Analysing directional cell migration in skin wound healing

1 Assessment

This session is assessed using MCQs (questions highlighted below). The actual MCQs can be found on the BS1070/MB1080 Blackboard site under Assessments and Feedback/Data analysis MCQs. The deadline is listed there and on the front page of the BS1070/MB1080 blackboard site. This assessment contributes 5% of module marks. You will receive feedback on this assessment after the submission deadline.

2 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")
  1. That means if you typed that into the console you would get the same effect (with your filepath not mine).
  2. Have a look at the data it should have 120 observations of 4 variables.

3 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.

3.1 Analysis for today

For each of the two variables, I need you to

  1. A quick explore of your data (maybe dfSummary or summary if you are having problems with dfSummary) and a boxplot to quickly, visually check if treatment has an effect
  2. Carry out an ANOVA
  3. Check the assumptions of your model (autoplot)
  4. If significant and assumptions are met, do a tukey test, otherwise carry out a Kruskal wallis and then a dunn’s test
  5. A final plot (pretty boxplot?)

4 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)?