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%\VignetteIndexEntry{How to parse gatingML into a GatingSet}
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---
title: "How to parse gatingML into a GatingSet"
author: "Mike Jiang"
date: "October 27, 2015"
output: html_document
---

This vignette demonstrates how the gatingML files exported from Cytobank can be imported into R as a GatingSet object.

### Load the gatingML file as a **graphGML** object
```{r message=FALSE}
library(flowWorkspace)
xmlfile <- system.file("extdata/cytotrol_tcell_cytobank.xml", package = "flowWorkspace")
g <- read.gatingML.cytobank(xmlfile)
class(g)
```


### **graphGML** stores the gating hierarchy
```{r}
g
```

### It can be inspected by various accessors.
```{r}
getNodes(g)
getParent(g, "GateSet_722318")
getChildren(g, "GateSet_722318")

```

### The Population tree can be plotted
```{r fig.width=4,fig.height=4}
plot(g)
```

The node with **dotted** border means the `tailored` gates(or sample-specific gates) are defined for that population.

### Read raw FCS files
```{r message=FALSE}
fcsFiles <- list.files(pattern = "CytoTrol", system.file("extdata", package = "flowWorkspaceData"), full = T)
fs <- read.ncdfFlowSet(fcsFiles)
```

### Compensate the data
```{r message=FALSE}
fs <- compensate(fs, g)
```

### Transform the data
```{r message=FALSE}
trans <- getTransformations(g)
trans
fs <- transform(fs, trans)
```


### Visualize the outcome of compensation and transformation
```{r fig.width=4,fig.height=4, fig.show='hold'}
 densityplot(~`CD4`, fs)
 xyplot(`CD4` ~`CD8`, fs, smooth = FALSE, xbin = 32)

```


### Construct the **GatingSet** and apply the gates
```{r message=FALSE}
gs <- GatingSet(fs)
gating(g, gs)
```


### Plot the gates
```{r}
plotGate(gs[[1]])
```

### Extract the population statistics
```{r results='markup'}
getPopStats(gs, statType = "count")
```