marcelcosta/Elispots-app
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

add core files

Browse Source
This commit is contained in:
marcelcosta
2020-11-03 07:34:13 +01:00
parent d235db420d
commit 642df836a9
3 changed files with 401 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files
app.R
+169
View File
@@ -0,0 +1,169 @@
library(shiny)
library(openxlsx)
library(readxl)
library(ggplot2)
library(reshape2)
library(dplyr)
library(ggbeeswarm)
library(magrittr)
library(flextable)
source("funcions.R")
ui <- fluidPage(
# Application title
titlePanel("ELISPOTs"),
sidebarLayout(
sidebarPanel(
fileInput(inputId = "file1", label = "Dades", multiple = F),
selectInput(inputId = "test", "Test Estadístic", selected = "Ttest", choices = c("T-test (adj Holm)","Wilcoxon (adj Holm)")),
sliderInput(inputId = "umbral_pos", "Mínimo para positivo:", min = 0, max=100, step = 5, value = 10),
checkboxInput(inputId = "positive", label = "Mostrar positivitat", value = F),
checkboxInput(inputId = "showstats", label = "Mostrar estadística", value = F),
downloadButton("downloadData", "Descarregar Informe")
),
mainPanel(
plotOutput("distPlot"),
uiOutput("flexstats")
)
)
)
# Define server logic required to draw a histogram
server <- function(input, output) {
dades<-reactiveValues()
dades$taula<-NULL
dades$stats<-NULL
observe({
if (!is.null(input$file1)){
dades$taula<-read_xlsx(input$file1$datapath)
}
})
output$distPlot <- renderPlot({
observeEvent(dades$taula, {})
if (!is.null(dades$taula)){
ctrl<-"Ctrl+"
mock<-"Mock"
table<-dades$taula[,colnames(dades$taula) != "Groups"]
t_mean<-dcast(melt(table, id="Mice"),Mice~variable, mean, na.rm=T)
t_substr<-data.frame("Mice"=t_mean[,1],
as.data.frame(t(apply(t_mean[,2:ncol(table)], 1, function(x) x-x[mock])))
)
t_mean_group<-merge(t_mean, unique(dades$taula[c("Mice","Groups")]), id="Mice")
mock_mean<-dcast(t_mean_group, Groups~., value.var=mock, mean)
mock_mean[,2]<-mock_mean[,2]*2
mock_mean[mock_mean$. < input$umbral_pos,2]<-input$umbral_pos
t_substr<-merge(t_substr, unique(dades$taula[c("Mice","Groups")]), id="Mice")
t_substr<-t_substr[,c(1, ncol(t_substr), 2:(ncol(t_substr)-1))]
t_substr<-t_substr[,c("Mice", "Groups", colnames(t_substr)[!colnames(t_substr) %in% c("Mice", "Groups")])]
t_substr[,3:ncol(t_substr)]<-apply(t_substr[,3:ncol(t_substr)],2, function(x) replace(x, which(x < 0),0))
colnames(t_substr)<-c("Mice", "Groups", colnames(t_mean)[2:ncol(t_mean)])
t_substr_gp<-t_substr
t_substr_gp[3:ncol(t_substr)]<-apply(t_substr[3:ncol(t_substr)], 2, function(x) gsub(".",",",x, fixed=T))
t_substr_gp<-t_substr_gp[order(factor(t_substr_gp$Mice, levels = unique(table$Mice))),]
doc<-t(t_substr_gp)
write.xlsx(doc, "data4graphpad.xlsx",rowNames=T)
t<-melt(t_substr[,!colnames(t_substr) %in% c(ctrl, mock)])
if (input$test == "T-test (adj Holm)"){
t_stats<-multi_stats(t, "value", "variable", "Groups", stat.test = "ttest")
}
if (input$test == "Wilcoxon (adj Holm)"){
t_stats<-multi_stats(t, "value", "variable", "Groups", stat.test = "wilcox")
}
dades$stats<<-t_stats
t_stats<-t_stats %>% filter(p.signif != "ns")
t_maps<-generate_labstats(t_stats, t, "value", "variable", "Groups")
if (input$showstats == F){
t_stats<-as.data.frame(matrix(nrow=0, ncol=6))
colnames(t_stats)<-c("variable", "group1", "group2", "p.adj", "p.signif", "Method")
t_maps<-list()
