fst.each.snp.hudson: Calculate the fixation index (Fst) for all SNPs between two...
In KRIS: Keen and Reliable Interface Subroutines for Bioinformatic Analysis
Description
Usage
Arguments
Value
References
See Also
Examples
View source: R/pvalue.R
View source: R/fst.R
Description
Fixation index (Fst) calculation was implemented using Hudson
method as in Bhatia (2013) and Hudson (1992).
Fixation index (Fst) calculation was implemented using Hudson
method as in Bhatia (2013) and Hudson (1992).
Usage
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fst.each.snp.hudson(X, idx.p1, idx.p2)
fst.each.snp.hudson(X, idx.p1, idx.p2)
Arguments
X
A matrix contains the number 0, 1, and 2 representing SNP in
additive coding. Rows represent individuals and columns represent SNP.
idx.p1
An integer vector contains the row indices of first population
in the matrix X.
idx.p2
An integer vector contains the row indices of second population
in the matrix X.
Value
The function returns a matrix of pairwise Fst values for all SNPs
between 2 specified groups.
The function returns a matrix of pairwise Fst values for all SNPs
between 2 specified groups.
References
Bhatia, G., Patterson, N., Sankararaman, S., and Price, A.L. (2013).
Estimating and interpreting FST: The impact of rare variants. Genome Res. 23,
1514-1521.
Hudson, R.R., Slatkin, M., and Maddison, W.P. (1992). Estimation of levels of
gene flow from DNA sequence data. Genetics 132, 583-589.
Bhatia, G., Patterson, N., Sankararaman, S., and Price, A.L. (2013).
Estimating and interpreting FST: The impact of rare variants. Genome Res. 23,
1514-1521.
Hudson, R.R., Slatkin, M., and Maddison, W.P. (1992). Estimation of levels of
gene flow from DNA sequence data. Genetics 132, 583-589.
See Also
Examples
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#Load simulated dataset
data(example_SNP)
idx1 <- which(sample_labels == 'pop1')
idx2 <- which(sample_labels == 'pop2')
fst.pairwise <- fst.each.snp.hudson(simsnp$snp, idx1, idx2)
#Print out the Fst values of the first three SNPs between 'pop1' and 'pop2'
print(fst.pairwise[1:3])
#Load simulated dataset
data(example_SNP)
idx1 <- which(sample_labels == 'pop1')
idx2 <- which(sample_labels == 'pop2')
fst.pairwise <- fst.each.snp.hudson(simsnp$snp, idx1, idx2)
#Print out the Fst values of the first three SNPs between 'pop1' and 'pop2'
print(fst.pairwise[1:3])
KRIS documentation built on Jan. 21, 2021, 5:08 p.m.
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Description Usage Arguments Value References See Also Examples
View source: R/pvalue.R View source: R/fst.R
Description
Fixation index (Fst) calculation was implemented using Hudson method as in Bhatia (2013) and Hudson (1992).
Fixation index (Fst) calculation was implemented using Hudson method as in Bhatia (2013) and Hudson (1992).
Usage
1 2 3 | fst.each.snp.hudson(X, idx.p1, idx.p2)
fst.each.snp.hudson(X, idx.p1, idx.p2)
|
Arguments
X |
A matrix contains the number 0, 1, and 2 representing SNP in additive coding. Rows represent individuals and columns represent SNP. |
idx.p1 |
An integer vector contains the row indices of first population in the matrix X. |
idx.p2 |
An integer vector contains the row indices of second population in the matrix X. |
Value
The function returns a matrix of pairwise Fst values for all SNPs between 2 specified groups.
The function returns a matrix of pairwise Fst values for all SNPs between 2 specified groups.
References
Bhatia, G., Patterson, N., Sankararaman, S., and Price, A.L. (2013). Estimating and interpreting FST: The impact of rare variants. Genome Res. 23, 1514-1521.
Hudson, R.R., Slatkin, M., and Maddison, W.P. (1992). Estimation of levels of gene flow from DNA sequence data. Genetics 132, 583-589.
Bhatia, G., Patterson, N., Sankararaman, S., and Price, A.L. (2013). Estimating and interpreting FST: The impact of rare variants. Genome Res. 23, 1514-1521.
Hudson, R.R., Slatkin, M., and Maddison, W.P. (1992). Estimation of levels of gene flow from DNA sequence data. Genetics 132, 583-589.
See Also
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | #Load simulated dataset
data(example_SNP)
idx1 <- which(sample_labels == 'pop1')
idx2 <- which(sample_labels == 'pop2')
fst.pairwise <- fst.each.snp.hudson(simsnp$snp, idx1, idx2)
#Print out the Fst values of the first three SNPs between 'pop1' and 'pop2'
print(fst.pairwise[1:3])
#Load simulated dataset
data(example_SNP)
idx1 <- which(sample_labels == 'pop1')
idx2 <- which(sample_labels == 'pop2')
fst.pairwise <- fst.each.snp.hudson(simsnp$snp, idx1, idx2)
#Print out the Fst values of the first three SNPs between 'pop1' and 'pop2'
print(fst.pairwise[1:3])
|
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