Cmikofsky - Bioinformatics Pathway

Certificates July 2020 Certificates Bioinformatics Workflow Self-Assessments
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1

Overview of things learned:

Technical - Using the various R Studio tools to prepare data for DE analysis
Tools - RStudio, Troubleshooting (stackoverflow, R website, etc), slack, google meet, stem away forum
Soft Skills Networking, intra-team communication.

Three achievement highlights:
1.) Successfully finding and working out an error in my code with the help of office hours
2.) Met with my group regularly, and completed assignments in a timely manner
3.) Learned valuable bug finding strategies which I’m sure will come in handy later

List of meetings attended including social team events:
Monday meeting, Tuesday bio webinar, Wednesday group meeting, Thursday office hours, Friday Happy Hour

Goals for the upcoming week:

Flesh out my linkedin profile
Work more closely with my group to complete our deliverables
Hone github skills and get practice with version control.
Relate my findings with the paper for a better understanding of the biology aspect

Detailed statement of tasks done:

Quality control using AffyQCReport
Normalization using RMA
Batch correction using ComBat()
PCA plotting ( prcomp() then ggplot() )

Challenges and how those challenges were overcome:

Technical:
I had a dimensions problem with my affy object which prevented me from conducting a batch correction. I went to office hours and tried many things to troubleshoot, and in the end we were successful in finding the (tiny) error.

I was initially unsure as to whether or not my PCA plots were correct because they didn’t match the document well, but after meeting with my group and talking it over, I couldn’t find any issues with my process or data so it was resolved as a non-issue.

No real non-technical issues. My team was quite responsive and helpful for the most part.

also, P.S. thanks @sona I kind of borrowed your formatting for the self assessment. I found yours very clear and easy to read.

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3

Overview of things learned:

Technical - Using R tools to conduct a differential gene expression analysis
Tools - RStudio, Troubleshooting (stackoverflow, R website, etc), slack, google meet, stem away forum
Soft Skills Cultural competency

Three achievement highlights:
1.) Successfully generated a heatmap and volcano plot
2.) Met with my group and troubleshot
3.) Fixed errors from week 1’s stuff

List of meetings attended including social team events:
Monday meeting, Tuesday bio webinar, Wednesday group meeting, Thursday office hours,

Goals for the upcoming week:

Figure out if previous steps were done correctly (i.e. the DEGs we find have biological signifigance
Relate my findings with the paper for a better understanding of the biology aspect

Detailed statement of tasks done:

Annotation - labeling gene probes w/ symbols, collapseRows()
Gene Filtering - Removing NA’s and removing low expression genes
Analysis w/ limma - Volcano plots and Heatmaps

Challenges and how those challenges were overcome:

Technical:

Had a problem with my model matrix as well as discovering an early error with the ordering of my affy object. Also a problem w/ our PCA and outliers.
All of these were solved through group or independent troubleshooting

No real non-technical issues. My team was responsive and helpful.

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4

Overview of things learned: Using R and other data science tools to conduct a functional analysis on colorectal cancer data.

Technical Using R for different types of functional analysis.
Tools RStudio, StringDb, clusterProfiler, GSEA
Soft Skills Troubleshooting with my team, meeting with tech leads, building a resume/CV

Three achievement highlights:
1.) Successfully conducting a functional analysis and finishing my analysis of the colorectal cancer dataset
2.) Worked with my team in a close way. Met and produced a presentation
3.) Understood the biological significance of the last few weeks

List of meetings attended including social team events:
2 group meetings, office hours, team meeting

Detailed statement of tasks done:
clusterProfiler: enrichGO() with CC, MF, BP and plotted
groupGO analysis
gene concept networks: enrichKEGG()
StringDB for protein-protein interactions
GSEA analysis of hallmark gene set

Challenges and how those challenges were overcome:
Had a lot of problems with my enrichment analyses returning no results. This was resolved after meeting and communicating with Anya. It ended up being a problem with my removal of my duplicate probeset ID’s which went on mess up my collapseRows() function. After this issue was resolved my enrichment analyses functioned again.

Also as a problem with GSEA() I had my p-values equal to 0 which I am yet to resolve.

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5

Overview of things learned: Using R and other data science skills to conduct QC, normalization, DGE analysis, and functional analysis on a clear cell Renal cell carcinoma (ccRCC) data set. Then, using google slides to make and present on my work

Technical All previous QC,DGE and Functional analysis tools
Tools RStudio, StringDB, GEPIA, google slides, ncbi website
Soft Skills Making and creating a bioinformatics presentation.

Three achievement highlights:
1.) Successful preformed QC, DGE analysis, and Functional Analysis on a dataset by myself
2.) Did external research to link enriched pathways with their biological signifigance
3.) Made and gave an informative presentation

List of meetings attended including social team events:
2 team meetings, multiple sub group meetings, 1 office hours

Detailed statement of tasks done:
Quality control using AffyQCReport, simpleaffy, affyPLM
Normalization using RMA
PCA plotting ( prcomp() then ggplot() )

clusterProfiler: enrichGO() with CC, MF, BP and plotted
groupGO analysis
gene concept networks: enrichKEGG()
StringDB for protein-protein interactions
GSEA analysis of hallmark gene set
Annotation - labeling gene probes w/ symbols, collapseRows()
Gene Filtering - Removing NA’s and removing low expression genes
Analysis w/ limma - Volcano plots and Heatmaps

Challenges and how those challenges were overcome:
Aside from some initial problems with my dataset (before I switched diseases). This project went very smoothly. The process was very similar to the colorectal cancer process. I used the extra time to do a more in-depth dive into the key genes and pathways.
Problems were resolved through troubleshooting on the STEM-Away form.

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