20261002

2026-10-02 03:32:03 +08:00
parent e398109a10
commit 06954e0e49
21 changed files with 4336 additions and 8560 deletions
@@ -0,0 +1,195 @@
+#! /usr/bin/env julia
+# Installing Dependencies
+## using Pkg
+## Pkg.add("Distributed")
+## Pkg.add("DataFrames")
+## Pkg.add("CSV")
+## Pkg.add("SNaQ")
+## Pkg.add("PhyloNetworks")
+## Pkg.add("RCall")
+## Pkg.add("PhyloPlots")
+## Pkg.add("QuartetNetworkGoodnessFit")
+
+# Running SNaQ Analysis
+using PhyloNetworks, SNaQ;
+using Distributed;
+addprocs(9);
+@everywhere using PhyloNetworks, SNaQ;
+nruns = 100; # number of runs for each hmax
+astralfile = joinpath("..", "species_tree", "coal.tre");
+astraltree = readnewick(astralfile);
+
+### Reading RAxML gene trees and ASTRAL species tree
+### running in raxml_snaq/ folder
+# raxmltrees = joinpath("..", "..", "species_tree", "all.trees");
+# inputCF = readtrees2CF(raxmltrees);
+# net0 = snaq!(astraltree, inputCF, hmax=0, filename="net0", seed=123, outgroup="ZJ", runs=nruns);
+# net1 = snaq!(net0, inputCF, hmax=1, filename="net1", seed=123, outgroup="ZJ", runs=nruns);
+# net2 = snaq!(net1, inputCF, hmax=2, filename="net2", seed=123, outgroup="ZJ", runs=nruns);
+# net3 = snaq!(net2, inputCF, hmax=3, filename="net3", seed=123, outgroup="ZJ", runs=nruns);
+# net4 = snaq!(net3, inputCF, hmax=4, filename="net4", seed=123, outgroup="ZJ", runs=nruns);
+
+### Alternatively, reading in the input files from Bucky
+### running in input_snaq/ folder
+inputCFfile = joinpath("..", "bucky", "bucky_1", "input.mb.CFs.csv");
+inputCF = readtableCF(inputCFfile);
+net0 = snaq!(astraltree, inputCF, hmax=0, filename="net0", seed=123, outgroup="ZJ", runs=nruns);
+net1 = snaq!(net0, inputCF, hmax=1, filename="net1", seed=123, outgroup="ZJ", runs=nruns);
+net2 = snaq!(net1, inputCF, hmax=2, filename="net2", seed=123, outgroup="ZJ", runs=nruns);
+net3 = snaq!(net2, inputCF, hmax=3, filename="net3", seed=123, outgroup="ZJ", runs=nruns);
+net4 = snaq!(net3, inputCF, hmax=4, filename="net4", seed=12345, outgroup="ZJ", runs=nruns);
+
+
+# Plotting the SNaQ results
+using PhyloPlots, RCall;
+using PhyloNetworks, SNaQ;
+## Network scores vs. hmax
+net0 = readsnaqnetwork("net0.out");
+net1 = readsnaqnetwork("net1.out");
+net2 = readsnaqnetwork("net2.out");
+net3 = readsnaqnetwork("net3.out");
+net4 = readsnaqnetwork("net4.out");
+scores = [loglik(net0), loglik(net1), loglik(net2), loglik(net3), loglik(net4)];
+hmax = collect(0:4);
+R"pdf"("snaq_network_scores.pdf", width=12, height=8);
+R"plot"(hmax, scores, type="b", ylab="network score", xlab="hmax", col="blue");
+R"dev.off"();
+
+## Rerooting and rotating the networks for better visualization
+rootatnode!(net1, "ZJ");
+rootatnode!(net2, "ZJ");
+rootatnode!(net3, "ZJ");
+rootatnode!(net4, "ZJ");
+### rotate!(net1, -2);
+### rotate!(net2, -2);
