title: "DLC: Deep Layer Chromatography"
===========
Artificial neural network for planar chromatographic image evaluation for denoising and feature extraction
To see it running:
http://shinyapps.ernaehrung.uni-giessen.de/tlc_denoising
To install locally:
Install R https://www.r-project.org/
In the console, install the package with those commands
install.packages('devtools')
devtools::install_github('DimitriF/DLC')
Then, run this command to launch the application
DLC::run.tlc_denoising()
To use it from the console:
library(DLC)
## get the path of the image, change it with your own
appDir <- system.file("shinyapps", "tlc_denoising", package = "DLC")
pict_path = paste0(appDir,"/www/bioassay-1.jpg")
## read the file
data = f.read.image(pict_path, height=256)
## check the image
str(data) ## should be an array with 3 dimensions
## plot it
par(mar = c(0,0,2,0),mfrow = c(3,2),xaxt="n",yaxt="n",xaxs="i",yaxs="i",oma=c(0,0,2,0)) ## graphic parameter, use ?par for help
raster(data,main="Original chromatograms")
## deconstruct the data ## use ?deconstruct.convol for help
decon = deconstruct.convol(data,margin = 3,transform = F,conv_width = 2)
## train the model ## use ?rbm.train for help
model <- rbm.train(decon,hidden = 4,numepochs = 10,batchsize = 1000,learningrate = 0.1,momentum = 0.5,cd = 2,verbose = T)
## cross the mode both way
up = rbm.up(model, decon)
down = rbm.down(model,up)
## reconstruct ## use ?reconstruct.convol for help
recon = reconstruct.convol(down,margin=3,transform = F,dimension = dim(data),conv_width = 2)
str(recon)
raster(recon,main="Reconstruct chromatograms")
## extract features ## use ?reconstruct for help
features = reconstruct(up,margin=3,transform = F,dimension = dim(data))
str(features)
for(i in seq(4)){
raster(features[,,i],main=paste0("Extracted chromatograms ",i))
}
## add the title
mtext("Denoising and feature extraction of chromatograms",outer=T)
Note that the package contain other shiny applications and functions, they must be considered as experimental though.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.