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pattern.R
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pattern.R
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###########################################################################################################
## Proteomics Visualization R Shiny App
##
##This software belongs to Biogen Inc. All right reserved.
##
##@file: pattern.R
##@Developer : Benbo Gao (benbo.gao@Biogen.com)
##@Date : 5/16/2018
##@version 1.0
###########################################################################################################
observe({
DataIn = DataReactive()
tests = c("ALL",DataIn$tests)
allgroups = DataIn$groups
groups = group_order()
updateSelectizeInput(session,'pattern_group', choices=allgroups, selected=groups)
updateSelectizeInput(session,'pattern_test',choices=tests, selected=tests[1])
})
observe({
DataIn = DataReactive()
ProteinGeneName = DataIn$ProteinGeneName
results_long = DataIn$results_long
if (!is.null(results_long)) {
pattern_test = input$pattern_test
pattern_fccut = log2(as.numeric(input$pattern_fccut))
pattern_pvalcut = as.numeric(input$pattern_pvalcut)
if (input$pattern_psel == "Padj") {
filteredgene1 = results_long %>%
dplyr::filter(abs(logFC) > pattern_fccut & Adj.P.Value < pattern_pvalcut) %>%
dplyr::select(UniqueID) %>% collect %>% .[["UniqueID"]] %>% as.character() %>% unique()
} else {
filteredgene1 = results_long %>%
dplyr::filter(abs(logFC) > pattern_fccut & P.Value < pattern_pvalcut) %>%
dplyr::select(UniqueID) %>% collect %>% .[["UniqueID"]] %>% as.character() %>% unique()
}
output$patternfilteredgene <- renderText({paste("Selected Genes:",length(filteredgene1),sep="")})
}
})
DatapatternReactive <- reactive({
DataIn = DataReactive()
ProteinGeneName = DataIn$ProteinGeneName
sample_group <- DataIn$sample_group
pattern_fccut = log2(as.numeric(input$pattern_fccut))
pattern_pvalcut = as.numeric(input$pattern_pvalcut)
sel_group = input$pattern_group
filteredgene=NULL
group_order(input$pattern_group)
results_long <- DataIn$results_long
data_long <- DataIn$data_long
if (!is.null(results_long)){
if (input$pattern_subset == "subset") {
if (input$pattern_psel == "Padj") {
filteredgene = results_long %>%
dplyr::filter(abs(logFC) > pattern_fccut & Adj.P.Value < pattern_pvalcut) %>%
dplyr::select(UniqueID) %>% collect %>% .[["UniqueID"]] %>% as.character()
} else {
filteredgene = results_long %>%
dplyr::filter(abs(logFC) > pattern_fccut & P.Value < pattern_pvalcut) %>%
dplyr::select(UniqueID) %>% collect %>% .[["UniqueID"]] %>% as.character()
}
}
}
if (input$pattern_subset == "upload genes") {
pattern_list <- input$pattern_list
if(grepl("\n",pattern_list)) {
pattern_list <- stringr::str_split(pattern_list, "\n")[[1]]
} else if(grepl(",",pattern_list)) {
pattern_list <- stringr::str_split(pattern_list, ",")[[1]]
}
pattern_list <- gsub(" ", "", pattern_list, fixed = TRUE)
pattern_list <- unique(pattern_list[pattern_list != ""])
validate(need(length(pattern_list)>2, message = "input gene list"))
filteredgene <- dplyr::filter(ProteinGeneName, (UniqueID %in% pattern_list) | (Protein.ID %in% pattern_list) | (Gene.Name %in% pattern_list)) %>%
dplyr::select(UniqueID) %>% collect %>% .[["UniqueID"]] %>% as.character()
