Introduction

A tutorial overview of flowPloidy is available on the Bioconductor website. This vignette is provided with the package, so once you have flowPloidy installed you can access it from with R (see below).

Installation

Stable Version

flowPloidy is available in Bioconductor.

To install it, you need to install the bioconductor R package (more details on the Bioconductor site ):

## try http:// if https:// URLs are not supported
if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install()

Once that’s installed, you can install flowPloidy using the Bioconductor tools:

BiocManager::install("flowPloidy")
BiocManager::install("flowPloidyData")   # (optional) data for the examples

This should pull in all the package dependencies for flowPloidy, after which you can load the package with the normal function library("flowPloidy").

Development Version

As of June 2018, I have added a new analysis method. This is aimed at assessing endopolyploidy, where a single sample may have four or more peaks. The intent is to compare the number of cells in each peak, rather than to determine a ratio relative to a co-chopped standard.

This new code will be incorporated into Bioconductor for the next release. If you’d like to try it now, you can install it directly from the GitHub repository as follows:

## Install Bioconductor tools first:
if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install()

## Install flowCore from Bioconductor:
BiocManager::install("flowCore")

## Install devtools so you can directly access GitHub
install.packages(devtools)
library(devtools)

## Install flowPloidy:
install_github("plantarum/flowPloidy", dependencies = TRUE, 
    build_vignettes = TRUE)

If the last command fails, particularly with complaints about building a vignette, or reference to Pandoc, try with build_vignettes = FALSE instead.

Note that I haven’t yet updated the documentation to match the new code. To use the endopolyploidy workflow, you need to use a new argument, g2 = FALSE in your call to FlowHist or batchFlowHist (NB: use g2, lowercase, not G2, uppercase. The original version of this README was incorrect!). This excludes the g2 peaks from peak fitting, treating each peak as an independent group of cells. You may also want to increase the samples argument to match the number of peaks; however, you can correct this in browseFlowHist, so that’s not critical.

## loading files for endopolyploidy analysis:
batch1 <- batchFlowHist(endo_files, channel="FL3.INT.LIN", g2 = FALSE,
    samples = 5)

batch1 <- browseFlowHist(batch1)

Expanding flowPloidy to handle up to six peaks (and now potentially an unlimited number if needed) required reworking a bunch of the existing code, and as part of this the column headings in the tables produced by tabulateFlowHist are now different from the previous release.

Getting Started

library("flowPloidy")

The flowPloidy workflow is documented in the vignette, which you can view from R:

fpVig <- vignette("flowPloidy-overview")
fpVig ## open vignette in a browser
edit(name = fpVig) ## open vignette source code in a text editor

It is also available online.

Getting Help

For general help using the package, you can post questions on the Bioconductor Support Site. Use the tag flowploidy to ensure your question is brought to my attention.

The development repository for flowPloidy is on Github, and you can file bugs there using the issues tab. You are also welcome to contribute features or bug-fixes via pull requests!