EvalAdmix

2019-07-21T12:25:17Z

Genis: /* Genotype data */

evalAdmix allows to evaluate the model fit of an admixture analysis. It takes as input the genotype data (either called genotypes in plink files or genotype likelihoods beagle files) used in the admixture analysis and the estimated ancestral frequency and admixture propotions files (P and Q files).

The output is a pairwise correlation of residuals matrix between individuals. The correlation will be 0 in case of a good fit of the data to the admixture model. When something is wrong, individuals from the same population will be positively correlated; and individuals from different populationts but that share one or more ancestral populations as admixture sources will have a negative correlation. Positive correlation between a pair of individuals might also be due to relatedness.

[[File:evalAdmix.png|thumb|550px]]

==Download and Installation==

evalAdmix can be installed from [https://github.com/GenisGE/evalAdmix github]

<pre>git clone https://github.com/GenisGE/evalAdmix.git
cd evalAdmix
make
</pre>

==Quick start==
:<code> ./evalAdmix -beagle inputBeagleFile.gz -fname myoutfiles.fopt.gz -qname myoutfiles.qopt -P 10 </code>
:<code> ./evalAdmix -plink inputPlinkPrefix -fname myoutfiles.fopt.gz -qname myoutfiles.qopt -P 10 </code>

* '''-beagle''' beagle file of genotype likelihoods
* '''-plink''' binary plink file prefix with genotype data
* '''-fname''' file with ancestral frequencies
* '''-qname''' file with admixture proportions
* '''-o''' prefix of output file names
* '''-P''' Number of threads used

==Parameters==

<pre>./evalAdmix </pre>

<pre>
Arguments:
Required:
-plink path to binary plink file (excluding the .bed)
or
-beagle path to beagle file containing genotype likelihoods (alternative to -plink)

-fname path to ancestral population frequencies file
-qname path to admixture proportions file

Optional:

-o name of the output file

Setup (optional):

-P 1 number of threads
-autosomeMax 23 autosome ends with this chromsome
-nIts 5 number of iterations to do for frequency correction; if set to 0 calculates correlation without correction (fast but biased)
-useSites 1.0 proportion of sites to use to calculate correlation of residuals
-useInds filename path to tab delimited file with first column containing all individuals ID and second column with 1 to include individual in analysis and 0 otherwise (default all individuals are included)
-misTol 0.05 tolerance for considering site as missing when using genotype likelihoods. Use same value as used in NGSadmix to keep compatibility when using genotype likelihoods (-beagle)
-minMaf 0.05 minimum minor allele frequency to keep site. Use same value as used in NGSadmix to keep compatibility when using genotype likelihoods (-beagle)
</pre>

==Input Files==

===Plink===
Genotype data files in binary PLINK format (.bed .fam .bim).
===Beagle genotype likelhoods===
The input file contains genotype likelihoods in a .beagle file format [http://faculty.washington.edu/browning/beagle/beagle.html].
and can be compressed with gzip.
==== BAM files ====
If you have BAM files you can use [[ANGSD]] to produce genotype likelihoods in .beagle format. Please
see [http://www.popgen.dk/angsd/index.php/Beagle_input Creation of Beagle files with ANGSD]

==== VCF files ====
If you already have made a VCF file that contains genotype likehood information then it should be possible to convert .vcf files with genotype likelihoods to .beagle file via vcftools [https://vcftools.github.io/man_latest.html]

<pre>
vcftools --vcf input.vcf --out test --BEAGLE-GL --chr 1,2
</pre>
Chromosome has to be specified.

You can also use bcftools' [https://samtools.github.io/bcftools/bcftools.html] 'query' option for generating a .beagle file from a .vcf file.

==Output File==
The analysis performed by evalAdmix produces one file, containing a tab delimited N times N symmetric correlation matrix, where column i in line j contains the correlation of residuals between individual i and j, and the diagonal values (self-correlation) are set to NA:

NA 0.008609 -0.006919 0.002731 0.020224 
0.008609 NA 0.000033 0.004968 -0.008470 
-0.006919 0.000033 NA 0.006982 0.005664 
0.002731 0.004968 0.006982 NA 0.000521 
0.020224 -0.008470 0.005664 0.000521 NA

==Run command example==

=== Genotype data ===
Download the input file containing genotypes in binary plink format
<pre>
wget http://pontus.popgen.dk/albrecht/open/admixTjeck/plink/admixTjeck2.bed
wget http://pontus.popgen.dk/albrecht/open/admixTjeck/plink/admixTjeck2.bim
wget http://pontus.popgen.dk/albrecht/open/admixTjeck/plink/admixTjeck2.fam
</pre>

Run ADMIXTURE [http://software.genetics.ucla.edu/admixture/] to obtain admixture proprotions

<pre>admixture admixTjeck2.bed 3</pre>

Run evalAdmix
<pre>./evalAdmix -plink admixTjeck2 -fname admixTjeck2.3.P -qname admixTjeck2.3.Q -P 20 </pre>

Plot results in R
<pre>
# read population labels and estimated admixture proportions
pop<-read.table("admixTjeck2.fam")
q<-read.table("admixTjeck2.3.Q")

# order according to population and plot the ADMIXTURE reults
ord<-order(pop[,2])
barplot(t(q)[,ord],col=2:10,space=0,border=NA,xlab="Individuals",ylab="Demo2 Admixture proportions for K=3")
text(tapply(1:nrow(pop),pop[ord,2],mean),-0.05,unique(pop[ord,2]),xpd=T)
abline(v=cumsum(sapply(unique(pop[ord,2]),function(x){sum(pop[ord,2]==x)})),col=1,lwd=1.2)

r<-as.matrix(read.table("output.corres.txt"))

# Plot correlation of residuals
source("NicePlotCorRes.R")
plotCorRes(cor_mat = r, pop = as.vector(pop[,2]), title="Evaluation of 1000G admixture proportions with K=3", max_z=0.1, min_z=-0.1)

</pre>

=== Low depth sequencing data ===
Download the input file containing genotype likelihoods in beagle format
<pre>
wget popgen.dk/software/download/NGSadmix/data/Demo2input.gz
wget popgen.dk/software/download/NGSadmix/data/Demo2pop.info
</pre>

Execute [[NgsAdmix |NGSadmix]] to obtain admixture proportions
<pre>./NGSadmix -likes Demo2input.gz -K 3 -P 20 -o myoutfiles -minMaf 0.05</pre>

::Input file = Demo2input.gz
::Ancestral Populations K=3
::Computer cores = 20 (-P 20).
::Output prefix = myoutfiles (-o myoutfiles)
::SNPs with MAF > 5% (-minMaf 0.05)

Run evalAdmix
<pre>./evalAdmix -beagle Demo2input.gz -fname myoutfiles.fopt.gz -qname myoutfiles.qopt -P 20 </pre>
::Input file = input.gz
::Ancestral Populations frequency file myoutfiles.fopt.gz
::Computer cores = 20 (-P 20).

