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Frequency_boxplots.Rmd
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Frequency_boxplots.Rmd
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---
title: "Frequency_boxplots"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Import
```{r}
library(DescTools)
library(ggplot2)
library(Seurat)
```
# Functions
```{r FUNCTIONS (get_freqs, freq_boxplot)}
#############################################################################################################################
get_freqs <- function(immune.combined, cluster_res) {
Idents(immune.combined) <- cluster_res
new.ident <- sort(unique(Idents(immune.combined)))
samples <- unique([email protected]$orig.ident)
tmp <- match(immune.combined$orig.ident, samples)
sample_ind <- unique(tmp)
ids <- Idents(immune.combined)
tmp_v <- matrix(0, nrow = length(samples), ncol = length(new.ident))
rownames(tmp_v) <- samples
total_in_sample <- rep(0, length(samples))
tmp <- immune.combined$orig.ident
tmp <- plyr::count(tmp)
total_in_sample = tmp$freq
names(total_in_sample) <- tmp$x
total_in_sample <- total_in_sample[match(samples, names(total_in_sample))]
for (i in 1:length(new.ident)) {
tmp <- ids[which(ids == new.ident[i])]
tmp <- immune.combined$orig.ident[match(names(tmp), rownames([email protected]))]
tmp <- plyr::count(tmp)
for (j in 1:nrow(tmp)) {
ind <- which(rownames(tmp_v) == tmp$x[j])
tmp_v[ind,i] <- tmp$freq[j]#/total_in_sample[which(names(total_in_sample) == samples[ind])]
}
}
colnames(tmp_v) <- new.ident
ind <- order(colnames(tmp_v))
tmp_v
}
##################################################################
freq_boxplot <- function(SEU, celltype, cellstoremove, metadata){
cellid <- paste('%', celltype, '%', sep = '')
myeloid_id <- as.character(unique(Idents(SEU))[unique(Idents(SEU)) %like any% c(cellid)])
if (cellstoremove != 'None'){
myeloid_id <- myeloid_id[!myeloid_id %like% cellstoremove]
}
t <- get_freqs(subset(SEU, ident = myeloid_id), metadata)
t2 <- t
for (i in 1:nrow(t2)) {
t2[i,] = t2[i,]/sum(t2[i,])
}
t2 <- as.matrix(t2)
meta <- [email protected][,c('meta', 'Stage')]
rownames(meta) <- NULL
meta <- unique(meta)
df <- NULL
for (i in 1:nrow(t2)) {
for (j in 1:ncol(t2)) {
df <- rbind(df, c(rownames(t2)[i], meta$Stage[which(meta$orig.ident == rownames(t2)[i])], colnames(t2)[j], as.numeric(t2[i,j])))
}
}
colnames(df) <- c('Sample', 'Group', 'Cluster', 'Freqs')
df <- as.data.frame(df)
df$Group <- as.character(df$Group)
df$Freqs <- as.numeric(as.character(df$Freqs))
#library(ggplot2)
#ggplot(df) +
#geom_boxplot(aes(x = Group, y = Freqs, color = Group, fill = Group), position = position_dodge(), alpha = 0.5, outlier.color = NA) + geom_point(aes(x = Group, y = Freqs, color = Group), alpha = 0.8, position = position_jitterdodge()) + facet_wrap(~ Cluster, scales = 'free', nrow = 2) + theme_bw() + theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.title.x = element_blank(), strip.text = element_text(size = 12)) + scale_shape_manual(values = 1:5) #needs to be equal to the number of samples that you have
