README.md
In mailund/admixture_graph: Admixture Graph Manipulation and Fitting
Admixture Graph Manipulation and Fitting
The package provides functionality to analyse and test admixture graphs against the f statistics described in the paper Ancient Admixture in Human History, Patterson et al., Genetics, Vol. 192, 1065--1093, 2012.
The f statistics --- f2, f3, and f4 --- extract information about correlations between gene frequencies in different populations (or single diploid genome samples), which can be informative about patterns of gene flow between these populations in form of admixture events. If a graph is constructed as a hypothesis for the relationship between the populations, equations for the expected values of the f statistics can be extracted, as functions of edge lenghs --- representing genetic drift --- and admixture proportions.
This package provides functions for extracting these equations and for fitting them against computed f statistics. It does not currently provide functions for computing the f statistics --- for that we refer to the ADMIXTOOLS software package.
Example
Below is a quick example of how the package can be used. The example uses data from polar bears and brown bears with a black bear as outgroup and is taken from Genomic evidence of geographically widespread effect of gene flow from polar bears into brown bears.
The BLK sample is the black bear, the PB sample is a polar bear, and the rest are brown bears.
I have taken the f statistics from Table 1 in the paper:
data(bears)
bears
#> W X Y Z D Z.value
#> 1 BLK PB Sweden Adm1 0.1258 12.8
#> 2 BLK PB Kenai Adm1 0.0685 5.9
#> 3 BLK PB Denali Adm1 0.0160 1.3
#> 4 BLK PB Sweden Adm2 0.1231 12.2
#> 5 BLK PB Kenai Adm2 0.0669 6.1
#> 6 BLK PB Denali Adm2 0.0139 1.1
#> 7 BLK PB Sweden Bar 0.1613 14.7
#> 8 BLK PB Kenai Bar 0.1091 8.9
#> 9 BLK PB Denali Bar 0.0573 4.3
#> 10 BLK PB Sweden Chi1 0.1786 17.7
#> 11 BLK PB Kenai Chi1 0.1278 11.3
#> 12 BLK PB Denali Chi1 0.0777 6.4
#> 13 BLK PB Sweden Chi2 0.1819 18.3
#> 14 BLK PB Kenai Chi2 0.1323 12.1
#> 15 BLK PB Denali Chi2 0.0819 6.7
#> 16 BLK PB Sweden Denali 0.1267 14.3
#> 17 BLK PB Kenai Denali 0.0571 5.6
#> 18 BLK PB Sweden Kenai 0.0719 9.6
The D column is the f4(W,X;Y,Z) statistic and the Z column is the Z-values obtained from a blocked jacknife (see Patterson et al. for details).
From the statistics we can see that the ABC bears (Adm, Bar and Chi) are closer related to the polar bears compared to the other brown bears. The paper explains this with gene flow from polar bears into the ABC bears and going further out from there, but we can also explain this by several waves of admixture from ancestral polar bears into brown bears:
leaves <- c("BLK", "PB",
"Bar", "Chi1", "Chi2", "Adm1", "Adm2",
"Denali", "Kenai", "Sweden")
inner_nodes <- c("R", "PBBB",
"Adm", "Chi", "BC", "ABC",
"x", "y", "z",
"pb_a1", "pb_a2", "pb_a3", "pb_a4",
"bc_a1", "abc_a2", "x_a3", "y_a4")
edges <- parent_edges(c(edge("BLK", "R"),
edge("PB", "pb_a1"),
edge("pb_a1", "pb_a2"),
edge("pb_a2", "pb_a3"),
edge("pb_a3", "pb_a4"),
edge("pb_a4", "PBBB"),
edge("Chi1", "Chi"),
edge("Chi2", "Chi"),
edge("Chi", "BC"),
edge("Bar", "BC"),
edge("BC", "bc_a1"),
edge("Adm1", "Adm"),
edge("Adm2", "Adm"),
admixture_edge("bc_a1", "pb_a1", "ABC", "a"),
edge("Adm", "ABC"),
edge("ABC", "abc_a2"),
admixture_edge("abc_a2", "pb_a2", "x", "b"),
edge("Denali", "x"),
edge("x", "x_a3"),
admixture_edge("x_a3", "pb_a3", "y", "c"),
edge("Kenai", "y"),
edge("y", "y_a4"),
admixture_edge("y_a4", "pb_a4", "z", "d"),
edge("Sweden", "z"),
edge("z", "PBBB"),
edge("PBBB", "R")))
bears_graph <- agraph(leaves, inner_nodes, edges)
plot(bears_graph, show_admixture_labels = TRUE)
#> fminbnd: Exiting: Maximum number of function evaluations has been exceeded
#> - increase MaxFunEvals option.