Plot results in R
<pre>
# read population labels and estimated admixture proportions
pop<-read.table("Demo2pop.info",as.is=T)
q<-read.table("myoutfiles.qopt")

# order according to population and plot the NGSadmix reults
ord<-order(pop[,1])
barplot(t(q)[,ord],col=2:10,space=0,border=NA,xlab="Individuals",ylab="Demo2 Admixture proportions for K=3")
text(tapply(1:nrow(pop),pop[ord,1],mean),-0.05,unique(pop[ord,1]),xpd=T)
abline(v=cumsum(sapply(unique(pop[ord,1]),function(x){sum(pop[ord,1]==x)})),col=1,lwd=1.2)

r<-read.table("output.corres.txt")

# Plot correlation of residuals
source("NicePlotCorRes.R")
plotCorRes(cor_mat = r, pop = as.vector(pop[,1]), title="Evaluation of 1000G admixture proportions with K=3", max_z=0.1, min_z=-0.1)

</pre>

==Citation==

evalAdmix has a preprint

[https://doi.org/10.1101/708883 Evaluation of Model Fit of Inferred Admixture Proportions]

EvalAdmix

2019-07-21T12:24:54Z

Genis: /* Output Files */

evalAdmix allows to evaluate the model fit of an admixture analysis. It takes as input the genotype data (either called genotypes in plink files or genotype likelihoods beagle files) used in the admixture analysis and the estimated ancestral frequency and admixture propotions files (P and Q files).

The output is a pairwise correlation of residuals matrix between individuals. The correlation will be 0 in case of a good fit of the data to the admixture model. When something is wrong, individuals from the same population will be positively correlated; and individuals from different populationts but that share one or more ancestral populations as admixture sources will have a negative correlation. Positive correlation between a pair of individuals might also be due to relatedness.

[[File:evalAdmix.png|thumb|550px]]

==Download and Installation==

evalAdmix can be installed from [https://github.com/GenisGE/evalAdmix github]

<pre>git clone https://github.com/GenisGE/evalAdmix.git
cd evalAdmix
make
</pre>

==Quick start==
:<code> ./evalAdmix -beagle inputBeagleFile.gz -fname myoutfiles.fopt.gz -qname myoutfiles.qopt -P 10 </code>
:<code> ./evalAdmix -plink inputPlinkPrefix -fname myoutfiles.fopt.gz -qname myoutfiles.qopt -P 10 </code>

* '''-beagle''' beagle file of genotype likelihoods
* '''-plink''' binary plink file prefix with genotype data
* '''-fname''' file with ancestral frequencies
* '''-qname''' file with admixture proportions
* '''-o''' prefix of output file names
* '''-P''' Number of threads used

==Parameters==

<pre>./evalAdmix </pre>

<pre>
Arguments:
Required:
-plink path to binary plink file (excluding the .bed)
or
-beagle path to beagle file containing genotype likelihoods (alternative to -plink)

-fname path to ancestral population frequencies file
-qname path to admixture proportions file

Optional:

-o name of the output file

Setup (optional):

-P 1 number of threads
-autosomeMax 23 autosome ends with this chromsome
-nIts 5 number of iterations to do for frequency correction; if set to 0 calculates correlation without correction (fast but biased)
-useSites 1.0 proportion of sites to use to calculate correlation of residuals
-useInds filename path to tab delimited file with first column containing all individuals ID and second column with 1 to include individual in analysis and 0 otherwise (default all individuals are included)
-misTol 0.05 tolerance for considering site as missing when using genotype likelihoods. Use same value as used in NGSadmix to keep compatibility when using genotype likelihoods (-beagle)
-minMaf 0.05 minimum minor allele frequency to keep site. Use same value as used in NGSadmix to keep compatibility when using genotype likelihoods (-beagle)
</pre>

==Input Files==

===Plink===
Genotype data files in binary PLINK format (.bed .fam .bim).
===Beagle genotype likelhoods===
The input file contains genotype likelihoods in a .beagle file format [http://faculty.washington.edu/browning/beagle/beagle.html].
and can be compressed with gzip.
==== BAM files ====
If you have BAM files you can use [[ANGSD]] to produce genotype likelihoods in .beagle format. Please
see [http://www.popgen.dk/angsd/index.php/Beagle_input Creation of Beagle files with ANGSD]

==== VCF files ====
If you already have made a VCF file that contains genotype likehood information then it should be possible to convert .vcf files with genotype likelihoods to .beagle file via vcftools [https://vcftools.github.io/man_latest.html]

<pre>
vcftools --vcf input.vcf --out test --BEAGLE-GL --chr 1,2
</pre>
Chromosome has to be specified.

You can also use bcftools' [https://samtools.github.io/bcftools/bcftools.html] 'query' option for generating a .beagle file from a .vcf file.

