# `r bead.data@fcs.filename` #

## Bead Stats ##
```{r results='asis',echo=FALSE}
    K <- dim(bead.data@beads.mef)[1]+1
    for (p in names(bead.data@mef.transform)) {
        d <- data.frame(bead.data@clustering.stats[,p, 1:K])
        names(d) <- paste(p, 1:K)
        print(xtable(d), type='html')
     }
``` 

## Plot ##
```{r plot, echo=FALSE, fig.path=paste(dirname(output.file), 'figures', gsub('.FCS','',toupper(basename(bead.data@fcs.filename))), sep='/')}
    plot(bead.data)
```

## Regression Summary ##
```{r results='asis',echo=FALSE}
    print( xtable(bead.data@mef.transform$APC$m), type='html' )
``` 

## Flow Cytometer Settings ##
```{r results='asis',echo=FALSE}
    print( xtable(t(data.frame(bead.data@description)[1,])), type='html' )
```





