Analysis and Classification of Anopheles Populations in AG1000-3R dataset using Intergenic SNPs

Objectives Covered

Exploratory Data Analysis
- SNPs Filtering
  - Mega Base Pair Selection
  - MAF Filtering
  - LD Pruning
- Minor Allele Filtering - Exploring the right MAF threhsold for rare allele filtering while preserving private alleles
- Unsupervised exploration - PCA and UMAP visualizations for 4.8 Million SNPs and samples from 16 populations. UMAP hyperparameter tuning for chromosome arm 3R.
Classification of 13 Populations
- Pipeline for population classification using genetic sequences
- Futher improvement through dimensionality reduction and domain related techniques
Pairwise analysis of 66 population pairs
Exploring SNP contribution and importance for population differentiation
Generic Python functions to reproduce and automate most of the analyses

BFcol, BFgam, AOcol, CIcol, CMgam, FRgam, GAgam, GHcol, GHgam, GM, GNcol, GNgam, GQgam, GW, KE, and UGgam

Name		Name	Last commit message	Last commit date
Latest commit History 108 Commits
.ipynb_checkpoints		.ipynb_checkpoints
Datasets		Datasets
Pairwise_Analysis		Pairwise_Analysis
plots		plots
AG1000_Dataset_EDA.ipynb		AG1000_Dataset_EDA.ipynb
Classification_Models.ipynb		Classification_Models.ipynb
Filtering_Statistics.csv		Filtering_Statistics.csv
Haplotype_Filtered_Combined_Population_Data.hd5		Haplotype_Filtered_Combined_Population_Data.hd5
PCALDA.ipynb		PCALDA.ipynb
Pairwise_Analysis.ipynb		Pairwise_Analysis.ipynb
Pairwise_Analysis_Truncated.ipynb		Pairwise_Analysis_Truncated.ipynb
README.md		README.md
Supervised_3d_UMAP_plot.html		Supervised_3d_UMAP_plot.html
Unsupervised_3d_UMAP_plot.html		Unsupervised_3d_UMAP_plot.html
hd5_datagen.py		hd5_datagen.py
nohup.out		nohup.out
pairwise.ipynb		pairwise.ipynb
private.ipynb		private.ipynb
requirements.txt		requirements.txt