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193 lines
5.2 KiB
193 lines
5.2 KiB
---
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title: "CytoR"
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author: "Marcel Costa-Garcia"
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date: "`r Sys.Date()`"
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output: html_document
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---
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```{r setup, include=FALSE}
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knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)
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```
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First we load the required libreries and import functions from `functionsCyto.R`:
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```{r}
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library(tidyverse)
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library(flowWorkspace)
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library(Biobase)
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library(flowGate)
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source("functionsCyto.R")
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```
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We import the .LMD or .fcs files to a FlowSet object that we convert into a GatingSet (in the case of LMD files, they include both the format FCS2 and FCS3, which is accessed as dataset=2):
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```{r}
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fs<-read.ncdfFlowSet(files=list.files("BcellPhenotype-Files/",".LMD", full.names = T), readonly = F, dataset=2)
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gs <- GatingSet(fs)
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gs
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```
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Next, we will compensate with the compensation matrix of the adquisition, which is embed into the file. We can import another compensation matrix:
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```{r}
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comp<-spillover(fs[[1]])$`$SPILLOVER`
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colnames(comp)<-colnames(gs)[5:14]
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rownames(comp)<-colnames(gs)[5:14]
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comp
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gs<-compensate(gs, comp)
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```
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To transform the axis in an interactive and visual way, I have created the following function:
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```{r, eval=FALSE}
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trans_params<-transform_gs(gs)
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```
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```{r, include=F}
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trans_params<-readRDS("BcellPhenotype-trans_params.rds")
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trans_apply(gs, trans_params = trans_params)
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```
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```{r}
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trans_params$`FL3-A`
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```
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We can save the transformation params in a file and latter we can import and apply directly (including in other experiments):
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```{r, eval=F}
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saveRDS(trans_params, "BcellPhenotype-trans_params.rds")
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```
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```{r, eval=F}
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trans_params<-readRDS("BcellPhenotype-trans_params.rds")
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trans_apply(gs, trans_params = trans_params)
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```
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As it wasn't done during the acquisition, we will define the marker name for the channels of interest:
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```{r}
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markers<-colnames(gs)
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markers[c(7,13)]<-c("CD19","L&D")
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names(markers)<-colnames(gs)
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markernames(gs)<-markers
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markernames(gs)
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```
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Finally, we will clean a bit the sample names:
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```{r}
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sampleNames(gs)
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sampleNames(gs)<-gsub("\\s[0-9]*.LMD","",sampleNames(gs))
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sampleNames(gs)<-gsub(".*\\s","",sampleNames(gs))
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pData(gs)$name<-rownames(pData(gs))
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sampleNames(gs)
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```
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And we are ready to gate! We will be using the `gs_interactive_gate` function from `flowGate` package.
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```{r, include=F}
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gates<-readRDS("BcellPhenotype-gates.rds")
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gs<-gates_apply(gs, gates)
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```
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```{r, eval=F}
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gs_gate_interactive(gs,
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filterId = "Leukocytes",
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dims = list("FS-A", "SS-A"))
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```
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```{r, eval=F}
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gs_gate_interactive(gs,
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subset = "Leukocytes",
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filterId = "CD19 L&D",
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dims = list("CD19", "L&D"))
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```
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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):
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```{r, eval=F}
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gates<-gates_save(gs, file = "BcellPhenotype-gates.rds")
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gates<-readRDS("BcellPhenotype-gates.rds")
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gs<-gates_apply(gs, gates)
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```
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```{r}
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plot(gs)
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```
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We can rapidly explore the results using the `autoplot` function:
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```{r}
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autoplot(gs, "Leukocytes", bins=128, nrow=1)
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autoplot(gs[["aCDE19"]], bins=128, nrow=1)
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```
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We can further personalize the plot with similar sintaxis as `ggplot` with the `ggcyto` package:
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```{r}
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g<-ggcyto(gs, subset = "Leukocytes", bins=128, aes(CD19,`L&D`))+
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facet_grid(.~factor(name, levels=c("Unst","aCDE19")))+
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geom_hex(bins=128)+
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geom_gate()+
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geom_stats()+
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scale_fill_gradient(low="black", high="violet")
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g
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```
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Finally, we can export the stats and plot them:
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```{r, fig.width=4}
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stats<-gs_pop_get_stats(gs, nodes=gs_get_pop_paths(gs, path = "auto")[3:6], type="perc")
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stats
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g2<-ggplot(stats, aes(factor(sample, levels=c("Unst","aCDE19")), percent, fill=pop))+
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geom_bar(stat="identity", color="black")+xlab("Samples")
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ggpubr::ggarrange(as.ggplot(g), g2, ncol=1)
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```
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Code:
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```r
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library(tidyverse)
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library(flowWorkspace)
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library(Biobase)
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library(flowGate)
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source("functionsCyto.R")
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fs<-read.ncdfFlowSet(files=list.files("BcellPhenotype-Files/",".LMD", full.names = T), readonly = F, dataset=2)
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gs <- GatingSet(fs)
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comp<-spillover(fs[[1]])$`$SPILLOVER`
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colnames(comp)<-colnames(gs)[5:14]
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rownames(comp)<-colnames(gs)[5:14]
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gs<-compensate(gs, comp)
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trans_params<-transform_gs(gs)
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markers<-colnames(gs)
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markers[c(7,13)]<-c("CD19","L&D")
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names(markers)<-colnames(gs)
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markernames(gs)<-markers
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sampleNames(gs)<-gsub("\\s[0-9]*.LMD","",sampleNames(gs))
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sampleNames(gs)<-gsub(".*\\s","",sampleNames(gs))
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pData(gs)$name<-rownames(pData(gs))
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gs_gate_interactive(gs,
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filterId = "Leukocytes",
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dims = list("FS-A", "SS-A"))
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gs_gate_interactive(gs,
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subset = "Leukocytes",
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filterId = "CD19 L&D",
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dims = list("CD19", "L&D"))
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g<-ggcyto(gs, subset = "Leukocytes", bins=128, aes(CD19,`L&D`))+
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facet_grid(.~factor(name, levels=c("Unst","aCDE19")))+
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geom_hex(bins=128)+
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geom_gate()+
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geom_stats()+
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scale_fill_gradient(low="black", high="violet")
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stats<-gs_pop_get_stats(gs, nodes=gs_get_pop_paths(gs, path = "auto")[3:6], type="perc")
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stats
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g2<-ggplot(stats, aes(factor(sample, levels=c("Unst","aCDE19")), percent, fill=pop))+
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geom_bar(stat="identity", color="black")+xlab("Samples")
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ggpubr::ggarrange(as.ggplot(g), g2, ncol=1)
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```
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