return(df)
}
############################################################################
```
# Luoma
## Stage_Path Response
```{r}
SEU <- luoma[,luoma$predicted.id %like% c('%Mac%', '%Mon%', '%DC%')]
SEU$orig.ident <- paste(SEU$Patient_ID, SEU$Stage)
SEU$meta <- paste(SEU$Patient_ID, SEU$Stage)
SEU$treatment_path_response <- paste(SEU$Stage, SEU$Path_response)
metadata <- 'predicted.id'
get_freqs <- function(immune.combined, cluster_res) {
Idents(immune.combined) <- cluster_res
new.ident <- sort(unique(Idents(immune.combined)))
samples <- unique([email protected]$orig.ident)
tmp <- match(immune.combined$orig.ident, samples)
sample_ind <- unique(tmp)
ids <- Idents(immune.combined)
tmp_v <- matrix(0, nrow = length(samples), ncol = length(new.ident))
rownames(tmp_v) <- samples
total_in_sample <- rep(0, length(samples))
tmp <- immune.combined$orig.ident
tmp <- plyr::count(tmp)
total_in_sample = tmp$freq
names(total_in_sample) <- tmp$x
total_in_sample <- total_in_sample[match(samples, names(total_in_sample))]
for (i in 1:length(new.ident)) {
tmp <- ids[which(ids == new.ident[i])]
tmp <- immune.combined$orig.ident[match(names(tmp), rownames([email protected]))]
tmp <- plyr::count(tmp)
for (j in 1:nrow(tmp)) {
ind <- which(rownames(tmp_v) == tmp$x[j])
tmp_v[ind,i] <- tmp$freq[j]
}
}
colnames(tmp_v) <- new.ident
ind <- order(colnames(tmp_v))
tmp_v
}
t <- get_freqs(SEU, metadata)
t2 <- t
for (i in 1:nrow(t2)) {
t2[i,] = t2[i,]/sum(t2[i,])
}
t2 <- as.matrix(t2)
meta <- [email protected][,c('meta', 'treatment_path_response')]
rownames(meta) <- NULL
meta <- unique(meta)
df <- NULL
for (i in 1:nrow(t2)) {
for (j in 1:ncol(t2)) {
df <- rbind(df, c(rownames(t2)[i], meta$treatment_path_response[which(meta$meta == rownames(t2)[i])], colnames(t2)[j], as.numeric(t2[i,j])))
}
}
colnames(df) <- c('Sample', 'Group', 'Cluster', 'Freqs')
df <- as.data.frame(df)
df$paired <- sapply(strsplit(df$Sample," "), `[`, 1)
df$Group <- as.character(df$Group)
df$Freqs <- as.numeric(as.character(df$Freqs))
df$Group <- factor(df$Group, levels = c('Pre-Tx Low', 'Post-Tx Low', 'Pre-Tx Medium', 'Post-Tx Medium', 'Pre-Tx High','Post-Tx High'))
ggplot(df) + geom_boxplot(aes(x = Group, y = Freqs, color = Group, fill = Group), position = position_dodge(), alpha = 0.5, outlier.color = NA) + geom_point(aes(x = Group, y = Freqs, color = Group), alpha = 0.8, position = position_jitterdodge()) + geom_line(aes(x = Group, y = Freqs, group = paired), alpha = 0.3) + facet_wrap(~ Cluster, scales = 'free', nrow = 2) + theme_bw() + theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.title.x = element_blank(), strip.text = element_text(size = 12)) + scale_shape_manual(values = 1:63)
ggsave('/Volumes/hqdinh2/Projects/Public_Data/Luoma_HNC/Luoma_boxplot_myeloid_stage_pathresponse.png', width = 14, height = 8)
```
## Path response
```{r}
SEU <- luoma[,luoma$predicted.id %like% c('%Mac%', '%Mon%', '%DC%')]
SEU$orig.ident <- paste(SEU$Patient_ID, SEU$Stage)