t_maps[["label"]]<-as.data.frame(matrix(nrow = 0, ncol=2))
colnames(t_maps$label)<-c("x", "y")
t_maps[["brackets"]]<-as.data.frame(matrix(nrow = 0, ncol=4))
colnames(t_maps$brackets)<-c("y1", "y2", "x1", "x2")
}
set.seed(123)
if (input$positive == T){
ggplot(melt(t_substr, id=c("Mice", ctrl, "Groups")), aes(variable, value))+
labs(x="", y="Spots/2.5*10^5 cells")+
# geom_errorbar(stat="summary", position=position_dodge(width=0.9), width=0.5, aes(fill=Groups))+
geom_hline(data=mock_mean, aes(color=Groups, yintercept = `.`))+
# geom_bar(stat="summary", position="dodge", color="black", aes(fill=Groups))+
geom_boxplot(color="black", aes(fill=Groups), alpha=0.4, outlier.alpha = 0)+
geom_jitter(position=position_jitterdodge(jitter.width = 0.2), shape=21, aes(fill=Groups), size=3)+
# geom_quasirandom(position = position_quasirandom(), shape=21)+
scale_x_discrete(limits=colnames(t_substr)[!colnames(t_substr) %in% c("Mice", "Groups", ctrl, mock)])+
geom_segment(data=t_maps$brackets, aes(x=x1, xend=x2, y=y1, yend=y2), color="black")+
geom_text(data=t_stats, aes(t_maps$label$x, t_maps$label$y, label=p.signif), color="black")+
theme_bw()+
theme(axis.text.x=element_text(angle=45, hjust=1))
}else{
ggplot(melt(t_substr, id=c("Mice", ctrl, "Groups")), aes(variable, value))+
labs(x="", y="Spots/2.5*10^5 cells")+
# geom_errorbar(stat="summary", position=position_dodge(width=0.9), width=0.5, aes(fill=Groups))+
# geom_bar(stat="summary", position="dodge", color="black", aes(fill=Groups))+
geom_boxplot(color="black", aes(fill=Groups), alpha=0.4, outlier.alpha = 0)+
geom_jitter(position=position_jitterdodge(jitter.width = 0.2), shape=21, aes(fill=Groups), size=3)+
# geom_quasirandom(width=0.2, position=position_dodge(), shape=21)+
scale_x_discrete(limits=colnames(t_substr)[!colnames(t_substr) %in% c("Mice", "Groups", ctrl, mock)])+
geom_segment(data=t_maps$brackets, aes(x=x1, xend=x2, y=y1, yend=y2), color="black")+
geom_text(data=t_stats, aes(t_maps$label$x, t_maps$label$y, label=p.signif), color="black")+
theme_bw()+
theme(axis.text.x=element_text(angle=45, hjust=1))
}
}
})
output$flexstats <- renderUI({
observeEvent(dades$stats, {})
t_stats<-dades$stats
if (!is.null(dades$stats)){
return(
t_stats %>%
flextable() %>%
theme_vanilla() %>%
fontsize(size=14, part="all") %>%
padding(padding=10, part="all") %>%
color(~ p.adj < 0.05, color = "red")%>%
autofit()
) %>%
htmltools_value()
}
})
output$downloadData <- downloadHandler(
filename = function() {
paste("elispot", ".zip", sep="")
},
content = function(file){
print(file)
# tempReport <- file.path(tempdir(), "elispots.Rmd")
# file.copy("elispots.Rmd", tempReport, overwrite = TRUE)
params=list(file=input$file1$datapath, positive=input$positive, showstats=input$showstats, test=input$test, umbral_pos=input$umbral_pos)
rmarkdown::render("elispots.Rmd", output_file="Results_elispot.html", params=params, envir = new.env(parent = globalenv()))
zip(file,
c("Results_elispot.html","data4graphpad.xlsx")
)
},
contentType="application/zip"
)
}
# Run the application
shinyApp(ui = ui, server = server)
elispots.Rmd
+95
View File
@@ -0,0 +1,95 @@
---
title: "Elispots"
output: html_document
params:
file: "../Elispot-validation-spots.xlsx"
positive: F
showstats: F
test: "Ttest"
umbral_pos: 10
---
```{r, echo=F, warning=F, message=F}
dades<-read_xlsx(params$file)
ctrl<-"Ctrl+"
mock<-"Mock"
