Skip to content

Added shiny app and ms code #1

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
lmanubens wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Added shiny app and ms code #1

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
The table of contents is too big for display.
Diff view
Diff view
  •  
  •  
  •  
477 changes: 477 additions & 0 deletions lmg/0401_gold163_all_soma_sort/gold163_consensus_features.nfb
View file
Open in desktop

Large diffs are not rendered by default.

65 changes: 65 additions & 0 deletions lmg/3DIQ_tosubset_v3.R
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,65 @@
library(TTR)
library(EBImage)

load('shiny_app/subsetdata_v2.Rdata')
load('shiny_app/groupsdf.Rdata')
ids <- sapply(strsplit(as.character(groupsdf$paths),'/'), "[", 8)

data <- my_data

data$Correlation <- NULL
data$FocusScore <- NA
data$LocalFocusScore <- NULL
data$MADIntensity <- NA
data$MaxIntensity <- NA
data$MeanIntensity <- NA
data$MedianIntensity <- NA
data$MinIntensity <- NA
data$PercentMaximal <- NA
data$PercentMinimal <- NA
data$PowerLogLogSlope <- NULL
data$StdIntensity <- NA
data$ThresholdOtsu <- NA
data$TotalArea <- NULL
data$TotalIntensity <- NULL
data$SNR_mean <- NA
data$CNR_mean <- NA
data$SNR_otsu <- NA
data$CNR_otsu <- NA

for(i in unique(ids))
{
print(i)
# if(is.na(groupsdf$paths[ids==i])==FALSE){
IQ <- read.csv(paste0("../BigNeurongit/Data/gold163_IQ/",i,"/",i,".v3dpbd.ImageQuality.csv"),header=T)
# try(data$Correlation[ids==i] <- mean(as.numeric(unlist(acf(as.numeric(swc$V12),plot=F)[4:8]))[1:5]))
data$FocusScore[ids==i] <- IQ$FocusScore
# try(data$LocalFocusScore[ids==i] <- mean(runSD(as.numeric(swc$V12))/runMean(as.numeric(swc$V12)),na.rm=T))
data$MADIntensity[ids==i] <- IQ$MADIntensity
data$MaxIntensity[ids==i] <- IQ$MaxIntensity
data$MeanIntensity[ids==i] <- IQ$MeanIntensity
data$MedianIntensity[ids==i] <- IQ$MedianIntensity
data$MinIntensity[ids==i] <- IQ$MinIntensity
data$PercentMaximal[ids==i] <- IQ$PercentMaximal
data$PercentMinimal[ids==i] <- IQ$PercentMinimal
# try(spect <- spectrum(as.numeric(swc$V12,na.rm=T),plot=F))
# logm1 <- lm(log(spect$spec) ~ log(spect$freq))
# data$PowerLogLogSlope[ids==i] <- as.numeric(coef(logm1)[2])
data$StdIntensity[ids==i] <-IQ$StdIntensity
data$SNR_mean[ids==i] <- IQ$SNR_mean
data$CNR_mean[ids==i] <- IQ$CNR_mean
data$ThresholdOtsu[ids==i] <- IQ$ThresholdOtsu
data$SNR_otsu[ids==i] <- IQ$SNR_otsu
data$CNR_otsu[ids==i] <- IQ$CNR_otsu
# data$TotalArea[ids==i] <- length(as.numeric(swc$V12))
# data$TotalIntensity[ids==i] <- sum(as.numeric(swc$V12))
# }
}

sum(complete.cases(data))

my_data <- do.call(data.frame, # Replace Inf in data by NA
lapply(data,
function(x) replace(x, is.infinite(x), NA)))

save(my_data,file='shiny_app/subsetdata_v2_3d.Rdata')
165 changes: 165 additions & 0 deletions lmg/3D_image_features.csv
View file
Open in desktop

Large diffs are not rendered by default.

