1 Assessment

This session is assessed using MCQs (questions below) and by pasting in your script in three separate questions. The actual questions can be found on the MB1080 Blackboard site. The deadline is listed there and on the front page of the MB1080 blackboard site. This assessment contributes 5% of module marks. You will receive feedback on this assessment after the submission deadline.

2 R scripts

This week you will be assessed on your script. As a reminder, below is what I said about scripts in the first semester.

One of the great advantages of R is reproducibility of your analysis (here’s a nice explanation to read in your own time). This is a major concern of modern biological research. But for the minute, lets imagine a simpler example. Imagine you have worked several years on an important project. The end result is actually the culmination of a large number of different analyses. You did this in a GUI based program e.g. graphpad prism (man I love those pressy buttons). You send the paper off for review. 3 months later (and years after you did your first analysis), one reviewer suggests that you should base your results on medians rather than means. Oh lordy, you now have to repeat all your analysis, trying to remember in which order you pressed all those lovely buttons.

Now lets assume that rather than heeding the alluring but ultimately unfufilling siren call of graphpad prism (or spss or minitab or the countless other products from companies trying to take money off you), you instead stayed on the true path of R. Well all your analyses would be in an R script. Then all you have to do would be to find the line of code where you asked for means and change that to medians.

In Rstudio, go to File and then New File and then R script. An empty sheet appears. I tend to write all my commands in there. Then I highlight them and press Run to test them. Save this script often. Things I will be looking for in your script is 1) you are not actually just using the console (there will be lots of > and 2) you comment at least three lines.

3 Getting the data into R

There are lots of ways of getting data into R. Its one of the most annoying things about it as a beginner. But I’m assuming everyone is using Rstudio, so I’ll show you how I get data in when someone gives me a csv file.

1. Look at the right hand top window in Rstudio. See the Import Dataset. Use this to import the data as textfile or From Text (base) in newer versions. Make sure that the heading option is on.
2. Notice what you really did was displayed in the console.

celldata <- read.csv("~/Dropbox/Teaching/first_year_stats/sessions/8.mb1080/celldata.csv")

3. That means if you pasted that into the script you would get the same effect (with your filepath not mine).
4. Have a look at the data it should have 120 observations of 3 variables.

4 Data analysis for hand-in

Today is about examining data generated in an experiment to look at the influence of two factors on the movement of cells. you will hear more about this in a separate briefing within MB1080, but essentially the study is looking at three conditions:

• Cell migration in the absence of an electric field
• Cell migration in the presence of a DC electric field
• Cell migration in the presence of a DC electric field plus an inhibitor of the protein Rac1.

Rac 1 is a small GTPase known to play a role in migration. It affects the ability of cells to migrate, depending on cell type, as it alters the formation of the membrane extrusions (lamellipodia and filopodia) which allow the cell to both attach to the surrounding extracellular matrix to initiate forward motion and sense their environment to migrate in a particular direction.

In the analysis of this experiment you are asking two questions:

1. Does treatment have an effect on the speed at which cells migrate and which treatments are different?
2. Does treatment have an effect on the direction to which cells migrate and which treatments are different?

A good general framework for any analysis is Plot -> Model -> Check assumptions -> Interpret -> Plot again. We will follow this below.

5 MCQs

1. Does treatment have an effect on the speed at which cells migrate (with correctly reported statistics (either parametric or nonparametric are fine))?
2. If yes, which which treatments are different in how fast cells migrate (with correctly reported statistics)?
3. Does treatment have an effect on the direction to which cells migrate (with correctly reported statistics (either parametric or nonparametric are fine)?
4. If yes, which which treatments are different in which direction cells migrate (with correctly reported statistics)?