table<-dades[,colnames(dades) != "Groups"]
t_mean<-dcast(melt(table, id="Mice"),Mice~variable, mean, na.rm=T)
t_substr<-data.frame("Mice"=t_mean[,1],
as.data.frame(t(apply(t_mean[,2:ncol(table)], 1, function(x) x-x[mock])))
)
t_mean_group<-merge(t_mean, unique(dades[c("Mice","Groups")]), id="Mice")
mock_mean<-dcast(t_mean_group, Groups~., value.var=mock, mean)
mock_mean[,2]<-mock_mean[,2]*2
mock_mean[mock_mean$. < params$umbral_pos,2]<-params$umbral_pos
t_substr<-merge(t_substr, unique(dades[c("Mice","Groups")]), id="Mice")
t_substr<-t_substr[,c(1, ncol(t_substr), 2:(ncol(t_substr)-1))]
t_substr<-t_substr[,c("Mice", "Groups", colnames(t_substr)[!colnames(t_substr) %in% c("Mice", "Groups")])]
t_substr[,3:ncol(t_substr)]<-apply(t_substr[,3:ncol(t_substr)],2, function(x) replace(x, which(x < 0),0))
colnames(t_substr)<-c("Mice", "Groups", colnames(t_mean)[2:ncol(t_mean)])
t<-melt(t_substr[,!colnames(t_substr) %in% c(ctrl, mock)])
if (params$test == "T-test (adj Holm)"){
t_stats<-multi_stats(t, "value", "variable", "Groups", stat.test = "ttest")
}
if (params$test == "Wilcoxon (adj Holm)"){
t_stats<-multi_stats(t, "value", "variable", "Groups", stat.test = "wilcox")
}
t_stats<-t_stats %>% filter(p.signif != "ns")
t_maps<-generate_labstats(t_stats, t, "value", "variable", "Groups")
if (params$showstats == F){
t_stats<-as.data.frame(matrix(nrow=0, ncol=6))
colnames(t_stats)<-c("variable", "group1", "group2", "p.adj", "p.signif", "Method")
t_maps<-list()
t_maps[["label"]]<-as.data.frame(matrix(nrow = 0, ncol=2))
colnames(t_maps$label)<-c("x", "y")
t_maps[["brackets"]]<-as.data.frame(matrix(nrow = 0, ncol=4))
colnames(t_maps$brackets)<-c("y1", "y2", "x1", "x2")
}
set.seed(123)
if (params$positive == T){
ggplot(melt(t_substr, id=c("Mice", ctrl, "Groups")), aes(variable, value))+
labs(x="", y="Spots/2.5*10^5 cells")+
geom_errorbar(stat="summary", position=position_dodge(width=0.9), width=0.5, aes(fill=Groups))+
geom_hline(data=mock_mean, aes(color=Groups, yintercept = `.`))+
geom_bar(stat="summary", position="dodge", color="black", aes(fill=Groups))+
geom_jitter(position=position_jitterdodge(jitter.width = 0.2), shape=21, aes(fill=Groups))+
# geom_quasirandom(position = position_quasirandom(), shape=21)+
scale_x_discrete(limits=colnames(t_substr)[!colnames(t_substr) %in% c("Mice", "Groups", ctrl, mock)])+
geom_segment(data=t_maps$brackets, aes(x=x1, xend=x2, y=y1, yend=y2), color="black")+
geom_text(data=t_stats, aes(t_maps$label$x, t_maps$label$y, label=p.signif), color="black")+
theme_bw()+
theme(axis.text.x=element_text(angle=45, hjust=1))
}else{
ggplot(melt(t_substr, id=c("Mice", ctrl, "Groups")), aes(variable, value))+
labs(x="", y="Spots/2.5*10^5 cells")+
geom_errorbar(stat="summary", position=position_dodge(width=0.9), width=0.5, aes(fill=Groups))+
geom_bar(stat="summary", position="dodge", color="black", aes(fill=Groups))+
geom_jitter(position=position_jitterdodge(jitter.width = 0.2), shape=21, aes(fill=Groups))+
# geom_quasirandom(width=0.2, position=position_dodge(), shape=21)+
scale_x_discrete(limits=colnames(t_substr)[!colnames(t_substr) %in% c("Mice", "Groups", ctrl, mock)])+
geom_segment(data=t_maps$brackets, aes(x=x1, xend=x2, y=y1, yend=y2), color="black")+
geom_text(data=t_stats, aes(t_maps$label$x, t_maps$label$y, label=p.signif), color="black")+
theme_bw()+
theme(axis.text.x=element_text(angle=45, hjust=1))
