In ome/rOMERO-gateway: OMERO R Gateway

Model yeast cell replication from timelapse image

Using the image YDA306_AGA1y_PRM1r_Mating from IDR to model the replication of yeast cells.

Load libraries

library(romero.gateway)
library(EBImage)

Connect to server

server <- OMEROServer(host = 'wss://idr.openmicroscopy.org/omero-ws', port = 443L, username='public', password='public')
server <- connect(server)
paste('Successfully logged in as', server@user$getUserName())

Load image

imageId <- 3491334
image <- loadObject(server, "ImageData", imageId)

Set # of timepoints and interval

(this information could be retrieved from the server too, but lets keep it simple)

tmax <- 42  # number of timepoints
tInMinutes <- 5  # separation between timepoints in minutes

Get the pixel values and display image

t <- as.integer(tmax / 2) # use half-time point
pixels <- getPixelValues(image, 1, t, 2)
ebi <- EBImage::Image(data = pixels, colormode = 'Grayscale')
img <- normalize(ebi)
EBImage::display(img)

Threshold the image

threshImg <- thresh(img, w=20, h=20, offset=0.05)
threshImg <- medianFilter(threshImg, 3)
threshImg <- fillHull(threshImg)
threshImg <- bwlabel(threshImg)
EBImage::display(colorLabels(threshImg))

Count the number of cells

count <- range(threshImg)
count[2]

Wrap up thresholding and counting into a function

countCells <- function (t) {
  pixels <- getPixelValues(image, 1, t, 2)
  ebi <- EBImage::Image(data = pixels, colormode = 'Grayscale')
  img <- normalize(ebi)
  threshImg <- thresh(img, w=20, h=20, offset=0.05)
  threshImg <- medianFilter(threshImg, 3)
  threshImg <- fillHull(threshImg)
  threshImg <- bwlabel(threshImg)
  count <- range(threshImg)
  as.integer(count[2])
}

Analyse all time points

#timepoints <- (1:tmax) # analyze every time point
timepoints <- seq(1,tmax,4) # only use every fourth time point (faster)
df <- data.frame(TimePoint=timepoints)
df$CellCount <- sapply(df$TimePoint, countCells)
df$TimePoint <- sapply(df$TimePoint, function(x) x * tInMinutes)
df

Plot the result

plot(df$TimePoint, df$CellCount, ylab = "Number of cells", xlab = "Time (in min)")

Fit a model

model <- lm(log(df$CellCount)~ df$TimePoint)
print(summary(model))

This gives us a model for the number of yeast cells N at time point t (in min): $N = e^{4.72710 + 0.00374 * t}$ .

Lets see how well it fits visually

plotTime <- as.list(df[,1])
predCounts <- exp(predict(model,plotTime))
plot(df$TimePoint, df$CellCount, ylab = "Number of cells", xlab = "Time (in min)")
lines(plotTime, predCounts,lwd=2, col = "red")

Disconnect from the server again

disconnect(server)

ome/rOMERO-gateway documentation built on Nov. 5, 2023, 9:29 a.m.