SEU$meta <- paste(SEU$Patient_ID, SEU$Stage)
#SEU$treatment_path_response <- paste(SEU$Stage, SEU$Path_response)
metadata <- 'predicted.id'
get_freqs <- function(immune.combined, cluster_res) {
Idents(immune.combined) <- cluster_res
new.ident <- sort(unique(Idents(immune.combined)))
samples <- unique([email protected]$orig.ident)
tmp <- match(immune.combined$orig.ident, samples)
sample_ind <- unique(tmp)
ids <- Idents(immune.combined)
tmp_v <- matrix(0, nrow = length(samples), ncol = length(new.ident))
rownames(tmp_v) <- samples
total_in_sample <- rep(0, length(samples))
tmp <- immune.combined$orig.ident
tmp <- plyr::count(tmp)
total_in_sample = tmp$freq
names(total_in_sample) <- tmp$x
total_in_sample <- total_in_sample[match(samples, names(total_in_sample))]
for (i in 1:length(new.ident)) {
tmp <- ids[which(ids == new.ident[i])]
tmp <- immune.combined$orig.ident[match(names(tmp), rownames([email protected]))]
tmp <- plyr::count(tmp)
for (j in 1:nrow(tmp)) {
ind <- which(rownames(tmp_v) == tmp$x[j])
tmp_v[ind,i] <- tmp$freq[j]
}
}
colnames(tmp_v) <- new.ident
ind <- order(colnames(tmp_v))
tmp_v
}
t <- get_freqs(SEU, metadata)
t2 <- t
for (i in 1:nrow(t2)) {
t2[i,] = t2[i,]/sum(t2[i,])
}
t2 <- as.matrix(t2)
meta <- [email protected][,c('meta', 'Path_response')]
rownames(meta) <- NULL
meta <- unique(meta)
df <- NULL
for (i in 1:nrow(t2)) {
for (j in 1:ncol(t2)) {
df <- rbind(df, c(rownames(t2)[i], meta$Path_response[which(meta$meta == rownames(t2)[i])], colnames(t2)[j], as.numeric(t2[i,j])))
}
}
colnames(df) <- c('Sample', 'Group', 'Cluster', 'Freqs')
df <- as.data.frame(df)
df$paired <- sapply(strsplit(df$Sample," "), `[`, 1)
df$Group <- as.character(df$Group)
df$Group[df$Group %like% 'Medium'] <- 'Medium/High'
df$Group[df$Group %like% 'High'] <- 'Medium/High'
df$Freqs <- as.numeric(as.character(df$Freqs))
df$Group <- factor(df$Group, levels = c('Low', 'Medium/High'))
ggplot(df) + geom_boxplot(aes(x = Group, y = Freqs, color = Group, fill = Group), position = position_dodge(), alpha = 0.5, outlier.color = NA) + geom_point(aes(x = Group, y = Freqs, color = Group), alpha = 0.8, position = position_jitterdodge()) + facet_wrap(~ Cluster, scales = 'free', nrow = 2) + theme_bw() + theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.title.x = element_blank(), strip.text = element_text(size = 12)) + scale_shape_manual(values = 1:63)
ggsave('/Volumes/hqdinh2/Projects/Public_Data/Luoma_HNC/Luoma_boxplot_myeloid_pathresponse.png', width = 14, height = 8)
```
### Wilcox test
```{r}
wilcox <- data.frame(Cluster=character(), pval = numeric())
for (x in unique(df$Cluster)){
df2 <- df[df$Cluster == x,]
l <- df2[df2$Group == 'Low', 'Freqs']
h <- df2[df2$Group %like% c('%High%'), 'Freqs']
wc <- wilcox.test(l, h)
p <- wc$p.value
row <- c(x, p)
wilcox <- rbind(wilcox, row)
}
write.csv(wilcox, file = '/Volumes/hqdinh2/Projects/Public_Data/Luoma_HNC/Luoma_boxplot_myeloid_pathresponse.csv')
```
## Stage
```{r}
SEU <- luoma[,luoma$predicted.id %like% c('%Mac%', '%Mon%', '%DC%')]