==Output File==
The analysis performed by evalAdmix produces one file, containing a tab delimited N times N symmetric correlation matrix, where column i in line j contains the correlation of residuals between individual i and j, and the diagonal values (self-correlation) are set to NA:

NA 0.008609 -0.006919 0.002731 0.020224 
0.008609 NA 0.000033 0.004968 -0.008470 
-0.006919 0.000033 NA 0.006982 0.005664 
0.002731 0.004968 0.006982 NA 0.000521 
0.020224 -0.008470 0.005664 0.000521 NA

==Run command example==

=== Genotype data ===
Download the input file containing genotype in binary plink format
<pre>
wget http://pontus.popgen.dk/albrecht/open/admixTjeck/plink/admixTjeck2.bed
wget http://pontus.popgen.dk/albrecht/open/admixTjeck/plink/admixTjeck2.bim
wget http://pontus.popgen.dk/albrecht/open/admixTjeck/plink/admixTjeck2.fam
</pre>

Run ADMIXTURE [http://software.genetics.ucla.edu/admixture/] to obtain admixture proprotions

<pre>admixture admixTjeck2.bed 3</pre>

Run evalAdmix
<pre>./evalAdmix -plink admixTjeck2 -fname admixTjeck2.3.P -qname admixTjeck2.3.Q -P 20 </pre>

Plot results in R
<pre>
# read population labels and estimated admixture proportions
pop<-read.table("admixTjeck2.fam")
q<-read.table("admixTjeck2.3.Q")

# order according to population and plot the ADMIXTURE reults
ord<-order(pop[,2])
barplot(t(q)[,ord],col=2:10,space=0,border=NA,xlab="Individuals",ylab="Demo2 Admixture proportions for K=3")
text(tapply(1:nrow(pop),pop[ord,2],mean),-0.05,unique(pop[ord,2]),xpd=T)
abline(v=cumsum(sapply(unique(pop[ord,2]),function(x){sum(pop[ord,2]==x)})),col=1,lwd=1.2)

r<-as.matrix(read.table("output.corres.txt"))

# Plot correlation of residuals
source("NicePlotCorRes.R")
plotCorRes(cor_mat = r, pop = as.vector(pop[,2]), title="Evaluation of 1000G admixture proportions with K=3", max_z=0.1, min_z=-0.1)

</pre>

=== Low depth sequencing data ===
Download the input file containing genotype likelihoods in beagle format
<pre>
wget popgen.dk/software/download/NGSadmix/data/Demo2input.gz
wget popgen.dk/software/download/NGSadmix/data/Demo2pop.info
</pre>

Execute [[NgsAdmix |NGSadmix]] to obtain admixture proportions
<pre>./NGSadmix -likes Demo2input.gz -K 3 -P 20 -o myoutfiles -minMaf 0.05</pre>

::Input file = Demo2input.gz
::Ancestral Populations K=3
::Computer cores = 20 (-P 20).
::Output prefix = myoutfiles (-o myoutfiles)
::SNPs with MAF > 5% (-minMaf 0.05)

Run evalAdmix
<pre>./evalAdmix -beagle Demo2input.gz -fname myoutfiles.fopt.gz -qname myoutfiles.qopt -P 20 </pre>
::Input file = input.gz
::Ancestral Populations frequency file myoutfiles.fopt.gz
::Computer cores = 20 (-P 20).

Plot results in R
<pre>
# read population labels and estimated admixture proportions
pop<-read.table("Demo2pop.info",as.is=T)
q<-read.table("myoutfiles.qopt")

# order according to population and plot the NGSadmix reults
ord<-order(pop[,1])
barplot(t(q)[,ord],col=2:10,space=0,border=NA,xlab="Individuals",ylab="Demo2 Admixture proportions for K=3")
text(tapply(1:nrow(pop),pop[ord,1],mean),-0.05,unique(pop[ord,1]),xpd=T)
abline(v=cumsum(sapply(unique(pop[ord,1]),function(x){sum(pop[ord,1]==x)})),col=1,lwd=1.2)

r<-read.table("output.corres.txt")

# Plot correlation of residuals
source("NicePlotCorRes.R")
plotCorRes(cor_mat = r, pop = as.vector(pop[,1]), title="Evaluation of 1000G admixture proportions with K=3", max_z=0.1, min_z=-0.1)

</pre>

==Citation==

evalAdmix has a preprint

[https://doi.org/10.1101/708883 Evaluation of Model Fit of Inferred Admixture Proportions]

EvalAdmix

2019-07-21T12:24:31Z

Genis: /* Input File */

evalAdmix allows to evaluate the model fit of an admixture analysis. It takes as input the genotype data (either called genotypes in plink files or genotype likelihoods beagle files) used in the admixture analysis and the estimated ancestral frequency and admixture propotions files (P and Q files).

The output is a pairwise correlation of residuals matrix between individuals. The correlation will be 0 in case of a good fit of the data to the admixture model. When something is wrong, individuals from the same population will be positively correlated; and individuals from different populationts but that share one or more ancestral populations as admixture sources will have a negative correlation. Positive correlation between a pair of individuals might also be due to relatedness.

[[File:evalAdmix.png|thumb|550px]]

==Download and Installation==

evalAdmix can be installed from [https://github.com/GenisGE/evalAdmix github]

<pre>git clone https://github.com/GenisGE/evalAdmix.git
cd evalAdmix
make
</pre>

==Quick start==
:<code> ./evalAdmix -beagle inputBeagleFile.gz -fname myoutfiles.fopt.gz -qname myoutfiles.qopt -P 10 </code>
:<code> ./evalAdmix -plink inputPlinkPrefix -fname myoutfiles.fopt.gz -qname myoutfiles.qopt -P 10 </code>

* '''-beagle''' beagle file of genotype likelihoods
* '''-plink''' binary plink file prefix with genotype data
* '''-fname''' file with ancestral frequencies
* '''-qname''' file with admixture proportions
* '''-o''' prefix of output file names
* '''-P''' Number of threads used

==Parameters==

<pre>./evalAdmix </pre>

<pre>
Arguments:
Required:
-plink path to binary plink file (excluding the .bed)
or
-beagle path to beagle file containing genotype likelihoods (alternative to -plink)

-fname path to ancestral population frequencies file
-qname path to admixture proportions file

Optional:

-o name of the output file

Setup (optional):

-P 1 number of threads
-autosomeMax 23 autosome ends with this chromsome
-nIts 5 number of iterations to do for frequency correction; if set to 0 calculates correlation without correction (fast but biased)
-useSites 1.0 proportion of sites to use to calculate correlation of residuals
-useInds filename path to tab delimited file with first column containing all individuals ID and second column with 1 to include individual in analysis and 0 otherwise (default all individuals are included)
-misTol 0.05 tolerance for considering site as missing when using genotype likelihoods. Use same value as used in NGSadmix to keep compatibility when using genotype likelihoods (-beagle)
-minMaf 0.05 minimum minor allele frequency to keep site. Use same value as used in NGSadmix to keep compatibility when using genotype likelihoods (-beagle)
</pre>

