.Rhistory

devtools::install_github("kkdey/Logolas")
library(Logolas)
library(Logolas)
library(grid)
library(gridBase)
library(ecostructure)
library(ggplot2)
library(Biobase)
library(dash)
xmat <- cbind(c(5, 0, 2, 0),
c(1, 1, 0, 1),
c(100, 100, 50, 100),
c(20, 50, 100, 10),
c(10, 10, 200, 20),
c(50, 54, 58, 53),
c(1,1,1,3),
c(2, 4, 1, 1))
rownames(xmat) <- c("A", "C", "G", "T")
colnames(xmat) <- paste0("pos-", 1:dim(xmat)[2])
xmat_norm <- apply(xmat, 2, function(x) return(x/sum(x)))
xmat
out <- dash(xmat, optmethod = "mixEM", verbose=FALSE, bf=TRUE)
grid.newpage()
layout.rows <- 1
layout.cols <- 2
top.vp <- viewport(layout=grid.layout(layout.rows, layout.cols,
widths=unit(rep(6,layout.cols), rep("null", 2)),
heights=unit(c(20,50), rep("lines", 2))))
plot_reg <- vpList()
l <- 1
for(i in 1:layout.rows){
for(j in 1:layout.cols){
plot_reg[[l]] <- viewport(layout.pos.col = j, layout.pos.row = i, name = paste0("plotlogo", l))
l <- l+1
}
}
plot_tree <- vpTree(top.vp, plot_reg)
color_profile = list("type" = "per_row",
"col" = RColorBrewer::brewer.pal(4,name ="Spectral"))
pushViewport(plot_tree)
seekViewport(paste0("plotlogo", 1))
logomaker(xmat_norm,color_profile = color_profile,
frame_width = 1,
pop_name = "pre dash PWM",
newpage = F)
seekViewport(paste0('plotlogo',2))
logomaker(out$posmean,color_profile = color_profile,
frame_width = 1,
pop_name = "post dash PWM",
newpage = F)
data <- get(load(system.file("extdata", "HimalayanBirdsData.rda",
package = "ecostructure")))
taxonomic_counts <- t(exprs(data))
m1 <- colMeans (taxonomic_counts)
rowSums(taxonomic_counts)
system.time(out <- dash(comp_data = taxonomic_counts+1,
optmethod = "mixEM",
mode = m1,
bf = TRUE,
verbose=TRUE))
data <- get(load(system.file("extdata", "HimalayanBirdsData.rda",
package = "ecostructure")))
taxonomic_counts <- t(exprs(data))
m1 <- colMeans(taxonomic_counts)
length(m1)
dim(taxonomic_counts)
system.time(out <- dash(comp_data = t(taxonomic_counts+1),
optmethod = "mixEM",
mode = m1,
bf = TRUE,
verbose=TRUE))
plot(out$posterior_weights[,1], log(rowSums(taxonomic_counts+1)), pch=20, col="red",
xlab = "posterior weights", ylab = "number of bird species in grid")
grid.newpage()
layout.rows <- 1
layout.cols <- 2
top.vp <- viewport(layout=grid.layout(layout.rows, layout.cols,
widths=unit(rep(6,layout.cols), rep("null", 2)),
heights=unit(c(20,50), rep("lines", 2))))
plot_reg <- vpList()
l <- 1
for(i in 1:layout.rows){
for(j in 1:layout.cols){
plot_reg[[l]] <- viewport(layout.pos.col = j, layout.pos.row = i, name = paste0("plotlogo", l))
l <- l+1
}
}
plot_tree <- vpTree(top.vp, plot_reg)
color_profile = list("type" = "per_row",
"col" = RColorBrewer::brewer.pal(4,name ="Spectral"))
pushViewport(plot_tree)
seekViewport(paste0("plotlogo", 1))
logomaker(xmat_norm,color_profile = color_profile,
frame_width = 1,
pop_name = "pre dash PWM",
newpage = F)
seekViewport(paste0('plotlogo',2))
logomaker(out$posmean,color_profile = color_profile,
frame_width = 1,
pop_name = "post dash PWM",
newpage = F)
xmat <- cbind(c(5, 0, 2, 0),
c(1, 1, 0, 1),
c(100, 100, 50, 100),
c(20, 50, 100, 10),
c(10, 10, 200, 20),
c(50, 54, 58, 53),
c(1,1,1,3),
c(2, 4, 1, 1))