SEU$orig.ident <- paste(SEU$Patient_ID, SEU$Stage)
SEU$meta <- paste(SEU$Patient_ID, SEU$Stage)
metadata <- 'predicted.id'
get_freqs <- function(immune.combined, cluster_res) {
Idents(immune.combined) <- cluster_res
new.ident <- sort(unique(Idents(immune.combined)))
samples <- unique([email protected]$orig.ident)
tmp <- match(immune.combined$orig.ident, samples)
sample_ind <- unique(tmp)
ids <- Idents(immune.combined)
tmp_v <- matrix(0, nrow = length(samples), ncol = length(new.ident))
rownames(tmp_v) <- samples
total_in_sample <- rep(0, length(samples))
tmp <- immune.combined$orig.ident
tmp <- plyr::count(tmp)
total_in_sample = tmp$freq
names(total_in_sample) <- tmp$x
total_in_sample <- total_in_sample[match(samples, names(total_in_sample))]
for (i in 1:length(new.ident)) {
tmp <- ids[which(ids == new.ident[i])]
tmp <- immune.combined$orig.ident[match(names(tmp), rownames([email protected]))]
tmp <- plyr::count(tmp)
for (j in 1:nrow(tmp)) {
ind <- which(rownames(tmp_v) == tmp$x[j])
tmp_v[ind,i] <- tmp$freq[j]
}
}
colnames(tmp_v) <- new.ident
ind <- order(colnames(tmp_v))
tmp_v
}
t <- get_freqs(SEU, metadata)
t2 <- t
for (i in 1:nrow(t2)) {
t2[i,] = t2[i,]/sum(t2[i,])
}
t2 <- as.matrix(t2)
meta <- [email protected][,c('meta', 'Stage')]
rownames(meta) <- NULL
meta <- unique(meta)
df <- NULL
for (i in 1:nrow(t2)) {
for (j in 1:ncol(t2)) {
df <- rbind(df, c(rownames(t2)[i], meta$Stage[which(meta$meta == rownames(t2)[i])], colnames(t2)[j], as.numeric(t2[i,j])))
}
}
colnames(df) <- c('Sample', 'Group', 'Cluster', 'Freqs')
df <- as.data.frame(df)
df$paired <- sapply(strsplit(df$Sample," "), `[`, 1)
df$Group <- as.character(df$Group)
df$Freqs <- as.numeric(as.character(df$Freqs))
df$Group <- factor(df$Group, levels = c('Pre-Tx', 'Post-Tx'))
ggplot(df) + geom_boxplot(aes(x = Group, y = Freqs, color = Group, fill = Group), position = position_dodge(), alpha = 0.5, outlier.color = NA) + geom_point(aes(x = Group, y = Freqs, color = Group), alpha = 0.8, position = position_jitterdodge()) + facet_wrap(~ Cluster, scales = 'free', nrow = 2) + theme_bw() + theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.title.x = element_blank(), strip.text = element_text(size = 12)) + scale_shape_manual(values = 1:63)
ggsave('/Volumes/hqdinh2/Projects/Public_Data/Luoma_HNC/Luoma_boxplot_myeloid_pathresponse.png', width = 14, height = 8)
```
### Wilcox test
```{r}
wilcox <- data.frame(Cluster=character(), pval = numeric())
for (x in unique(df$Cluster)){
df2 <- df[df$Cluster == x,]
l <- df2[df2$Group == 'Pre-Tx', 'Freqs']
h <- df2[df2$Group %like% c('%Post-Tx%'), 'Freqs']
wc <- wilcox.test(l, h)
p <- wc$p.value
row <- c(x, p)
wilcox <- rbind(wilcox, row)
}
write.csv(wilcox, file = '/Volumes/hqdinh2/Projects/Public_Data/Luoma_HNC/Luoma_boxplot_myeloid_stage.csv')
```
# Freq barplots (human)
```{r compute frequencies INDIVIDUAL VALUES}
myeloid_id <- as.character(unique(Idents(seurat))[unique(Idents(seurat)) %like any% c('%Mon%', '%Mac%', '%DC%')])
myeloid_id <- as.character(unique(Idents(seurat))[unique(Idents(seurat)) %like any% c('%N0%', '%N1%', '%N2%', '%N3%')])
