Pseudocode of the statical analysis with three groups according to the group descriptions.

# If n. of groups = 3 and n. of unique descriptions = 2, then perform
# the statistical analysis between singleton and each doubleton group.
# Else if n. of groups = 3 and descriptions are all identical/different,
# then perform the statistical analysis between each one of them.

if (NofGroups==3 & length(unique(groupdescriptions))==2) {
    statistical_analysis(singleton, doubleton_1)
    statistical_analysis(singleton, doubleton_2)
    plot_results
} else {
    statistical_analysis(group_1, group_2)
    statistical_analysis(group_1, group_3)
    statistical_analysis(group_2, group_3)
    plot_results
}

if(NofGroups == 3 & length(unique(groupdescriptions))==2){
  
  doubleton <- list()
  singleton <- list()
  
  if (groupdescriptions[1] == groupdescriptions[2]) {
     doubleton = c(unique(groupdescriptions[1], groupdescriptions[2]), 
                   groupnames[1], groupnames[2])
     singleton = c(groupdescriptions[3], groupnames[3])
  }  else if (groupdescriptions[2] == groupdescriptions[3]) {
     doubleton = c(unique(groupdescriptions[2], groupdescriptions[3]), 
                   groupnames[2], groupnames[3])
     singleton = c(groupdescriptions[1], groupnames[1])
  }  else {
     doubleton = c(unique(groupdescriptions[1], groupdescriptions[3]),  
                   groupnames[1], groupnames[3])
     singleton = c(groupdescriptions[2], groupnames[2])
  }
}

Given three descriptions, splits them into three variables. Two out of three of these are the same while the other is not: the single set to be compared to the two “identical” sets is emplaced in the singleton list while the other two sets are emplaced in the doubleton list.

-Introduced Sayani to the wonderful world of Drosophila

-Been doing some DTS coding

-Preparing flies to do optomotor response for Mathias Raß

Updates in DTS code

Refreshing dissection skills

DTS coding
-Added progressbar for data validation
-Updated the progress bar (see figure 1)
-Fixed bug with wrong sample size (see figure 2)
-Fixed bug with unorganized barplots (see figure 2)

Exp always to the right:
plotOMparams <- plotOMparams[order(plotOMparams$desc),]
plotOMparams$group <- factor(plotOMparams$group, levels=paste(unique(plotOMparams$group)))

Samplesize fix:
samplesizes.annotate(boxes, as.numeric(table(plotOMparams$desc)))

Progressbar:
progress <- c(round(l(100/(length(xml_list)))),round(flycount(100/(totalflies))))

Rescreening:
-Finished rescreening last Thursday. Started to evaluate the new data

Optomotor platform: Ran a few more tests to confirm that the machine was still working, it is. I also adjusted the 0 line so that it is at 0, by readjusting the “zero line” screw. Looks much better now but it is still not perfectly at 0. A difference 0.1 on the computer screen translates to 100 in the evaluation chart.

Optomotor platform:
Ran a few more tests to confirm that the 0 line is always at 0. Readjusted the “zero line” screw. Looks much better now. It is still not perfectly at 0 but a difference of 0.1 in the chart translates to 100 in the evaluation graph.

Recently I measured the optomotor response in T4/T5 flies. As expected, they did not respond to the optomotor stimulus as seen in the left chart below. However, statistical evaluation struggles to quantify this difference. It might be that this is because of the low sample size, or that we are using the wrong statistical analysis?

Mean or median?

A highly hypothetical scenario of how the distribution of dwelling times could be. Even if unrealistic, it still illustrates the problem of using median instead of mean.

Plotting the dwelling times as means

Grouped analysis of dwelling times

Cumming estimation plot

• Added a timer
• Previous version generated faulty xml files. This bug has been squashed (see image below)
• Display window of the software now also prints which direction the striped arena turns