manual_plot

elijahedmondson · Jun 24, 2022 · 1a1db3b · 1a1db3b
1 parent be5c3f7
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diff --git a/A01 Humanized.R b/A01 Humanized.R
@@ -15,21 +15,38 @@ library(lme4)
 library(multcomp)
 ########### 1. Generate Counts From Manual Gating ########### 
 ########### 
-data_dir <- "C:/Users/edmondsonef/Desktop/Humanized/Flow/Aggregate/" 
+data_dir <- "C:/Users/edmondsonef/Desktop/Humanized/Flow/Aggregate/TD033/" 
 study_dir <- ""
 
-ws <- open_flowjo_xml(paste0(data_dir,study_dir,"19-331-122 Full Dataset WSP 220414.wsp"))
+ws <- open_flowjo_xml(paste0(data_dir,"19-331-122 Full Dataset WSP 220414.wsp"))
 ws
-#fj_ws_get_samples(ws, group_id = c(1))
-gs <- flowjo_to_gatingset(ws, name = 13, path=paste0(data_dir,study_dir))#,compensation = compensation_matrix)
+
+fj_ws_get_samples(ws, group_id = c(1))$name
+
+
+#myfiles <- list.files(path="C:/Users/edmondsonef/Desktop/Humanized/Flow/Aggregate", pattern = "Samples", ignore.case = TRUE)
+#fs <- read.flowSet(myfiles, path="C:/Users/edmondsonef/Desktop/Humanized/Flow/Aggregate/TD033", truncate_max_range = FALSE)
+#CS <- load_cytoset_from_fcs(files = myfiles, path="C:/Users/edmondsonef/Desktop/Humanized/Flow/Aggregate/TD033")
+
+###THE ISSUE IS ALL THE NEW FILES IN THE COMBING WS HAVE THE NO. OF CELLS AT THE END .fsc_xxxx????###
+
+
+ gs <- flowjo_to_gatingset(ws, name = 6, path=data_dir)#, 
+                           channel.ignore.case = F) #,
+                           skip_faulty_gate = T,
+                           include_empty_tree = F,
+                           cytoset = CS) #,compensation = compensation_matrix)
+gs
 gs_get_pop_paths(gs)
 recompute(gs)
-#plot(gs)
+plot(gs)
+
+
 
 ######### % Human
 ######### % Human
 ######### % Human
-human_counts <- gs_pop_get_count_fast(gs, format = "long", subpopulations = gs_get_pop_paths(gs)[4])
+human_counts <- gs_pop_get_count_fast(gs, format = "long", subpopulations = gs_get_pop_paths(gs)[1])
 human_counts
 human_counts <- human_counts %>% pivot_wider(id_cols = name, 
                                              names_from = Population, 
@@ -380,7 +397,7 @@ agg <- AggregateFlowFrames(paste0(dir_prepr, files),
 ########### 5. Train FlowSOM model ###########
 ###########  ###########
 
-#agg <- read.FCS(paste0(dir_results,'aggregate.fcs'), truncate_max_range = FALSE)
+agg <- read.FCS(paste0(dir_results,'aggregate.fcs'), truncate_max_range = FALSE)
 