rownames(xmat) <- c("A", "C", "G", "T")
colnames(xmat) <- paste0("pos-", 1:dim(xmat)[2])
xmat_norm <- apply(xmat, 2, function(x) return(x/sum(x)))
xmat
out <- dash(xmat, optmethod = "mixEM", verbose=FALSE, bf=TRUE)
grid.newpage()
layout.rows <- 1
layout.cols <- 2
top.vp <- viewport(layout=grid.layout(layout.rows, layout.cols,
widths=unit(rep(6,layout.cols), rep("null", 2)),
heights=unit(c(20,50), rep("lines", 2))))
plot_reg <- vpList()
l <- 1
for(i in 1:layout.rows){
for(j in 1:layout.cols){
plot_reg[[l]] <- viewport(layout.pos.col = j, layout.pos.row = i, name = paste0("plotlogo", l))
l <- l+1
}
}
plot_tree <- vpTree(top.vp, plot_reg)
color_profile = list("type" = "per_row",
"col" = RColorBrewer::brewer.pal(4,name ="Spectral"))
pushViewport(plot_tree)
seekViewport(paste0("plotlogo", 1))
logomaker(xmat_norm,color_profile = color_profile,
frame_width = 1,
pop_name = "pre dash PWM",
newpage = F)
seekViewport(paste0('plotlogo',2))
logomaker(out$posmean,color_profile = color_profile,
frame_width = 1,
pop_name = "post dash PWM",
newpage = F)
grid.newpage()
layout.rows <- 1
layout.cols <- 2
top.vp <- viewport(layout=grid.layout(layout.rows, layout.cols,
widths=unit(rep(6,layout.cols), rep("null", 2)),
heights=unit(c(20,50), rep("lines", 2))))
plot_reg <- vpList()
l <- 1
for(i in 1:layout.rows){
for(j in 1:layout.cols){
plot_reg[[l]] <- viewport(layout.pos.col = j, layout.pos.row = i, name = paste0("plotlogo", l))
l <- l+1
}
}
plot_tree <- vpTree(top.vp, plot_reg)
color_profile = list("type" = "per_row",
"col" = RColorBrewer::brewer.pal(4,name ="Spectral"))
pushViewport(plot_tree)
seekViewport(paste0("plotlogo", 1))
logomaker(xmat_norm,color_profile = color_profile,
frame_width = 1,
pop_name = "pre dash PWM",
newpage = F)
seekViewport(paste0('plotlogo',2))
logomaker(out$posmean,color_profile = color_profile,
frame_width = 1,
pop_name = "post dash PWM",
newpage = F)
grid.newpage()
layout.rows <- 1
layout.cols <- 2
top.vp <- viewport(layout=grid.layout(layout.rows, layout.cols,
widths=unit(rep(6,layout.cols), rep("null", 2)),
heights=unit(c(20,50), rep("lines", 2))))
plot_reg <- vpList()
l <- 1
for(i in 1:layout.rows){
for(j in 1:layout.cols){
plot_reg[[l]] <- viewport(layout.pos.col = j, layout.pos.row = i, name = paste0("plotlogo", l))
l <- l+1
}
}
plot_tree <- vpTree(top.vp, plot_reg)
color_profile = list("type" = "per_row",
"col" = RColorBrewer::brewer.pal(4,name ="Spectral"))
pushViewport(plot_tree)
seekViewport(paste0("plotlogo", 1))
logomaker(xmat_norm,color_profile = color_profile,
frame_width = 1,
pop_name = "pre dash PWM",
newpage = F)
seekViewport(paste0('plotlogo',2))
logomaker(out$posmean,color_profile = color_profile,
frame_width = 1,
pop_name = "post dash PWM",
newpage = F)
grid.newpage()
layout.rows <- 1
layout.cols <- 2
top.vp <- viewport(layout=grid.layout(layout.rows, layout.cols,
widths=unit(rep(6,layout.cols), rep("null", 2)),
heights=unit(c(20,50), rep("lines", 2))))
plot_reg <- vpList()
l <- 1
for(i in 1:layout.rows){
for(j in 1:layout.cols){
plot_reg[[l]] <- viewport(layout.pos.col = j, layout.pos.row = i, name = paste0("plotlogo", l))
l <- l+1
}
}
plot_tree <- vpTree(top.vp, plot_reg)
color_profile = list("type" = "per_row",
