T-test

Comparison between two group means

In the paper PFOS induces proliferation, cell-cycle progression, and malignant phenotype in human breast epithelial cells, 2018 (PFOS phenotype paper), the adverse effects of PFOS are tested on a human cell line (MCF-10F), in multiple doses and exposure times. The adverse effects were assessed by investigating phenotypes such as proliferation, invasion and migration.

Here, we will reproduce the statistics and the figure 3. This figure shows the cell migration (A) and cell invasion (B) phenotypes of 10 µM PFOS for 72 h. Cell migration and invasion are by quantifying the cell nuclei count with DAPI, a staining method.

TipCompare to the original

Keep the figure 3 open in a separate tab during the tutorial so can easily compare it to ours.

Important

All code are ran inside R. If you need to install R follow this tutorial.

Installing packages

We’ll be needing some basic packages to help us along the way. Install forcats, readr and ggplot2.

# Once per machine
1install.packages("forcats")
install.packages("readr")
install.packages("ggplot2")

2library(forcats)
1
Install packages with install.packages("<package-name>"), use quotes
2
Load library with library(<package-name>)

Import the data

Lets import the dataset of nuclei count. The dataset is found here. We will download it directly into R with the read_csv() function (readr packages)

library(readr)
url <- "https://raw.githubusercontent.com/KarlssonLaboratory/mi8016-Research-Approaches/main/data/migration.csv"

# Import dataset
migration <- read_csv(url)
Important

The rest of the tutorial will use the migration dataset. Since the setup of the invasion dataset is identical you can use the same code to analysis as well.

url <- "https://raw.githubusercontent.com/KarlssonLaboratory/mi8016-Research-Approaches/main/data/invasion.csv"

# Import dataset
invasion <- read_csv(url)

Inspect the dataset with the head() and summary() functions:

1head(migration)
1
head() shows the top 6 rows of the data frame 
# A tibble: 6 × 2
  name    value
  <chr>   <dbl>
1 Control     0
2 Control     2
3 Control     2
4 Control     0
5 Control     0
6 Control     2
1summary(migration)
1
summary() shows ranges and types for each column 
     name               value       
 Length:32          Min.   : 0.000  
 Class :character   1st Qu.: 0.000  
 Mode  :character   Median : 2.000  
                    Mean   : 4.379  
                    3rd Qu.:10.000  
                    Max.   :17.000  
                    NA's   :3

The data frame is composed:

  • 2 columns : named “name” and “value”.
  • 3 values are NA’s.

To inspect the data frame fully, type the data frames name!

Use the forcats function fct_inorder() to make the name column in to factors (categorical) and in the order they appear.

library(forcats)
1migration$name <- fct_inorder(migration$name)
1
forcats::fct_inorder makes the categorices of the column the order they appear.

What happens if you do not include migration$name <- in the code above, and only write fct_inorder(migration$name)?

Statistics

Lets compare the means of the observed values for PFOS and control. Since there are two groups being compared a student’s t-test is suffice.

The t.test() function runs a t-test and takes the arguments for each group. t.test() returns a named list, with its elements accessable via the dollar sign $ notation.

Run the t-test and check the results:

res <- t.test(formula = value ~ name, data = migration)

res

    Welch Two Sample t-test

data:  value by name
t = -6.3497, df = 13.221, p-value = 2.347e-05
alternative hypothesis: true difference in means between group Control and group PFOS is not equal to 0
95 percent confidence interval:
 -10.285708  -5.070061
sample estimates:
mean in group Control    mean in group PFOS 
             0.937500              8.615385

To single out the p-value run:

res$p.value
[1] 2.346662e-05

The result is very significant!

Lets make a simple plot!

The plot() function generates boxes for our two columns, migration$Control and migration$PFOS:

plot(x = migration$name, y = migration$value)

Tip

Try plot(migration) aswell, any difference?