==Input Files==

===Plink===
Genotype data files in binary PLINK format (.bed .fam .bim).
===Beagle genotype likelhoods===
The input file contains genotype likelihoods in a .beagle file format [http://faculty.washington.edu/browning/beagle/beagle.html].
and can be compressed with gzip.
==== BAM files ====
If you have BAM files you can use [[ANGSD]] to produce genotype likelihoods in .beagle format. Please
see [http://www.popgen.dk/angsd/index.php/Beagle_input Creation of Beagle files with ANGSD]

==== VCF files ====
If you already have made a VCF file that contains genotype likehood information then it should be possible to convert .vcf files with genotype likelihoods to .beagle file via vcftools [https://vcftools.github.io/man_latest.html]

<pre>
vcftools --vcf input.vcf --out test --BEAGLE-GL --chr 1,2
</pre>
Chromosome has to be specified.

You can also use bcftools' [https://samtools.github.io/bcftools/bcftools.html] 'query' option for generating a .beagle file from a .vcf file.

==Output Files==
The analysis performed by evalAdmix produces 1 file, containing a tab delimited N times N symmetric correlation matrix, where column i in line j contains the correlation of residuals between individual i and j, and the diagonal values (self-correlation) are set to NA:

NA 0.008609 -0.006919 0.002731 0.020224 
0.008609 NA 0.000033 0.004968 -0.008470 
-0.006919 0.000033 NA 0.006982 0.005664 
0.002731 0.004968 0.006982 NA 0.000521 
0.020224 -0.008470 0.005664 0.000521 NA

==Run command example==

=== Genotype data ===
Download the input file containing genotype in binary plink format
<pre>
wget http://pontus.popgen.dk/albrecht/open/admixTjeck/plink/admixTjeck2.bed
wget http://pontus.popgen.dk/albrecht/open/admixTjeck/plink/admixTjeck2.bim
wget http://pontus.popgen.dk/albrecht/open/admixTjeck/plink/admixTjeck2.fam
</pre>

Run ADMIXTURE [http://software.genetics.ucla.edu/admixture/] to obtain admixture proprotions

<pre>admixture admixTjeck2.bed 3</pre>

Run evalAdmix
<pre>./evalAdmix -plink admixTjeck2 -fname admixTjeck2.3.P -qname admixTjeck2.3.Q -P 20 </pre>

Plot results in R
<pre>
# read population labels and estimated admixture proportions
pop<-read.table("admixTjeck2.fam")
q<-read.table("admixTjeck2.3.Q")

# order according to population and plot the ADMIXTURE reults
ord<-order(pop[,2])
barplot(t(q)[,ord],col=2:10,space=0,border=NA,xlab="Individuals",ylab="Demo2 Admixture proportions for K=3")
text(tapply(1:nrow(pop),pop[ord,2],mean),-0.05,unique(pop[ord,2]),xpd=T)
abline(v=cumsum(sapply(unique(pop[ord,2]),function(x){sum(pop[ord,2]==x)})),col=1,lwd=1.2)

r<-as.matrix(read.table("output.corres.txt"))

# Plot correlation of residuals
source("NicePlotCorRes.R")
plotCorRes(cor_mat = r, pop = as.vector(pop[,2]), title="Evaluation of 1000G admixture proportions with K=3", max_z=0.1, min_z=-0.1)

</pre>

=== Low depth sequencing data ===
Download the input file containing genotype likelihoods in beagle format
<pre>
wget popgen.dk/software/download/NGSadmix/data/Demo2input.gz
wget popgen.dk/software/download/NGSadmix/data/Demo2pop.info
</pre>

Execute [[NgsAdmix |NGSadmix]] to obtain admixture proportions
<pre>./NGSadmix -likes Demo2input.gz -K 3 -P 20 -o myoutfiles -minMaf 0.05</pre>

::Input file = Demo2input.gz
::Ancestral Populations K=3
::Computer cores = 20 (-P 20).
::Output prefix = myoutfiles (-o myoutfiles)
::SNPs with MAF > 5% (-minMaf 0.05)

Run evalAdmix
<pre>./evalAdmix -beagle Demo2input.gz -fname myoutfiles.fopt.gz -qname myoutfiles.qopt -P 20 </pre>
::Input file = input.gz
::Ancestral Populations frequency file myoutfiles.fopt.gz
::Computer cores = 20 (-P 20).

Plot results in R
<pre>
# read population labels and estimated admixture proportions
pop<-read.table("Demo2pop.info",as.is=T)
q<-read.table("myoutfiles.qopt")

# order according to population and plot the NGSadmix reults
ord<-order(pop[,1])
barplot(t(q)[,ord],col=2:10,space=0,border=NA,xlab="Individuals",ylab="Demo2 Admixture proportions for K=3")
text(tapply(1:nrow(pop),pop[ord,1],mean),-0.05,unique(pop[ord,1]),xpd=T)
abline(v=cumsum(sapply(unique(pop[ord,1]),function(x){sum(pop[ord,1]==x)})),col=1,lwd=1.2)

r<-read.table("output.corres.txt")

# Plot correlation of residuals
source("NicePlotCorRes.R")
plotCorRes(cor_mat = r, pop = as.vector(pop[,1]), title="Evaluation of 1000G admixture proportions with K=3", max_z=0.1, min_z=-0.1)

</pre>

==Citation==

evalAdmix has a preprint

[https://doi.org/10.1101/708883 Evaluation of Model Fit of Inferred Admixture Proportions]

EvalAdmix

2019-07-21T12:01:03Z

Genis:

evalAdmix allows to evaluate the model fit of an admixture analysis. It takes as input the genotype data (either called genotypes in plink files or genotype likelihoods beagle files) used in the admixture analysis and the estimated ancestral frequency and admixture propotions files (P and Q files).

The output is a pairwise correlation of residuals matrix between individuals. The correlation will be 0 in case of a good fit of the data to the admixture model. When something is wrong, individuals from the same population will be positively correlated; and individuals from different populationts but that share one or more ancestral populations as admixture sources will have a negative correlation. Positive correlation between a pair of individuals might also be due to relatedness.