 
 #Level 1 - create model to separate human cells from mouse

diff --git a/A02 Humanized manual data.R b/A02 Humanized manual data.R
@@ -0,0 +1,188 @@
+
+library(flowCore)
+library(flowWorkspace)
+library(ggcyto)
+library(flowAI)
+library(gridExtra)
+library(tidyverse)
+library(flowStats)
+library(CytoML)
+library(Rtsne)
+library(FlowSOM)
+library(ggplot2)
+library(ggpubr)
+library(dplyr)
+library(lme4)
+library(multcomp)
+#manual
+#Load data
+#myfiles <- list.files(path="C:/Users/edmondsonef/Desktop/Humanized Mouse Models/Flow/15701 02Feb2022 Simone/", pattern = ".FCS", ignore.case = TRUE)
+#fs <- read.flowSet(myfiles, path="C:/Users/edmondsonef/Desktop/Humanized Mouse Models/Flow/15701 02Feb2022 Simone/")#, truncate_max_range = FALSE)
+
+# data_dir <- "C:/Users/edmondsonef/Desktop/Humanized/Flow/Aggregate/FCS/" 
+# ws <- open_flowjo_xml("C:/Users/edmondsonef/Desktop/Humanized/Flow/Aggregate/FCS/19-331-122 Full Dataset WSP 220414.wsp")
+# #ws <- open_flowjo_xml(paste0(data_dir,"15761 12Apr2022 LASP.wsp"))
+# ws
+# 
+# head(fj_ws_get_samples(ws, group_id = c(1)))
+# tail(fj_ws_get_sample_groups(ws))
+# fj_ws_get_keywords(ws, 28)[1:8]
+# 
+# myfiles <- list.files(path=data_dir, pattern = "Samples", ignore.case = TRUE)
+# 
+# path_comp <- "C:/Users/edmondsonef/Desktop/0-Autospill/6-10Mar2022/autospill_compensation.csv"
+# compensation_matrix <- read.csv(path_comp, check.names = FALSE, row.names = 1)
+# 
+
+
+
+
+
+
+#####
+##### Creating manual plots
+
+
+
+
+data_dir <- "C:/Users/edmondsonef/Desktop/Humanized/Flow/Aggregate/FCS/marrow/19-331-122/" 
+#myfiles <- list.files(path=data_dir, pattern = "Samples", ignore.case = TRUE)
+#fs <- read.flowSet(myfiles, path=data_dir, truncate_max_range = FALSE)
+
+
+
+ws <- open_flowjo_xml("C:/Users/edmondsonef/Desktop/Humanized/Flow/Aggregate/FCS/19-331-122 Full Dataset WSP 220414.wsp")
+gs <- flowjo_to_gatingset(ws, name = 1, path=data_dir)
+plot(gs)
+
+pData(gs) %>% head(15)
+pData(gs)$animal <-  gsub(".*_.*_...(.*)_.*.fcs_.*","\\1",sampleNames(gs))
+pData(gs) %>% head(15)
+
+out <- pData(gs)
+write.csv(out, "C:/Users/edmondsonef/Desktop/Human1.csv")
+out <- read.csv("C:/Users/edmondsonef/Desktop/Human1.csv", header = T, stringsAsFactors = F)
+pData(gs)$strain <- out$strain
+
+
+colnames(gs)
+colnames(gs)[colnames(gs)=="Comp-BB515-A"] <- "CD8"
+colnames(gs)[colnames(gs)=="Comp-BB700-P-A"] <- "CD4"
+colnames(gs)[colnames(gs)=="Comp-APC-A"] <- "CD11b"
+colnames(gs)[colnames(gs)=="Comp-APC-Cy7-A"] <- "CD19"
+colnames(gs)[colnames(gs)=="Comp-BV421-A"] <- "CD3"
+colnames(gs)[colnames(gs)=="Comp-BV786-A"] <- "CD33"
+colnames(gs)[colnames(gs)=="Comp-BUV395-A"] <- "mCD45"
+colnames(gs)[colnames(gs)=="Comp-BUV805-A"] <- "huCD45"
+colnames(gs)[colnames(gs)=="Comp-PE-A"] <- "CD56"
+colnames(gs)[colnames(gs)=="Comp-PE-CF594-A"] <- "CD66b"
+colnames(gs)[colnames(gs)=="Comp-PE-Cy7-A"] <- "CD25"
+colnames(gs)
+
+ggcyto(gs[1],aes(x="FSC-A",y="FSC-H"),subset="root")+geom_gate("sing")+
+  geom_hex(bins = 200)+
+  geom_stats(adjust = 0.8)+ggcyto_par_set(limits = "instrument") +
+  facet_wrap(~animal,ncol = 10)
+
+colnames(gs)
+plot(gs)
+gs_get_pop_paths(gs, path = 2)
+nodelist <- gs_get_pop_paths(gs, path = "auto")[c(11,15,18,22,23,27,30,31,32,35)]
+nodelist
+node <- nodelist[3]
+g <- gs_pop_get_gate(gs, node)
+g
+gs_pop_get_stats(gs)[1:10,]
+
+
+autoplot(gs, nodelist[1], digits = 2)+
+  facet_wrap(~animal) + geom_hex(bins = 300)
+
+autoplot(gs, nodelist[2], digits = 2)+
+  facet_wrap(~animal)
+autoplot(gs, nodelist[3], digits = 2)+
+  facet_wrap(~animal)
