- Learning how to work with different quality control packages and finding outliers.
- Learning how to Normalizing, background correcting, and log transforming data.
- Being familiar with biomaRt annotation package in collaboration with group A for the R Shiny app.
- Learning adding professional features to arrayQualitymetrix results for the R Shiny app.
- Learning how to work with GitHub by R Studio
- Trying to build my first Shiny app
- Elevating writing and verbal English skills in communicating with mentors and teammates.
- Attending several meetings and reporting work progress
- Time management
- Elevating self-learning skills
- RMA package
- SVA package
- ArrayQualityMetrix package
- Simpleaffy package
- affyPLM package
- QCReport package
- Git in RStudio
- R Shiny package
- Finding outliers with a high degree of confidence
- Successfully removing batch effect and preprocessing microarray data
- Mastering working with Git functions
- Finding the best quality control packages for finding outliers for the R Shiny app
- The color of annotations in the heatmap was not shown. After several searches and reading different tutorials I found that the row names of data fram that we build for annotation should be identical with columns names of the correlation matrix.
- Some quality control packages need a lot of memory that was fixed by the memory.limit() function.
- We didn’t know where we should put the deliverables of module 3. After speaking with Dravie (my teammate) about it we asked Anya and she added us to mentorchain repository on Github for doing that.