}
```
```{r, echo=F, warning=F, message=F}
t_stats %>%
flextable() %>%
theme_vanilla() %>%
fontsize(size=14, part="all") %>%
padding(padding=10, part="all") %>%
color(~ p.adj < 0.05, color = "red")%>%
autofit()
```
funcions.R
+137
View File
@@ -0,0 +1,137 @@
library(dplyr)
library(dunn.test)
library(tibble)
library(reshape2)
library(ggplot2)
corr_multi<-function(table, genes1, genes2, font_size=5, ruta="./", exportar=F)
{
cor.list<-list()
cont<-1
for (gene1 in genes1){
for (gene2 in genes2){
print(gene1)
print(gene2)
cor_temp<-cor.test(table[,gene1], table[,gene2])
if (!is.na(cor_temp$p.value)){
pval_plot<-paste("cor =",format(cor_temp$estimate, digits=2),if(cor_temp$p.value < 2.2e-16){" , p < 2.2e-16"}else{paste(", p=",format(cor_temp$p.value,digits=3))})
}
cor.list[[cont]]<-ggplotGrob(ggplot(data=table, aes(x=table[,gene1], y=table[,gene2]))+
geom_point()+
geom_smooth(method="lm")+
geom_text(aes(x=min(table[,gene1], na.rm=T), y=(max(table[,gene2],na.rm = T)+max(table[,gene2],na.rm = T)*0.2), label=pval_plot), hjust="inward", family="serif", size=font_size)+
labs(x=gene1, y=gene2)+
theme_bw())
if (exportar == T){
png(paste0(ruta,gene1,"vs",gene2,".png"), res=900, width=3000, height=3000)
plot(cor.list[[cont]])
dev.off()
}
cont<-cont+1
}
}
return(cor.list)
}
multi_stats<-function(table, value.var, x, group, stat.test, adjust="default", paired=F){
## Requires dplyr and tibble packets
defaults=c("dunn"="none", "ttest"="holm", "wilcox"="holm")
funs<-c("ttest"="pairwise.t.test", "wilcox"="pairwise.wilcox.test")
if (adjust == "default"){adjust=defaults[stat.test]}
stat.def<-as.data.frame(matrix(nrow=0, ncol=5))
colnames(stat.def)<-c(x, "group1", "group2", "p.adj", "p.signif")
for (point in unique(table[,x])){
condition<-all(table %>% filter(table[,x] == point) %>% pull(value.var) == 0) == F
len_group<-length(unique(table %>% filter(table[,x] == point) %>% pull(group)))
if (condition == T & !is.na(condition) & len_group > 1){
if(stat.test == "dunn"){
test<-dunn.test(table %>% filter(table[,x] == point) %>% pull(value.var), table %>% filter(table[,x] == point) %>% pull(group), method=adjust)
comp<-strsplit(test$comparisons, " - ")
stat.temp<-data.frame(matrix(unlist(comp), nrow=length(comp), byrow=T), "p.adj"=test$P.adjusted, stringsAsFactors = F, check.names = F)
colnames(stat.temp)[1:2]<-c("group1", "group2")
}else if (stat.test %in% names(funs)){
test<-get(funs[stat.test])(table %>% filter(table[,x] == point) %>% pull(value.var), table %>% filter(table[,x] == point) %>% pull(group), method=adjust, paired=paired)$p.value
stat.temp<-melt(test)
colnames(stat.temp)<-c("group1", "group2","p.adj")
}
stat.temp["p.signif"]<-case_when(
stat.temp$p.adj >= 0.05 ~ "ns",
stat.temp$p.adj < 0.0001 ~ "****",
stat.temp$p.adj < 0.001 ~ "***",
stat.temp$p.adj < 0.01 ~ "**",
stat.temp$p.adj < 0.05 ~ "*"
)
stat.temp<-stat.temp %>% add_column(x=point, .before=T)
colnames(stat.temp)[1]<-x
stat.def<-rbind(stat.def, stat.temp)
}
}
stat.def["Method"]<-stat.test
return(stat.def)
}
generate_labstats<-function(table_stat, table, value.var, x, group, y="max", bracket.offset=0.05, bracket.length=0.02){
table[,group]<-as.factor(table[,group])
table[,x]<-as.factor(table[,x])
se<-function(x, na.rm=F) sd(x, na.rm = na.rm)/sqrt(length(x))
if (y == "max"){
formula<-as.formula(paste0(colnames(table_stat)[1], "~."))
