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EDN_ECP_summary.Rmd
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EDN_ECP_summary.Rmd
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---
title: "EDN_ECP_summary"
author: "Xiang Ji"
date: "January 12, 2016"
output: pdf_document
---
1, Read in tables
```{r}
rm(list=ls()) # clean up workspace
path <- "/Users/xji3/GitFolders/EDN_ECP/Summary/MG94"
summary.list <- c("_clock_summary",
"_nonclock_summary",
"_Force_clock_summary",
"_Force_nonclock_summary"
)
pair = c("EDN", "ECP")
for (target.summary in summary.list){
summary_file <- paste(path, "_EDN_ECP", target.summary, '.txt', sep = '')
all <- readLines(summary_file, n = -1)
row.names <- strsplit(all[length(all)], ' ')[[1]][-1]
col.name <- paste("MG94", target.summary, sep = "")
summary_mat <- as.matrix(read.table(summary_file,
row.names = row.names,
col.names = col.name))
assign(paste("MG94", target.summary, sep = ""), summary_mat)
}
ECP.EDN.MG94 <- cbind(MG94_nonclock_summary, MG94_clock_summary,
MG94_Force_nonclock_summary, MG94_Force_clock_summary)
ECP.EDN.MG94
```
2, Now show branch specific % changes due to IGC
```{r}
(ECP.EDN.MG94[26:33, ] + ECP.EDN.MG94[34:41, ])/(ECP.EDN.MG94[42:49, ] + ECP.EDN.MG94[26:33, ] + ECP.EDN.MG94[34:41, ])
```
3, % changes due to IGC in all branches
```{r}
colSums(ECP.EDN.MG94[26:33, ] + ECP.EDN.MG94[34:41, ])/colSums(ECP.EDN.MG94[42:49, ] + ECP.EDN.MG94[26:33, ] + ECP.EDN.MG94[34:41, ])
```
<!-- \newpage -->
<!-- 04212017 update -->
<!-- Now plot posterior log likelihood ratio: $ln(\frac{Pr(S_i = 1 | x)}{Pr(S_i = 0 | x)}$. -->
<!-- The derivatives are $\frac{{\partial \ln L}}{{\partial \ln {p_{tract}}}}$ for -->
<!-- the first order and $\frac{{\partial^2 \ln L}}{{\partial \ln {p_{tract}}}^2}$ for -->
<!-- second order. -->
<!-- The variance is calculated by: -->
<!-- $Var(ln(p_{tract}))=\frac{1}{I(ln(p_{tract}))} \approx - \frac{1}{\frac{{\partial^2 \ln L}}{{\partial \ln {p_{tract}}}^2}}$ -->
<!-- 95% confidence interval for $ln(p_{tract})$ is $ln(p_{tract}) \pm 1.96 * \sqrt {Var(\ln ({p_{tract}}))}$ -->
<!-- By transforming to $3.0 / p_{tract}$ to get the average tract length in nucleotide. -->
<!-- ```{r} -->
<!-- # plot one paralog -->
<!-- paralog = "EDN_ECP" -->
<!-- lnL.ratio <- as.vector(read.table(paste("./summary/", paralog, "_MG94_nonclock_HMM_log_posterior_ratio.txt", sep = ""))) -->
<!-- Viterbi.path <- as.vector(read.table(paste("./summary/", paralog, "_MG94_nonclock_HMM_Viterbi_path.txt", sep = ""))) -->
<!-- lnL.surface <- as.vector(read.table(paste("./summary/", paralog, "_MG94_nonclock_HMM_lnL_surface.txt", sep = ""))) -->
<!-- IGC.sw.lnL <- as.vector(read.table(paste("./summary/", paralog, "_MG94_nonclock_sw_lnL.txt", sep = ""))) -->
<!-- Force.sw.lnL <- as.vector(read.table(paste("./summary/Force_", paralog, "_MG94_nonclock_sw_lnL.txt", sep = ""))) -->
