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https://github.com/laderast/flowDashboard
Shiny Modules for visualizing flow cytometry data
https://github.com/laderast/flowDashboard
dashboard flow-cytometry flow-cytometry-analysis shiny shiny-modules visualization
Last synced: 3 months ago
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Shiny Modules for visualizing flow cytometry data
- Host: GitHub
- URL: https://github.com/laderast/flowDashboard
- Owner: laderast
- License: other
- Created: 2016-11-03T23:01:22.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2018-06-14T22:30:19.000Z (over 6 years ago)
- Last Synced: 2024-04-11T16:16:19.474Z (7 months ago)
- Topics: dashboard, flow-cytometry, flow-cytometry-analysis, shiny, shiny-modules, visualization
- Language: R
- Homepage: http://laderast.github.io/flowDashboard
- Size: 10.7 MB
- Stars: 8
- Watchers: 4
- Forks: 2
- Open Issues: 5
-
Metadata Files:
- Readme: readme.md
- Contributing: contributing.md
- License: license.md
Awesome Lists containing this project
- awesome-shiny-extensions - flowDashboard - Shiny Modules for visualizing flow cytometry data. (Visualization / Biological Data)
- jimsghstars - laderast/flowDashboard - Shiny Modules for visualizing flow cytometry data (R)
README
# flowDashboard
[](https://zenodo.org/badge/latestdoi/72794399)
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 `GatingSet`s 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](https://tladeras.shinyapps.io/sampleFlowDashboard/) 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](contributing.md) 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.