}
subdatlong <- dplyr::filter(data_long, (group %in% sel_group) & (UniqueID %in% filteredgene)) %>%
group_by(., group, UniqueID) %>%
dplyr::summarise(mean=mean(expr, na.rm = TRUE))
subdatwide <- subdatlong %>%
tidyr::spread(.,group, mean, fill = 0) %>% as.data.frame() %>%
remove_rownames(.) %>%
column_to_rownames(.,var="UniqueID") %>%
dplyr::select(sel_group)
return(list("subdatlong"= subdatlong,"subdatwide"= subdatwide, "filteredgene" = filteredgene))
})
pattern_out <- reactive({withProgress(message = 'Processing...', value = 0, {
Datapattern <-DatapatternReactive ()
subdatwide <- Datapattern$subdatwide
subdatlong <- Datapattern$subdatlong
sel_group=input$pattern_group
k=input$k
set.seed(123)
if (input$ClusterMehtod == "kmeans") {
cl <- kmeans(subdatwide, k)
cluster<-cl$cluster
cluster.df <- data.frame(UniqueID=names(cluster), cluster=paste('Cluster',cluster,sep=' '), row.names=NULL)
subdatlong <- subdatlong %>%
left_join(., cluster.df, by="UniqueID")
subdatlong$group = factor(subdatlong$group,levels = sel_group)
p <- ggplot(subdatlong, aes(x=group, y=mean)) +
facet_wrap(~ cluster,scales = "free", ncol = input$pattern_ncol) +
geom_line(aes(group=UniqueID, color="UniqueID")) +
stat_summary(aes(color="red", group=1), fun=mean, geom="line", size=1.2, group=1)
p <- p + theme_bw(base_size = input$pattern_font) + ylab("Expression") + xlab(" ") +
theme (plot.margin = unit(c(1,1,1,1), "cm"), axis.text.x = element_text(angle = input$pattern_Xangle),legend.position="none")
return(p)
#} else if (input$ClusterMehtod == "pam") {
# clpam <- pam(subdatwide, k)
# cluster <- clpam$clustering
#
# cluster.df <- data.frame(UniqueID=names(cluster), cluster=cluster, row.names=NULL)
# subdatlong <- subdatlong %>%
# left_join(., cluster.df, by="UniqueID")
# subdatlong$group = factor(subdatlong$group,levels = sel_group)
#
# p <- ggplot(subdatlong, aes(x=group, y=mean)) +
# facet_wrap(~ cluster,scales = "free", ncol = 3) +
# geom_line(aes(group=UniqueID, color="UniqueID")) +
# stat_summary(aes(color="red", group=1), fun=mean, geom="line", size=1.2, group=1)
# p <- p + theme_bw(base_size = 14) + ylab("expr") + xlab(" ") +
# theme (plot.margin = unit(c(1,1,1,1), "cm"), axis.text.x = element_text(angle = 45),legend.position="none")
# return(p)
#
} else if (input$ClusterMehtod == "mfuzz") {
tmp_expr <- new('ExpressionSet', exprs = as.matrix(subdatwide))
m1 <- mestimate(tmp_expr)
cl <- mfuzz(tmp_expr, c = k, m = m1, iter.max = 200)
nrow=ceiling(k/input$pattern_ncol)
mfuzz.plot(tmp_expr, cl = cl, mfrow = c(nrow, input$pattern_ncol), min.mem=0.4, time.labels=colnames(subdatwide),new.window = FALSE)
p = recordPlot()
return(p)
}
})
})
output$pattern<- renderPlot({
if (input$ClusterMehtod == "kmeans") {
pattern_out()
} else if (input$ClusterMehtod == "pam") {
pattern_out()
} else if (input$ClusterMehtod == "mfuzz") {
replayPlot(pattern_out())
}
})
observeEvent(input$pattern, {
if (input$ClusterMehtod == "kmeans") {
saved_plots$patternkmeans <- pattern_out()
} else if (input$ClusterMehtod == "pam") {
saved_plots$patternpam <- pattern_out()
} else if (input$ClusterMehtod == "mfuzz") {
saved_plots$patternmfuzz <- pattern_out()
}
})
output$dat_pattern<- DT::renderDataTable({withProgress(message = 'Processing...', value = 0, {