75 changes: 75 additions & 0 deletions lmg/3D_int_dists.R
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,75 @@
library(TTR)
library(EBImage)

load('shiny_app/subsetdata.Rdata')
load('shiny_app/groupsdf.Rdata')
ids <- sapply(strsplit(as.character(groupsdf$paths),'/'), "[", 8)

data <- my_data

data$Correlation <- NULL
data$FocusScore <- NA
data$LocalFocusScore <- NULL
data$MADIntensity <- NA
data$MaxIntensity <- NA
data$MeanIntensity <- NA
data$SNR_mean <- NA
data$CNR_mean <- NA
data$MedianIntensity <- NA
data$MinIntensity <- NA
data$PercentMaximal <- NA
data$PercentMinimal <- NA
data$PowerLogLogSlope <- NULL
data$StdIntensity <- NA
data$ThresholdOtsu <- NULL
data$SNR_otsu <- NA
data$CNR_otsu <- NA
data$TotalArea <- NULL
data$TotalIntensity <- NULL

for(i in unique(ids))
{
print(i)
im <- readImage(paste0("../BigNeurongit/Data/gold166_wids_vols_tiff/",i,"/",i,".v3dpbd.tiff"))
# swc <- read.csv2(paste0(gsub("gold_163_all_soma_sort_s1","gold_163_all_soma_sort_s1_int",groupsdf$paths[ids==i]),'.missing_branches_int.eswc'),sep=' ',header=F)
# try(data$Correlation[ids==i] <- mean(as.numeric(unlist(acf(im,plot=F)[4:8]))[1:5]))
# "Correlation."
# multiplied = np.dot(self.levels[:, np.newaxis] + 1, self.levels[np.newaxis] + 1)
# repeated = np.tile(multiplied[np.newaxis], (self.nobjects, 1, 1))
# summed = (repeated * self.P).sum(2).sum(1)
# h3 = (summed - self.mux * self.muy) / (self.sigmax * self.sigmay)
# h3[np.isinf(h3)] = 0
data$FocusScore[ids==i] <- sd(im)/mean(im)
# try(data$LocalFocusScore[ids==i] <- mean(runSD(im)/runMean(im),na.rm=T))
data$MADIntensity[ids==i] <- mad(im)
im_max <- max(im)
data$MaxIntensity[ids==i] <- im_max
im_mean <- mean(im)
noise_sd <- sd(im[im < im_mean])
data$MeanIntensity[ids==i] <- im_mean
data$SNR_mean[ids==i] <- mean(im[im > im_mean])/noise_sd
data$CNR_mean[ids==i] <- abs(im_max-im_mean)/noise_sd
data$MedianIntensity[ids==i] <- median(im)
data$MinIntensity[ids==i] <- min(im)
data$PercentMaximal[ids==i] <- sum(im==max(im))/length(im)
data$PercentMinimal[ids==i] <- sum(im==min(im))/length(im)
# spect <- spectrum(im,plot=F)
# logm1 <- lm(log(spect$spec) ~ log(spect$freq))
# data$PowerLogLogSlope[ids==i] <- as.numeric(coef(logm1)[2])
data$StdIntensity[ids==i] <-sd(im)
im_otsu <- otsu(as.Image(as.numeric(im)))
otsu_noise_sd <- sd(im[im < im_otsu])
data$ThresholdOtsu[ids==i] <- im_otsu
data$SNR_otsu[ids==i] <- mean(im[im > im_otsu])/otsu_noise_sd
data$CNR_otsu[ids==i] <- abs(im_max-im_otsu)/otsu_noise_sd
# data$TotalArea[ids==i] <- length(im)
# data$TotalIntensity[ids==i] <- sum(im)
}

sum(complete.cases(data))

my_data <- do.call(data.frame, # Replace Inf in data by NA
lapply(data,
function(x) replace(x, is.infinite(x), NA)))

save(my_data,file='shiny_app/subsetdata3D.Rdata')
88 changes: 88 additions & 0 deletions lmg/3D_int_dists_Hanbo.R
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,88 @@
# library(TTR)
# library(EBImage)
library(autothresholdr)
library(bioimagetools)

