Adding a Rmarkdown workflow example.

CytoR-Workflow.Rmd

@@ -0,0 +1,193 @@
+---
+title: "CytoR"
+author: "Marcel Costa-Garcia"
+date: "`r Sys.Date()`"
+output: html_document
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)
+```
+
+First we load the required libreries and import functions from `functionsCyto.R`:
+```{r}
+library(tidyverse)
+library(flowWorkspace)
+library(Biobase)
+library(flowGate)
+source("functionsCyto.R")
+```
+
+We import the .LMD or .fcs files to a FlowSet object that we convert into a GatingSet (in the case of LMD files, they include both the format FCS2 and FCS3, which is accessed as dataset=2):
+```{r}
+fs<-read.ncdfFlowSet(files=list.files("BcellPhenotype-Files/",".LMD", full.names = T), readonly = F, dataset=2)
+gs <- GatingSet(fs)
+gs
+```
+
+Next, we will compensate with the compensation matrix of the adquisition, which is embed into the file. We can import another compensation matrix:
+
+```{r}
+comp<-spillover(fs[[1]])$`$SPILLOVER`
+colnames(comp)<-colnames(gs)[5:14]
+rownames(comp)<-colnames(gs)[5:14]
+comp
+gs<-compensate(gs, comp)
+```
+
+To transform the axis in an interactive and visual way, I have created the following function:
+```{r, eval=FALSE}
+trans_params<-transform_gs(gs)
+```
+
+![](trans_params.jpg)
+
+```{r, include=F}
+trans_params<-readRDS("BcellPhenotype-trans_params.rds")
+trans_apply(gs, trans_params = trans_params)
+```
+
+```{r}
+trans_params$`FL3-A`
+```
+We can save the transformation params in a file and latter we can import and apply directly (including in other experiments):
+```{r, eval=F}
+saveRDS(trans_params, "BcellPhenotype-trans_params.rds")
+```
+
+
+```{r, eval=F}
+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:
+```{r}
+markers<-colnames(gs)
+markers[c(7,13)]<-c("CD19","L&D")
+names(markers)<-colnames(gs)
+markernames(gs)<-markers
+markernames(gs)
+```
+
+Finally, we will clean a bit the sample names:
+```{r}
+sampleNames(gs)
+sampleNames(gs)<-gsub("\\s[0-9]*.LMD","",sampleNames(gs))
+sampleNames(gs)<-gsub(".*\\s","",sampleNames(gs))
+pData(gs)$name<-rownames(pData(gs))
+sampleNames(gs)
+```
+
+
+And we are ready to gate! We will be using the `gs_interactive_gate` function from `flowGate` package.
+```{r, include=F}
+gates<-readRDS("BcellPhenotype-gates.rds")
+gs<-gates_apply(gs, gates)
+```
+
+```{r, eval=F}
+gs_gate_interactive(gs,
+                    filterId = "Leukocytes",
+                    dims = list("FS-A", "SS-A"))
+```
+![](Gates.jpg)
+
+```{r, eval=F}
+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):
+```{r, eval=F}
+gates<-gates_save(gs, file = "BcellPhenotype-gates.rds")
+
+gates<-readRDS("BcellPhenotype-gates.rds")
+gs<-gates_apply(gs, gates)
+```
+
+```{r}
+plot(gs)
+```
+
+We can rapidly explore the results using the `autoplot` function:
+```{r}
+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:
+```{r}
+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:
+```{r, fig.width=4}
+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)
+
+```
+
+Code:
+```r
+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)
+```