<!-- plot(lnL.ratio[, 1], xlab = "codon number", ylab = "log values", -->
<!-- type = "l", col = "black", lty = 1, -->
<!-- main = paste(paralog, " HMM result"), -->
<!-- ylim = c(min(-0.5, min(lnL.ratio)), max(lnL.ratio))) -->
<!-- lines(1:dim(Viterbi.path)[1], Viterbi.path[, 1], type = "S", lty = 2, col = "red") -->
<!-- #lines(1:dim(IGC.sw.lnL)[1], IGC.sw.lnL[, 2] - Force.sw.lnL[, 2], type = "l", lty = 3, col = "red") -->
<!-- legend(1, max(lnL.ratio), legend = c("log posterior ratio", "Viterbi path"), -->
<!-- lty = c(1, 2), col = c( "black", "red")) -->
<!-- plot(-lnL.surface[, 1], xlab = "tract length in nucleotide", ylab= "lnL", type = "l", col = "black", lty = 1, -->
<!-- main = paste(paralog, " lnL surface")) -->
<!-- summary.mat <- read.table("./HMM_tract_MG94_nonclock_summary.txt") -->
<!-- # Now calculate standard deviation of lnP -->
<!-- lnP <- log(3.0 / summary.mat[, 3]) -->
<!-- sd.lnP <- 1.0 / sqrt(-summary.mat[, 7]) -->
<!-- low.cf <- exp(lnP - 1.96 * sd.lnP) -->
<!-- up.cf <- exp(lnP + 1.96 * sd.lnP) -->
<!-- up.cf[up.cf > 1] <- 1.0 -->
<!-- summary.mat <- cbind(summary.mat, 3.0 / up.cf, 3.0 / low.cf) -->
<!-- rownames(summary.mat) <- c("EDN_ECP") -->
<!-- colnames(summary.mat) <- c("lnL", "max lnL", "tract length", -->
<!-- "Pr(S_0)", "Pr(S_1)", -->
<!-- "df", "d^2f", "c.i. tract length", "c.i tract length") -->
<!-- summary.mat -->
<!-- ``` -->
<!-- \newpage -->
<!-- 09132017 update -->
<!-- Now plot lnL surface of MG94+IS-IGC+HMM model. -->
<!-- The derivatives are $\frac{{\partial \ln L}}{{\partial \ln {p_{tract}}}}$ for -->
<!-- the first order and $\frac{{\partial^2 \ln L}}{{\partial \ln {p_{tract}}}^2}$ for -->
<!-- second order. -->
<!-- The variance is calculated by: -->
<!-- $Var(ln(p_{tract}))=\frac{1}{I(ln(p_{tract}))} \approx - \frac{1}{\frac{{\partial^2 \ln L}}{{\partial \ln {p_{tract}}}^2}}$ -->
<!-- 95% confidence interval for $ln(p_{tract})$ is $ln(p_{tract}) \pm 1.96 * \sqrt {Var(\ln ({p_{tract}}))}$ -->
<!-- By transforming to $3.0 / p_{tract}$ to get the average tract length in nucleotide. -->
<!-- ```{r} -->
<!-- # plot one paralog -->
<!-- paralog = "EDN_ECP" -->
<!-- lnL.ratio <- as.vector(read.table(paste("./summary/", paralog, "_Ind_MG94_HMM_Posterior_lnL.txt", sep = ""))) -->
<!-- Viterbi.path <- as.vector(read.table(paste("./summary/", paralog, "_Ind_MG94_HMM_Viterbi_array.txt", sep = ""))) -->
<!-- lnL.surface <- as.vector(read.table(paste("./plot/HMM_", paralog, "_lnL_1D_surface.txt", sep = ""))) -->
<!-- plot(1:dim(lnL.ratio)[1], lnL.ratio[, 2] - lnL.ratio[, 1], xlab = "codon number", ylab = "log values", -->
<!-- type = "l", col = "black", lty = 1, -->
<!-- main = paste(paralog, " HMM result"), -->
<!-- ylim = c(min(-0.5, min(lnL.ratio[, 2] - lnL.ratio[, 1])), max(lnL.ratio[, 2] - lnL.ratio[, 1]))) -->
<!-- lines(1:dim(Viterbi.path)[1], Viterbi.path[, 1], type = "S", lty = 2, col = "red") -->