folder <- "../BigNeurongit/Data/gold166_wids_vols_tiff"
ids <- list.dirs(folder, full.names = F, recursive = F)

data <- data.frame(ids=ids)

data$FocusScore <- NA
data$MADIntensity <- NA
data$MaxIntensity <- NA
data$MeanIntensity <- NA
data$SNR_mean <- NA
data$CNR_mean <- NA
data$MedianIntensity <- NA
data$MinIntensity <- NA
data$PercentMaximal <- NA
data$PercentMinimal <- NA
data$StdIntensity <- NA
data$SNR_otsu <- NA
data$CNR_otsu <- NA

for(i in unique(ids))
{
print(i)
# im <- readImage(paste0(folder,"/",i,"/",i,".v3dpbd.tiff"))
im <- readTIF(paste0(folder,"/",i,"/",i,".v3dpbd.tiff"), as.is=T, channels=1)
gc()
im <- as.integer(im)
gc()
# im <- as.numeric(im)*256
# gc()
# im <- as.integer(im)
# gc()
# swc <- read.csv2(paste0(gsub("gold_163_all_soma_sort_s1","gold_163_all_soma_sort_s1_int",groupsdf$paths[ids==i]),'.missing_branches_int.eswc'),sep=' ',header=F)
# try(data$Correlation[ids==i] <- mean(as.numeric(unlist(acf(im,plot=F)[4:8]))[1:5]))
# "Correlation."
# multiplied = np.dot(self.levels[:, np.newaxis] + 1, self.levels[np.newaxis] + 1)
# repeated = np.tile(multiplied[np.newaxis], (self.nobjects, 1, 1))
# summed = (repeated * self.P).sum(2).sum(1)
# h3 = (summed - self.mux * self.muy) / (self.sigmax * self.sigmay)
# h3[np.isinf(h3)] = 0
# try(data$LocalFocusScore[ids==i] <- mean(runSD(im)/runMean(im),na.rm=T))
im_sd <- sd(im)
data$StdIntensity[ids==i] <- im-sd
gc()
data$MADIntensity[ids==i] <- mad(im)
im_max <- max(im)
data$MaxIntensity[ids==i] <- im_max
im_mean <- mean(im)
noise_sd <- sd(im[im < im_mean])
gc()
data$MeanIntensity[ids==i] <- im_mean
data$FocusScore[ids==i] <- im_sd^2/im_mean
gc()
data$SNR_mean[ids==i] <- mean(im[im > im_mean])/noise_sd
data$CNR_mean[ids==i] <- abs(im_max-im_mean)/noise_sd
data$MedianIntensity[ids==i] <- median(im)
data$MinIntensity[ids==i] <- min(im)
data$PercentMaximal[ids==i] <- sum(im==max(im))/length(im)
data$PercentMinimal[ids==i] <- sum(im==min(im))/length(im)
# spect <- spectrum(im,plot=F)
# logm1 <- lm(log(spect$spec) ~ log(spect$freq))
# data$PowerLogLogSlope[ids==i] <- as.numeric(coef(logm1)[2])
im_otsu <- auto_thresh(im,"Otsu")
gc()
# im_otsu <- otsu(im)
otsu_noise_sd <- sd(im[im < im_otsu[1]])
gc()
data$ThresholdOtsu[ids==i] <- im_otsu[1]
data$SNR_otsu[ids==i] <- mean(im[im > im_otsu[1]])/otsu_noise_sd
data$CNR_otsu[ids==i] <- abs(im_max-im_otsu[1])/otsu_noise_sd
# data$TotalArea[ids==i] <- length(im)
# data$TotalIntensity[ids==i] <- sum(im)
rm(im)
gc()
}