[[File:evalAdmix.png|thumb|550px]]

==Download and Installation==

evalAdmix can be installed from [https://github.com/GenisGE/evalAdmix github]

<pre>git clone https://github.com/GenisGE/evalAdmix.git
cd evalAdmix
make
</pre>

==Quick start==
:<code> ./evalAdmix -beagle inputBeagleFile.gz -fname myoutfiles.fopt.gz -qname myoutfiles.qopt -P 10 </code>
:<code> ./evalAdmix -plink inputPlinkPrefix -fname myoutfiles.fopt.gz -qname myoutfiles.qopt -P 10 </code>

* '''-beagle''' beagle file of genotype likelihoods
* '''-plink''' binary plink file prefix with genotype data
* '''-fname''' file with ancestral frequencies
* '''-qname''' file with admixture proportions
* '''-o''' prefix of output file names
* '''-P''' Number of threads used

==Parameters==

<pre>./evalAdmix </pre>

<pre>
Arguments:
Required:
-plink path to binary plink file (excluding the .bed)
or
-beagle path to beagle file containing genotype likelihoods (alternative to -plink)

-fname path to ancestral population frequencies file
-qname path to admixture proportions file

Optional:

-o name of the output file

Setup (optional):

-P 1 number of threads
-autosomeMax 23 autosome ends with this chromsome
-nIts 5 number of iterations to do for frequency correction; if set to 0 calculates correlation without correction (fast but biased)
-useSites 1.0 proportion of sites to use to calculate correlation of residuals
-useInds filename path to tab delimited file with first column containing all individuals ID and second column with 1 to include individual in analysis and 0 otherwise (default all individuals are included)
-misTol 0.05 tolerance for considering site as missing when using genotype likelihoods. Use same value as used in NGSadmix to keep compatibility when using genotype likelihoods (-beagle)
-minMaf 0.05 minimum minor allele frequency to keep site. Use same value as used in NGSadmix to keep compatibility when using genotype likelihoods (-beagle)
</pre>

==Input File==

===Plink===
Genotype data files in binary PLINK format (.bed .fam .bim).
===Beagle genotype likelhoods===
The input file contains genotype likelihoods in a .beagle file format [http://faculty.washington.edu/browning/beagle/beagle.html].
and can be compressed with gzip.
==== BAM files ====
If you have BAM files you can use [[ANGSD]] to produce genotype likelihoods in .beagle format. Please
see [http://www.popgen.dk/angsd/index.php/Beagle_input Creation of Beagle files with ANGSD]

==== VCF files ====
If you already have made a VCF file that contains genotype likehood information then it should be possible to convert .vcf files with genotype likelihoods to .beagle file via vcftools [https://vcftools.github.io/man_latest.html]

<pre>
vcftools --vcf input.vcf --out test --BEAGLE-GL --chr 1,2
</pre>
Chromosome has to be specified.

You can also use bcftools' [https://samtools.github.io/bcftools/bcftools.html] 'query' option for generating a .beagle file from a .vcf file.

==Output Files==
The analysis performed by evalAdmix produces 1 file, containing a tab delimited N times N symmetric correlation matrix, where column i in line j contains the correlation of residuals between individual i and j, and the diagonal values (self-correlation) are set to NA:

NA 0.008609 -0.006919 0.002731 0.020224 
0.008609 NA 0.000033 0.004968 -0.008470 
-0.006919 0.000033 NA 0.006982 0.005664 
0.002731 0.004968 0.006982 NA 0.000521 
0.020224 -0.008470 0.005664 0.000521 NA

==Run command example==

=== Genotype data ===
Download the input file containing genotype in binary plink format
<pre>
wget http://pontus.popgen.dk/albrecht/open/admixTjeck/plink/admixTjeck2.bed
wget http://pontus.popgen.dk/albrecht/open/admixTjeck/plink/admixTjeck2.bim
wget http://pontus.popgen.dk/albrecht/open/admixTjeck/plink/admixTjeck2.fam
</pre>

Run ADMIXTURE [http://software.genetics.ucla.edu/admixture/] to obtain admixture proprotions

<pre>admixture admixTjeck2.bed 3</pre>

Run evalAdmix
<pre>./evalAdmix -plink admixTjeck2 -fname admixTjeck2.3.P -qname admixTjeck2.3.Q -P 20 </pre>

Plot results in R
<pre>
# read population labels and estimated admixture proportions
pop<-read.table("admixTjeck2.fam")
q<-read.table("admixTjeck2.3.Q")

# order according to population and plot the ADMIXTURE reults
ord<-order(pop[,2])
barplot(t(q)[,ord],col=2:10,space=0,border=NA,xlab="Individuals",ylab="Demo2 Admixture proportions for K=3")
text(tapply(1:nrow(pop),pop[ord,2],mean),-0.05,unique(pop[ord,2]),xpd=T)
abline(v=cumsum(sapply(unique(pop[ord,2]),function(x){sum(pop[ord,2]==x)})),col=1,lwd=1.2)

r<-as.matrix(read.table("output.corres.txt"))

# Plot correlation of residuals
source("NicePlotCorRes.R")
plotCorRes(cor_mat = r, pop = as.vector(pop[,2]), title="Evaluation of 1000G admixture proportions with K=3", max_z=0.1, min_z=-0.1)

</pre>

=== Low depth sequencing data ===
Download the input file containing genotype likelihoods in beagle format
<pre>
wget popgen.dk/software/download/NGSadmix/data/Demo2input.gz
wget popgen.dk/software/download/NGSadmix/data/Demo2pop.info
</pre>

Execute [[NgsAdmix |NGSadmix]] to obtain admixture proportions
<pre>./NGSadmix -likes Demo2input.gz -K 3 -P 20 -o myoutfiles -minMaf 0.05</pre>

::Input file = Demo2input.gz
::Ancestral Populations K=3
::Computer cores = 20 (-P 20).
::Output prefix = myoutfiles (-o myoutfiles)
::SNPs with MAF > 5% (-minMaf 0.05)

Run evalAdmix
<pre>./evalAdmix -beagle Demo2input.gz -fname myoutfiles.fopt.gz -qname myoutfiles.qopt -P 20 </pre>
::Input file = input.gz
::Ancestral Populations frequency file myoutfiles.fopt.gz
::Computer cores = 20 (-P 20).

