In enbrown/eye-canalogram: Generalized Additive Modeling of Ophthalmic Fluorescence Canalograms

Load the Packages

library(EyeCanalogram)

Load Data

demo_directory <- system.file('extdata', 'Demo-Eye', 'Pre', package = 'EyeCanalogram')
demo_root <- file.path(demo_directory, 'Pre-')
images <- read.images(demo_root, low = 10)

Display raw data

plot(images)

Fit a Generalized Additive Model

fit <- fit.gam(images)

plot.images(fit, frames = c(1, 10, 20), masked = FALSE)

plot.images(fit, frames = c(1, 10, 20), masked = TRUE)

Plot the Fit Model

plot(fit)

plot.ring(fit, which = 'all')

plot.dot(fit, scale.range = c(1, 5.5), time.max = 50)

Process Pre- and Post-Treatment Data

Load Data

demo_directory <- system.file('extdata', 'Demo-Eye', 'Pre', package = 'EyeCanalogram')
demo_before <- file.path(demo_directory, 'Pre-')
before.images <- read.images(demo_before, low = 10)

demo_directory <- system.file('extdata', 'Demo-Eye', 'Post', package = 'EyeCanalogram')
demo_after <- file.path(demo_directory, 'Post-')
after.images <- read.images(demo_after, low = 10)

Fit Models

before <- fit.gam(before.images, r_bands = 3,
                  fit.grid = TRUE, fit.bands = TRUE)
after <- fit.gam(after.images, r_bands = 3,
                 fit.grid = TRUE, fit.bands = TRUE)

rm(before.images)
rm(after.images)

Plot figures

Create a ggplot theme

library(ggplot2)
mytheme <- ggplot2::theme_bw(base_size = 14) +
  ggplot2::theme(legend.position="right",
                 axis.ticks = ggplot2::element_blank(),
                 axis.text.x = ggplot2::element_blank(),
                 axis.text.y = ggplot2::element_blank(),
                 panel.border = ggplot2::element_blank(),
                 panel.grid = ggplot2::element_blank(),
                 panel.margin = grid::unit(1, 'mm'),
                 legend.margin = grid::unit(0, 'mm'),
                 legend.key.size = grid::unit(0.8, 'lines'),
                 legend.key = ggplot2::element_rect(fill = NULL, color = "white"),
                 plot.margin = grid::unit(c(0,0,0,0), 'mm'))

Plot the individual images

p1 <- plot.dot(before, scale.range = c(1, 5.5), time.max = 50) +
  mytheme 
print(p1)

p2 <- plot.ring(before, which = 'rate', resolution = 100, rate.max = 10) +
  mytheme 
print(p2)

p3 <- plot.dot(after, scale.range = c(1, 5.5), time.max = 50) +
  mytheme
print(p3)

p4 <- plot.ring(after, which = 'rate', resolution = 100, rate.max = 10) +
  mytheme
print(p4)

Plot all images together

EyeCanalogram:::multiplot(
  p1 + ylab("pre") + ggtitle("") + theme(axis.title=element_text(size = 32)),
  p2 + ggtitle(""),
  p3 + ylab("post") + ggtitle("") + theme(axis.title=element_text(size = 32)),
  p4 + ggtitle(""),
  layout = matrix(c(1,2,3,4), nrow = 2, byrow = TRUE)
)

Compute difference maps

delta <- delta.gam(before, after)

p1 <- plot(delta, which ='intensity') + mytheme
p2 <- plot(delta, which = 'rate') + mytheme
EyeCanalogram:::multiplot(
  p1, p2,
  layout = matrix(c(1,2), nrow = 1, byrow = TRUE)
)

Manipulate data (average pre and post)

Load support packages

library(dplyr)

Join all fitted datasets together

a <- before$gam$grid_metrics[, c('x', 'y', 'r_band', 'theta_band',
                                 'min_fit', 'mid_fit', 'max_fit',
                                 'mid_t', 'mid_rate')]
b <- after$gam$grid_metrics[, c('x', 'y', 'r_band', 'theta_band',
                                'min_fit', 'mid_fit', 'max_fit',
                                'mid_t', 'mid_rate')]

all <- rbind(a, b)

all <- all %>%
  dplyr::group_by(r_band, theta_band) %>%
  dplyr::summarise_each(dplyr::funs(mean)) %>%
  dplyr::ungroup()

Create a dummy grid with a hole for a circular cornea

size <- 64
grid <- expand.grid(x = -floor(size*4/3):ceiling(size*4/3), y = -size:size)
grid$r <- round(sqrt(grid$x**2 + grid$y**2) - size / 4)
grid$r_band <- round(grid$r / 1)
grid <- grid[grid$r_band >= 0,]
grid$theta <- atan2(-grid$x, -grid$y) * 180.0 / pi + 180.0
grid$theta_band <- 10 * round(grid$theta / 10)

ggplot(grid, aes(x = x, y = y, fill = r_band)) + geom_raster() + 
  coord_fixed(ratio = 1) + mytheme

Merge fitted metrics onto the dummy grid

all <- dplyr::inner_join(all, grid, by = c('r_band', 'theta_band'))
all <- all %>%
  dplyr::mutate(x = x.y, y = y.y) %>%
  dplyr::group_by(x, y) %>%
  dplyr::summarise_each(dplyr::funs(mean)) %>%
  dplyr::ungroup()

Restrict the range of values for the fitted metrics

all$mid_fit[all$mid_fit < 0.0] <- 0.0
all$mid_fit[all$mid_fit > 1.0] <- 1.0
all$mid_t[all$mid_t < 0] <- 0.0
all$mid_t[all$mid_t > 50] <- 50.0
all$mid_rate[all$mid_rate < 0.0] <- 0.0
all$mid_rate[all$mid_rate > 5.0] <- 5.0

Plot data

ggplot(all, aes(x = x, y = y, fill = mid_fit)) + geom_raster() +
  coord_fixed(ratio = 1) + ggtitle("Average Intensity") + xlab("") + ylab("") +
  scale_fill_gradient("Intensity", high = "red", limits = c(0.0, 1.0)) + mytheme

ggplot(all, aes(x = x, y = y, fill = mid_t)) + geom_raster() +
  coord_fixed(ratio = 1) + ggtitle("Average Filling Time") + xlab("") + ylab("") +
  scale_fill_gradient("Time", low = "red", high = "black", limits = c(0, 50)) + mytheme

ggplot(all, aes(x = x, y = y, fill = mid_rate)) + geom_raster() +
  coord_fixed(ratio = 1) + ggtitle("Average Filling Rate") + xlab("") + ylab("") +
  scale_fill_gradient("Rate", high = "red", limits = c(0.0, 5.0)) + mytheme

enbrown/eye-canalogram documentation built on April 10, 2020, 12:04 a.m.