HIV Flow Cytometry

About the Data

Analysis of FACS data from cells derived from 5-6 mL of cerebrospinal fluid (CSF) or 1-2 million peripheral blood cells from the same individuals. All subjects were virally-suppressed people with HIV. CSF-derived cells were processed fresh. PBMCs were analyzed from frozen aliquots.

Load the Data

library(ggplot2)
library(cowplot)
library(tidyverse)

data <- read.csv("20220817_02_STATS.csv", stringsAsFactors = TRUE)
CSF <- data %>% filter(Type == "CSF")
PBMC <- data %>% filter(Type == "PBMC")
data[,3:4]

Plots

data %>% select(1:6) %>% pivot_longer(cols = starts_with("perc"),names_to = "perc.clean", values_to = "percent") %>% 
  ggplot(., aes(x = perc.clean, y = percent, fill = Type)) + 
  geom_boxplot(outlier.shape = NA) +
  geom_point(position = position_dodge(width = 0.75)) + theme_cowplot()  + scale_fill_manual(values = c("cornflowerblue", "firebrick2")) +
  ylab("% of total") + xlab("") + ylim(0,100) + scale_x_discrete(labels= c("lymphoid", "myeloid"))

data %>% select(c(1:6)) %>% group_by(Type) %>% 
  summarise(cells = "myeloid",
            mean = mean(perc.myel),
            sem = sd(perc.myel) / sqrt(n()),
            lower = (mean - sem),
            upper = (mean + sem)) -> m

data %>% select(c(1:6)) %>% group_by(Type) %>% 
  summarise(cells = "lymphoid",
            mean = mean(perc.lym),
            sem = sd(perc.lym) / sqrt(n()),
            lower = (mean(perc.lym) + mean(perc.myel) - sem),
            upper = (mean(perc.lym) + mean(perc.myel) + sem)) -> l

errs = rbind(m,l)
errs

#Draw bar plot with ggplot2
ggplot(data=errs, aes(x=Type, y=mean, fill=cells)) + 
  geom_bar(stat="identity", position="stack", width = 0.4) +
  geom_errorbar(data = filter(errs, cells == 'myeloid'), aes(ymax=upper,  ymin=lower), stat = 'identity', position = 'identity', width=0.15) + 
   geom_errorbar(data = filter(errs, cells == 'lymphoid'), aes(ymax=upper,  ymin=lower), stat = 'identity', position = 'identity', width=0.15) + 
  scale_fill_manual(values = c("hotpink", "seagreen3")) +
  xlab(" ") + 
  ylab("Percentage (%)") + theme_cowplot() +
  scale_y_continuous(labels = function(bs) {paste0(bs, '%')}) +  geom_text(aes(y = errs$mean, label = round(mean,1)), vjust = 3, colour = "white", size = 4)

Statistics

paired T-test between PBMC vs CSF

% Lymphoid & Myeloid

a <- data %>% filter(Type == "CSF") %>% mutate(pval = shapiro.test(perc.lym)$p.value)
b <- data %>% filter(Type == "PBMC") %>% mutate(pval = shapiro.test(perc.lym)$p.value)
c <- data %>% filter(Type == "CSF") %>% mutate(pval = shapiro.test(perc.myel)$p.value)
d <- data %>% filter(Type == "PBMC") %>% mutate(pval = shapiro.test(perc.myel)$p.value)
tmp=rbind(a$pval[1], b$pval[1], c$pval[1], d$pval[1])
rownames(tmp)=c("CSF:Lym","PBMC:Lym","CSF:Myel","PBMC:Myel")
colnames(tmp)=c("Shapiro Test for Normality")
round(tmp,2)
rm(a,b,c,d,tmp)

var.test(CSF$perc.lym, PBMC$perc.lym)
t.test(CSF$perc.lym, PBMC$perc.lym, paired = TRUE, var.equal = TRUE)

var.test(CSF$perc.myel, PBMC$perc.myel)
t.test(CSF$perc.myel, PBMC$perc.myel, paired = TRUE, var.equal = TRUE)

Session Info

sessionInfo()