"col" = RColorBrewer::brewer.pal(4,name ="Spectral"))
pushViewport(plot_tree)
seekViewport(paste0("plotlogo", 1))
logomaker(xmat_norm,color_profile = color_profile,
frame_width = 1,
pop_name = "pre dash PWM",
newpage = F)
seekViewport(paste0('plotlogo',2))
logomaker(out$posmean,color_profile = color_profile,
frame_width = 1,
pop_name = "post dash PWM",
newpage = F)
xmat <- cbind(c(5, 0, 2, 0),
c(1, 1, 0, 1),
c(100, 100, 50, 100),
c(20, 50, 100, 10),
c(10, 10, 200, 20),
c(50, 54, 58, 53),
c(1,1,1,3),
c(2, 4, 1, 1))
rownames(xmat) <- c("A", "C", "G", "T")
colnames(xmat) <- paste0("pos-", 1:dim(xmat)[2])
xmat_norm <- apply(xmat, 2, function(x) return(x/sum(x)))
xmat
out <- dash(xmat, optmethod = "mixEM", verbose=FALSE, bf=TRUE)
grid.newpage()
layout.rows <- 1
layout.cols <- 2
top.vp <- viewport(layout=grid.layout(layout.rows, layout.cols,
widths=unit(rep(6,layout.cols), rep("null", 2)),
heights=unit(c(20,50), rep("lines", 2))))
plot_reg <- vpList()
l <- 1
for(i in 1:layout.rows){
for(j in 1:layout.cols){
plot_reg[[l]] <- viewport(layout.pos.col = j, layout.pos.row = i, name = paste0("plotlogo", l))
l <- l+1
}
}
plot_tree <- vpTree(top.vp, plot_reg)
color_profile = list("type" = "per_row",
"col" = RColorBrewer::brewer.pal(4,name ="Spectral"))
pushViewport(plot_tree)
seekViewport(paste0("plotlogo", 1))
logomaker(xmat_norm,color_profile = color_profile,
frame_width = 1,
pop_name = "pre dash PWM",
newpage = F)
seekViewport(paste0('plotlogo',2))
logomaker(out$posmean,color_profile = color_profile,
frame_width = 1,
pop_name = "post dash PWM",
newpage = F)
mat <- Matrix::rsparsematrix(nrow = 500, ncol = 100, density = 0.3)
mat2 <- as.matrix(mat)
mat2[mat2 != 0] = 1
m1 <- colMeans(as.matrix(mat2))
system.time(out <- dash(comp_data = mat2,
optmethod = "mixEM",
mode = m1,
def_positions = list("center" = Inf, "null" = 1, "corner" = 1),
concentration = c(Inf, 100, 50, 20, 10, 5, 2, 1),
weight = list("center" = 100, "null" = 1, "corner" = 1),
bf = TRUE,
verbose=TRUE))
mat <- Matrix::rsparsematrix(nrow = 500, ncol = 100, density = 0.3)
mat2 <- as.matrix(mat)
mat2[mat2 != 0] = 1
m1 <- colMeans(as.matrix(mat2))
system.time(out <- dash(comp_data = mat2,
optmethod = "mixEM",
mode = m1,
bf = TRUE,
verbose=TRUE))
mat <- Matrix::rsparsematrix(nrow = 500, ncol = 100, density = 0.3)
mat2 <- as.matrix(mat)
mat2[mat2 != 0] = 1
m1 <- colMeans(as.matrix(mat2))
system.time(out <- dash(comp_data = t(mat2),
optmethod = "mixEM",
mode = m1,
bf = TRUE,
verbose=TRUE))
out
which(out$center_prob_local > 0.99)
dim(mat2)
length(which(out$center_prob_local > 0.99))/dim(mat2)[1]
knit_with_parameters('~/Documents/dash/vignettes/dash.Rmd')
xmat <- cbind(c(5, 0, 2, 0),
c(1, 1, 0, 1),
c(100, 100, 50, 100),
c(20, 50, 100, 10),
c(10, 10, 200, 20),
c(50, 54, 58, 53),
c(1,1,1,3),
c(2, 4, 1, 1))
rownames(xmat) <- c("A", "C", "G", "T")
colnames(xmat) <- paste0("pos-", 1:dim(xmat)[2])
xmat <- xmat[1:2,]
xmat_norm <- apply(xmat, 2, function(x) return(x/sum(x)))
xmat
out <- dash(xmat, optmethod = "mixEM", verbose=FALSE, bf=TRUE)
out
library(metagenomeSeq)
source("https://bioconductor.org/biocLite.R")
biocLite("metagenomeSeq")
library(metagenomeSeq)