myeloid_ids <- as.character(unique(Idents(SEU))[unique(Idents(SEU)) %like any% c('%Mac')])
t <- get_freqs(SEU, 'global.cluster4')
t2 <- t
for (i in 1:nrow(t2)) {
t2[i,] = t2[i,]/sum(t2[i,])
}
t2 <- as.matrix(t2)
barplot(t(t2))
meta <- [email protected][,c('orig.ident', 'hpv_status', 'global.cluster4')]
rownames(meta) <- NULL
meta <- unique(meta)
df <- NULL
for (i in 1:nrow(t2)) {
for (j in 1:ncol(t2)) {
df <- rbind(df, c(rownames(t2)[i], meta$tissue[which(meta$orig.ident == rownames(t2)[i])], colnames(t2)[j], as.numeric(t2[i,j])))
}
}
colnames(df) <- c('Sample', 'Group', 'Cluster', 'Freqs')
df <- as.data.frame(df)
df$Group <- as.character(df$Group)
df$Freqs <- as.numeric(as.character(df$Freqs))
ggplot(df) +
geom_boxplot(aes(x = Group, y = Freqs, color = Group, fill = Group), position = position_dodge(), alpha = 0.5, outlier.color = NA) + geom_point(aes(x = Group, y = Freqs, color = Group), alpha = 0.8, position = position_jitterdodge()) + facet_wrap(~ Cluster, scales = 'free', nrow = 2) + theme_bw() + theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.title.x = element_blank(), strip.text = element_text(size = 12)) + scale_shape_manual(values = 1:63) #needs to be equal to the number of samples that you have
ggsave('/Volumes/hdlab/Projects/HNC_SPORE/Wangetal2021/moc2_monmacdc_boxplot.png', width = 14, height = 7)
##################this is for human data########################################################################'
SEU <- luoma
df <- freq_boxplot(SEU, 'Mac', cellstoremove = 'None', metadata ='predicted.id')
df <- as.data.frame(df)
ggplot(df) +
geom_boxplot(aes(x = Group, y = Freqs, color = Group, fill = Group), position = position_dodge(), alpha = 0.5, outlier.color = NA) + geom_point(aes(x = Group, y = Freqs, color = Group), alpha = 0.8, position = position_jitterdodge()) + facet_wrap(~ Cluster, scales = 'free', nrow = 2) + theme_bw() + theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.title.x = element_blank(), strip.text = element_text(size = 12)) + scale_shape_manual(values = 1:5) #needs to be equal to the number of samples that you have
ggsave('/Volumes/hdlab/Projects/HNC_SPORE/Golfinosetal2022/split_mregdcs_cillo_hnc_myeloidcellboxplot_allTILmyeloid_OUTOFALLCD45+.png', width = 8, height = 12)
```
# Freq barplots (human)
```{r compute frequencies INDIVIDUAL VALUES}
myeloid_id <- as.character(unique(Idents(seurat))[unique(Idents(seurat)) %like any% c('%Mon%', '%Mac%', '%DC%')])
myeloid_id <- as.character(unique(Idents(seurat))[unique(Idents(seurat)) %like any% c('%N0%', '%N1%', '%N2%', '%N3%')])
myeloid_ids <- as.character(unique(Idents(SEU))[unique(Idents(SEU)) %like any% c('%Mac')])
t <- get_freqs(SEU, 'global.cluster4')
t2 <- t
for (i in 1:nrow(t2)) {
t2[i,] = t2[i,]/sum(t2[i,])
}
t2 <- as.matrix(t2)
barplot(t(t2))
meta <- [email protected][,c('orig.ident', 'hpv_status', 'global.cluster4')]
rownames(meta) <- NULL
meta <- unique(meta)
df <- NULL
for (i in 1:nrow(t2)) {
for (j in 1:ncol(t2)) {