+### rotate!(net3, -2);
+### rotate!(net4, -2);
+
+## Plotting the networks
+R"pdf"("snaq_networks_fulltree.pdf", width=14, height=10);
+R"layout(matrix(1:4, 2, 2, byrow=TRUE))"; # to get 4 plots into a single figure: 2 row, 2 columns
+R"par"(mar=[0, 0, 1.5, 0]);        # for smaller margins
+xmin, xmax = PhyloPlots.PhyloPlots.edgenode_coordinates(net1, false, false)[13:14];
+xmax += (xmax - xmin) * 0.3;
+plot(net1, showgamma=true, tipoffset=0.1, xlim=[xmin, xmax]);
+R"mtext"(string("hmax=1, loglik=-", round(loglik(net1), digits=2)), font=2);
+xmin, xmax = PhyloPlots.PhyloPlots.edgenode_coordinates(net2, false, false)[13:14];
+xmax += (xmax - xmin) * 0.3;
+plot(net2, showgamma=true, tipoffset=0.1, xlim=[xmin, xmax]);
+R"mtext"(string("hmax=2, loglik=-", round(loglik(net2), digits=2)), font=2);
+xmin, xmax = PhyloPlots.PhyloPlots.edgenode_coordinates(net3, false, false)[13:14];
+xmax += (xmax - xmin) * 0.3;
+plot(net3, showgamma=true, tipoffset=0.1, xlim=[xmin, xmax]);
+R"mtext"(string("hmax=3, loglik=-", round(loglik(net3), digits=2)), font=2);
+xmin, xmax = PhyloPlots.PhyloPlots.edgenode_coordinates(net4, false, false)[13:14];
+xmax += (xmax - xmin) * 0.3;
+plot(net4, showgamma=true, tipoffset=0.1, xlim=[xmin, xmax]);
+R"mtext"(string("hmax=4, loglik=-", round(loglik(net4), digits=2)), font=2);
+R"dev.off"();
+
+R"pdf"("snaq_networks_majortree.pdf", width=14, height=10);
+R"layout(matrix(1:4, 2, 2, byrow=TRUE))"; # to get 4 plots into a single figure: 2 row, 2 columns
+R"par"(mar=[0, 0, 1.5, 0]);        # for smaller margins
+xmin, xmax = PhyloPlots.PhyloPlots.edgenode_coordinates(net1, false, false)[13:14];
+xmax += (xmax - xmin) * 0.3;
+plot(net1, style=:majortree, showgamma=true, tipoffset=0.1, xlim=[xmin, xmax]);
+R"mtext"(string("hmax=1, loglik=-", round(loglik(net1), digits=2)), font=2);
+xmin, xmax = PhyloPlots.PhyloPlots.edgenode_coordinates(net2, false, false)[13:14];
+xmax += (xmax - xmin) * 0.3;
+plot(net2, style=:majortree, showgamma=true, tipoffset=0.1, xlim=[xmin, xmax]);
+R"mtext"(string("hmax=2, loglik=-", round(loglik(net2), digits=2)), font=2);
+xmin, xmax = PhyloPlots.PhyloPlots.edgenode_coordinates(net3, false, false)[13:14];
+xmax += (xmax - xmin) * 0.3;
+plot(net3, style=:majortree, showgamma=true, tipoffset=0.1, xlim=[xmin, xmax]);
+R"mtext"(string("hmax=3, loglik=-", round(loglik(net3), digits=2)), font=2);
+xmin, xmax = PhyloPlots.PhyloPlots.edgenode_coordinates(net4, false, false)[13:14];
+xmax += (xmax - xmin) * 0.3;
+plot(net4, style=:majortree, showgamma=true, tipoffset=0.1, xlim=[xmin, xmax]);
+R"mtext"(string("hmax=4, loglik=-", round(loglik(net4), digits=2)), font=2);
+R"dev.off"();
+
+## expected vs. observed quartet concordance factors
+using CSV, DataFrames, Distributions, Random, RCall;
+using PhyloNetworks, SNaQ;
