CytoR
Marcel Costa-Garcia
2024-04-10
First we load the required libreries and import functions from
functionsCyto.R:
library(tidyverse)
library(flowWorkspace)
library(Biobase)
library(flowGate)
source("functionsCyto.R")We import the .LMD or .fcs files to a FlowSet object that we convert into a GatingSet (in the case of LMD files, they include both the format FCS2 and FCS3, which is accessed as dataset=2):
fs<-read.ncdfFlowSet(files=list.files("BcellPhenotype-Files/",".LMD", full.names = T), readonly = F, dataset=2)
gs <- GatingSet(fs)
gs## A GatingSet with 2 samplesNext, we will compensate with the compensation matrix of the adquisition, which is embed into the file. We can import another compensation matrix:
comp<-spillover(fs[[1]])$`$SPILLOVER`
colnames(comp)<-colnames(gs)[5:14]
rownames(comp)<-colnames(gs)[5:14]
comp## FL1-A FL2-A FL3-A FL4-A FL5-A FL6-A FL7-A FL8-A FL9-A FL10-A
## FL1-A 1.000 0.699 0.118 0.000 0.000 0.000 0.000 0.000 0.000 0.265
## FL2-A 0.002 1.000 0.542 0.000 0.000 0.000 0.000 0.000 0.000 0.000
## FL3-A 0.000 0.036 1.000 0.949 0.000 0.000 0.000 0.000 0.000 0.000
## FL4-A 0.000 0.000 0.003 1.000 0.182 0.168 0.161 0.000 0.000 0.000
## FL5-A 0.000 0.000 0.000 0.002 1.000 0.000 0.006 0.000 0.000 0.000
## FL6-A 0.000 0.000 0.000 0.000 0.000 1.000 0.293 0.000 0.000 0.000
## FL7-A 0.000 0.000 0.000 0.000 0.000 0.027 1.000 0.166 0.000 0.000
## FL8-A 0.000 0.000 0.000 0.000 0.000 0.000 0.090 1.000 0.000 0.000
## FL9-A 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.136
## FL10-A 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.055 1.000gs<-compensate(gs, comp)To transform the axis in an interactive and visual way, I have created the following function:
trans_params<-transform_gs(gs)
trans_params$`FL3-A`## $channel
## [1] "FL3-A"
##
## $scale
## [1] "biexp"
##
## $maxvalue
## [1] 250000
##
## $pos
## [1] 5
##
## $widthBasis
## [1] -400
##
## $max
## [1] 5011.872
##
## $min
## [1] 956We can save the transformation params in a file and latter we can import and apply directly (including in other experiments):
saveRDS(trans_params, "BcellPhenotype-trans_params.rds")trans_params<-readRDS("BcellPhenotype-trans_params.rds")
trans_apply(gs, trans_params = trans_params)As it wasn’t done during the acquisition, we will define the marker name for the channels of interest:
markers<-colnames(gs)
markers[c(7,13)]<-c("CD19","L&D")
names(markers)<-colnames(gs)
markernames(gs)<-markers
markernames(gs)## FS-H FS-A FS-W SS-A FL1-A FL2-A FL3-A FL4-A
## "FS-H" "FS-A" "FS-W" "SS-A" "FL1-A" "FL2-A" "CD19" "FL4-A"
## FL5-A FL6-A FL7-A FL8-A FL9-A FL10-A
## "FL5-A" "FL6-A" "FL7-A" "FL8-A" "L&D" "FL10-A"Finally, we will clean a bit the sample names:
sampleNames(gs)## [1] "FC20339 2020.09.14 LD CD19PECF594 aCDE19 002.LMD"
## [2] "FC20339 2020.09.14 LD CD19PECF594 Unst 001.LMD"sampleNames(gs)<-gsub("\\s[0-9]*.LMD","",sampleNames(gs))
sampleNames(gs)<-gsub(".*\\s","",sampleNames(gs))
pData(gs)$name<-rownames(pData(gs))
sampleNames(gs)## [1] "aCDE19" "Unst"And we are ready to gate! We will be using the
gs_interactive_gate function from flowGate
package.
