Re-implementation of lostruct in Python, used to compare local population structure across populations.
This is a reimplementation of lostruct from the original code: Lostruct. Please cite the original paper
Demonstration / How to use
Please see the Example Notebook
Lostruct-py is available on PyPi
pip install lostruct-py is the easiest way to get started.
Please use our DOI to cite this specific project. Also please cite the original Lostruct paper and CyVCF2.
Original Lostruct Paper
Please cite the original lostruct paper:
Li, Han, and Peter Ralph. "Local PCA shows how the effect of population structure differs along the genome." Genetics 211.1 (2019): 289-304.
This paper also uses cyvcf2 for fast VCF processing and should be cited:
Brent S Pedersen, Aaron R Quinlan, cyvcf2: fast, flexible variant analysis with Python, Bioinformatics, Volume 33, Issue 12, 15 June 2017, Pages 1867–1869, https://doi.org/10.1093/bioinformatics/btx057
Changes from Lostruct R package
Please note numpy and R are different when it comes to row-major vs. column-major. Essentially, many things in the python version will be transposed from R.
Python >= 3.6 (may work with older versions). Developed on Python 3.8.5
CyVCF2 requires zlib-dev, libbz2-dev, libcurl-dev, liblzma-dev, and probably others
Easiest to install all of these through conda
Used Medicago HapMap sister taxa chromsome 1, processed, and run with LoStruct
bcftools annotate chr1-filtered-set-2014Apr15.bcf -x INFO,FORMAT | bcftools view -a -i 'F_MISSING<=0.2' | bcftools view -q 0.05 -q 0.95 -m2 -M2 -a -Oz -o chr1-filtered.vcf.gz
Rscript run_lostruct.R -t SNP -s 95 -k 10 -m 10 -i data/
This generates the mds_coords.tsv that is used in the correlation comparison.
FAQ / Notes
Currently the end-user is expected to save the outputs. But could be good to save it in a similar way to lostruct R-code. Please open an issue if you need this.
PCA, MDS, PCoA
PCoA returns the same results as lostruct's MDS implementation (cmdscale). In the example Jupyter notebook you can see the correlation is R =~ 0.998. Some examples of other methods of clustering / looking at differences are included in the notebook.
Casting complex values to real discards the imaginary part
This is fine.
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