Classically one can use fan plots to visualize the full profile of a given population over a set of conditions, but if you are interested in the effect of treatment on a given channel, you can `pivot` or `invert` the fan plot and visualize the effect of treatment over all populations

inverted_fan_plots.R

library(ggplot2)
library(dplyr)
library(tidyr)
library(cytofan)
library(bodenmiller)

## let's prepare some data:
cur.mat <- cbind(rbind(refPhenoMat,untreatedPhenoMat),
                 rbind(refFuncMat,untreatedFuncMat))
cur.annot <- bind_rows(refAnnots %>% 
                         mutate(Treatment='reference') %>% 
                         mutate_all(as.character),
                       untreatedAnnots %>% 
                         mutate(Treatment=as.character(Treatment)) %>% 
                         mutate_all(as.character)) %>% 
  mutate(Treatment=factor(Treatment),
         Treatment=relevel(Treatment,'reference'),
         Cells=factor(Cells,levels=levels(refAnnots$Cells)))

cur.df <- bind_cols(data.frame(cur.mat),cur.annot)

## classically one can use fan plots to visualize the full profile of a given population
## over a set of conditions:
cur.df %>% 
  filter(Cells=='cd4+') %>% 
  gather('Channel','value',one_of(colnames(cur.mat))) %>% 
  ggplot(aes(x=Channel,y=value))+
  geom_fan()+
  facet_grid(Treatment~.)+
  theme_light(base_size = 18)+
  theme(axis.text.x=element_text(angle=45,hjust=1))

## but if you are interested in the effect of treatment on a given channel,
## you can `pivot` or `invert` the fan plot and visualize
## the effect of treatment over all populations:
cur.ch <- 'pS6'
cur.annot %>% 
  mutate(value=cur.mat[,cur.ch]) %>% 
  ggplot(aes(x=Cells,y=value))+
  geom_fan()+
  facet_grid(Treatment~.)+
  theme_light(base_size = 18)+
  theme(axis.text.x=element_text(angle=45,hjust=1))

## you can add a reference line to make changes easier to visualize:
cur.annot %>% 
  mutate(value=cur.mat[,cur.ch]) %>% 
  ggplot(aes(x=Cells,y=value))+
  geom_fan()+
  geom_line(data=cur.annot %>% 
              mutate(value=cur.mat[,cur.ch]) %>% 
              filter(Treatment=='reference') %>%
              group_by(Cells) %>% 
              do((function(df,quants=c(0.25,0.5,0.75)) {
                res <- quantile(df$value,quants)
                res <- data.frame(Quantile=names(res),
                                  value=res)
                return(res)
              })(.)) %>%
              mutate(LineType=ifelse(Quantile=='50%','dashed','dotted')),
            aes(group=Quantile,linetype=LineType),
            col='blue')+
  scale_linetype_identity()+
  facet_grid(Treatment~.)+
  theme_light(base_size = 18)+
  theme(axis.text.x=element_text(angle=45,hjust=1))
## eg treatment with BCR/FcR-XL increases pS6 in B cells (`igm+` and `igm-`),
## cd14-hladrhigh, cd14-hladrmid and 
## monocytes (`cd14+hladr-`, `cd14+hladrhigh`, `cd14+hladrmid`)