Created
November 17, 2019 03:49
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hastaLaVista demo file for scoresExplorer
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# to install hastaLaVista package use the following: | |
# devtools::install_github("jwist/hastaLaVista") | |
# the simply load the library | |
library(hastaLaVista) | |
# we use the MetaboMate package for normalization | |
# and for multivariate analysis | |
# to install it use: | |
# devtools::install_github("kimsche/MetaboMate") | |
library(MetaboMate) | |
library(car) | |
library(corrplot) | |
# load demo dataset | |
data("bariatricRat") | |
metadata <- bariatricRat$metadata | |
# check that folers are there | |
# l = list.dirs("/run/media/jul/44B6-1E16/data/Julien Bariatric Rat", recursive = FALSE) | |
# l = as.numeric(unlist(lapply(l, function(x) strsplit(x, "/")[[1]][8]))) | |
# length(match(metadata$Experiment.Number, l)) | |
write.table(file = "./bariatricRatsMetadata.csv", metadata[, c(5, 1, 2, 3, 4, 6:ncol(metadata))], quote=FALSE, sep="\t", col.names = NA) | |
data("bariatricRat.binned.5") | |
metadata['JcampUrl'] <- paste0('http://127.0.0.1:5474/data/jcamp/', metadata$Experiment.Number, '-1.jdx') | |
Xn.binned <- bariatricRat.binned.5$binned.data | |
ppm.binned <- bariatricRat.binned.5$binned.ppm | |
matspec(ppm.binned, Xn.binned, shift=c(7,8)) | |
ID <- metadata$Sample.Label | |
group <- metadata$Class | |
metadata <- data.frame(metadata) | |
x <- matrix(Xn.binned, dim(Xn.binned)[1], dim(Xn.binned)[2]) | |
x_axis <- as.numeric( ppm.binned ) | |
color = sapply(group, function(x) getColor2(as.character(x))) | |
d = list() | |
c <- data.frame(ID = ID, | |
group = group, | |
color = color, | |
"_highlight" = seq_along(group) - 1, | |
dataMatrix = I(matrix( c(rbind(repRow(x_axis, nrow(x)), x)), nrow(x), ncol(x)*2)), | |
metadata = I(metadata), | |
check.names = FALSE | |
) | |
# do not modify variableName, the view is expecting it! | |
d <- appendData(data = d, variableName = "data", variable = c, type = "table") | |
# do not modify variableName, the view is expecting it! | |
d <- appendData(data = d, variableName = "xAxis", variable = x_axis, type = "table") | |
corX <- abs(cor(x)) | |
# do not modify variableName, the view is expecting it! | |
d <- appendData(data = d, variableName = "correlationMatrix", variable = corX, type = "table") | |
mod <- MetaboMate::pca(x) | |
chart12 <- data.frame("x" = mod@t[,1], | |
"y" = mod@t[,2], | |
"highlight" = seq_along(group) - 1, | |
"info"= ID, | |
"group" = as.numeric( group ), | |
"color" = color | |
) | |
# do not modify variableName, the view is expecting it! | |
d <- appendData( data = d, variableName = "score12", variable = chart12, type = "score") | |
d[['scores']] <- mod@t | |
d[['loadings']] <- cov(mod@t, x) | |
d[['loadingsColor']] <- abs(cor(mod@t, x)) | |
ellipse <- dataEllipse(mod@t[,1], mod@t[,2], levels=0.80) | |
ellipseChart <- data.frame("x" = ellipse[,1], | |
"y" = ellipse[,2], | |
"color" = rep('black', length(ellipse[,1]))) | |
# do not modify variableName, the view is expecting it! | |
d <- appendData( data = d, variableName = "ellipse", variable = ellipseChart, type = "color") | |
v <- new("visualization") | |
v@data <- "rat_bariatric_pcaExplorer.data.json" | |
v@view <- "scoresExplorer_2_0.view.json" | |
push(v, type="data", d) | |
visualize(v) |
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