Shiny modules for visualizing flow cytometry data as well as data transformation methods to enable the rapid display of cytometry data.
flowDashboard uses data objects that are derived from
GatingSets to enable rapid deployment of comparative dashboards for large experiments. It is designed to scale to very large comparisons (100+) across patient populations. Visualizations can be subset, sorted, and colored according to annotations provided in the data.
flowDashboard currently transforms
GatingSets (derived from the flowWorkspace package) into data objects. Using the
CytoML package, gating schemes from flowJo and Cytobank can also be imported into
GatingSets for use with
The shiny modules are intended to address each step of an analysis workflow (preprocessing/data transformation, normalization, gating and comparative analysis).
Why New Data Objects?
One might ask why new data objects are even necessary. The short answer is that the current data object for storing gating results in Bioconductor, the
GatingSet, is really designed to display results of one FCS file at a time. The
flowDashboard objects allow for rapid visualization and aggregation across samples based on their annotation. There are three main data objects:
qcFlowObj- made for QC assessment of markers
gatingObj- made for assessment of automated gating and population percentages
populationExpressionObj- made for comparison and assessment of marker expression within populations of interest
Additionally, these objects set default display options (such as what Populations and markers to display) for the Shiny Dashboards, allowing you to drop them into our reference implementation with only a small amount of effort.
The shiny modules themselves are not dependent on any Bioconductor packages. However, in building the data objects that plug into the dashboards,
flowDashboard is dependent upon
flowWorkspace, mostly for the
source("http://www.bioconductor.org/biocLite.R") biocLite(c("flowCore", "flowWorkspace"), dependencies=TRUE) library(devtools) install_github("laderast/flowDashboard")
Once you have
flowDashboard installed, you can try out the sample dashboard code here:
Building Data Objects for
Please refer to the vignette in the
sampleFlowDashboard repo for more info on building the data objects that plug into
Also, please refer to
?PEOFromGatingSet for what inputs you need. If you have provided your annotation as
phenoData for your
GatingSet, you should be able to build the objects easily.
More documentation on the data objects is forthcoming.
Dropping Objects Into the Reference Implementation
If you save your objects (named
PEO) as an
.RData file and your gating images into the
sampleFlowDashboard/data folder, you should just be able to load them directly into the reference implementation in
global.R using the
load() command (make sure to comment out the
Interested in Contributing?
We're always interested in having people improve our software!
Please read the Contributing file about ways to contribute to this project.
Copyright 2017 Ted Laderas
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
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