PCAngsdTutorial - Revision history

Albrecht: /* Admixture based on two PC */

2020-05-29T16:10:28Z

Admixture based on two PC

← Older revision		Revision as of 18:10, 29 May 2020
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	Let's try to use the PCA to infer admixture proportions based on the first 2 principal components. For the optimization we will use a small penalty on the admixture proportions (alpha).		Let's try to use the PCA to infer admixture proportions based on the first 2 principal components. For the optimization we will use a small penalty on the admixture proportions (alpha).

	<code>python $PCANGSD -beagle $IN_DIR/Demo1input.gz -o $OUT_DIR/Demo2PCANGSD_3 -admix -admix_alpha 50</code>		<code>python $PCANGSD -beagle $IN_DIR/Demo1input.gz -e 2 -o $OUT_DIR/Demo2PCANGSD_3 -admix -admix_alpha 50</code>

	Plot the results in R		Plot the results in R

Albrecht: /* Admixture based on 1st two PC */

2020-05-29T16:09:49Z

Admixture based on 1st two PC

← Older revision		Revision as of 18:09, 29 May 2020
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	<code>evince PCAngsd2.pdf</code>		<code>evince PCAngsd2.pdf</code>

	==== Admixture based on ~~1st~~ two PC ====		==== Admixture based on two PC ====
	Let's try to use the PCA to infer admixture proportions based on the first 2 principal components. For the optimization we will use a small penalty on the admixture proportions (alpha).		Let's try to use the PCA to infer admixture proportions based on the first 2 principal components. For the optimization we will use a small penalty on the admixture proportions (alpha).

Albrecht: /* Admixture based on 1st two PC */

2020-05-29T16:08:27Z

Admixture based on 1st two PC

← Older revision		Revision as of 18:08, 29 May 2020
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	library(RcppCNPy) # Numpy library for R		library(RcppCNPy) # Numpy library for R

	pop<-read.table("Demo1pop.info",as.is=T)		pop<-read.table("Demo/Data/Demo1pop.info",as.is=T)

	q <- npyLoad("Demo2PCANGSD_3.admix.Q.npy")		q <- npyLoad("Demo/Results/Demo2PCANGSD_3.admix.Q.npy")

	## order according to population		## order according to population

Albrecht: /* Estimating Individual Allele Frequencies */

2020-05-29T16:07:11Z

Estimating Individual Allele Frequencies

← Older revision		Revision as of 18:07, 29 May 2020
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	<pre>		<pre>
	#open R		#open R
	pop<-read.table("Demo1pop.info")		pop<-read.table("Demo/Data/Demo1pop.info")
	C <- as.matrix(read.table("Demo1PCANGSD_1.cov"))		C <- as.matrix(read.table("Demo/Results/Demo1PCANGSD_1.cov"))
	e <- eigen(C)		e <- eigen(C)

Albrecht: /* Demo 1: Allele Frequencies */

2020-05-29T16:06:03Z

Demo 1: Allele Frequencies

← Older revision		Revision as of 18:06, 29 May 2020
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	<code>mv Demo1input.gz $IN_DIR</code>		<code>mv Demo1input.gz $IN_DIR</code>

	<code>mv Demo1pop.info $~~OUT_DIR~~</code>		<code>mv Demo1pop.info $IN_DIR</code>

	==== View the genotype likelihood beagle file ====		==== View the genotype likelihood beagle file ====

Albrecht: /* Download the input and population information files */

2020-05-29T16:04:40Z

Download the input and population information files

← Older revision		Revision as of 18:04, 29 May 2020
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	To download the files and move them to your input folder (for example, $IN_DIR):		To download the files and move them to your input folder (for example, $IN_DIR):

	~~'''ANDERS'''~~

	<code>wget popgen.dk/software/download/NGSadmix/data/Demo1input.gz</code>		<code>wget popgen.dk/software/download/NGSadmix/data/Demo1input.gz</code>
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	<code>mv Demo1pop.info $OUT_DIR</code>		<code>mv Demo1pop.info $OUT_DIR</code>

	~~'''ANDERS'''~~

	==== View the genotype likelihood beagle file ====		==== View the genotype likelihood beagle file ====

Albrecht: /* Create directories */

2020-05-29T16:03:48Z

Create directories

← Older revision		Revision as of 18:03, 29 May 2020
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	<code>mkdir Demo</code>		<code>mkdir Demo</code>

	~~<code>cd Demo</code>~~

	<code>mkdir Data</code>		<code>mkdir Demo/Data</code>

	<code>mkdir Results</code>		<code>mkdir Demo/Results</code>

	==== Set the paths to your local directories ====		==== Set the paths to your local directories ====

Albrecht at 16:02, 29 May 2020

2020-05-29T16:02:52Z

← Older revision		Revision as of 18:02, 29 May 2020
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	<code>		<code>
	PCANGSD=~/Software/pcangsd		PCANGSD=~/Software/pcangsd/pcangsd.py
	</code>		</code>

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	==== Estimating Individual Allele Frequencies ====		==== Estimating Individual Allele Frequencies ====

	<code>python $PCANGSD~~/pcangsd.py~~ -beagle $IN_DIR/Demo1input.gz -o $OUT_DIR/Demo1PCANGSD_1</code>		<code>python $PCANGSD -beagle $IN_DIR/Demo1input.gz -o $OUT_DIR/Demo1PCANGSD_1</code>

	Plot the results in R		Plot the results in R
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	This is the same as using the first iteration of the algorithm.		This is the same as using the first iteration of the algorithm.

