My self-assessments will be posted here.
Self-Assessment for Week 2
Technical Area:
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Learning how to work with the GEO database
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Learning how to use functions in RShiny, including functions relating to page layout
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Practiced presenting multiple figures from academic paper and providing my teammates with basic understanding of statistical concepts
Tools:
- R, RShiny, GEO2R
Soft Skills:
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Communication with team members
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Patience, particularly with learning basic app-building without having prior experience
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Troubleshooting
Three Achievement Highlights:
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Able to upload CSV file and visualize heatmap from file in RShiny
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Learned how to use GEO2R for statistical analysis
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Worked with other leads to develop project structure and provide timeline for team
Goals for Week 3:
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Finish L1M3
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Develop RShiny implementation of some basic QC features
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Get started on exploring other QC methods as part of Transcriptomics Group in overall project structure
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Help coordinate first meeting for Transcriptomics and Functional Components groups
Other details of tasks done and challenges faced:
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I started off the week by finishing the Journal Club module with my team. I provided my teammates with an overview of complicated terms they may not understand and facilitated discussion of the paper with fellow lead Samuel.
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I then worked on the basic RShiny app in anticipation of the technical session. I struggled to import large data files and still need to determine the best way to accomodate large files in RShiny, but I got the heatmap app I was trying to make to be successful on a smaller file.
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I assisted Samuel in making a project structure and organizing groups.
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Lastly, I planned out tasks for my team for the next week in regard to completing Module 3.
Self-Assessment for Week 3
Technical Area:
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Learned how to work with affy package and how to work with affy objects
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Researched why different QC measures matter
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Worked with other leads on logistics of project development
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Organized meeting with rest of team
Tools:
- R, AirTable
Soft Skills:
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Time management
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Organization
Three Achievement Highlights:
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Researched and found explanations of why different QC methods matter
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Found a few articles for exploring alternative QC methods to what was included in the module
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Solved problem I was running into in getting annotations for heatmap (problem was that annotation vectors were not equal despite me thinking they were initially)
Goals for the rest of Week 3:
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Implement QC features in R Shiny
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Organize first meetings of Transcriptomics and Functional Components project groups
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Find more QC methods for Transcriptomics group to explore
Other details of tasks done and challenges faced:
- It was more challenging than I expected to get some of the data files to upload properly. Reading CEL files into Rstudio took some time to get used to, but after reading many posts on StackExchange and realizing how the error I was running into was different than those, I was able to get it to work.
Self-Assessment for Week 4
Technical Areas:
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Learned about how to create action buttons in RShiny
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Practiced implementing QC methods in RShiny
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Worked through L1M4
Tools:
- R, RShiny
Soft Skills:
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Helping Samuel organize tasks for A and B1
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Virtual communication
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Encouraging teammate participation
Three Achievement Highlights:
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Able to successfully complete deliverables for L1M4
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Able to encourage team participation
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Developed working RLE and NUSE RShiny app
Goals for Week 5:
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Lead Groups A and B1 through implementing data importation
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Start working on QC methods with Groups A and B1
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Provide Team 3 with an overview of Module 5
Other details of tasks done and challenges faced:
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I found the gene annotation part of L1M4 to be quite difficult. Eventually I got something that gave me a unique mapping from probeID to gene symbol, but it was difficult to come up with a systematic way to approach it.
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I found it hard to communicate over Slack this week, but as a pathway I think Samuel and the other leads are encouraging greater communication, so in the last few days it has definitely improved.
Self-Assessment for Week 5
Technical Areas:
- Learned how to create dynamic user inputs in RShiny
- Learned how to work with GEOQuery package
- Researched and implemented importation for IDAT files
Tools:
- R, RShiny, GEO Database
Soft Skills:
- Researching different approaches to obtaining microarray data
- Organization for team tasks
- Assisting those who need help with completing their tasks
Three Achievement Highlights:
- Figured out how to make drop down menu that changes based on what user inputs
- Encouraged participation in meetings
- Implemented GEOquery functions in RShiny app
Goals for Week 6:
- Get Groups A and B1 to finish QC tasks
- Provide Team 3 with overview of Module 5
- Get to work on Statistical Analysis for final app
Other details of tasks done and challenges faced:
- I found that working through data importation tasks and organizing the data importation tasks of other sub-groups took longer than expected, making it hard for me to finish going through Module 5 in time to provide Team 3 with an overview.
- I am still struggling with getting SOFT files from the GEO database into the app; I might work with others to see how this can be done.
