In maxmeyl/excyte_1.0: Exhaustive eXploration of CYTometry data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Introduction

Excyte is a pipeline that allows exhaustive exploration of cytometry data. Excyte first preprocess compensated fcs data, then performs unsuppervised clustering of selected events with PhenoGraph (Jacob H. Levine et.al; Cell, 2015) and visualization via Umap (Leland McInnes et.al; arXiv:1802.03426). This pipeline outputs proportions of identified clusters for each input sample as well as intuitive visualizations.

To get started

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)

maxmeyl/excyte_1.0 documentation built on March 7, 2020, 2:01 a.m.