df <- rbind(df, c(rownames(t2)[i], meta$tissue[which(meta$orig.ident == rownames(t2)[i])], colnames(t2)[j], as.numeric(t2[i,j])))
}
}
colnames(df) <- c('Sample', 'Group', 'Cluster', 'Freqs')
df <- as.data.frame(df)
df$Group <- as.character(df$Group)
df$Freqs <- as.numeric(as.character(df$Freqs))
ggplot(df) +
geom_boxplot(aes(x = Group, y = Freqs, color = Group, fill = Group), position = position_dodge(), alpha = 0.5, outlier.color = NA) + geom_point(aes(x = Group, y = Freqs, color = Group), alpha = 0.8, position = position_jitterdodge()) + facet_wrap(~ Cluster, scales = 'free', nrow = 2) + theme_bw() + theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.title.x = element_blank(), strip.text = element_text(size = 12)) + scale_shape_manual(values = 1:63) #needs to be equal to the number of samples that you have
ggsave('/Volumes/hdlab/Projects/HNC_SPORE/Wangetal2021/moc2_monmacdc_boxplot.png', width = 14, height = 7)
##################this is for human data########################################################################'
SEU <- luoma
df <- freq_boxplot(SEU, 'Mac', cellstoremove = 'None', metadata ='predicted.id')
df <- as.data.frame(df)
ggplot(df) +
geom_boxplot(aes(x = Group, y = Freqs, color = Group, fill = Group), position = position_dodge(), alpha = 0.5, outlier.color = NA) + geom_point(aes(x = Group, y = Freqs, color = Group), alpha = 0.8, position = position_jitterdodge()) + facet_wrap(~ Cluster, scales = 'free', nrow = 2) + theme_bw() + theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.title.x = element_blank(), strip.text = element_text(size = 12)) + scale_shape_manual(values = 1:5) #needs to be equal to the number of samples that you have
ggsave('/Volumes/hdlab/Projects/HNC_SPORE/Golfinosetal2022/split_mregdcs_cillo_hnc_myeloidcellboxplot_allTILmyeloid_OUTOFALLCD45+.png', width = 8, height = 12)
```
# Freq barplots (mouse)
```{r}
#t <- get_freqs(subset(seurat, ident = myeloid_id), 'global.cluster2')
t <- get_freqs(seurat, 'global.cluster2')
t2 <- t
for (i in 1:nrow(t2)) {
t2[i,] = t2[i,]/sum(t2[i,])
}
t2 <- as.matrix(t2)
barplot(t(t2))
meta <- [email protected][,c('orig.ident', 'tissue')]
rownames(meta) <- NULL
meta <- unique(meta)
df <- NULL
for (i in 1:nrow(t2)) {
for (j in 1:ncol(t2)) {
df <- rbind(df, c(rownames(t2)[i], meta$tissue[which(meta$orig.ident == rownames(t2)[i])], colnames(t2)[j], as.numeric(t2[i,j])))
}
}
colnames(df) <- c('Sample', 'Group', 'Cluster', 'Freqs')
df <- as.data.frame(df)
df$Group <- as.character(df$Group)
df$Freqs <- as.numeric(as.character(df$Freqs))
#uncomment this if you want to do it out of all CD45+ cells
df <- df[df$Cluster %in% myeloid_id,]
ggplot(df) + geom_boxplot(aes(x = Group, y = Freqs, color = Group, fill = Group), position = position_dodge(), alpha = 0.5, outlier.color = NA) + geom_point(aes(x = Group, y = Freqs, color = Group), alpha = 0.8, position = position_jitterdodge()) + facet_wrap(~ Cluster, scales = 'free', nrow = 2) + theme_bw() + theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.title.x = element_blank(), strip.text = element_text(size = 12)) + scale_shape_manual(values = 1:63) #needs to be equal to the number of samples that you have
