In WendyLiuLab/BiactivationFlow: What the package does (short line)

Protocol:

t = -24h: Seed cells
t = 0h: Stimulate cells with M1 and M2 stimuli, simultaneously.
t = 24h: Harvest 24 h condition. For remaining conditions, rinse and replace media with blank D10.
t = 48h: Harvest 48 h condition. (24 h of decay time)
t = 72h: Harvest 72 h condition. (48 h of decay time)
t = 96h: Harvest 96 h condition. (72 h of decay time)

library(FlowAnalysis)
library(dplyr)
library(ggplot2)
library(testthat)

# Load data
pulsechase = load_experiments("../data-raw/pulse-chase", cleanup=rename_fluorophores) %>%
  decorate_timecourse_from_filename()
expect_equal(length(unique(pulsechase$Experiment)), 2)

medians = pulsechase %>%
  group_by(Experiment, antibody, timepoint, m1_concentration, m2_concentration) %>%
  summarize(median.CD86=median(CD86), median.CD206=median(CD206))

Populations

Top facet row is LPS+IFN-$\gamma$ concentration in ng/ml. Bottom facet row is IL-4+IL-13 concentration in ng/ml.

for(experiment in unique(pulsechase$Experiment)) {
  g = pulsechase %>%
    filter(Experiment == experiment) %>%
    ggplot(aes(CD86)) +
      facet_grid(antibody+timepoint~m1_concentration+m2_concentration) +
      geom_density() +
      geom_vline(data=filter(medians, Experiment == experiment), mapping=aes(xintercept=median.CD86), alpha=0.5) +
      scale_x_continuous(trans=biexp_trans(lim=100, decade.size=800)) +
      theme_bw() +
      labs(title=sprintf("%s CD86", experiment))
  print(g)

  g = pulsechase %>%
    filter(Experiment == experiment) %>%
    ggplot(aes(CD206)) +
      facet_grid(antibody+timepoint~m1_concentration+m2_concentration) +
      geom_density() +
      geom_vline(data=filter(medians, Experiment == experiment), mapping=aes(xintercept=median.CD206), alpha=0.5) +
      scale_x_continuous(trans=biexp_trans(lim=100, decade.size=800)) +
      theme_bw() +
      labs(title=sprintf("%s CD206", experiment))
  print(g)
}

Medians vs. time

Column facets are IL-4+IL-13 concentration in ng/ml. Row facets are LPS+IFN-$\gamma$ concentration in ng/ml.

for(experiment in unique(pulsechase$Experiment)) {
  g = medians %>%
    filter(Experiment == experiment) %>%
    ggplot(aes(timepoint, median.CD86, color=antibody, group=antibody)) +
      facet_grid(m1_concentration~m2_concentration) +
      geom_point() +
      geom_line() +
      theme_bw() +
      labs(title=sprintf("%s CD86", experiment))
  print(g)

  g = medians %>%
    filter(Experiment == experiment) %>%
    ggplot(aes(timepoint, median.CD206, color=antibody, group=antibody)) +
      facet_grid(m1_concentration~m2_concentration) +
      geom_point() +
      geom_line() +
      theme_bw() +
      labs(title=sprintf("%s CD206", experiment))
  print(g)
}

Overview plots

pulsechase_normalized = normalize_by_positive_controls_timepoint(pulsechase)
medians_normalized = pulsechase_normalized %>%
  group_by(Experiment, antibody, timepoint, m1_concentration, m2_concentration) %>%
  summarize(median.CD86=median(CD86), median.CD206=median(CD206))

g = medians_normalized %>%
  filter(antibody == "exp") %>%
  ggplot(aes(timepoint, median.CD86, color=Experiment, group=Experiment)) +
    facet_grid(m1_concentration~m2_concentration) +
    geom_point() +
    geom_line() +
    stat_summary(mapping=aes(color=NULL, group=1), fun.y=mean, geom="point") +
    stat_summary(mapping=aes(color=NULL, group=1), fun.y=mean, geom="line") +
    theme_bw()
#print(g)

g = medians_normalized %>%
  filter(antibody == "exp") %>%
  ggplot(aes(timepoint, median.CD86, group=Experiment)) +
    facet_grid(m1_concentration~m2_concentration) +
    geom_point(alpha=0.2) +
    geom_line(alpha=0.2) +
    stat_summary(mapping=aes(color=NULL, group=1), fun.y=mean, geom="point") +
    stat_summary(mapping=aes(color=NULL, group=1), fun.y=mean, geom="line") +
    labs(x="Timepoint", y="CD86 intensity relative to 0.3x0") +
    ylim(0, NA) +
    theme_bw() +
    theme(text=element_text(size=8))
print(g)

g = medians_normalized %>%
  filter(antibody == "exp") %>%
  ggplot(aes(timepoint, median.CD206, color=Experiment, group=Experiment)) +
    facet_grid(m1_concentration~m2_concentration) +
    geom_point() +
    geom_line() +
    stat_summary(mapping=aes(color=NULL, group=1), fun.y=mean, geom="point") +
    stat_summary(mapping=aes(color=NULL, group=1), fun.y=mean, geom="line") +
    theme_bw()
#print(g)

g = medians_normalized %>%
  filter(antibody == "exp") %>%
  ggplot(aes(timepoint, median.CD206, group=Experiment)) +
    facet_grid(m1_concentration~m2_concentration) +
    geom_point(alpha=0.2) +
    geom_line(alpha=0.2) +
    stat_summary(mapping=aes(color=NULL, group=1), fun.y=mean, geom="point") +
    stat_summary(mapping=aes(color=NULL, group=1), fun.y=mean, geom="line") +
    labs(x="Timepoint", y="CD206 intensity relative to 0x1") +
    ylim(0, NA) +
    theme_bw() +
    theme(text=element_text(size=8))
print(g)