis. tools.
    • Analyze generated plots.
  • Tools
    • R: AnnotationDbi
    • EnrichR, DAVID, Metascape
    • STRING and PPI
    • GEPIA
  • Soft Skills
    • Research
    • Teamwork
    • Team Management
    • Communication and Organization

2. Achievement Highlights

  • Used differentially-expressed gene data generated from Module 5 to generate a protein-protein interaction network and STRING.db table as web-based functional analysis.
  • Identify significant protein-protein interactions from the PPI network.
  • Identify the highest co-expressed genes in the network.

3. Details of Tasks and Hurdles

  • Uploading the gene vector generated from Module 5 to the STRING.db website initially failed. However, changing the output to the generated format generated a plot.
  • My module partner, @sanisetti, and I had different gene vectors from our previous work.

4. Goals for the Upcoming Week

  • Work with @veyssi as Group B2 Project Managers to code the layout of the R Shiny app with @ivanlam27.
  • Work with Team-1 members on functional analysis for the Capstone project.
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BIOINFORMATICS: LEVEL 1 MODULE 7

1. Overview

  • Technical Area
    • Use a transcriptomics pipeline to analyze a new GEO dataset, GSE4107: colorectal cancer. tools.
    • Conduct quality control and categorize the dataset.
    • Identify differentially-expressed genes and understand underlying pathways, categories, and protein-protein interactions with these DEGs.
  • Tools
    • GEO and GEO2R
    • R: Affy, AnnotationDbi, enrichPlot, limma, clusterProfiler
    • QC and PCA
    • Heatmap and VolcanoPlot
    • KEGG, Gene-Ontology Network, GSEA, Survival analysis
  • Soft Skills
    • Presentation Skills
    • Teamwork
    • Team Management
    • Communication and Organization

2. Achievement Highlights

  • Identified KEGG pathways with the highest differentially-expressed genes.
  • Created a gene-concept network for further analysis, and a GSEA plot to target hallmark categories.
  • Creating, practicing, and presenting a capstone presentation for GSE4107 with teammates: @Ananya_Kaushik, @veyssi, @ivanlam27, @KellyZhang, @Leila.

3. Details of Tasks and Hurdles

  • Our team had to coordinate meetings across multiple, different time-zones.

4. Goals for the Upcoming Week

  • Present the R-Shiny product with the R-Shiny team.
  • Work with my capstone team on further transcriptomics analysis.
  • Possibly meet with @anya’s research group about STEM-Away internship experiences.