Gene function can be uncovered by examining when and where gene expression occurs. Different quantification methods are needed to gain this insight.

This folder contains a project demonstrating the analysis of gene expression using quantitative techniques such as Principal Component Analysis (PCA). The goals of this project are:

• Explore and Explain how to spot "bad" genetic data.
  • Compare several independent data sets.
  • Gene level vs probeset level
• Explore genetic algorithms.
• Methods for interpreting and using PCA results.
  • Create PCA visual for each data set, analyze independently, then create overlay for each
• A comparisson between analysis methods in R and in Python.
  • princomp vs prcomp in R

References

1. Quantitative Understanding in Biology Principal Component Analysis
2. Visualization and PCA with Gene Expression Data
3. PCA analysis for differential gene expression
4. PRINCIPAL COMPONENTS ANALYSIS TO SUMMARIZE MICROARRAY EXPERIMENTS
5. [https://burakkanber.com/blog/machine-learning-genetic-algorithms-part-1-javascript/](Machine Learning: Introduction to Genetic Algorithms)
6. Introduction to Genetic Algorithm & their application in data science
7. Recommended Data Repositories
8. RefEx, a reference gene expression dataset as a web tool for the functional analysis of genes
9. GEO2R How To

Data Sources

1. NCBI GEO2R
2. DAVID Bioinformatics Resources
3. NCBI GEO
4. DATA.gov