set.seed(123)
Datapattern <-DatapatternReactive ()
subdatwide <- Datapattern$subdatwide
#subdatwide[,sapply(subdatwide,is.numeric)] <- signif(subdatwide[,sapply(subdatwide,is.numeric)],3)
subdatlong <- Datapattern$subdatlong
#subdatlong[,sapply(subdatlong,is.numeric)] <- signif(subdatlong[,sapply(subdatlong,is.numeric)],3)
sel_group=input$pattern_group
k=input$k
if (input$ClusterMehtod == "kmeans") {
cl <- kmeans(subdatwide, k)
cluster<-cl$cluster
cluster.df <- data.frame(UniqueID=names(cluster), cluster=cluster, row.names=NULL)
if (input$DataFormat == "long") {
#subdatlong[,sapply(subdatlong,is.numeric)] <- signif(subdatlong[,sapply(subdatlong,is.numeric)],3)
subdatlong <- subdatlong %>%
left_join(., cluster.df, by="UniqueID")
DT::datatable(subdatlong)
} else if (input$DataFormat == "wide") {
subdatwide[,sapply(subdatwide,is.numeric)] <- signif(subdatwide[,sapply(subdatwide,is.numeric)],3)
subdatwide <- subdatwide %>%
rownames_to_column(.,var="UniqueID") %>%
left_join(., cluster.df, by="UniqueID")%>%
separate(UniqueID, c("Gene", "ID"), sep = "_")
DT::datatable(subdatwide,
extensions = 'Buttons',
options = list(dom = "Blfrtip",
buttons = list("copy", list(extend = "collection",
buttons = c("csv", "excel", "pdf"),
text = "Download")),
lengthMenu = list( c(10, 20, -1), c(10, 20, "All")), pageLength = 10),
filter = 'top')
}
#} else if (input$ClusterMehtod == "pam") {
# clpam <- pam(subdatwide, k)
# cluster <- clpam$clustering
# cluster.df <- data.frame(UniqueID=names(cluster), cluster=cluster, row.names=NULL)
#
# if (input$DataFormat == "long") {
# subdatlong <- subdatlong %>%
# left_join(., cluster.df, by="UniqueID")
# DT::datatable(subdatlong)
#
# } else if (input$DataFormat == "wide") {
# subdatwide <- subdatwide %>%
# rownames_to_column(.,var="UniqueID") %>%
# left_join(., cluster.df, by="UniqueID")
# DT::datatable(subdatwide, filter = 'top')
# }
#
} else if (input$ClusterMehtod == "mfuzz") {
tmp_expr <- new('ExpressionSet', exprs = as.matrix(subdatwide))
m1 <- mestimate(tmp_expr)
cl <- mfuzz(tmp_expr, c = k, m = m1, iter.max = 200)
cluster <- cl$cluster
cluster.df <- data.frame(UniqueID=names(cluster), cluster=cluster, row.names=NULL)
if (input$DataFormat == "long") {
#subdatlong[,sapply(subdatlong,is.numeric)] <- signif(subdatlong[,sapply(subdatlong,is.numeric)],3)
subdatlong <- subdatlong %>%
left_join(., cluster.df, by="UniqueID")
DT::datatable(subdatlong)
} else if (input$DataFormat == "wide") {
subdatwide[,sapply(subdatwide,is.numeric)] <- signif(subdatwide[,sapply(subdatwide,is.numeric)],3)
subdatwide <- subdatwide %>%
rownames_to_column(.,var="UniqueID") %>%
left_join(., cluster.df, by="UniqueID") %>%
separate(UniqueID, c("Gene", "ID"), sep = "_")
DT::datatable(subdatwide,
extensions = 'Buttons',
options = list(dom = "Blfrtip",
buttons = list("copy", list(extend = "collection",
buttons = c("csv", "excel", "pdf"),
text = "Download")),
lengthMenu = list( c(10, 20, -1), c(10, 20, "All")), pageLength = 10),
filter = 'top')
}
}
})
})
#output$nbclust <- renderPlot({withProgress(message = 'Processing...', value = 0, {
# Datapattern <-DatapatternReactive ()
# subdatwide <- Datapattern$subdatwide
# fviz_nbclust(subdatwide, kmeans, method = "silhouette", k.max = 12) + theme_minimal()
#})
#})
#