Plot Fan Plots for Cytometry Data using 'ggplot2'

An implementation of Fan plots for cytometry data in 'ggplot2'. For reference see Britton, E.; Fisher, P. & J. Whitley (1998) The Inflation Report Projections: Understanding the Fan Chart < https://www.bankofengland.co.uk/quarterly-bulletin/1998/q1/the-inflation-report-projections-understanding-the-fan-chart>).


cytofan

Travis-CI Build Status

cytofan implements the concept of fan plots (Britton, E.; Fisher, P. & J. Whitley (1998) The Inflation Report Projections: Understanding the Fan Chart) for cytometry data in ggplot2.

The cytofan package was implemented following up on a gist written shortly after the fanplot package was released. Compared to ggfan, cytofan uses categorical data as input on the x axis.

Installation

You can install cytofan from github with:

devtools::install_github("yannabraham/cytofan")

Example

cytofan can be used to visualize differences between populations identifed using mass cytometry:

library(cytofan)
#> Loading required package: ggplot2
library(bodenmiller)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(reshape2)
 
data("refPhenoMat")
data("refAnnots")
 
bind_cols(refAnnots,
          as.data.frame(refPhenoMat)) %>%
  melt(.,measure.vars=colnames(refPhenoMat),
       variable.name='Channel') %>%
  filter(Cells %in% c('cd4+','cd8+','igm+','igm-')) %>%
  ggplot(aes(x=Channel,y=value))+
  geom_fan()+
  facet_grid(Cells~.)

News

cytofan 0.1.0

First release on CRAN

Reference manual

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install.packages("cytofan")

0.1.0 by Yann Abraham, 10 months ago


https://github.com/yannabraham/cytofan


Report a bug at https://github.com/yannabraham/cytofan/issues


Browse source code at https://github.com/cran/cytofan


Authors: Yann Abraham [aut, cre]


Documentation:   PDF Manual  


GPL-3 license


Imports RColorBrewer

Depends on ggplot2

Suggests dplyr, reshape2, bodenmiller, knitr, rmarkdown


See at CRAN