PCAngsd - Revision history

Albrecht at 11:26, 24 October 2023

2023-10-24T11:26:25Z

← Older revision		Revision as of 13:26, 24 October 2023
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	The HWE test was published in 2019 and can be found here: [https://onlinelibrary.wiley.com/doi/abs/10.1111/1755-0998.13019]		The HWE test was published in 2019 and can be found here: [https://onlinelibrary.wiley.com/doi/abs/10.1111/1755-0998.13019]
	~~[[File:Pcangsd_admix.gif\|frame]]~~
	[[File:Pcangsd_admix3.gif\|frame]]		[[File:Pcangsd_admix3.gif\|frame]]

Albrecht at 11:12, 24 October 2023

2023-10-24T11:12:40Z

← Older revision		Revision as of 13:12, 24 October 2023
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	The HWE test was published in 2019 and can be found here: [https://onlinelibrary.wiley.com/doi/abs/10.1111/1755-0998.13019]		The HWE test was published in 2019 and can be found here: [https://onlinelibrary.wiley.com/doi/abs/10.1111/1755-0998.13019]
	[[File:Pcangsd_admix.gif\|frame]]		[[File:Pcangsd_admix.gif\|frame]]
			[[File:Pcangsd_admix3.gif\|frame]]

	[[File:Pcangsd_pca.png\|thumb\|400px\|Simulated low depth NGS data of 3 populations]]		[[File:Pcangsd_pca.png\|thumb\|400px\|Simulated low depth NGS data of 3 populations]]

Jonas2 at 14:38, 20 January 2021

2021-01-20T14:38:24Z

← Older revision		Revision as of 16:38, 20 January 2021
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	; -admix_seed [int]		; -admix_seed [int]
	Specify seed for random initializations of factor matrices in admixture estimations.		Specify seed for random initializations of factor matrices in admixture estimations.

			==Tree==
			; -tree
			Construct neighbour-joining tree of samples from estimated covariance matrix estimated based on indivdual allele frequencies.
			; -tree_samples
			Provide a list of sample names of all individuals to construct a beautiful tree.

	=Citation=		=Citation=

Jonas2 at 14:13, 20 January 2021

2021-01-20T14:13:28Z

Show changes

Albrecht: /* Quick start */

2020-05-29T15:59:32Z

Quick start

← Older revision		Revision as of 17:59, 29 May 2020
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	# Estimate covariance matrix and perform selection scan		# Estimate covariance matrix and perform selection scan
	python pcangsd.py -beagle input.gz -selection -o test4 -threads 10		python pcangsd.py -beagle input.gz -selection -o test4 -threads 10

			# perform PCA on covarince matrix in R
			## open R
			C <- as.matrix(read.table("test1.cov"))
			e <- eigen(C)
			plot(e$vectors[,1:2],xlab="PC1",ylab="PC2")
	</pre>		</pre>

Albrecht: /* Output */

2020-05-29T15:56:45Z

Output

← Older revision		Revision as of 17:56, 29 May 2020
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	<pre>		<pre>
	import numpy as np		import numpy as np
	~~C = np.genfromtxt("output.cov") # Reads in estimated covariance matrix~~
	S = np.load("output.selection.npy") # Reads results from selection scan		S = np.load("output.selection.npy") # Reads results from selection scan
	</pre>		</pre>
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	<pre>		<pre>
	library(RcppCNPy)		library(RcppCNPy)
	~~C <- as.matrix(read.table("output.cov")) # Reads in estimated covariance matrix~~
	S <- npyLoad("output.selection.npy") # Reads results from selection scan		S <- npyLoad("output.selection.npy") # Reads results from selection scan
	</pre>		</pre>

Albrecht: /* Download and Installation */

2020-05-29T15:51:56Z

Download and Installation

← Older revision		Revision as of 17:51, 29 May 2020
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	Installation has only been tested on Linux systems.		Installation has only been tested on Linux systems.

	~~It is assumed that OpenMP is installed [https://www.openmp.org/].~~		Get PCAngsd and build

	~~1. Login to your server using ssh on your terminal window.~~

	~~2. Create the directory where you will install your software~~ and ~~enter it, such as:~~

	~~<pre>~~
	~~mkdir ~/Software~~
	~~cd ~/Software~~
	~~</pre>~~

	~~3. Download the source code:~~

	<pre>		<pre>
	git clone https://github.com/Rosemeis/pcangsd.git		git clone https://github.com/Rosemeis/pcangsd.git
	~~</pre>~~

	~~4. Configure, Compile and Install:~~

	~~<pre>~~
	cd pcangsd/		cd pcangsd/
	python setup.py build_ext --inplace		python setup.py build_ext --inplace
	</pre>		</pre>
			Install dependencies:
	5. Install dependencies:

	The required set of Python packages are easily installed using the pip command and the requirements.txt file included in the pcangsd folder.		The required set of Python packages are easily installed using the pip command and the requirements.txt file included in the pcangsd folder.

Albrecht: /* Overview */

2020-05-29T15:50:32Z

Overview

← Older revision		Revision as of 17:50, 29 May 2020
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	=Overview=		=Overview=
			Framework for analyzing low depth next-generation sequencing (NGS) data in heterogeneous populations using principal component analysis (PCA). Population structure is inferred to detect the number of significant principal components which is used to estimate individual allele frequencies using genotype dosages in a SVD model. The estimated individual allele frequencies are then used in an probabilistic framework to update the genotype dosages such that an updated set of individual allele frequencies can be estimated iteratively based on inferred population structure. A covariance matrix can be estimated using the updated prior information of the estimated individual allele frequencies.

	Based on population structure inference, PCAngsd is able to detect the number of significant principal components which is then used to estimate individual allele frequencies using genotype dosages in a SVD model. These individual allele frequencies can be used in various population genetic methods for heterogeneous populations, such that PCAngsd can perform PCA (estimate covariance matrix), call genotypes, estimate individual admixture proportions, estimate inbreeding coefficients (per-individual and per-site) and perform a genome selection scan using principal components.
	The estimated individual allele frequencies and principal components can be used as prior knowledge in other probabilistic methods based on a same Bayesian principle. PCAngsd can perform the following analyses:		The estimated individual allele frequencies and principal components can be used as prior knowledge in other probabilistic methods based on a same Bayesian principle. PCAngsd can perform the following analyses:
	*Covariance matrix		*Covariance matrix

Albrecht at 15:49, 29 May 2020

2020-05-29T15:49:16Z

Show changes

Albrecht at 11:05, 23 August 2019

2019-08-23T11:05:01Z

← Older revision		Revision as of 13:05, 23 August 2019
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	<pre>		<pre>
	./angsd -GL 1 -out data -nThreads 10 -doGlf 2 -doMajorMinor 1 -doMaf 2 -SNP_pval 1e-6 -bam bam.filelist		./angsd -GL 2 -out data -nThreads 10 -doGlf 2 -doMajorMinor 1 -doMaf 1 -SNP_pval 1e-6 -bam bam.filelist
	</pre>		</pre>