Plot results in R
<pre>
# read population labels and estimated admixture proportions
pop<-read.table("Demo2pop.info",as.is=T)
q<-read.table("myoutfiles.qopt")

# order according to population and plot the NGSadmix reults
ord<-order(pop[,1])
barplot(t(q)[,ord],col=2:10,space=0,border=NA,xlab="Individuals",ylab="Demo2 Admixture proportions for K=3")
text(tapply(1:nrow(pop),pop[ord,1],mean),-0.05,unique(pop[ord,1]),xpd=T)
abline(v=cumsum(sapply(unique(pop[ord,1]),function(x){sum(pop[ord,1]==x)})),col=1,lwd=1.2)

r<-read.table("output.corres.txt")

# Plot correlation of residuals
source("NicePlotCorRes.R")
plotCorRes(cor_mat = r, pop = as.vector(pop[,1]), title="Evaluation of 1000G admixture proportions with K=3", max_z=0.1, min_z=-0.1)

</pre>

==Citation==

evalAdmix has a preprint

[https://doi.org/10.1101/708883 Evaluation of Model Fit of Inferred Admixture Proportions]

2019-07-20T16:10:51Z

Genis:

EvalAdmix

2019-07-20T16:08:30Z

Genis: /* Output Files */

evalAdmix allows to evaluate the results of an admixture analysis. It takes as input the genotype data (either called genotypes in plink files or genotype likelihoods beagle files) used in the admixture analysis and the frequency
and admixture propotions (P and Q files) generated.

The output is a pairwise correlation of residuals matrix between individuals The correlation will be 0 in case of a good fit of the data to the admixture model. When something is wrong, individuals from the same population will be positively correlated; and individuals from different populationts but that share one or more ancestral populations as admixture sources will have a negative correlation. Positive correlation between a pair of individuals might also be due to relatedness.

[[File:evalAdmix.png|thumb]]

==Download and Installation==

evalAdmix can be installed from [https://github.com/GenisGE/evalAdmix github]

<pre>
git clone https://github.com/GenisGE/evalAdmix.git
cd evalAdmix
make
</pre>

==Quick start==
:<code> ./evalAdmix -beagle inputBeagleFile.gz -fname myoutfiles.fopt.gz -qname myoutfiles.qopt -P 10 </code>
:<code> ./evalAdmix -plink inputPlinkPrefix -fname myoutfiles.fopt.gz -qname myoutfiles.qopt -P 10 </code>

* '''-beagle''' beagle file of genotype likelihoods
* '''-plink''' binary plink file prefix with genotype data
* '''-fname''' file with ancestral frequencies
* '''-qname''' file with admixture proportions
* '''-o''' prefix of output file names
* '''-P''' Number of threads used

==Parameters==

<pre>./evalAdmix </pre>

<pre>
Arguments:
-plink path to binary plink file (excluding the .bed)
-beagle path to beagle file containing genotype likelihoods (alternative to -plink)
-fname path to ancestral population frequencies file
-qname path to admixture proportions file
-o name of the output file
Setup:
-P 1 number of threads
-autosomeMax 23 autosome ends with this chromsome
-nIts 5 number of iterations to do for frequency correction; if set to 0 calculates correlation without correction (fast but biased)
-useSites 1.0 proportion of sites to use to calculate correlation of residuals
-misTol 0.05 tolerance for considering site as missing when using genotype likelihoods. Use same value as used in NGSadmix to keep compatibility when using genotype likelihoods (-beagle)
-minMaf 0.05 minimum minor allele frequency to keep site. Use same value as used in NGSadmix to keep compatibility when using genotype likelihoods (-beagle)
</pre>

==Input File==

===Plink===
Genotype data files in binary PLINK format (.bed .fam .bim).
===Beagle genotype likelhoods===
The input file contains genotype likelihoods in a .beagle file format [http://faculty.washington.edu/browning/beagle/beagle.html].
and can be compressed with gzip.
==== BAM files ====
If you have BAM files you can use [[ANGSD]] to produce genotype likelihoods in .beagle format. Please
see [http://www.popgen.dk/angsd/index.php/Beagle_input Creation of Beagle files with ANGSD]

==== VCF files ====
If you already have made a VCF file that contains genotype likehood information then it should be possible to convert .vcf files with genotype likelihoods to .beagle file via vcftools [https://vcftools.github.io/man_latest.html]

<pre>
vcftools --vcf input.vcf --out test --BEAGLE-GL --chr 1,2
</pre>
Chromosome has to be specified.

You can also use bcftools' [https://samtools.github.io/bcftools/bcftools.html] 'query' option for generating a .beagle file from a .vcf file.

==Output Files==
The analysis performed by evalAdmix produces 1 file, containing a tab delimited N times N symmetric correlation matrix, where column i in line j contains the correlation of residuals between individual i and j, and the diagonal values (self-correlation) are set to NA:

NA 0.008609 -0.006919 0.002731 0.020224 
0.008609 NA 0.000033 0.004968 -0.008470 
-0.006919 0.000033 NA 0.006982 0.005664 
0.002731 0.004968 0.006982 NA 0.000521 
0.020224 -0.008470 0.005664 0.000521 NA

==Run command example==

=== Genotype data ===
Download the input file containing genotype in binary plink format
<pre>
wget http://pontus.popgen.dk/albrecht/open/admixTjeck/plink/admixTjeck2.bed
wget http://pontus.popgen.dk/albrecht/open/admixTjeck/plink/admixTjeck2.bim
wget http://pontus.popgen.dk/albrecht/open/admixTjeck/plink/admixTjeck2.fam
</pre>

Run ADMIXTURE to obtain admixture proprotions

<pre>admixture admixTjeck2.bed 3</pre>

Run evalAdmix
<pre>./evalAdmix -plink admixTjeck2 -fname admixTjeck2.3.P -qname admixTjeck2.3.Q -P 20 </pre>

Plot results in R
<pre>
# read population labels and estimated admixture proportions
pop<-read.table("admixTjeck2.fam")
q<-read.table("admixTjeck2.3.Q")

