The Department of Internal Medicine at the University of Michigan needed a data warehousing solution to store biological experiments and gene expressions. I researched the requirements for the lab, architected the database and scraped the data from various data sources, built a web application, REST APIs and libraries to support the API interface for the database. As of today, the database stores over 3.5 million data points. The stack is Django, PostgreSQL & Nginx.

1. Hello!
I AM Manu Gupta
I am the product owner for the data
warehousing solution for Department of
Internal Medicine, University of Michigan
You can find me at gmanu@umich.edu

2. BIG CONCEPT
A data warehousing solution for
storing expression data.

3. Tech Stack
Our stack includes
◦ openSUSE Linux Server
◦ PostgreSQL
◦ Django
◦ R
◦ Nginx
Currently, we have over 3.5 million
data points in the database and
46 relations.

4. What we store?
What it is intended to do?
◦ Internal data storage & analysis
◦ Expression data with their annotation.
◦ Data includes
▫ Platforms (Affymetrix Array)
▫ Dataset (GDS* / GSE* based on GEO)
▫ Series (GSE* based on GEO)
▫ Samples (GSM* based on GEO)
▫ Annotations (Technical & Biological)
▫ Expression Values
▫ QC Results
▫ Sequence Data

5. Quick Review
What it is not intended to do
◦ Store clinical data

6. OUR MANTRA
UsableFlexible Efficient

7. “
◦ Accommodate for upcoming
technology ( for example
RNASeq)
◦ Answer variety of questions.
- Flexibility

8. Flexibility
The questions we can answer
◦ Dataset
▫ Find all the series in a sample.
▫ Find the QC Results, Expression Value of a series.
▫ Find group of genes which are very highly
expressed or very poorly expressed.
▫ Find expression values between specific sequence
start & end positions.
◦ Sequence Groups
▫ Find all the homologenes given a particular gene.
▫ Find the annotation of a sequence
▫ Find all the sequence between a particular
sequence start and end positions.

9. “
◦ Effort should be minimized to
store and retrieve data from the
dataset.
- Usability

10. OUR PROCESS IS EASY
Use batch uploads for uploading loads of data
(demo)
Retrieve the datasets really fast
Use a web interface

11. “
◦ Wherever possible, allow
computations.
- Efficiency

12. OUR PROCESS IS EASY
Statistical analysis over R, Python or any programming
language
Visualizations
Data transformations to external databases such as
Nephromine, Transmart

13. THANK YOU
ANY QUESTIONS

14. CREDITS
Special thanks to
◦ Felix Eichinger, Viji Nair
◦ Shruti, Fan, Adam, Lauren & Izhar for
visualizations
◦ Presentation template by SlidesCarnival
◦ Photographs by Unsplash