flowDashboard

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.

For a sample reference dashboard click here for a demo and the sample dashboard repo: https://github.com/laderast/sampleFlowDashboard

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 flowDashboard.

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.

Installing flowDashboard

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 flowCore and flowWorkspace, mostly for the GatingSet objects.

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:

shiny::runGitHub("laderast/sampleFlowDashboard")

Building Data Objects for flowDashboard

Please refer to the vignette in the sampleFlowDashboard repo for more info on building the data objects that plug into flowDashboard: https://github.com/laderast/sampleFlowDashboard/blob/master/docs/gvhdVignette.Rmd

Also, please refer to ?QCOFromGatingSet, ?GOFromGatingSet, and ?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 QCO, GO and 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 load("data/GvHD.rda") line).

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.

Licensing

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

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

See the License for the specific language governing permissions and limitations under the License.