agg<-dcast(table, formula, value.var = value.var, fun.aggregate = max, na.rm=T)
}else if (y == "mean"){
formula<-as.formula(paste0(colnames(table_stat)[1], "~", group))
agg<-dcast(table, formula, value.var = value.var, fun.aggregate = mean, na.rm=T)
agg<- data.frame(x=agg[,1], "."=apply(agg[,2:ncol(agg)], 1, max, na.rm=T))
colnames(agg)[1]<-x
}else if (y == "mean+sd"){
formula<-as.formula(paste0(colnames(table_stat)[1], "~", group))
agg<- dcast(table, formula, value.var = value.var, fun.aggregate = function(x) mean(x,na.rm=T)+sd(x,na.rm=T))
agg<- data.frame(x=agg[,1], "."=apply(agg[,2:ncol(agg)], 1, max, na.rm=T))
colnames(agg)[1]<-x
}else if (y == "mean+se"){
formula<-as.formula(paste0(colnames(table_stat)[1], "~", group))
agg<- dcast(table, formula, value.var = value.var, fun.aggregate = function(x) mean(x,na.rm=T)+se(x,na.rm=T))
agg<- data.frame(agg[,1], "."=apply(agg[,2:ncol(agg)], 1, max, na.rm=T))
colnames(agg)[1]<-x
}
t<-data.frame("y1"=merge(table_stat, agg ,sort=F)[,"."]+diff(range(table[value.var], na.rm = T))*bracket.offset,
"y2"=merge(table_stat, agg ,sort=F)[,"."]+diff(range(table[value.var], na.rm = T))*bracket.offset,
"x1"= match(table_stat[,x], unique(table[,x]))+
0.75*((match(table_stat$group1, levels(table[,group]))-0.5)/length(levels(table[,group]))-0.5),
"x2"= match(table_stat[,x], unique(table[,x]))+
0.75*((match(table_stat$group2, levels(table[,group]))-0.5)/length(levels(table[,group]))-0.5)
)
for (dia in unique(table_stat[,1])){
t[table_stat[,x] == dia,"y1"]<-seq(t[table_stat[,x] == dia,"y1"][1],
t[table_stat[,x] == dia,"y1"][1]+diff(range(table[,value.var], na.rm = T))*0.05*(nrow(table_stat[table_stat[,x] == dia,])-1),
by=diff(range(table[,value.var], na.rm = T))*0.05)
t[table_stat[,x] == dia,"y2"]<-t[table_stat[,x] == dia,"y1"]
}
t_def<-as.data.frame(matrix(ncol=4, nrow=0))
for (row in 1:nrow(t)){
t_def<-rbind(t_def, t[row,],
c(t[row,"y1"]-diff(range(table[,value.var], na.rm = T))*bracket.length, t[row,"y1"], t[row,"x1"], t[row,"x1"]),
c(t[row,"y1"]-diff(range(table[,value.var], na.rm = T))*bracket.length, t[row,"y1"], t[row,"x2"], t[row,"x2"]))
}
t_lab<-data.frame("x"=t$x1+(t$x2-t$x1)/2, "y"=t$y1+diff(range(table[,value.var], na.rm = T))*0.005, check.names = F)
return(list("label"=t_lab, "brackets"=t_def))
}
secfile<-function(file){
ext<-strsplit(file, ".", fixed = T)[[1]]
ext<-ext[length(ext)]
num<-1
while(file.exists(file) == T){
if(num == 1){
file_tmp<-strsplit(file, ".", fixed=T)[[1]]
file<-paste0(paste(file_tmp[1:(length(file_tmp)-1)], collapse = "."),"_",num,".",file_tmp[length(file_tmp)])
}else{
file_tmp<-paste(strsplit(file, "_", fixed=T)[[1]][-length(strsplit(file, "_", fixed=T)[[1]])], collapse = "_")
file<-paste0(file_tmp, "_", num,".",ext)
}
num<-num+1
}
return(file)
}