+using QuartetNetworkGoodnessFit;
+using Statistics, Printf;
+
+inputCFfile = joinpath("..", "bucky", "bucky_1", "input.mb.CFs.csv");
+inputCF = readtableCF(inputCFfile);
+Random.seed!(123);
+jitter = 0.005;
+hmax_list = 0:3;
+
+R"pdf"("expCFs_obsvsfitted.pdf", width=6, height=6);
+R"par(mfrow=c(2,2), mar=c(4,4,3,1))"
+gof_results = DataFrame(h=Int[], pvalue_normal=Float64[], pvalue_empirical=Float64[], z_uncorrected=Float64[], sigma=Float64[], nsim=Int[])
+
+for h in hmax_list
+    net = readsnaqnetwork("net$(h).out")
+    topologymaxQpseudolik!(net, inputCF)
+
+    df_long = fittedquartetCF(inputCF, :long)
+    df_jittered = deepcopy(df_long)
+    df_jittered.obsCF .+= rand(Uniform(-jitter/2, jitter/2), nrow(df_long))
+    df_jittered.expCF .+= rand(Uniform(-jitter/2, jitter/2), nrow(df_long))
+
+    res = quarnetGoFtest!(net, inputCF, false; seed=123, nsim=1000)
+    z_obs = res[2]
+    z_sim = sort!(res[6])
+    p_emp = mean(z_sim .>= z_obs)      # 经验 p 值：模拟 z 超过观测 z 的比例
+
+    # 存表时两种 p 值都留，加上模拟分布的特征备查
+    push!(gof_results, (h=h,
+        pvalue_normal=res[1],
+        pvalue_empirical=p_emp,
+        z_uncorrected=z_obs,
+        sigma=res[3],
+        nsim=1000))
+
+
+    ptxt = p_emp == 0 ? "p < 0.001 (emp.)" : @sprintf("p = %.3f (emp.)", p_emp)
+    ttl = "h = $h, $ptxt"
+    R"plot"(df_jittered.obsCF, df_jittered.expCF,
+        xlim=[0, 1.05], ylim=[0, 1.05],
+        xlab="quartet CF observed in gene trees",
+        ylab="quartet CF expected from network",
+        main=ttl,
+        pch=16, col="#008080", cex=0.5)
+    R"abline"(0, 1, col="#008080", lwd=0.5)
+end
+
+R"dev.off"();
+
+CSV.write("gof_results.csv", gof_results)
+println(gof_results)
+
+
+net_final = readsnaqnetwork("net0.out");
+topologymaxQpseudolik!(net_final, inputCF);
+df_long = fittedquartetCF(inputCF, :long);
+
+### Adding jitter to the points for better visualization
+df_jittered = deepcopy(df_long);
+df_jittered.obsCF .+= rand(Uniform(-jitter / 2, jitter / 2), nrow(df_long));
+df_jittered.expCF .+= rand(Uniform(-jitter / 2, jitter / 2), nrow(df_long));
+df_wide = fittedquartetCF(inputCF, :wide)
+println(names(df_wide))
+df_wide.outlier_p = res1[4]
+outliers = sort(df_wide[df_wide.outlier_p .< 0.01, :], :outlier_p)
+CSV.write("outlier_quartets_h1.csv", outliers)
+
+
+R"plot"(df_jittered.obsCF, df_jittered.expCF, xlab="quartet CF observed in gene trees",
+    ylab="quartet CF expected from network", pch=16,
+    col="#008080",
+    cex=0.7);
+R"abline"(0, 1, col="#008080", lwd=0.5);
+R"dev.off"();
+
+# Goodness of fit of the SNaQ networks
+using QuartetNetworkGoodnessFit;
+res1 = quarnetGoFtest!(net_final, inputCF, true; seed=123, nsim=1000);
+res1[[1, 2, 3]] # p-value, uncorrected z, σ
+z_obs = res1[2]
+z_sim = sort!(res1[6])
+p_emp = mean(z_sim .>= z_obs)