gs_gate_interactive(gs,
filterId = "Leukocytes",
dims = list("FS-A", "SS-A"))
gs_gate_interactive(gs,
subset = "Leukocytes",
filterId = "CD19 L&D",
dims = list("CD19", "L&D"))We can save the created gates into a file to import latter on (which may be also used to apply the gating strategy into a new experiment):
gates<-gates_save(gs, file = "BcellPhenotype-gates.rds")
gates<-readRDS("BcellPhenotype-gates.rds")
gs<-gates_apply(gs, gates)plot(gs)
We can rapidly explore the results using the autoplot
function:
autoplot(gs, "Leukocytes", bins=128, nrow=1)
autoplot(gs[["aCDE19"]], bins=128, nrow=1)
We can further personalize the plot with similar sintaxis as
ggplot with the ggcyto package:
g<-ggcyto(gs, subset = "Leukocytes", bins=128, aes(CD19,`L&D`))+
facet_grid(.~factor(name, levels=c("Unst","aCDE19")))+
geom_hex(bins=128)+
geom_gate()+
geom_stats()+
scale_fill_gradient(low="black", high="violet")
g
Finally, we can export the stats and plot them:
stats<-gs_pop_get_stats(gs, nodes=gs_get_pop_paths(gs, path = "auto")[3:6], type="perc")
stats## sample pop percent
## <char> <char> <num>
## 1: aCDE19 CD19-L&D+ 0.013749535
## 2: aCDE19 CD19+L&D+ 0.101820884
## 3: aCDE19 CD19+L&D- 0.880936455
## 4: aCDE19 CD19-L&D- 0.003493125
## 5: Unst CD19-L&D+ 0.069043075
## 6: Unst CD19+L&D+ 0.119411123
## 7: Unst CD19+L&D- 0.373977644
## 8: Unst CD19-L&D- 0.437568157g2<-ggplot(stats, aes(factor(sample, levels=c("Unst","aCDE19")), percent, fill=pop))+
geom_bar(stat="identity", color="black")+xlab("Samples")
ggpubr::ggarrange(as.ggplot(g), g2, ncol=1)
Code:
library(tidyverse)
library(flowWorkspace)
library(Biobase)
library(flowGate)
source("functionsCyto.R")
fs<-read.ncdfFlowSet(files=list.files("BcellPhenotype-Files/",".LMD", full.names = T), readonly = F, dataset=2)
gs <- GatingSet(fs)
comp<-spillover(fs[[1]])$`$SPILLOVER`
colnames(comp)<-colnames(gs)[5:14]
rownames(comp)<-colnames(gs)[5:14]
gs<-compensate(gs, comp)
trans_params<-transform_gs(gs)
markers<-colnames(gs)
markers[c(7,13)]<-c("CD19","L&D")
names(markers)<-colnames(gs)
markernames(gs)<-markers
sampleNames(gs)<-gsub("\\s[0-9]*.LMD","",sampleNames(gs))
sampleNames(gs)<-gsub(".*\\s","",sampleNames(gs))
pData(gs)$name<-rownames(pData(gs))
gs_gate_interactive(gs,
filterId = "Leukocytes",
dims = list("FS-A", "SS-A"))
gs_gate_interactive(gs,
subset = "Leukocytes",
filterId = "CD19 L&D",
dims = list("CD19", "L&D"))
g<-ggcyto(gs, subset = "Leukocytes", bins=128, aes(CD19,`L&D`))+
facet_grid(.~factor(name, levels=c("Unst","aCDE19")))+
geom_hex(bins=128)+
geom_gate()+
geom_stats()+
scale_fill_gradient(low="black", high="violet")
stats<-gs_pop_get_stats(gs, nodes=gs_get_pop_paths(gs, path = "auto")[3:6], type="perc")
stats
g2<-ggplot(stats, aes(factor(sample, levels=c("Unst","aCDE19")), percent, fill=pop))+
geom_bar(stat="identity", color="black")+xlab("Samples")
ggpubr::ggarrange(as.ggplot(g), g2, ncol=1)