blood_serum: Acute Exercise, Prostate Cancer Cell Growth, and Immune...

In npaterno/data_hunter: Data set repository for OpenIntro textbooks

Acute Exercise, Prostate Cancer Cell Growth, and Immune System Cell Growth.

Description

Blood serum is a component of blood important to the immune system. In this study, blood serum was collected from 10 men, both before and immediately after exercise. Each of these serum samples, two from each man, were then exposed to two types of cells. The first type, LNCaP, is a type of prostate cancer cell. The second type, NIH3T3, is an immune system cell. The growth of the two types of cells was recorded after 48 hours.

Usage

blood_serum

Format

A data frame with 40 rows and 4 variables.

patient

A number assigned to each patient.

cell

Type of cell, either "LNCaP" (prostate cancer cell) or "NIH3T3" (immune system cell).

serum

When the serum was collected, either "before" or "after" exercise.

growth

Cell proliferation, measured in arbitrary units. (Arbitrary units mean relative values are important but the absolute values are not.)

Source

Rundqvist H, Augsten M, et al. 2013. Effect of Acute Exercise on Prostate Cancer Cell Growth. PLOS One 8(7):e67579.

Examples

library(ggplot2)
head(blood_serum)
table(blood_serum$patient)
qplot(
  ifelse(serum == "after", 1, 0),
  growth,
  data = blood_serum,
  geom = "line",
  color = patient
) +
  facet_grid(cols = vars(cell)) +
  xlab("Before (0) and After (1)")


x <- subset(blood_serum, patient == 13 & cell == "NIH3T3")
blood_serum_change <- do.call(rbind.data.frame, by(
  blood_serum,
  data.frame(blood_serum$patient, blood_serum$cell),
  function(x) {
    x$change <- x$growth[x$serum == "after"] - x$growth[x$serum == "before"]
    x$growth <- NULL
    x$serum <- NULL
    return(x[1, ])
  }
))

# t-test methodologies follow.

# First, look at the groups separately, which are similar to the analyses
# done in the paper.
bsc_cancer <- subset(blood_serum_change, cell == "LNCaP")
t.test(bsc_cancer$change)
bsc_immune <- subset(blood_serum_change, cell == "NIH3T3")
t.test(bsc_immune$change)

# In this next test, we treat the immune cells as the controls for the
# cancer cells from the corresponding individual, and the results are
# no longer statistically different from zero. This is effectively a
# "difference of differences" analysis.
# (Not 100% sure this comparison is valid, particularly if the units
# of growth vary from one cell type to the next.)
immune_to_cancer_map <- match(bsc_cancer$patient, bsc_immune$patient)
t.test(bsc_immune$change[immune_to_cancer_map] - bsc_cancer$change)

# Visualize the differences between the two types of cells for each
# of the 10 individuals.
qplot(
  ifelse(cell == "NIH3T3", 0, 1),
  change,
  data = blood_serum_change,
  geom = "line",
  color = patient
) + xlab("Immune Cell (0) and Cancer Cell (1)")
# Another way to run the test using both cell types is with multiple
# regression. The result exactly matches the last t-test above.
m <- lm(
  change ~ cell + patient,
  data = blood_serum_change
)
summary(m)

npaterno/data_hunter documentation built on July 22, 2022, 10:20 a.m.