#> Current function value: 3027.37262644651
Fitting a graph to data
The graph makes predictions on how the f4 statistics should look. The graph parameters can be fit to observed statistics using the fit_graph function:
fit <- fit_graph(bears, bears_graph)
fit
#>
#> Call: inner_fit_graph(data, graph, point, Z.value, concentration, optimisation_options,
#> parameters, iteration_multiplier, qr_tol)
#>
#> None of the admixture proportions are properly fitted!
#> Not all of the admixture proportions are properly fitted!
#> See summary.agraph_fit for a more detailed analysis.
#>
#> Minimal error: 12.98523
You can get detailsabout the fit by calling the summary.agraph_fit function:
summary(fit)
#>
#> Call: inner_fit_graph(data, graph, point, Z.value, concentration, optimisation_options,
#> parameters, iteration_multiplier, qr_tol)
#>
#> None of the proportions {a, b, c, d} affect the quality of the fit!
#>
#> Optimal admixture proportions:
#> a b c d
#> 0.3666992 0.4977105 0.9565926 0.7986799
#>
#> Optimal edge lengths:
#> edge_R_BLK edge_R_PBBB edge_PBBB_z edge_PBBB_pb_a4
#> 0.00000000 0.00000000 0.00000000 0.07852837
#> edge_Adm_Adm1 edge_Adm_Adm2 edge_Chi_Chi1 edge_Chi_Chi2
#> 0.00000000 0.00000000 0.00000000 0.00000000
#> edge_BC_Bar edge_BC_Chi edge_ABC_Adm edge_ABC_bc_a1
#> 0.00000000 0.00000000 0.00000000 0.00000000
#> edge_x_Denali edge_x_abc_a2 edge_y_Kenai edge_y_x_a3
#> 0.00000000 0.00000000 0.00000000 0.00000000
#> edge_z_Sweden edge_z_y_a4 edge_pb_a1_PB edge_pb_a1_bc_a1
#> 0.00000000 0.00000000 0.00000000 0.00000000
#> edge_pb_a2_pb_a1 edge_pb_a2_abc_a2 edge_pb_a3_pb_a2 edge_pb_a3_x_a3
#> 0.13643125 0.00000000 0.02156832 0.00000000
#> edge_pb_a4_pb_a3 edge_pb_a4_y_a4 edge_bc_a1_BC edge_abc_a2_ABC
#> 0.04010857 0.00000000 0.00000000 0.00000000
#> edge_x_a3_x edge_y_a4_y
#> 0.00000000 0.00000000
#>
#> Solution to a homogeneous system of edge lengths with the optimal admixture proportions:
#> Adding any such solution to the optimal one will not affect the error.
#>
#> Free edge lengths:
#> edge_R_BLK
#> edge_R_PBBB
#> edge_PBBB_z
#> edge_Adm_Adm1
#> edge_Adm_Adm2
#> edge_Chi_Chi1
#> edge_Chi_Chi2
#> edge_BC_Bar
#> edge_BC_Chi
#> edge_ABC_Adm
#> edge_ABC_bc_a1
#> edge_x_Denali
#> edge_x_abc_a2
#> edge_y_Kenai
#> edge_y_x_a3
#> edge_z_Sweden
#> edge_z_y_a4
#> edge_pb_a1_PB
#> edge_pb_a1_bc_a1
#> edge_pb_a2_abc_a2
#> edge_pb_a3_x_a3
#> edge_pb_a4_y_a4
#> edge_bc_a1_BC
#> edge_abc_a2_ABC
#> edge_x_a3_x
#> edge_y_a4_y
#>
#> Bounded edge lengths:
#> edge_PBBB_pb_a4 = 0
#> edge_pb_a2_pb_a1 = 0
#> edge_pb_a3_pb_a2 = 0
#> edge_pb_a4_pb_a3 = 0
#>
#> Minimal error:
#> 12.98523
You can make a plot of the fit against the data by calling the plot.agraph_fit function:
plot(fit)
The plot shows the observed f4 statistics with error bars (in black) plus the predicted values from the graph.
The result of this is a ggplot2 object that you can modify by adding ggplot2 commands in the usual way.
