PCAngsdv2 - Revision history

Jonas2: /* Citation */

2019-08-16T11:34:33Z

Citation

← Older revision		Revision as of 13:34, 16 August 2019
Line 235:		Line 235:
	Our methods for inferring population structure have been published in GENETICS:		Our methods for inferring population structure have been published in GENETICS:

	~~Population structure:~~ [http://www.genetics.org/content/early/2018/08/21/genetics.118.301336 Inferring Population Structure and Admixture Proportions in Low Depth NGS Data]		[http://www.genetics.org/content/early/2018/08/21/genetics.118.301336 Inferring Population Structure and Admixture Proportions in Low Depth NGS Data]

	HWE ~~test~~: [https://onlinelibrary.wiley.com/doi/abs/10.1111/1755-0998.13019 Testing for Hardy‐Weinberg Equilibrium in Structured Populations using Genotype or Low‐Depth NGS Data]
			Our method for testing for HWE in structured populations has been published in Molecular Ecology Resources:

			[https://onlinelibrary.wiley.com/doi/abs/10.1111/1755-0998.13019 Testing for Hardy‐Weinberg Equilibrium in Structured Populations using Genotype or Low‐Depth NGS Data]

Albrecht: /* Quick start */

2019-08-08T07:00:58Z

Quick start

← Older revision		Revision as of 09:00, 8 August 2019
Line 65:		Line 65:
	<pre>		<pre>

	# Download the input file		# Download the input beagle file with genotype likelihoods
	~~'''ANDERS'''~~
	wget popgen.dk/software/download/NGSadmix/data/input.gz		wget popgen.dk/software/download/NGSadmix/data/input.gz
	~~maybe change it to:~~
	~~wget popgen.dk/software/download/PCAngsd/data/input.gz~~
	~~'''ANDERS'''~~

	# Only estimate covariance matrix using 10 threads		# Only estimate covariance matrix using 10 threads

Sonia4: /* Inbreeding */

2019-08-07T09:35:29Z

Inbreeding

← Older revision		Revision as of 11:35, 7 August 2019
Line 196:		Line 196:
	; -inbreed 1		; -inbreed 1
	Simple estimator computed by an EM algorithm. Allows for F-values between -1 and 1. Based on [http://genome.cshlp.org/content/23/11/1852.full ngsF].		Simple estimator computed by an EM algorithm. Allows for F-values between -1 and 1. Based on [http://genome.cshlp.org/content/23/11/1852.full ngsF].

	; -inbreed 2		; -inbreed 2
	A maximum likelihood estimator also computed by an EM algorithm. Only allows for F-values between 0 and 1. Based on [https://www.cambridge.org/core/journals/genetics-research/article/maximum-likelihood-estimation-of-individual-inbreeding-coefficients-and-null-allele-frequencies/2DEBA0C0C2B92DF0EE89BD27DFCAD3FB].		A maximum likelihood estimator also computed by an EM algorithm. Only allows for F-values between 0 and 1. Based on [https://www.cambridge.org/core/journals/genetics-research/article/maximum-likelihood-estimation-of-individual-inbreeding-coefficients-and-null-allele-frequencies/2DEBA0C0C2B92DF0EE89BD27DFCAD3FB].

	; -inbreed 3		; -inbreed 3
	Estimator using an estimated kinship matrix. Allows for F-values between -1 and 1. Based on [http://www.cell.com/ajhg/abstract/S0002-9297(15)00493-0 PC-Relate].		Estimator using an estimated kinship matrix. Allows for F-values between -1 and 1. Based on [http://www.cell.com/ajhg/abstract/S0002-9297(15)00493-0 PC-Relate].

	; -inbreed_iter [int]		; -inbreed_iter [int]
	Maximum number of iterations for the EM algorithm methods. (Default: 200)		Maximum number of iterations for the EM algorithm methods. (Default: 200)

	; -inbreed_tole [float]		; -inbreed_tole [float]
	Tolerance value for the EM algorithms for inbreeding coefficients estimation. (Default: 1e-4)		Tolerance value for the EM algorithms for inbreeding coefficients estimation. (Default: 1e-4)


	Per-site inbreeding coefficients incorporating population structure alongside likehood ratio tests for HWE can be computed as follows:		Per-site inbreeding coefficients incorporating population structure alongside likehood ratio tests for HWE can be computed as follows:

	; -inbreedSites		; -inbreedSites

	Use likelihood ratio tests (.lrt.sites.gz) generated from '''-inbreedSites''' to filter out variable sites using a given threshold for HWE test p-value:		Use likelihood ratio tests (.lrt.sites.gz) generated from '''-inbreedSites''' to filter out variable sites using a given threshold for HWE test p-value:

	; -hwe [LRT filename]		; -hwe [LRT filename]

	; -hwe_tole [float]		; -hwe_tole [float]
	Tolerance value for HWE test. (Default: 1e-6)		Tolerance value for HWE test. (Default: 1e-6)

Sonia4: /* Output */

2019-08-07T09:31:39Z

Output

← Older revision		Revision as of 11:31, 7 August 2019
Line 102:		Line 102:
	=Output=		=Output=

	Since version 0.98, PCAngsd is ~~now outputting solely~~ in binary Numpy format (.npy) except for the covariance matrix.		Since version 0.98, PCAngsd's output is only in binary Numpy format (.npy) except for the covariance matrix.

