196 lines
7.9 KiB
Julia
196 lines
7.9 KiB
Julia
#! /usr/bin/env julia
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# Installing Dependencies
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## using Pkg
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## Pkg.add("Distributed")
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## Pkg.add("DataFrames")
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## Pkg.add("CSV")
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## Pkg.add("SNaQ")
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## Pkg.add("PhyloNetworks")
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## Pkg.add("RCall")
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## Pkg.add("PhyloPlots")
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## Pkg.add("QuartetNetworkGoodnessFit")
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# Running SNaQ Analysis
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using PhyloNetworks, SNaQ;
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using Distributed;
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addprocs(9);
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@everywhere using PhyloNetworks, SNaQ;
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nruns = 100; # number of runs for each hmax
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astralfile = joinpath("..", "species_tree", "coal.tre");
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astraltree = readnewick(astralfile);
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### Reading RAxML gene trees and ASTRAL species tree
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### running in raxml_snaq/ folder
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# raxmltrees = joinpath("..", "..", "species_tree", "all.trees");
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# inputCF = readtrees2CF(raxmltrees);
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# net0 = snaq!(astraltree, inputCF, hmax=0, filename="net0", seed=123, outgroup="ZJ", runs=nruns);
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# net1 = snaq!(net0, inputCF, hmax=1, filename="net1", seed=123, outgroup="ZJ", runs=nruns);
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# net2 = snaq!(net1, inputCF, hmax=2, filename="net2", seed=123, outgroup="ZJ", runs=nruns);
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# net3 = snaq!(net2, inputCF, hmax=3, filename="net3", seed=123, outgroup="ZJ", runs=nruns);
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# net4 = snaq!(net3, inputCF, hmax=4, filename="net4", seed=123, outgroup="ZJ", runs=nruns);
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### Alternatively, reading in the input files from Bucky
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### running in input_snaq/ folder
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inputCFfile = joinpath("..", "bucky", "bucky_1", "input.mb.CFs.csv");
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inputCF = readtableCF(inputCFfile);
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net0 = snaq!(astraltree, inputCF, hmax=0, filename="net0", seed=123, outgroup="ZJ", runs=nruns);
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net1 = snaq!(net0, inputCF, hmax=1, filename="net1", seed=123, outgroup="ZJ", runs=nruns);
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net2 = snaq!(net1, inputCF, hmax=2, filename="net2", seed=123, outgroup="ZJ", runs=nruns);
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net3 = snaq!(net2, inputCF, hmax=3, filename="net3", seed=123, outgroup="ZJ", runs=nruns);
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net4 = snaq!(net3, inputCF, hmax=4, filename="net4", seed=12345, outgroup="ZJ", runs=nruns);
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# Plotting the SNaQ results
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using PhyloPlots, RCall;
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using PhyloNetworks, SNaQ;
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## Network scores vs. hmax
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net0 = readsnaqnetwork("net0.out");
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net1 = readsnaqnetwork("net1.out");
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net2 = readsnaqnetwork("net2.out");
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net3 = readsnaqnetwork("net3.out");
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net4 = readsnaqnetwork("net4.out");
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scores = [loglik(net0), loglik(net1), loglik(net2), loglik(net3), loglik(net4)];
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hmax = collect(0:4);
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R"pdf"("snaq_network_scores.pdf", width=12, height=8);
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R"plot"(hmax, scores, type="b", ylab="network score", xlab="hmax", col="blue");
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R"dev.off"();
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## Rerooting and rotating the networks for better visualization
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rootatnode!(net1, "ZJ");
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rootatnode!(net2, "ZJ");
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rootatnode!(net3, "ZJ");
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rootatnode!(net4, "ZJ");
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### rotate!(net1, -2);
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### rotate!(net2, -2);
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### rotate!(net3, -2);
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### rotate!(net4, -2);
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## Plotting the networks
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R"pdf"("snaq_networks_fulltree.pdf", width=14, height=10);
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R"layout(matrix(1:4, 2, 2, byrow=TRUE))"; # to get 4 plots into a single figure: 2 row, 2 columns
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R"par"(mar=[0, 0, 1.5, 0]); # for smaller margins
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xmin, xmax = PhyloPlots.PhyloPlots.edgenode_coordinates(net1, false, false)[13:14];
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xmax += (xmax - xmin) * 0.3;
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plot(net1, showgamma=true, tipoffset=0.1, xlim=[xmin, xmax]);
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R"mtext"(string("hmax=1, loglik=-", round(loglik(net1), digits=2)), font=2);
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xmin, xmax = PhyloPlots.PhyloPlots.edgenode_coordinates(net2, false, false)[13:14];
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xmax += (xmax - xmin) * 0.3;
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plot(net2, showgamma=true, tipoffset=0.1, xlim=[xmin, xmax]);
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R"mtext"(string("hmax=2, loglik=-", round(loglik(net2), digits=2)), font=2);
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xmin, xmax = PhyloPlots.PhyloPlots.edgenode_coordinates(net3, false, false)[13:14];
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xmax += (xmax - xmin) * 0.3;
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plot(net3, showgamma=true, tipoffset=0.1, xlim=[xmin, xmax]);
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R"mtext"(string("hmax=3, loglik=-", round(loglik(net3), digits=2)), font=2);
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xmin, xmax = PhyloPlots.PhyloPlots.edgenode_coordinates(net4, false, false)[13:14];
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xmax += (xmax - xmin) * 0.3;
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plot(net4, showgamma=true, tipoffset=0.1, xlim=[xmin, xmax]);
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R"mtext"(string("hmax=4, loglik=-", round(loglik(net4), digits=2)), font=2);
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R"dev.off"();
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R"pdf"("snaq_networks_majortree.pdf", width=14, height=10);
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R"layout(matrix(1:4, 2, 2, byrow=TRUE))"; # to get 4 plots into a single figure: 2 row, 2 columns
