In darachm/rheocyto: Some FlowCytometry Analysis Tools

Vignettes are long form documentation commonly included in packages. Because they are part of the distribution of the package, they need to be as compact as possible. The html_vignette output type provides a custom style sheet (and tweaks some options) to ensure that the resulting html is as small as possible. The html_vignette format:

• Never uses retina figures
• Has a smaller default figure size
• Uses a custom CSS stylesheet instead of the default Twitter Bootstrap style

Vignette Info

Note the various macros within the vignette section of the metadata block above. These are required in order to instruct R how to build the vignette. Note that you should change the title field and the \VignetteIndexEntry to match the title of your vignette.

Styles

The html_vignette template includes a basic CSS theme. To override this theme you can specify your own CSS in the document metadata as follows:

output: 
  rmarkdown::html_vignette:
    css: mystyles.css

Figures

The figure sizes have been customised so that you can easily put two images side-by-side.

plot(1:10)
plot(10:1)

You can enable figure captions by fig_caption: yes in YAML:

output:
  rmarkdown::html_vignette:
    fig_caption: yes

Then you can use the chunk option fig.cap = "Your figure caption." in knitr.

More Examples

You can write math expressions, e.g. $Y = X\beta + \epsilon$, footnotes^[A footnote here.], and tables, e.g. using knitr::kable().

knitr::kable(head(mtcars, 10))

Also a quote using >:

"He who gives up [code] safety for [code] speed deserves neither." (via)

z<- (dlply(cz2,"fileName",function(x){ unlist(apply(x,1,function(x){as.numeric(rep(x["binDiameter"],x["binCounts"]))})) }))

datadir <- "./exampleFlowDataset2/"
souqce("./src/coulterZ2reader.R")
lrgdf <- coulterZ2reader(datadir)

This next part must be customized for your application! Look at the comments!

# here we put into the next column (ncol+1) through to 
#   enough columns (ncol+4), the colsplit of
lrgdf[,(ncol(lrgdf)+1):(ncol(lrgdf)+4)] <- colsplit(
# splitting the filename, sans extension, on underscores...
  string=sub(".=#Z2","",lrgdf$fz),pattern="_",
# and give them names for each field.
  names=c("date","time","media","blankOrNot"))
# for this to work, you have to have the same number of underscore
#   delimited fields in each filename. You can use something other
#   than underscore too, just change it in colsplit argument pattern
head(lrgdf)

lrgdf$fz <- as.character(lrgdf$fz)
lrgdf$date <- as.Date(x=as.character(lrgdf$date),format="%y%m%d")
lrgdf$time <- 
  as.numeric(sub("(\\d{1,2})\\d{2}","\\1",lrgdf$time))*60+
  as.numeric(sub("\\d{1,2}(\\d{2})","\\1",lrgdf$time))
lrgdf$blankOrNot <- grepl("blank",lrgdf$blankOrNot)
lrgdf$media<-factor(lrgdf$media)
head(lrgdf)

For coulter, blanks are important to see how big the background bubbles and crud are.

ggplot(lrgdf)+aes(x=bins,y=height,col=factor(time))+
    geom_point()+facet_wrap(~blankOrNot)+scale_y_log10()

ggplot(subset(lrgdf,bins>2.0))+aes(x=bins,y=height,col=factor(time))+
    geom_point()+facet_wrap(~blankOrNot)+scale_y_log10()

ggplot(subset(lrgdf,bins>2.5))+aes(x=bins,y=height,col=factor(time))+
    geom_point()+facet_wrap(~blankOrNot)+scale_y_log10()

ggplot(subset(lrgdf,bins>3.0))+aes(x=bins,y=height,col=factor(time))+
    geom_point()+facet_wrap(~blankOrNot)+scale_y_log10()

subdf <- subset(lrgdf,bins>2.5&bins<7.0&blankOrNot==F)

Below we use aggregate to just wrap up all events detected per sample. Then we adjust by a scaling factor to adjust for the ammount counted, and the dilution factor. You will need to change this!

counts <- aggregate(height~fz+date+time+media,data=subdf,FUN=sum)
counts$e6cellsml <- counts$height/2000
# that adjustment must be adjusted!

g<-ggplot(counts)+
    aes(x=time/60,y=e6cellsml)+
    geom_point()+
    scale_y_log10(limits=c(1,7),breaks=seq(1,7,1))+
    scale_x_continuous(breaks=seq(09,21,1),labels=(09:21))+
    xlab("Hours")+
    ylab("million cells per ml")
g
#ggsave("151206exp152counts.pdf",g,width=8,height=6)

How about going back to distributions for alternative stats and representations?

distributions <- list()
for (fz in unique(subdf$fz)) {
  distributions[[fz]] <- df2distribution(subset(subdf,fz==fz))
}
hist(distributions[[1]])

darachm/rheocyto documentation built on May 14, 2019, 6:07 p.m.