+autoplot(gs, nodelist[4], digits = 2)+
+  facet_wrap(~animal)
+autoplot(gs, nodelist[5], digits = 2)+
+  facet_wrap(~animal)
+autoplot(gs, nodelist[6], digits = 2)+
+  facet_wrap(~animal)
+
+ps <- gs_pop_get_count_with_meta(gs)
+ps <- ps %>% mutate(percent_of_parent=Count/ParentCount)
+head(ps)
+
+
+
+
+######### % Human
+######### % Human
+######### % Human
+human_counts <- gs_pop_get_count_fast(gs, format = "long", subpopulations = gs_get_pop_paths(gs)[1])
+human_counts
+human_counts <- human_counts %>% pivot_wider(id_cols = name, 
+                                             names_from = Population, 
+                                             values_from = c("Count", "ParentCount"))
+write.csv(human_counts, "C:/Users/edmondsonef/Desktop/Humanized.count.csv")
+
+
+######### STACKED BAR CHART
+######### STACKED BAR CHART
+######### STACKED BAR CHART
+gs_get_pop_paths(gs)[c(11,15,18,22,23,27,30,31,32,35)]
+#counts_table <-gs_pop_get_count_fast(gs[1])
+counts_table <- gs_pop_get_count_fast(gs, format = "long", subpopulations = gs_get_pop_paths(gs)[c(11,15,18,22,23,27,30,31,32,35)])
+counts_table
+counts_table <- counts_table %>% pivot_wider(id_cols = name, 
+                                             names_from = Population, 
+                                             values_from = c("Count", "ParentCount"))
+write.csv(counts_table, "C:/Users/edmondsonef/Desktop/counts_table.csv")
+
+
+counts_table <- read.csv("C:/Users/edmondsonef/Desktop/counts_table.csv")
+counts_table_t <- counts_table[-1] %>% t() %>% as.data.frame() %>% setNames(counts_table[,1])
+props_table <- t(t(counts_table_t) / colSums(counts_table_t[])) * 100
+props_table_t <- props_table[] %>% t() %>% as.data.frame() %>% setNames(row.names(props_table))
+counts <- as.data.frame.matrix(counts_table_t)
+props <- as.data.frame.matrix(props_table)
+write.csv(props, "C:/Users/edmondsonef/Desktop/props.csv")
+
+
+props <- read.csv("C:/Users/edmondsonef/Desktop/props.csv", header = T, stringsAsFactors = F)
+props <- data.frame(props[,-1], row.names = props[,1])
+ggdf <- reshape2::melt(data.frame(cluster = rownames(props), props), 
+                       id.vars = "cluster", value.name = "proportion", variable.name = "sample_id")
+write.csv(ggdf, "C:/Users/edmondsonef/Desktop/ggdf.csv")
+#ggdf <- read.csv("C:/Users/edmondsonef/Desktop/ggdf.csv", header = T, stringsAsFactors = F)
+
+
+
+
+data <- read_excel("C:/Users/edmondsonef/Desktop/agg.xlsx", sheet = "Final.blood")
+ggdf <- data
+
+
+color_clusters <- c("#7BAFDE", "#7570B3", "#882E72", "#B17BA6", "#FF7F00", 
+                    "#DC050C", "#FB8072", "#FDB462", #"#E7298A", "#E78AC3", 
+                    "#33A02C", "#B2DF8A", "#55A1B1", "#8DD3C7", "#A6761D", 
+                    "#E6AB02", "#1965B0", "#BEAED4", "#666666", "#999999", 
+                    "#aa8282", "#d4b7b7", "#8600bf", "#ba5ce3", "#808000", 
+                    "#aeae5c", "#1e90ff", "#00bfff", "#56ff0d", "#ffff00")
+
+plot <- ggplot(ggdf, aes(x = ID, y = proportion, fill = cluster)) +
+  geom_bar(stat = "identity") +
+  facet_wrap(~ GroupX, scales = "free_x") +
+  theme_bw() +
+  theme(axis.title.x=element_blank(), text = element_text(size = 10))+
+  labs(title="Blood") +
+  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
+  scale_fill_manual(values = color_clusters) 
+plot
+setwd("C:/Users/edmondsonef/Desktop/R-plots/")
+tiff("blood_plots.tiff", units="in", width=10, height=4, res=600)
+plot
+dev.off()
+######### STACKED BAR CHART
+######### STACKED BAR CHART
+######### STACKED BAR CHART