The plot() function also takes in additional arguments, like main, ylab and xlab. This will help us make the plot more understandable.

plot(
    migration,
1    main = "Migration",
    ylab = "Number of migrating cells",
    xlab = ""
)
1
main = plot title, ylab = text on y-axis, xlab = text on x-asis

All functions in R comes with documentation, where the function and all the arguments the function takes in are described. To open the documentation use the question mark ? before the name of the function, like ?plot will show:

plot                   package:base                    R Documentation

Generic X-Y Plotting

Description:

     Generic function for plotting of R objects.

     For simple scatter plots, ‘plot.default’ will be used.  However,
     there are ‘plot’ methods for many R objects, including
     ‘function’s, ‘data.frame’s, ‘density’ objects, etc.  Use
     ‘methods(plot)’ and the documentation for these. Most of these
     methods are implemented using traditional graphics (the ‘graphics’
     package), but this is not mandatory.

     . . .

To exit the documentation, press q.

Put a little flare on it

R comes with many packages to help customise plots. ggplot2 is the most popular one and builts on “layers” of geometic objects, called geom. The dataset is loaded via the ggplot() function and mapped via aesthetics (aes). Aesthetics contols x, y, outline colors, inside fill colors, sizes, shapes etc.

ggplot(<dataframe>, aes(x = <column_x_values>, y = <column_y_values>, colors = <column>)) +
  geom_<layer> +
  geom_<layer> +
  geom_<layer> +

  . . .

Lets style the plot with ggplot2! Run the code below:

# Every R session
library(ggplot2)

1ggplot(migration, aes(name, value, fill = name)) +
2    geom_bar(fun = "mean", stat = "summary", color = "black") +
3    theme_classic(base_size = 18) +
  labs(
4    title = "Migration",
    x = "",
    y = "Number of migrating cells")
1
By adding a plus sign + multiple rows are chained fo the plot.
2
geom_bar() uses the mean to calculate the top part of the bar.
3
theme_classic() is on of many themes for decorating a plot. base_size sets the overall size.
4
labs() holds arguments for text inputs for title, x-axis, y-axis, etc.

The legend is not needed, remove it with theme(legend.position = "none"):

ggplot(migration, aes(name, value, fill = name)) + 
  geom_bar(fun = "mean", stat = "summary", color = "black") +
  theme_classic(base_size = 18) +
  
1  theme(legend.position = "none") +
  
  labs(
    title = "Migration",
    x = "",
    y = "Number of migrating cells")
1
legend.position is set to “none” and therefor not rendered. Default value is legend.position = "right".

We need to add “whiskers” to the plot, which shows mean +/- standard deviation, i.e the variance. Use geom_errorbar:

ggplot(migration, aes(name, value, fill = name)) + 
  geom_bar(fun = "mean", stat = "summary", color = "black") +
  
  geom_errorbar(
    stat = "summary",
1    fun.min = function(x) mean(x) - sd(x),
2    fun.max = function(x) mean(x) + sd(x),
3    width = 0.4) +
  
  theme_classic(base_size = 18) +
  theme(legend.position = "none") +
  labs(
    title = "Migration",
    x = "",
    y = "Number of migrating cells")
1
Bottom whisker
2
Top whisker
3
Width of whiskars

Color the bars according to the original figure.

Manually set the colors:

bar_colors <- c(
  "white",
  "grey40"
)

1barplot(rep(1, length(bar_colors)), col = bar_colors)
1
Inspect the colors with a box-plot

Put it all together!

ggplot(migration, aes(name, value, fill = name)) + 
  geom_bar(fun = "mean", stat = "summary", color = "black") +
  geom_errorbar(
    stat = "summary",
    fun.min = function(x) mean(x) - sd(x),
    fun.max = function(x) mean(x) + sd(x),
    width = 0.4) +
  
1  scale_fill_manual(values = bar_colors) +
  
  theme_classic(base_size = 18) +
  theme(legend.position = "none") +
  labs(
    title = "Migration",
    x = "",
    y = "Number of migrating cells")
1
Set the fill colors manually