ggsave('/Volumes/hdlab/Projects/HNC_SPORE/Wangetal2021/moc2_monmacdc_boxplot.png', width = 14, height = 7)
#ggsave('/Users/agolfinos/Desktop/moc2_neutrophil_boxplot.png', width = 5, height = 7)
```
# Mon to Mac ratio
```{r mon to mac ratio}
SEU <- subset(hnc, subset = tissue == 'TIL')
hnc$patient_hpvstatus <- paste(hnc$orig.ident, hnc$hpv_status, sep = '_')
test <- table(SEU$patient_hpvstatus, SEU$global.cluster4)
test <- as.data.frame.matrix(test)
mons <- test[,stringr::str_detect(colnames(test), "Mon")]
macs <- test[,stringr::str_detect(colnames(test), 'Mac')]
mons$mon_sum <- rowSums(mons)
macs$mac_sum <- rowSums(macs)
summary <- as.data.frame(mons$mon_sum/macs$mac_sum)
summary$id <- rownames(mons)
summary <- tidyr::separate(data = summary, col = id, into = c("orig.ident", "hpv_status"), sep = "_")
summary$freqs <- summary$`mons$mon_sum/macs$mac_sum`
summary$`mons$mon_sum/macs$mac_sum` <- NULL
ggplot(summary) +
geom_boxplot(aes(x = hpv_status, y = freqs, color = hpv_status, fill = hpv_status), position = position_dodge(), alpha = 0.5, outlier.color = NA) + geom_point(aes(x = hpv_status, y = freqs, color = hpv_status), alpha = 0.8, position = position_jitterdodge()) + theme_bw() + theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.title.x = element_blank(), strip.text = element_text(size = 12)) + scale_shape_manual(values = 1:63) #needs to be equal to the number of samples that you have
ggsave('/Users/agolfinos/Desktop/hnc_montomacratio_boxplot.png', width = 6, height = 12)
df <- summary
df1 <- df[df$hpv_status %in% 'HPV+', ]
df2 <- df[df$hpv_status %in% 'HPV-',]
temp1 <- df1[['freqs']]
temp2 <- df2[['freqs']]
test <- wilcox.test(temp1, temp2, alternative = "two.sided", exact = TRUE)
pval <- test$p.value
write.csv(comparison, file = '/Users/agolfinos/Desktop/hnctil_mac_comparison_pvals.csv')
```
```{r Wilcoxan test of each cluster mouse}
df_moc2 <- df[df$Group %in% 'MOC2', ]
df_k17ko <- df[df$Group %in% 'K17KOMOC2',]
comparison <- list()
for (x in unique(df$Cluster)) {
temp_moc2 <- df_moc2[df_moc2[,3]==x,4]
temp_k17ko <- df_k17ko[df_k17ko[,3]==x,4]
test <- wilcox.test(temp_moc2, temp_k17ko, alternative = "two.sided", exact = TRUE)
pval <- test$p.value
comparison[[x]] = pval
}
```
```{r Wilcoxan test of each cluster human}
#df <- as.data.frame(df$data)
df1 <- df[df$Group %in% 'HNSCC:PBMC:HPV-', ]
df2 <- df[df$Group %in% 'HNSCC:TIL:HPV-',]
df3 <- df[df$Group %in% 'HNSCC:PBMC:HPV+',]
df4 <- df[df$Group %in% "HNSCC:TIL:HPV+", ]
comparison <- list()
for (x in unique(df$Cluster)) {
temp1 <- df1[df1[,3]==x,4]
temp2 <- df2[df2[,3]==x,4]
temp3 <- df3[df3[,3]==x,4]
temp4 <- df4[df4[,3]==x,4]
test <- wilcox.test(temp2, temp4, alternative = "two.sided", exact = TRUE)
pval <- test$p.value
comparison[[x]] = pval
}
write.csv(comparison, file = '/Users/agolfinos/Desktop/hnctil_mac_comparison_pvals.csv')
```