Read the vignette admixturegraph for more examples.
mailund/admixture_graph documentation built on May 21, 2019, 11:06 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Admixture Graph Manipulation and Fitting
The package provides functionality to analyse and test admixture graphs against the f statistics described in the paper Ancient Admixture in Human History, Patterson et al., Genetics, Vol. 192, 1065--1093, 2012.
The f statistics --- f2, f3, and f4 --- extract information about correlations between gene frequencies in different populations (or single diploid genome samples), which can be informative about patterns of gene flow between these populations in form of admixture events. If a graph is constructed as a hypothesis for the relationship between the populations, equations for the expected values of the f statistics can be extracted, as functions of edge lenghs --- representing genetic drift --- and admixture proportions.
This package provides functions for extracting these equations and for fitting them against computed f statistics. It does not currently provide functions for computing the f statistics --- for that we refer to the ADMIXTOOLS software package.
Example
Below is a quick example of how the package can be used. The example uses data from polar bears and brown bears with a black bear as outgroup and is taken from Genomic evidence of geographically widespread effect of gene flow from polar bears into brown bears.
The BLK sample is the black bear, the PB sample is a polar bear, and the rest are brown bears.
I have taken the f statistics from Table 1 in the paper:
data(bears)
bears
#> W X Y Z D Z.value
#> 1 BLK PB Sweden Adm1 0.1258 12.8
#> 2 BLK PB Kenai Adm1 0.0685 5.9
#> 3 BLK PB Denali Adm1 0.0160 1.3
#> 4 BLK PB Sweden Adm2 0.1231 12.2
#> 5 BLK PB Kenai Adm2 0.0669 6.1
#> 6 BLK PB Denali Adm2 0.0139 1.1
#> 7 BLK PB Sweden Bar 0.1613 14.7
#> 8 BLK PB Kenai Bar 0.1091 8.9
#> 9 BLK PB Denali Bar 0.0573 4.3
#> 10 BLK PB Sweden Chi1 0.1786 17.7
#> 11 BLK PB Kenai Chi1 0.1278 11.3
#> 12 BLK PB Denali Chi1 0.0777 6.4
#> 13 BLK PB Sweden Chi2 0.1819 18.3
#> 14 BLK PB Kenai Chi2 0.1323 12.1
#> 15 BLK PB Denali Chi2 0.0819 6.7
#> 16 BLK PB Sweden Denali 0.1267 14.3
#> 17 BLK PB Kenai Denali 0.0571 5.6
#> 18 BLK PB Sweden Kenai 0.0719 9.6
The D column is the f4(W,X;Y,Z) statistic and the Z column is the Z-values obtained from a blocked jacknife (see Patterson et al. for details).
From the statistics we can see that the ABC bears (Adm, Bar and Chi) are closer related to the polar bears compared to the other brown bears. The paper explains this with gene flow from polar bears into the ABC bears and going further out from there, but we can also explain this by several waves of admixture from ancestral polar bears into brown bears:
leaves <- c("BLK", "PB",
"Bar", "Chi1", "Chi2", "Adm1", "Adm2",
"Denali", "Kenai", "Sweden")
inner_nodes <- c("R", "PBBB",
"Adm", "Chi", "BC", "ABC",
"x", "y", "z",
"pb_a1", "pb_a2", "pb_a3", "pb_a4",
"bc_a1", "abc_a2", "x_a3", "y_a4")
edges <- parent_edges(c(edge("BLK", "R"),
edge("PB", "pb_a1"),
edge("pb_a1", "pb_a2"),
edge("pb_a2", "pb_a3"),
edge("pb_a3", "pb_a4"),
edge("pb_a4", "PBBB"),
edge("Chi1", "Chi"),
edge("Chi2", "Chi"),
edge("Chi", "BC"),
edge("Bar", "BC"),
edge("BC", "bc_a1"),
edge("Adm1", "Adm"),
edge("Adm2", "Adm"),
admixture_edge("bc_a1", "pb_a1", "ABC", "a"),
edge("Adm", "ABC"),
edge("ABC", "abc_a2"),
admixture_edge("abc_a2", "pb_a2", "x", "b"),
edge("Denali", "x"),
edge("x", "x_a3"),
admixture_edge("x_a3", "pb_a3", "y", "c"),
edge("Kenai", "y"),
edge("y", "y_a4"),
admixture_edge("y_a4", "pb_a4", "z", "d"),
edge("Sweden", "z"),
edge("z", "PBBB"),
edge("PBBB", "R")))
bears_graph <- agraph(leaves, inner_nodes, edges)
plot(bears_graph, show_admixture_labels = TRUE)
#> fminbnd: Exiting: Maximum number of function evaluations has been exceeded
#> - increase MaxFunEvals option.