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R"par"(mar=[0, 0, 1.5, 0]); # for smaller margins
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xmin, xmax = PhyloPlots.PhyloPlots.edgenode_coordinates(net1, false, false)[13:14];
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xmax += (xmax - xmin) * 0.3;
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plot(net1, style=:majortree, showgamma=true, tipoffset=0.1, xlim=[xmin, xmax]);
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R"mtext"(string("hmax=1, loglik=-", round(loglik(net1), digits=2)), font=2);
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xmin, xmax = PhyloPlots.PhyloPlots.edgenode_coordinates(net2, false, false)[13:14];
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xmax += (xmax - xmin) * 0.3;
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plot(net2, style=:majortree, showgamma=true, tipoffset=0.1, xlim=[xmin, xmax]);
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R"mtext"(string("hmax=2, loglik=-", round(loglik(net2), digits=2)), font=2);
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xmin, xmax = PhyloPlots.PhyloPlots.edgenode_coordinates(net3, false, false)[13:14];
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xmax += (xmax - xmin) * 0.3;
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plot(net3, style=:majortree, showgamma=true, tipoffset=0.1, xlim=[xmin, xmax]);
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R"mtext"(string("hmax=3, loglik=-", round(loglik(net3), digits=2)), font=2);
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xmin, xmax = PhyloPlots.PhyloPlots.edgenode_coordinates(net4, false, false)[13:14];
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xmax += (xmax - xmin) * 0.3;
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plot(net4, style=:majortree, showgamma=true, tipoffset=0.1, xlim=[xmin, xmax]);
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R"mtext"(string("hmax=4, loglik=-", round(loglik(net4), digits=2)), font=2);
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R"dev.off"();
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## expected vs. observed quartet concordance factors
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using CSV, DataFrames, Distributions, Random, RCall;
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using PhyloNetworks, SNaQ;
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using QuartetNetworkGoodnessFit;
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using Statistics, Printf;
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inputCFfile = joinpath("..", "bucky", "bucky_1", "input.mb.CFs.csv");
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inputCF = readtableCF(inputCFfile);
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Random.seed!(123);
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jitter = 0.005;
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hmax_list = 0:3;
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R"pdf"("expCFs_obsvsfitted.pdf", width=6, height=6);
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R"par(mfrow=c(2,2), mar=c(4,4,3,1))"
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gof_results = DataFrame(h=Int[], pvalue_normal=Float64[], pvalue_empirical=Float64[], z_uncorrected=Float64[], sigma=Float64[], nsim=Int[])
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for h in hmax_list
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net = readsnaqnetwork("net$(h).out")
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topologymaxQpseudolik!(net, inputCF)
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df_long = fittedquartetCF(inputCF, :long)
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df_jittered = deepcopy(df_long)
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df_jittered.obsCF .+= rand(Uniform(-jitter/2, jitter/2), nrow(df_long))
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df_jittered.expCF .+= rand(Uniform(-jitter/2, jitter/2), nrow(df_long))
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res = quarnetGoFtest!(net, inputCF, false; seed=123, nsim=1000)
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z_obs = res[2]
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z_sim = sort!(res[6])
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p_emp = mean(z_sim .>= z_obs) # 经验 p 值：模拟 z 超过观测 z 的比例
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# 存表时两种 p 值都留，加上模拟分布的特征备查
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push!(gof_results, (h=h,
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pvalue_normal=res[1],
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pvalue_empirical=p_emp,
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z_uncorrected=z_obs,
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sigma=res[3],
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nsim=1000))
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ptxt = p_emp == 0 ? "p < 0.001 (emp.)" : @sprintf("p = %.3f (emp.)", p_emp)
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ttl = "h = $h, $ptxt"
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R"plot"(df_jittered.obsCF, df_jittered.expCF,
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xlim=[0, 1.05], ylim=[0, 1.05],
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xlab="quartet CF observed in gene trees",
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ylab="quartet CF expected from network",
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main=ttl,
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pch=16, col="#008080", cex=0.5)
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R"abline"(0, 1, col="#008080", lwd=0.5)
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end
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R"dev.off"();
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CSV.write("gof_results.csv", gof_results)
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println(gof_results)
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net_final = readsnaqnetwork("net0.out");
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topologymaxQpseudolik!(net_final, inputCF);
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df_long = fittedquartetCF(inputCF, :long);
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### Adding jitter to the points for better visualization
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df_jittered = deepcopy(df_long);
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df_jittered.obsCF .+= rand(Uniform(-jitter / 2, jitter / 2), nrow(df_long));
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df_jittered.expCF .+= rand(Uniform(-jitter / 2, jitter / 2), nrow(df_long));
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df_wide = fittedquartetCF(inputCF, :wide)
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println(names(df_wide))
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df_wide.outlier_p = res1[4]
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outliers = sort(df_wide[df_wide.outlier_p .< 0.01, :], :outlier_p)
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CSV.write("outlier_quartets_h1.csv", outliers)
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R"plot"(df_jittered.obsCF, df_jittered.expCF, xlab="quartet CF observed in gene trees",
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ylab="quartet CF expected from network", pch=16,
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col="#008080",
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cex=0.7);
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R"abline"(0, 1, col="#008080", lwd=0.5);
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R"dev.off"();
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# Goodness of fit of the SNaQ networks
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using QuartetNetworkGoodnessFit;
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res1 = quarnetGoFtest!(net_final, inputCF, true; seed=123, nsim=1000);
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res1[[1, 2, 3]] # p-value, uncorrected z, σ
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z_obs = res1[2]
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z_sim = sort!(res1[6])
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p_emp = mean(z_sim .>= z_obs)
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