<!-- legend(1, max(lnL.ratio[, 2] - lnL.ratio[, 1]), legend = c("log posterior ratio", "Viterbi path"), -->
<!-- lty = c(1, 2), col = c( "black", "red")) -->
<!-- plot.new() -->
<!-- plot(-lnL.surface[, 1],lnL.surface[, 2], xlab = "log tract length in codon", ylab= "lnL", type = "l", col = "black", lty = 1, -->
<!-- main = paste(paralog, " lnL surface")) -->
<!-- abline(v = log(6.7/3.), col = "red") -->
<!-- summary.mat <- read.table("./Summary/EDN_ECP_Ind_MG94_HMM_1D_summary.txt") -->
<!-- hessian <- read.table("./Summary/EDN_ECP_Ind_MG94_HMM_Hessian.txt") -->
<!-- # Now calculate standard deviation of lnP -->
<!-- lnP <- log(summary.mat[10,1]) -->
<!-- sd.lnP <- 1.0 / sqrt(-hessian[2, 1]) -->
<!-- low.cf <- exp(lnP - 1.96 * sd.lnP) -->
<!-- up.cf <- exp(lnP + 1.96 * sd.lnP) -->
<!-- up.cf[up.cf > 1] <- 1.0 -->
<!-- show.mat <- matrix(c(summary.mat[1,1], max(-lnL.surface[, 2]), 3.0/summary.mat[10, 1], -->
<!-- hessian[1,1], hessian[2,1], 3.0 / up.cf, 3.0 / low.cf), 1, 7) -->
<!-- rownames(show.mat) <- c("EDN_ECP") -->
<!-- colnames(show.mat) <- c("lnL", "max lnL", "tract length", -->
<!-- "df", "d^2f", "c.i. tract length", "c.i tract length") -->
<!-- show.mat -->
<!-- ``` -->
\newpage
12212017 update
HKY+PS-IGC results
```{r}
all <- readLines("./Summary/PSJS_HKY_EDN_ECP_One_rate_Guess_1_rv_SCOK_nonclock_summary.txt", n = -1)
col.names <- "Guess_1"
row.names <- strsplit(all[length(all)], ' ')[[1]][-1]
EDN.ECP.guess.1.result <- as.matrix(read.table("./Summary/PSJS_HKY_EDN_ECP_One_rate_Guess_1_rv_SCOK_nonclock_summary.txt", row.names = row.names, col.names = col.names))
all <- readLines("./Summary/PSJS_HKY_EDN_ECP_One_rate_Guess_2_rv_SCOK_nonclock_summary.txt", n = -1)
col.names <- "Guess_2"
row.names <- strsplit(all[length(all)], ' ')[[1]][-1]
EDN.ECP.guess.2.result <- as.matrix(read.table("./Summary/PSJS_HKY_EDN_ECP_One_rate_Guess_2_rv_SCOK_nonclock_summary.txt", row.names = row.names, col.names = col.names))
gradient.file <- "./Summary/PSJS_HKY_EDN_ECP_One_rate_Guess_1_nonclock_gradient.txt"
gradient.list <- read.table(gradient.file)
cat("Verify gradient ~ 0: Gradient = ", colSums(gradient.list), ". Gradient/Objective = ", colSums(gradient.list)/EDN.ECP.guess.1.result["ll",1], "\n")
Godambe.matrix.guess.1 <- read.table("./Summary/PSJS_HKY_EDN_ECP_One_rate_Guess_1_nonclock_godambe.txt")
gradient.file <- "./Summary/PSJS_HKY_EDN_ECP_One_rate_Guess_2_nonclock_gradient.txt"
gradient.list <- read.table(gradient.file)
cat("Verify gradient ~ 0: Gradient = ", colSums(gradient.list), ". Gradient/Objective = ", colSums(gradient.list)/EDN.ECP.guess.2.result["ll",1], "\n")
Godambe.matrix.guess.2 <- read.table("./Summary/PSJS_HKY_EDN_ECP_One_rate_Guess_2_nonclock_godambe.txt")
Results <- cbind(EDN.ECP.guess.1.result, EDN.ECP.guess.2.result)
Results
Godambe.inverse <- cbind(c(solve(Godambe.matrix.guess.1)/dim(gradient.list)[1]),
