Determining population structure from k-mer frequencies using simulated data

This repository includes all the required code to analyze simulated human genome data from different populations and determine population structure based on k-mer frequencies within each genome, utilizing principal component analysis (PCA) and K-Means clustering.

Introduction

Population stratification using genome sequences often involves analyzing the genetic diversity among populations to identify subgroups or clusters based on genetic similarity. When using k-mers, PCA, and K-Means clustering, the approach involves counting k-mers in each genome, calculating the intersection of k-mer profiles (k-mer and the number of times it is found in the sequence) across all genomes (samples), applying PCA on the k-mer profiles (scaled) to extract principal components (PCs) and applying K-Means clustering algorithm to the first two PCs. See the manuscript cited below for an explanation of how to pick the length of a k-mer, the number of PCs, and the "number of clusters" parameter when applying K-Means method.

Usage

Each directory is numbered in sequential order of steps to perform population stratification using k-mers and simulated data. MOST OF THIS CODE IS COPIED (and revised) FROM https://github.com/YanaHrytsenko/determining_population_structure_from_k-mer_frequencies For reproducibility purposes:

• Follow the steps in the 1_simulate_genome_sequences directory to generate simulated sequences using SLiM, an evolutionary simulation framework, following SLiM's "recipes" provided here: https://messerlab.org/slim/
• Use directory 2_generate_kmer_profile for step-by-step instructions/scripts on counting k-mers in genome sequences using JellyFish software.
• Directory 3_fastSTRUCTURE lists the steps to generate structure plots using fastSTRUCTURE software.
• Apply PCA and K-Means clustering to the k-mer profiles constructed in step 2_generate_kmer_profile following steps in the 4_PCA_and_clustering directory.
• You can find the outputs from this work in the 5_outputs directory for comparison between the population stratification methods using k-mers, PCA and clustering to the fastSTRUCTURE approach using SNPs data.

Software packages used

• SAMtools (version 1.12)
• BCFtools (version 1.12)
• Jellyfish (version 2.2.10)
• SLiMguiLegacy (version 3.7.1)
• fastSTRUCTURE (version 1.0)

Python Dependencies

• Python (version 3.6.6)
• pandas (version 1.3)
• numpy (version 1.19)
• matplotlib (version 3.3.3)
• scikit-learn (for StandardScaler, PCA and K-Means) (version 0.23.2)

Citation

Yana Hrytsenko, Noah M. Daniels, Rachel S. Schwartz et al. Determining population structure from k-mer frequencies., 01 December 2023, PREPRINT [https://doi.org/10.21203/rs.3.rs-1689838/v2]

License

MIT License

Copyright (c) 2023 Yana Hrytsenko, Rachel Schwartz

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.