# order according to population and plot the ADMIXTURE reults
ord<-order(pop[,2])
barplot(t(q)[,ord],col=2:10,space=0,border=NA,xlab="Individuals",ylab="Demo2 Admixture proportions for K=3")
text(tapply(1:nrow(pop),pop[ord,2],mean),-0.05,unique(pop[ord,2]),xpd=T)
abline(v=cumsum(sapply(unique(pop[ord,2]),function(x){sum(pop[ord,2]==x)})),col=1,lwd=1.2)

r<-read.table("output.corres.txt")

# Plot correlation of residuals
mypalette <- colorRampPalette(colors = c("blue", "green", "red"), space="Lab")(10)
mylegend <- as.raster(mypalette, ncol=1)[10:1,]

layout(matrix(1:2,ncol=2), width = c(4,1),height = c(1,1))
par(mar=c(5,4,4,0))
image(as.matrix(r)[ord,ord], col=mypalette,
yaxt="n",xaxt="n", zlim=c(-0.25,0.25),useRaster=T,
main="Correlation of residuals",
oldstyle=T,cex.main=1)
text(tapply(1:nrow(pop),pop[ord,2],mean)/length(pop[ord,2]),-0.1,unique(pop[ord,2]),xpd=T)
text(-0.1,tapply(1:nrow(pop),pop[ord,2],mean)/length(pop[ord,2]),unique(pop[ord,2]),xpd=T)
abline(v=cumsum(sapply(unique(pop[ord,2]),function(x){sum(pop[ord,2]==x)}))/length(pop[ord,2]),col=1,lwd=1.2)
abline(h=cumsum(sapply(unique(pop[ord,2]),function(x){sum(pop[ord,2]==x)}))/length(pop[ord,2]),col=1,lwd=1.2)
par(mar=c(5,0.5,4,2))
plot(c(0,1),c(0,1),type = 'n', axes = F,xlab = '', ylab = '', main = '')
rasterImage(mylegend, 0, 0.25, 0.4,0.75)
text(x=0.8, y = c(0.25,0.5, 0.75), labels = c(-0.25, 0, 0.25),cex=0.8)

</pre>
=== Low depth sequencing data ===
Download the input file containing genotype likelihoods in beagle format
<pre>
wget popgen.dk/software/download/NGSadmix/data/Demo2input.gz
wget popgen.dk/software/download/NGSadmix/data/Demo2pop.info
</pre>

Execute [[NgsAdmix |NGSadmix]] to obtain admixture proportions
<pre>./NGSadmix -likes Demo2input.gz -K 3 -P 20 -o myoutfiles -minMaf 0.05</pre>

::Input file = Demo2input.gz
::Ancestral Populations K=3
::Computer cores = 20 (-P 20).
::Output prefix = myoutfiles (-o myoutfiles)
::SNPs with MAF > 5% (-minMaf 0.05)

Run evalAdmix
<pre>./evalAdmix -beagle Demo2input.gz -fname myoutfiles.fopt.gz -qname myoutfiles.qopt -P 20 </pre>
::Input file = input.gz
::Ancestral Populations frequency file myoutfiles.fopt.gz
::Computer cores = 20 (-P 20).

Plot results in R
<pre>
# read population labels and estimated admixture proportions
pop<-read.table("Demo2pop.info",as.is=T)
q<-read.table("myoutfiles.qopt")

# order according to population and plot the NGSadmix reults
ord<-order(pop[,1])
barplot(t(q)[,ord],col=2:10,space=0,border=NA,xlab="Individuals",ylab="Demo2 Admixture proportions for K=3")
text(tapply(1:nrow(pop),pop[ord,1],mean),-0.05,unique(pop[ord,1]),xpd=T)
abline(v=cumsum(sapply(unique(pop[ord,1]),function(x){sum(pop[ord,1]==x)})),col=1,lwd=1.2)

r<-read.table("output.corres.txt")
image(as.matrix(r))

</pre>

==Citation==

EvalAdmix

2019-07-19T13:35:04Z

Genis:

evalAdmix allows to evaluate the results of an admixture analysis. It takes as input the genotype data (either called genotypes in plink files or genotype likelihoods beagle files) used in the admixture analysis and the frequency
and admixture propotions (P and Q files) generated.

The output is a pairwise correlation of residuals matrix between individuals The correlation will be 0 in case of a good fit of the data to the admixture model. When something is wrong, individuals from the same population will be positively correlated; and individuals from different populationts but that share one or more ancestral populations as admixture sources will have a negative correlation. Positive correlation between a pair of individuals might also be due to relatedness.

[[File:evalAdmix.png|thumb]]

==Download and Installation==

evalAdmix can be installed from [https://github.com/GenisGE/evalAdmix github]

<pre>
git clone https://github.com/GenisGE/evalAdmix.git
cd evalAdmix
make
</pre>

==Quick start==
:<code> ./evalAdmix -beagle inputBeagleFile.gz -fname myoutfiles.fopt.gz -qname myoutfiles.qopt -P 10 </code>
:<code> ./evalAdmix -plink inputPlinkPrefix -fname myoutfiles.fopt.gz -qname myoutfiles.qopt -P 10 </code>

* '''-beagle''' beagle file of genotype likelihoods
* '''-plink''' binary plink file prefix with genotype data
* '''-fname''' file with ancestral frequencies
* '''-qname''' file with admixture proportions
* '''-o''' prefix of output file names
* '''-P''' Number of threads used

==Parameters==

<pre>./evalAdmix </pre>

<pre>
Arguments:
-plink path to binary plink file (excluding the .bed)
-beagle path to beagle file containing genotype likelihoods (alternative to -plink)
-fname path to ancestral population frequencies file
-qname path to admixture proportions file
-o name of the output file
Setup:
-P 1 number of threads
-autosomeMax 23 autosome ends with this chromsome
-nIts 5 number of iterations to do for frequency correction; if set to 0 calculates correlation without correction (fast but biased)
-useSites 1.0 proportion of sites to use to calculate correlation of residuals
-misTol 0.05 tolerance for considering site as missing when using genotype likelihoods. Use same value as used in NGSadmix to keep compatibility when using genotype likelihoods (-beagle)
-minMaf 0.05 minimum minor allele frequency to keep site. Use same value as used in NGSadmix to keep compatibility when using genotype likelihoods (-beagle)
</pre>