	In order to read files in python:		In order to read files in python:

Sonia4 at 10:51, 29 July 2019

2019-07-29T10:51:27Z

← Older revision		Revision as of 12:51, 29 July 2019
Line 11:		Line 11:


	==Overview==		=Overview=

	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.		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.
Line 26:		Line 26:
	The older version, based on the Numba library (only working with Python 2.7) is still available in version 0.973 and can be found here [https://github.com/Rosemeis/pcangsd/releases/tag/0.973].		The older version, based on the Numba library (only working with Python 2.7) is still available in version 0.973 and can be found here [https://github.com/Rosemeis/pcangsd/releases/tag/0.973].

	==Download and Installation==		=Download and Installation=

	PCAngsd should work on all platforms meeting the requirements but server-side usage is highly recommended.		PCAngsd should work on all platforms meeting the requirements but server-side usage is highly recommended.

Sonia4: /* Quick start */

2019-07-29T10:50:51Z

Quick start

← Older revision		Revision as of 12:50, 29 July 2019
Line 61:		Line 61:
	<code>pip install --user -r requirements.txt</code>		<code>pip install --user -r requirements.txt</code>

	==Quick start==		=Quick start=

	<pre>		<pre>
Line 84:		Line 84:
	python pcangsd.py -beagle input.gz -selection -o test4 -threads 10		python pcangsd.py -beagle input.gz -selection -o test4 -threads 10
	</pre>		</pre>

			==Detailed Examples and Tutorial==

			Please refer to the tutorial's page [http://www.popgen.dk/software/index.php/PCAngsdTutorial]

	=Input=		=Input=

Sonia4: /* Quick start */

2019-07-29T10:10:51Z

Quick start

← Older revision		Revision as of 12:10, 29 July 2019
Line 73:		Line 73:

	# Only estimate covariance matrix using 10 threads		# Only estimate covariance matrix using 10 threads
	python pcangsd.py -beagle input.gz -o ~~test~~ -threads 10		python pcangsd.py -beagle input.gz -o test1 -threads 10

	# Estimate covariance matrix and individual admixture proportions		# Estimate covariance matrix and individual admixture proportions
	python pcangsd.py -beagle input.gz -admix -o ~~test~~ -threads 10		python pcangsd.py -beagle input.gz -admix -o test2 -threads 10

	# Estimate covariance matrix and inbreeding coefficients		# Estimate covariance matrix and inbreeding coefficients
	python pcangsd.py -beagle input.gz -inbreed 2 -o ~~test~~ -threads 10		python pcangsd.py -beagle input.gz -inbreed 2 -o test3 -threads 10

	# Estimate covariance matrix and perform selection scan		# Estimate covariance matrix and perform selection scan
	python pcangsd.py -beagle input.gz -selection -o ~~test~~ -threads 10		python pcangsd.py -beagle input.gz -selection -o test4 -threads 10
	</pre>		</pre>

Sonia4: /* Quick start */

2019-07-29T10:09:40Z

Quick start

← Older revision		Revision as of 12:09, 29 July 2019
Line 82:		Line 82:

	# Estimate covariance matrix and perform selection scan		# Estimate covariance matrix and perform selection scan
	python pcangsd.py -beagle input.gz -selection 1 -o test -threads 10		python pcangsd.py -beagle input.gz -selection -o test -threads 10
	</pre>		</pre>

Sonia4: /* Quick start */

2019-07-29T10:05:02Z

Quick start

← Older revision		Revision as of 12:05, 29 July 2019
Line 64:		Line 64:

	<pre>		<pre>

			# Download the input file
			'''ANDERS'''
			wget popgen.dk/software/download/NGSadmix/data/input.gz
			maybe change it to:
			wget popgen.dk/software/download/PCAngsd/data/input.gz
			'''ANDERS'''

	# Only estimate covariance matrix using 10 threads		# Only estimate covariance matrix using 10 threads
	python pcangsd.py -beagle ~~data.beagle~~.gz -o test -threads 10		python pcangsd.py -beagle input.gz -o test -threads 10

	# Estimate covariance matrix and individual admixture proportions		# Estimate covariance matrix and individual admixture proportions
	python pcangsd.py -beagle ~~data.beagle~~.gz -admix -o test -threads 10		python pcangsd.py -beagle input.gz -admix -o test -threads 10

	# Estimate covariance matrix and inbreeding coefficients		# Estimate covariance matrix and inbreeding coefficients
	python pcangsd.py -beagle ~~data.beagle~~.gz -inbreed 2 -o test -threads 10		python pcangsd.py -beagle input.gz -inbreed 2 -o test -threads 10

	# Estimate covariance matrix and perform selection scan		# Estimate covariance matrix and perform selection scan
	python pcangsd.py -beagle ~~data.beagle~~.gz -selection 1 -o test -threads 10		python pcangsd.py -beagle input.gz -selection 1 -o test -threads 10
	</pre>		</pre>

Sonia4: /* Quick start */

2019-07-29T09:56:04Z

Quick start

← Older revision		Revision as of 11:56, 29 July 2019
Line 62:		Line 62:

	==Quick start==		==Quick start==

	<pre>		<pre>
	~~# See all options in PCAngsd~~
	~~python pcangsd.py -h~~

	# Only estimate covariance matrix using 10 threads		# Only estimate covariance matrix using 10 threads
	python pcangsd.py -beagle data.beagle.gz -o test -threads 10		python pcangsd.py -beagle data.beagle.gz -o test -threads 10