c(solve(Godambe.matrix.guess.2)/dim(gradient.list)[1]))
Godambe.inverse
# Guess 2 has higher lnL
which.max(Results["ll",])
# effective Tau
Results["init_rate", ] * Results["tract_length", ] * 3 / (1.0 + colSums(Results[c("r2", "r3"), ]))
Results["init_rate", ] * Results["tract_length", ] * 3 /exp(1.96*sqrt(Godambe.inverse[1, ])) / (1.0 + colSums(Results[c("r2", "r3"), ]))
Results["init_rate", ] * Results["tract_length", ] * 3 *exp(1.96*sqrt(Godambe.inverse[1, ])) / (1.0 + colSums(Results[c("r2", "r3"), ]))
# Tract length
Results["tract_length", ]
exp(log(Results["tract_length", ]-1.0)-1.96*sqrt(Godambe.inverse[4,]))+1.0
exp(log(Results["tract_length", ]-1.0)+1.96*sqrt(Godambe.inverse[4,]))+1.0
```
HKY + IS-IGC results
```{r}
all <- readLines("./Summary/JS_HKY_EDN_ECP_One_rate_rv_nonclock_summary.txt", n = -1)
col.names <- c("HKY+IS-IGC")
row.names <- strsplit(all[length(all)], ' ')[[1]][-1]
EDN.ECP.HKY.result <- as.matrix(read.table("./Summary/JS_HKY_EDN_ECP_One_rate_rv_nonclock_summary.txt", row.names = row.names, col.names = col.names))
all <- readLines("./Summary/Force_JS_HKY_EDN_ECP_One_rate_rv_nonclock_summary.txt", n = -1)
col.names <- c("Force_HKY+IS-IGC")
row.names <- strsplit(all[length(all)], ' ')[[1]][-1]
Force.EDN.ECP.HKY.result <- as.matrix(read.table("./Summary/Force_JS_HKY_EDN_ECP_One_rate_rv_nonclock_summary.txt", row.names = row.names, col.names = col.names))
gradient.file <- "./Summary/JS_HKY_EDN_ECP_One_rate_rv_nonclock_gradient.txt"
gradient.list <- read.table(gradient.file)
cat("Verify gradient ~ 0: Gradient = ", colSums(gradient.list), ". Gradient/Objective = ", colSums(gradient.list)/EDN.ECP.guess.1.result["ll",1], "\n")
Godambe.matrix <- read.table("./Summary/JS_HKY_EDN_ECP_One_rate_rv_nonclock_godambe.txt")
Godambe.JS <- cbind(diag(solve(Godambe.matrix)/dim(gradient.list)[1]))
show.mat <- c(EDN.ECP.HKY.result["ll", ],
EDN.ECP.HKY.result["ll", ] - Force.EDN.ECP.HKY.result["ll",],
EDN.ECP.HKY.result["Tau", ]*3.0/(1. + sum(EDN.ECP.HKY.result[c("r2", "r3"),])),
EDN.ECP.HKY.result["Tau",]/exp(1.96*sqrt(Godambe.JS[7, ]))*3.0/(1. + sum(EDN.ECP.HKY.result[c("r2", "r3"),])),
EDN.ECP.HKY.result["Tau",]*exp(1.96*sqrt(Godambe.JS[7, ]))*3.0/(1. + sum(EDN.ECP.HKY.result[c("r2", "r3"),])),
EDN.ECP.HKY.result["r2", ],
EDN.ECP.HKY.result["r2",]/exp(1.96*sqrt(Godambe.JS[5,])),
EDN.ECP.HKY.result["r2",]*exp(1.96*sqrt(Godambe.JS[5,])),
EDN.ECP.HKY.result["r3",],
EDN.ECP.HKY.result["r3",]/exp(1.96*sqrt(Godambe.JS[6,])),
EDN.ECP.HKY.result["r3",]*exp(1.96*sqrt(Godambe.JS[6,]))
)
names(show.mat) <- c("ll", "Diff", "Tau", "min", "max",
"r2", "min", "max",
"r3", "min", "max")
round(show.mat, digits = 2)
```
####12302017 %changes due to IGC
```{r}
num.IGC <- sum(EDN.ECP.HKY.result[20:28,])
num.Mut <- sum(EDN.ECP.HKY.result[c(29, 32:37), ])
# % changes due to IGC
num.IGC/(num.IGC+num.Mut)
```
Now save workspace.
```{r}
save.image("./EDN_ECP_Summary.RData")
```