A1: Data set selection and initial processing

Objective

Find a suitable data set for analysis throughout the course.

• Query the GEO database with the appropriate conditions.
• Download the dataset and clean the data.

Started: 2023-02-02 | Completed: 2023-02-14
Estimated time: 12 hrs | Actual time: 15 hrs

Procedure

• Select an Expression Data Set
  • In order to find a data set, you have to prepare the GEOmetadb database.
  • if(!file.exists('GEOmetadb.sqlite')) GEOmetadb::getSQLiteFile() file.info('GEOmetadb.sqlite')
  • Connect to the data base
  • con <- DBI::dbConnect(RSQLite::SQLite(),'GEOmetadb.sqlite'
  • Going to query the database.
  • I wanted to find a data set that fit the following parameters: the organisms should contain Homo sapiens and the title of the series should contain one of multiple sclerosis, lupus. Technology should be Highthroughoutput RNA sequencing and was submitted within the past 10 years.
• Clean the data and map to HUGO symbols
• Apply Normalization
• Interpret, and document

Results

• I chose the series GSE111972.
  • I initially had problems with connecting to the database. It worked the first time but when I tried it again the next day, the same error Warning: Couldn't set synchronous mode: database disk image is malformed Use synchronous = NULL to turn off this warning. came up. I tried reinstalling it multiple times but it did not work.
  • I realized it was because I did not have enough space on my disk.
• Gene IDs were already annotated according to HGNC
• The distribution and spread of the samples were similar across all the groups.
• I created a new RNotebook with code that completes the tasks for the assignment (found here)
• Exported a .csv file of my final normalized data frame.

Conclusion

• Data has been normalized.
• Gene IDs are already annotated according to HGNC which helped a lot in the process.

References

Isserlin, R. (2021) Lecture 4 - Exploring the data and basics of Normalization.
Isserlin, R. (2021) Lecture 5 - Data exploration and Identifier mapping.
Xie, Y., Dervieux, C., Riederer, E. (2022) R Markdown Cookbook https://bookdown.org/yihui/rmarkdown-cookbook/