Self-Assessment for Week 6
Technical Area:
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Learned how to implement interactive boxplots and PCA plots in R Shiny
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Figured out how to create algorithm to allow user to remove outliers from data set
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Learned how to work with conditional panels in R Shiny layout
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Technical writing: began to work on documentation with the help of others working on the app
Tools:
- R, RShiny, referenced websites like StackOverflow, R Shiny Community
Soft Skills:
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Communication: had to assign QC tasks related to the app and work with my group members to make sure all tasks get completed
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Attention to detail: team members helped me find shortcomings in the current layout of the app and we made a plan to improve them
Three Achievement Highlights:
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Created code to remove outliers from dataset
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Adapted R code from others to R Shiny for batch correction
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Made small layout changes
Goals for Week 7:
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Refine QC code so that all combinations of functions are working on all possible data types
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Begin work on statistical analysis
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Make sure all subgroups working in Groups A and B1 are engaged
Other details of tasks done and challenges faced:
- I was responsible for putting everyone’s QC code together in one file, and I had trouble making sure everything was working with the data importation code. Overall, though, I have tried to address the issue by dedicating time to testing functions and by talking with each subgroup about what their code does and what they used to test it.
- I haven’t found a good way to integrate multiple files together (e.g. data importation code for Group B1 and QC code for Group B1), but I will work with Samuel and Disha on this.
Self-Assessment for Week 7
Technical Area:
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Finished integrating subgroups’ code into one file
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Worked on writing R Shiny code to perform Limma analysis
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Integrated subgroups’ statistical analysis code into one file
Tools:
- R, R Shiny
Soft Skills:
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Strengthened my ability to take responsibility for the project and finished portions of the code from subgroups I didn’t hear back from
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Learned how to rely on others for help, particularly Maryam Momeni who was able to help me figure out gene annotation
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Time management: worked with my groups to make sure we could work on both QC and statistical analysis
Three Achievement Highlights:
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Created code for conducting Limma analysis with user input of p cut-off value
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Worked with Disha Chauhan, Shreya Vora, Sneha Raj, and Maryam Momeni to make sure all aspects of statistical analysis were finished
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Got both QC and statistical analysis to work on an imported data set
Goals for Week 8:
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Finish functional analysis
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Optimize user inputs
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Figure out how to get app to run on many devices, not just on my PC
Other details of tasks done and challenges faced:
- While I can get all of the code to run on a dataset imported from my laptop, the code is not working properly on the laptops of others. It seems related to how the data importation uses the working directory, and Samuel and I will try to solve the issue.
Self-Assessment for Week 8
Technical Area:
- Polished statistical analysis file with all subgroups’ code
- Worked on fixing data importation for raw data
- Got statistical analysis to work for CSV & TXT data type
- Worked with subgroups, Samuel, Disha, and other groups to edit layout of app and add text explanations to different app pages
- With Samuel’s help split code into multiple files and learned how to add it to GitHub
Tools:
- R, R Shiny
Soft Skills:
- Task delegation: I learned a lot about how to think pragmatically about the tasks I will have time to complete and how to assign tasks to others. Being reasonable and honest about what each individual can actually complete allowed us to finish the app this week.
- Growth mindset: Multiple meetings this week were spent figuring out how we could improve different aspects of our app, and it was an important learning experience to take feedback from others not as harsh criticisms, but as practical pieces of advice that allowed me and my groups to make the app a lot better.
- Listening skills: I built upon my listening skills by making sure that I was paying attention to the stress levels and availability of my teammates, and I listened carefully to their ideas and thought about how we could implement their ideas within our short time frame.
Three Achievement Highlights:
- Getting the statistical analysis and functional analysis to successfully work on CSV data type
- Dividing tasks and collaborating to put finishing touches on the app between Ivan, Arian, Roman, Samuel, Disha, Shreya, Kelly, and I.
- Finishing the app!
Goals for External Presentation:
- Go through all functions one more time to make sure there are no bugs
- Practice how we want to demo the app and what we want to say about ourselves
Other details of tasks done and challenges faced:
- This week was the most challenging so far, as we had a lot of tasks to do and a short time to do it. However, we found that we could accomplish everything we wanted by dividing the tasks and delegating tasks such that each person’s tasks were closely related to work they had already done or work they felt very comfortable doing. For example, I had spent the most time out of us on the statistical analysis code, so it made most sense for me to work on getting the statistical analysis code to work on the CSV data type. This strategy allowed us to build a successful app.