dat <- data("lungData")
dat
data(lungData
)
lungData
data("lungData")
lungData
pData(lungData)
counts <- exprs(lungData)
counts <- data(lungData)
dim(counts)
counts
featureData(lungData)
ff <- fData(lungData)
dim(ff)
ff
MRcounts(lungData)
dim(MRcounts(lungData))
data(mouseData)
mouseData
pData(mouseData)
dd <- MRcounts(mouseData)
dd
rowSums(dd)
colSums(dd)
mat <- rbind(c(5, 0, 2, 0),
#'              c(1, 1, 0, 1),
#'              c(100, 100, 50, 100),
#'              c(20, 50, 100, 10),
#'              c(10, 10, 200, 20),
#'              c(50, 54, 58, 53),
#'              c(1,1,1,3),
#'              c(2, 4, 1, 1))
#' mat <- rbind(c(5, 0, 2, 0),
#'              c(1, 1, 0, 1),
#'              c(100, 100, 50, 100),
#'              c(20, 50, 100, 10),
#'              c(10, 10, 200, 20),
#'              c(50, 54, 58, 53),
#'              c(1,1,1,3),
#'              c(2, 4, 1, 1))
source('~/Documents/dash/R/dash.R')
mat <- rbind(c(5, 0, 2, 0),
c(1, 1, 0, 1),
c(100, 100, 50, 100),
c(20, 50, 100, 10),
c(10, 10, 200, 20),
c(50, 54, 58, 53),
c(1,1,1,3),
c(2, 4, 1, 1))
out <- dash(xmat, optmethod = "mixEM", verbose=TRUE)
out <- dash(xmat, optmethod = "w_mixEM", verbose=TRUE)
mat <- rbind(c(5, 0, 2, 0),
c(1, 1, 0, 1),
c(100, 100, 50, 100),
c(20, 50, 100, 10),
c(10, 10, 200, 20),
c(50, 54, 58, 53),
c(1,1,1,3),
c(2, 4, 1, 1))
out <- dash(mat, optmethod = "mixEM", verbose=TRUE)
out <- dash(mat, optmethod = "w_mixEM", verbose=TRUE)
library(ashr)
mat <- rbind(c(5, 0, 2, 0),
c(1, 1, 0, 1),
c(100, 100, 50, 100),
c(20, 50, 100, 10),
c(10, 10, 200, 20),
c(50, 54, 58, 53),
c(1,1,1,3),
c(2, 4, 1, 1))
out <- dash(mat, optmethod = "mixEM", verbose=TRUE)
out <- dash(mat, optmethod = "w_mixEM", verbose=TRUE)
mat <- rbind(c(5, 0, 2, 0),
c(1, 1, 0, 1),
c(100, 100, 50, 100),
c(20, 50, 100, 10),
c(10, 10, 200, 20),
c(50, 54, 58, 53),
c(1,1,1,3),
c(2, 4, 1, 1))
library(dash)
out <- dash(mat, optmethod = "mixEM", verbose=TRUE)
out <- dash(mat, optmethod = "w_mixEM", verbose=TRUE)
source('~/Documents/dash/R/dash.R')
mat <- rbind(c(5, 0, 2, 0),
c(1, 1, 0, 1),
c(100, 100, 50, 100),
c(20, 50, 100, 10),
c(10, 10, 200, 20),
c(50, 54, 58, 53),
c(1,1,1,3),
c(2, 4, 1, 1))
out <- dash(mat, optmethod = "mixEM", verbose=TRUE)
out <- dash(mat, optmethod = "w_mixEM", verbose=TRUE)
install.packages("CorShrink")
library(CorShrink)
CorShrinkMatrix()
CorShrinkMatrix
library(CorShrink)
setwd("~/Documents/dash/vignettes/test")
dat <- get(load("reads_all_1_93297593_93307481.Robj"))
length(dat)
dat[[1]]
dim(dat[[1]])
dim(dat[[2]])
dim(dat[[3]])
length(dat[[3]])
dat[[3]]
dat[[4]]
dat[[5]]
dat[[6]]
dat[[7]]
dat[[8]]
dat[[9]]
dat[[1]]
dat[[1]][1:5,1:5]
class(dat[[1]])
dat[[1]]
dat[[1]][[1]]
dat[[1]][[2]]
dim(dat[[1]][[2]])
adipose_data <- dat[[1]][2]
dim(adipose_data)
plot(adipose_data[1,])
adipose_data <- dat[[1]][[2]]
dim(adipose_data)
plot(adipose_data[1,])
sig <- adipose_data[1,]
sig <- adipose_data[1,]
n = length(sig)
J = log2(n)
n
J
dmat = matrix(0, nrow=J+1, ncol=n)
dim(dmat)
dmat[1,] = sig
#dmat[1,] = as.matrix(sig)
dmat2 = matrix(0, nrow=J, ncol=2*n) #the parent table
dim(dmat2)
nD = 2^(J-D);
nDo2 = nD/2;
twonD = 2*nD;
D <- 0
nD = 2^(J-D);
nDo2 = nD/2;
twonD = 2*nD;