# sum(complete.cases(data))

my_data <- do.call(data.frame, # Replace Inf in data by NA
lapply(data,
function(x) replace(x, is.infinite(x), NA)))

save(my_data,file='3D_image_features.Rdata')
write.csv(my_data,file='3D_image_features.csv')
88 changes: 88 additions & 0 deletions lmg/3D_int_dists_standalone.R
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,88 @@
# library(TTR)
# library(EBImage)
library(autothresholdr)
library(bioimagetools)

folder <- "../BigNeurongit/Data/gold166_wids_vols_tiff"
ids <- list.dirs(folder, full.names = F, recursive = F)

data <- data.frame(ids=ids)

data$FocusScore <- NA
data$MADIntensity <- NA
data$MaxIntensity <- NA
data$MeanIntensity <- NA
data$SNR_mean <- NA
data$CNR_mean <- NA
data$MedianIntensity <- NA
data$MinIntensity <- NA
data$PercentMaximal <- NA
data$PercentMinimal <- NA
data$StdIntensity <- NA
data$SNR_otsu <- NA
data$CNR_otsu <- NA

for(i in unique(ids))
{
print(i)
# im <- readImage(paste0(folder,"/",i,"/",i,".v3dpbd.tiff"))
im <- readTIF(paste0(folder,"/",i,"/",i,".v3dpbd.tiff"), as.is=T, channels=1)
gc()
im <- as.integer(im)
gc()
# im <- as.numeric(im)*256
# gc()
# im <- as.integer(im)
# gc()
# swc <- read.csv2(paste0(gsub("gold_163_all_soma_sort_s1","gold_163_all_soma_sort_s1_int",groupsdf$paths[ids==i]),'.missing_branches_int.eswc'),sep=' ',header=F)
# try(data$Correlation[ids==i] <- mean(as.numeric(unlist(acf(im,plot=F)[4:8]))[1:5]))
# "Correlation."
# multiplied = np.dot(self.levels[:, np.newaxis] + 1, self.levels[np.newaxis] + 1)
# repeated = np.tile(multiplied[np.newaxis], (self.nobjects, 1, 1))
# summed = (repeated * self.P).sum(2).sum(1)
# h3 = (summed - self.mux * self.muy) / (self.sigmax * self.sigmay)
# h3[np.isinf(h3)] = 0
# try(data$LocalFocusScore[ids==i] <- mean(runSD(im)/runMean(im),na.rm=T))
im_sd <- sd(im)
data$StdIntensity[ids==i] <- im-sd
gc()
data$MADIntensity[ids==i] <- mad(im)
im_max <- max(im)
data$MaxIntensity[ids==i] <- im_max
im_mean <- mean(im)
noise_sd <- sd(im[im < im_mean])
gc()
data$MeanIntensity[ids==i] <- im_mean
data$FocusScore[ids==i] <- im_sd^2/im_mean
gc()
data$SNR_mean[ids==i] <- mean(im[im > im_mean])/noise_sd
data$CNR_mean[ids==i] <- abs(im_max-im_mean)/noise_sd
data$MedianIntensity[ids==i] <- median(im)
data$MinIntensity[ids==i] <- min(im)
data$PercentMaximal[ids==i] <- sum(im==max(im))/length(im)
data$PercentMinimal[ids==i] <- sum(im==min(im))/length(im)
# spect <- spectrum(im,plot=F)
# logm1 <- lm(log(spect$spec) ~ log(spect$freq))
# data$PowerLogLogSlope[ids==i] <- as.numeric(coef(logm1)[2])
im_otsu <- auto_thresh(im,"Otsu")
gc()
# im_otsu <- otsu(im)
otsu_noise_sd <- sd(im[im < im_otsu[1]])
gc()
data$ThresholdOtsu[ids==i] <- im_otsu[1]
data$SNR_otsu[ids==i] <- mean(im[im > im_otsu[1]])/otsu_noise_sd
data$CNR_otsu[ids==i] <- abs(im_max-im_otsu[1])/otsu_noise_sd
# data$TotalArea[ids==i] <- length(im)
# data$TotalIntensity[ids==i] <- sum(im)
rm(im)
gc()
}

# sum(complete.cases(data))

my_data <- do.call(data.frame, # Replace Inf in data by NA
lapply(data,
function(x) replace(x, is.infinite(x), NA)))

save(my_data,file='3D_image_features.Rdata')
write.csv(my_data,file='3D_image_features.csv')
Loading