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

Load package and data

library(FlowAnalysis)
library(magrittr)
library(dplyr)
library(ggplot2)
names(simul_timecourse)

Density plots

simul_timecourse %>%
  filter(antibody == "exp") %>%
  group_by(Experiment) %>%
  do(plot=(ggplot(., aes(x=CD206, y=CD86)) +
             facet_grid(timepoint~m1_concentration+m2_concentration) +
             stat_density2d(aes(fill=..level..), geom="polygon") +
             scale_fill_gradient(low="navyblue", high="red") +
             scale_x_continuous(trans=biexp_trans(lim=100, decade.size=200)) +
             scale_y_continuous(trans=biexp_trans(lim=100, decade.size=200)) +
             coord_cartesian(xlim=c(-100,3e5), ylim=c(-100,3e5)) +
             ggtitle(.$Experiment) +
             theme_bw()) %T>%
       print)

Individual experiments (raw)

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

medians$group1 = paste(medians$m1_concentration, medians$antibody)
medians$group2 = paste(medians$m2_concentration, medians$antibody)

medians %>% group_by(Experiment) %>%
    do(
      cd206_plot=(ggplot(., aes(timepoint, CD206, shape=antibody, color=m1_concentration, group=group1)) +
                    facet_grid(m2_concentration~.) +
                    geom_point() +
                    geom_line() +
                    ggtitle(paste0(.$Experiment, ": CD206 vs M1 concentration")) +
                    theme_bw()) %T>%
        print,
      cd86_plot=(ggplot(., aes(timepoint, CD86, shape=antibody, color=m2_concentration, group=group2)) +
                   facet_grid(m1_concentration~.) +
                   geom_point() +
                   geom_line() +
                   ggtitle(paste0(.$Experiment, ": CD86 vs M2 concentration")) +
                   theme_bw()) %T>%
        print)

Normalized

normalize_by_null_condition_timepoint = function(df) {
  blank_isotype = df %>%
    filter(antibody == "exp", m1_concentration == 0, m2_concentration == 0, timepoint == "24h") %>%
    group_by(Experiment) %>%
    summarize(iso_CD206=median(CD206), iso_CD86=median(CD86))

  normalized = inner_join(df, blank_isotype, by="Experiment")
  normalized$CD206 = normalized$CD206 / normalized$iso_CD206
  normalized$CD86 = normalized$CD86 / normalized$iso_CD86

  normalized[! names(normalized) %in% c("iso_CD206", "iso_CD86")]
}

medians = simul_timecourse %>%
  normalize_by_positive_controls_timepoint %>%
  filter(antibody == "exp") %>%
  group_by(Experiment, timepoint, m1_concentration, m2_concentration) %>%
  summarize(CD206 = median(CD206), CD86 = median(CD86))

medians %>% group_by(Experiment) %>%
    do(
      cd206_plot=(ggplot(., aes(timepoint, CD206, color=m1_concentration, group=m1_concentration)) +
                    facet_grid(m2_concentration~.) +
                    geom_point() +
                    geom_line() +
                    ggtitle(paste0(.$Experiment, ": CD206 vs M1 concentration")) +
                    theme_bw()) %T>%
        print,
      cd86_plot=(ggplot(., aes(timepoint, CD86, color=m2_concentration, group=m2_concentration)) +
                   facet_grid(m1_concentration~.) +
                   geom_point() +
                   geom_line() +
                   ggtitle(paste0(.$Experiment, ": CD86 vs M2 concentration")) +
                   theme_bw()) %T>%
        print)

medians$M1xM2 = paste(medians$m1_concentration, medians$m2_concentration, sep="x")

medians %>% group_by(Experiment) %>%
  do(
    plot=(ggplot(., aes(CD86, CD206, color=M1xM2)) +
            geom_path(alpha=0.6, size=1) +
            geom_point(aes(size=timepoint)) +
            scale_size_discrete(range=c(3,7)) +
            theme_bw()) %T>% print
    )

g = ggplot(medians, aes(CD86, CD206, color=Experiment)) +
  facet_grid(timepoint~m1_concentration+m2_concentration) +
  geom_point() +
  theme_bw()
print(g)

median_means = medians %>%
  group_by(m1_concentration, m2_concentration, timepoint) %>%
  summarize(mean.CD86=mean(CD86), sd.CD86=sd(CD86),
            mean.CD206=mean(CD206), sd.CD206=sd(CD206),
            n=n()) %>%
  mutate(sem.CD86=sd.CD86/sqrt(n), sem.CD206=sd.CD206/sqrt(n)) %>%
  mutate(ymax.CD86=mean.CD86+sem.CD86, ymin.CD86=mean.CD86-sem.CD86,
         ymax.CD206=mean.CD206+sem.CD206, ymin.CD206=mean.CD206-sem.CD206,
         XbyX=paste(m1_concentration, m2_concentration, sep="x"))

g = ggplot(median_means, aes(mean.CD86, mean.CD206, color=XbyX, group=XbyX, shape=timepoint)) +
  geom_point(size=4) +
  geom_path() +
  geom_errorbar(aes(ymin=ymin.CD206, ymax=ymax.CD206)) +
  geom_errorbarh(aes(xmin=ymin.CD86, xmax=ymax.CD86)) +
  xlim(0, NA) + ylim(0, NA) +
  theme_bw()
print(g)