==Input File==

===Plink===
Genotype data files in binary PLINK format (.bed .fam .bim).
===Beagle genotype likelhoods===
The input file contains genotype likelihoods in a .beagle file format [http://faculty.washington.edu/browning/beagle/beagle.html].
and can be compressed with gzip.
==== BAM files ====
If you have BAM files you can use [[ANGSD]] to produce genotype likelihoods in .beagle format. Please
see [http://www.popgen.dk/angsd/index.php/Beagle_input Creation of Beagle files with ANGSD]

==== VCF files ====
If you already have made a VCF file that contains genotype likehood information then it should be possible to convert .vcf files with genotype likelihoods to .beagle file via vcftools [https://vcftools.github.io/man_latest.html]

<pre>
vcftools --vcf input.vcf --out test --BEAGLE-GL --chr 1,2
</pre>
Chromosome has to be specified.

You can also use bcftools' [https://samtools.github.io/bcftools/bcftools.html] 'query' option for generating a .beagle file from a .vcf file.

==Output Files==
The analysis performed by evalAdmix produces 1 correlation file:

==Run command example==

=== Genotype data ===
Download the input file containing genotype in binary plink format
<pre>
wget http://pontus.popgen.dk/albrecht/open/admixTjeck/plink/admixTjeck2.bed
wget http://pontus.popgen.dk/albrecht/open/admixTjeck/plink/admixTjeck2.bim
wget http://pontus.popgen.dk/albrecht/open/admixTjeck/plink/admixTjeck2.fam
</pre>

Run ADMIXTURE to obtain admixture proprotions

<pre>admixture admixTjeck2.bed 3</pre>

Run evalAdmix
<pre>./evalAdmix -plink admixTjeck2 -fname admixTjeck2.3.P -qname admixTjeck2.3.Q -P 20 </pre>

Plot results in R
<pre>
# read population labels and estimated admixture proportions
pop<-read.table("admixTjeck2.fam")
q<-read.table("admixTjeck2.3.Q")

# order according to population and plot the ADMIXTURE reults
ord<-order(pop[,2])
barplot(t(q)[,ord],col=2:10,space=0,border=NA,xlab="Individuals",ylab="Demo2 Admixture proportions for K=3")
text(tapply(1:nrow(pop),pop[ord,2],mean),-0.05,unique(pop[ord,2]),xpd=T)
abline(v=cumsum(sapply(unique(pop[ord,2]),function(x){sum(pop[ord,2]==x)})),col=1,lwd=1.2)

r<-read.table("output.corres.txt")

# Plot correlation of residuals
mypalette <- colorRampPalette(colors = c("blue", "green", "red"), space="Lab")(10)
mylegend <- as.raster(mypalette, ncol=1)[10:1,]

layout(matrix(1:2,ncol=2), width = c(4,1),height = c(1,1))
par(mar=c(5,4,4,0))
image(as.matrix(r)[ord,ord], col=mypalette,
yaxt="n",xaxt="n", zlim=c(-0.25,0.25),useRaster=T,
main="Correlation of residuals",
oldstyle=T,cex.main=1)
text(tapply(1:nrow(pop),pop[ord,2],mean)/length(pop[ord,2]),-0.1,unique(pop[ord,2]),xpd=T)
text(-0.1,tapply(1:nrow(pop),pop[ord,2],mean)/length(pop[ord,2]),unique(pop[ord,2]),xpd=T)
abline(v=cumsum(sapply(unique(pop[ord,2]),function(x){sum(pop[ord,2]==x)}))/length(pop[ord,2]),col=1,lwd=1.2)
abline(h=cumsum(sapply(unique(pop[ord,2]),function(x){sum(pop[ord,2]==x)}))/length(pop[ord,2]),col=1,lwd=1.2)
par(mar=c(5,0.5,4,2))
plot(c(0,1),c(0,1),type = 'n', axes = F,xlab = '', ylab = '', main = '')
rasterImage(mylegend, 0, 0.25, 0.4,0.75)
text(x=0.8, y = c(0.25,0.5, 0.75), labels = c(-0.25, 0, 0.25),cex=0.8)

</pre>
=== Low depth sequencing data ===
Download the input file containing genotype likelihoods in beagle format
<pre>
wget popgen.dk/software/download/NGSadmix/data/Demo2input.gz
wget popgen.dk/software/download/NGSadmix/data/Demo2pop.info
</pre>

Execute [[NgsAdmix |NGSadmix]] to obtain admixture proportions
<pre>./NGSadmix -likes Demo2input.gz -K 3 -P 20 -o myoutfiles -minMaf 0.05</pre>

::Input file = Demo2input.gz
::Ancestral Populations K=3
::Computer cores = 20 (-P 20).
::Output prefix = myoutfiles (-o myoutfiles)
::SNPs with MAF > 5% (-minMaf 0.05)

Run evalAdmix
<pre>./evalAdmix -beagle Demo2input.gz -fname myoutfiles.fopt.gz -qname myoutfiles.qopt -P 20 </pre>
::Input file = input.gz
::Ancestral Populations frequency file myoutfiles.fopt.gz
::Computer cores = 20 (-P 20).

Plot results in R
<pre>
# read population labels and estimated admixture proportions
pop<-read.table("Demo2pop.info",as.is=T)
q<-read.table("myoutfiles.qopt")

# order according to population and plot the NGSadmix reults
ord<-order(pop[,1])
barplot(t(q)[,ord],col=2:10,space=0,border=NA,xlab="Individuals",ylab="Demo2 Admixture proportions for K=3")
text(tapply(1:nrow(pop),pop[ord,1],mean),-0.05,unique(pop[ord,1]),xpd=T)
abline(v=cumsum(sapply(unique(pop[ord,1]),function(x){sum(pop[ord,1]==x)})),col=1,lwd=1.2)

r<-read.table("output.corres.txt")
image(as.matrix(r))

</pre>

==Citation==

EvalAdmix

2019-07-19T13:08:04Z

Genis:

EvalAdmix

2019-07-19T13:06:45Z

Genis:

EvalAdmix

2019-07-19T13:04:58Z

Genis: