pcangsd 1.36.4
Framework for analyzing low depth NGS data in heterogeneous populations using PCA
Requires Python: >=3.10

# PCAngsd (v1.36.4)

Framework for analyzing low-depth next-generation sequencing (NGS) data in heterogeneous/structured populations using principal component analysis (PCA). Population structure is inferred by estimating individual allele frequencies in an iterative approach using a truncated SVD model. The covariance matrix is estimated using the estimated individual allele frequencies as prior information for the unobserved genotypes in low-depth NGS data.

The estimated individual allele frequencies can further be used to account for population structure in other probabilistic methods. `pcangsd` can be used for the following analyses:

* Covariance matrix
* Admixture estimation
* Inbreeding coefficients (both per-sample and per-site)
* HWE test
* Genome-wide selection scans
* Genotype calling
* Estimate NJ tree of samples


## Installation
```bash
# Option 1: Build and install via PyPI
pip install pcangsd

# Option 2: Download source and install via pip
git clone https://github.com/Rosemeis/pcangsd.git
cd pcangsd
pip install .

# Option 3: Download source and install in a new Conda environment
git clone https://github.com/Rosemeis/pcangsd.git
conda env create -f pcangsd/environment.yml
conda activate pcangsd
```
You can now run the program with the `pcangsd` command.

## Usage
`pcangsd` works directly on genotype likelihood files or PLINK files.
```bash
# See all options
pcangsd -h

# Genotype likelihood file in Beagle format with 2 eigenvectors using 64 threads
pcangsd --beagle input.beagle.gz --eig 2 --threads 64 --out pcangsd
# Outputs by default log-file (pcangsd.log) and covariance matrix (pcangsd.cov)

# PLINK files (using file-prefix, *.bed, *.bim, *.fam)
pcangsd --plink input.plink --eig 2 --threads 64 --out pcangsd

# Perform PC-based selection scan and estimate admixture proportions
pcangsd --beagle input.beagle.gz --eig 2 --threads 64 --out pcangsd --selection --admix
# Outputs the following files:
# log-file (pcangsd.log)
# covariance matrix (pcangsd.cov)
# selection statistics (pcangsd.selection)
# admixture proportions (pcangsd.admix.3.Q)
# ancestral allele frequencies (pcangsd.admix.3.F)
```
`pcangsd` will output most files in text-format.

Quick example of reading output and creating PCA plot in *R*:
```R
C <- as.matrix(read.table("pcangsd.cov")) # Reads estimated covariance matrix
D <- as.matrix(read.table("pcangsd.selection")) # Reads PC-based selection statistics

# Plot PCA plot
e <- eigen(C)
plot(e$vectors[,1:2], xlab="PC1", ylab="PC2", main="PCAngsd")

# Obtain p-values from PC-based selection scan
p <- pchisq(D, 1, lower.tail=FALSE)
```

Read files in *python* and create PCA plot using matplotlib:
```python
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import chi2
C = np.loadtxt("pcangsd.cov") # Reads estimated covariance matrix
D = np.loadtxt("pcangsd.selection") # Reads PC based selection statistics

# Plot PCA plot
evals, evecs = np.linalg.eigh(C)
evecs = evecs[:,::-1]
plt.scatter(evecs[:,0], evecs[:,1])
plt.xlabel("PC1")
plt.ylabel("PC2")
plt.title("PCAngsd")
plt.show()

# Obtain p-values from PC-based selection scan
p = chi2.sf(D, 1)
```

Beagle genotype likelihood files can be generated from BAM files using [ANGSD](https://github.com/ANGSD/angsd). For inference of population structure in genotype data with non-random missigness, we recommend our [EMU](https://github.com/Rosemeis/emu) software that performs accelerated EM-PCA, however with fewer functionalities than *PCAngsd*.

## Citation
Please cite our papers if you use the `pcangsd` framework:

Population structure: [Inferring Population Structure and Admixture Proportions in Low-Depth NGS Data](http://www.genetics.org/content/210/2/719).\
HWE test: [Testing for Hardy‐Weinberg Equilibrium in Structured Populations using Genotype or Low‐Depth NGS Data](https://onlinelibrary.wiley.com/doi/abs/10.1111/1755-0998.13019).\
Selection: [Detecting Selection in Low-Coverage High-Throughput Sequencing Data using Principal Component Analysis](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04375-2).