	<code>python $PCANGSD~~/pcangsd.py~~ -beagle $IN_DIR/Demo1input.gz -o $OUT_DIR/Demo1PCANGSD_2 -iter 0</code>		<code>python $PCANGSD -beagle $IN_DIR/Demo1input.gz -o $OUT_DIR/Demo1PCANGSD_2 -iter 0</code>

	Plot the results in R		Plot the results in R
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	Let's try to use the PCA to infer admixture proportions based on the first 2 principal components. For the optimization we will use a small penalty on the admixture proportions (alpha).		Let's try to use the PCA to infer admixture proportions based on the first 2 principal components. For the optimization we will use a small penalty on the admixture proportions (alpha).

	<code>python $PCANGSD~~/pcangsd.py~~ -beagle $IN_DIR/Demo1input.gz -o $OUT_DIR/Demo2PCANGSD_3 -admix -admix_alpha 50</code>		<code>python $PCANGSD -beagle $IN_DIR/Demo1input.gz -o $OUT_DIR/Demo2PCANGSD_3 -admix -admix_alpha 50</code>

	Plot the results in R		Plot the results in R
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	Inbreeding in admixed samples is usually not possible to estimate using standard approaches. Let's try to estimate the inbreeding coefficient of the samples using the average allele frequency.		Inbreeding in admixed samples is usually not possible to estimate using standard approaches. Let's try to estimate the inbreeding coefficient of the samples using the average allele frequency.

	<code>python $PCANGSD~~/pcangsd.py~~ -beagle $IN_DIR/Demo1input.gz -o $OUT_DIR/Demo1PCANGSD_4 -inbreed 2 -iter 0</code>		<code>python $PCANGSD -beagle $IN_DIR/Demo1input.gz -o $OUT_DIR/Demo1PCANGSD_4 -inbreed 2 -iter 0</code>

	Join names and results, sort the values and look at the results		Join names and results, sort the values and look at the results
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	Now let's try to estimate the inbreeding coefficient of the samples by using the individual allele frequencies predicted by the PCA		Now let's try to estimate the inbreeding coefficient of the samples by using the individual allele frequencies predicted by the PCA

	<code>python $PCANGSD~~/pcangsd.py~~ -beagle $IN_DIR/Demo1input.gz -o $OUT_DIR/Demo1PCANGSD_5 -inbreed 2 </code>		<code>python $PCANGSD -beagle $IN_DIR/Demo1input.gz -o $OUT_DIR/Demo1PCANGSD_5 -inbreed 2 </code>

	Join names and results, sort the values and look at the results		Join names and results, sort the values and look at the results
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	The objective is to show the differences among individuals.		The objective is to show the differences among individuals.

	<code>python $PCANGSD~~/pcangsd.py~~ -beagle $IN_DIR/Demo2input.gz -o $OUT_DIR/Demo2PCANGSD_1 -threads 4</code>		<code>python $PCANGSD -beagle $IN_DIR/Demo2input.gz -o $OUT_DIR/Demo2PCANGSD_1 -threads 4</code>

	==== Plot the results in R ====		==== Plot the results in R ====
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	Now let's try to use the PC to infer selection along the genome based on the PCA		Now let's try to use the PC to infer selection along the genome based on the PCA

	<code>python $PCANGSD~~/pcangsd.py~~ -beagle $IN_DIR/Demo2input.gz -o $OUT_DIR/Demo2PCANGSD_2 -selection -sites_save #-n $N </code>		<code>python $PCANGSD -beagle $IN_DIR/Demo2input.gz -o $OUT_DIR/Demo2PCANGSD_2 -selection -sites_save #-n $N </code>

Sonia4: /* Plot the results */

2019-08-07T10:53:51Z

Plot the results

← Older revision		Revision as of 12:53, 7 August 2019
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	<pre>		<pre>

			library(RcppCNPy) # Numpy library for R

	## function for QQplot		## function for QQplot
	qqchi<-function(x,...){		qqchi<-function(x,...){
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	### read in seleciton statistics (chi2 distributed)		### read in seleciton statistics (chi2 distributed)
	s<-~~scan~~("Demo2PCANGSD_2.selection.gz")		s<-npyLoad("Demo2PCANGSD_2.selection.npy")

	## make QQ plot to QC the test statistics		## make QQ plot to QC the test statistics

Sonia4: /* Infer selection along the genome */

2019-08-07T10:52:35Z

Infer selection along the genome

← Older revision		Revision as of 12:52, 7 August 2019
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	names(p)<-c("chr","pos")		names(p)<-c("chr","pos")

	~~pdf("PCAngsdDemo2_2_1.pdf")~~

	## make manhatten plot		## make manhatten plot
	plot(-log10(pval),col=p$chr,xlab="Chromosomes",main="Manhattan plot")		plot(-log10(pval),col=p$chr,xlab="Chromosomes",main="Manhattan plot")

	~~pdf("PCAngsdDemo2_2_2.pdf")~~

	## zoom into region		## zoom into region