#> Current function value: 3027.37262644651
Fitting a graph to data
The graph makes predictions on how the f4 statistics should look. The graph parameters can be fit to observed statistics using the fit_graph function:
fit <- fit_graph(bears, bears_graph)
fit
#>
#> Call: inner_fit_graph(data, graph, point, Z.value, concentration, optimisation_options,
#> parameters, iteration_multiplier, qr_tol)
#>
#> None of the admixture proportions are properly fitted!
#> Not all of the admixture proportions are properly fitted!
#> See summary.agraph_fit for a more detailed analysis.
#>
#> Minimal error: 12.98523
You can get detailsabout the fit by calling the summary.agraph_fit function:
summary(fit)
#>
#> Call: inner_fit_graph(data, graph, point, Z.value, concentration, optimisation_options,
#> parameters, iteration_multiplier, qr_tol)
#>
#> None of the proportions {a, b, c, d} affect the quality of the fit!
#>
#> Optimal admixture proportions:
#> a b c d
#> 0.3666992 0.4977105 0.9565926 0.7986799
#>
#> Optimal edge lengths:
#> edge_R_BLK edge_R_PBBB edge_PBBB_z edge_PBBB_pb_a4
#> 0.00000000 0.00000000 0.00000000 0.07852837
#> edge_Adm_Adm1 edge_Adm_Adm2 edge_Chi_Chi1 edge_Chi_Chi2
#> 0.00000000 0.00000000 0.00000000 0.00000000
#> edge_BC_Bar edge_BC_Chi edge_ABC_Adm edge_ABC_bc_a1
#> 0.00000000 0.00000000 0.00000000 0.00000000
#> edge_x_Denali edge_x_abc_a2 edge_y_Kenai edge_y_x_a3
#> 0.00000000 0.00000000 0.00000000 0.00000000
#> edge_z_Sweden edge_z_y_a4 edge_pb_a1_PB edge_pb_a1_bc_a1
#> 0.00000000 0.00000000 0.00000000 0.00000000
#> edge_pb_a2_pb_a1 edge_pb_a2_abc_a2 edge_pb_a3_pb_a2 edge_pb_a3_x_a3
#> 0.13643125 0.00000000 0.02156832 0.00000000
#> edge_pb_a4_pb_a3 edge_pb_a4_y_a4 edge_bc_a1_BC edge_abc_a2_ABC
#> 0.04010857 0.00000000 0.00000000 0.00000000
#> edge_x_a3_x edge_y_a4_y
#> 0.00000000 0.00000000
#>
#> Solution to a homogeneous system of edge lengths with the optimal admixture proportions:
#> Adding any such solution to the optimal one will not affect the error.
#>
#> Free edge lengths:
#> edge_R_BLK
#> edge_R_PBBB
#> edge_PBBB_z
#> edge_Adm_Adm1
#> edge_Adm_Adm2
#> edge_Chi_Chi1
#> edge_Chi_Chi2
#> edge_BC_Bar
#> edge_BC_Chi
#> edge_ABC_Adm
#> edge_ABC_bc_a1
#> edge_x_Denali
#> edge_x_abc_a2
#> edge_y_Kenai
#> edge_y_x_a3
#> edge_z_Sweden
#> edge_z_y_a4
#> edge_pb_a1_PB
#> edge_pb_a1_bc_a1
#> edge_pb_a2_abc_a2
#> edge_pb_a3_x_a3
#> edge_pb_a4_y_a4
#> edge_bc_a1_BC
#> edge_abc_a2_ABC
#> edge_x_a3_x
#> edge_y_a4_y
#>
#> Bounded edge lengths:
#> edge_PBBB_pb_a4 = 0
#> edge_pb_a2_pb_a1 = 0
#> edge_pb_a3_pb_a2 = 0
#> edge_pb_a4_pb_a3 = 0
#>
#> Minimal error:
#> 12.98523
You can make a plot of the fit against the data by calling the plot.agraph_fit function:
plot(fit)
The plot shows the observed f4 statistics with error bars (in black) plus the predicted values from the graph.
The result of this is a ggplot2 object that you can modify by adding ggplot2 commands in the usual way.
Read the vignette admixturegraph for more examples.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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