flowDashboard
In order to enable comparison of flow cytometry data across samples, we have developed three R6 objects that enable different portions of the analysis in our Shiny Dashboard.
qcFlowObj - used to visualize qc analysisgatingObj - used to visualize gating and population percentagespopulationExpressionObj - used to compare expression across conditions.Why R6 objects? We wanted to make the data easily comparable and reusable. Putting data into these objects allows for rapid deployment of a comparative flow analysis dashboard. Once the objects are built, they can be loaded into a Shiny dashboard with relatively little work.
All three data objects have an annotation slot, which holds a data.table. This annotation slot allows for subsetting the data according to covariates in the data. There is a constraint on these objects that forces the annotation and associated data slot (qcData, popTable, and expressionData) to have sample ids that reconcile with each other. The mapping variable between these two data.tables must be specified as a named vector (for example: c("idVar"="FCSFiles")) in the mapVar slot.
qcData can be generated from both flowSets and gatingSets. popTable and expressionData can only be generated from gatingSets.
flowDashboard ObjectsEach object type has display options associated with the object. Setting these options on the objects greatly simplifies deployment of a modular dashboard. For example, you can control what variables in your annotation to sort on and subset on using the subsetModule. For the GatingObj, you can control which populations are visible in your dashboard using the setPopulations method.
Common display options across all of the classes are:
setSubsetAndSortOptions() - Setting columns for subsetting in the annotation slot setAnnotationDisplayOptions() - Setting the columns in the annotation slot to display subsetAnnotation() - subset the annotation and data slots to be restricted to a set of ids (this is a destructive operation)
qcFlowObj, gatingObj and populationExpressionObj from a GatingSet
Here we build a qcFlowObj from a subset of GvHD dataset. Before we do, we look at the phenotypes available in the phenoData slot in the GatingSet.
library(flowWorkspace)## Loading required package: flowCore## Loading required package: ncdfFlow## Loading required package: RcppArmadillo## Loading required package: BHgsFile <- system.file("extdata/gvHDgs/", package = "flowDashboard")
gs <- load_gs(gsFile)## loading R object...## loading tree object...## Done##look at phenoData slot in gs
phenotypes <- pData(gs@data@phenoData)
phenotypes## Patient Visit Days Grade name
## s5a01 5 1 -6 3 s5a01
## s5a02 5 2 0 3 s5a02
## s5a03 5 3 6 3 s5a03
## s5a04 5 4 12 3 s5a04
## s5a05 5 5 19 3 s5a05
## s5a06 5 6 26 3 s5a06
## s5a07 5 7 33 3 s5a07
## s6a01 6 1 -8 3 s6a01
## s6a02 6 2 0 3 s6a02
## s6a03 6 3 5 3 s6a03
## s6a04 6 4 12 3 s6a04
## s6a05 6 5 19 3 s6a05
## s6a06 6 6 27 3 s6a06
## s6a07 6 7 32 3 s6a07
## s7a01 7 1 -4 3 s7a01
## s7a02 7 2 0 3 s7a02
## s7a03 7 3 7 3 s7a03
## s7a04 7 4 14 3 s7a04
## s7a05 7 5 21 3 s7a05
## s7a06 7 6 28 3 s7a06##look at marker information in gs
markerInfo <- pData(parameters(gs))
markerInfo#build a qcFlowObj
QCO <- QCOFromGatingSet(gs, samplePop = 1000)
#show annotation slot
QCO$annotation
#show first few lines of qcData
QCO$qcDataHere we build a gatingObj from our GatingSet. If you want to show the gating for each sample, you will also need to set plotGraphs=TRUE and provide an imageDir as arguments to gatingObjFromGatingSet.
objId <- "GOgvhd"
#GO <- GOFromGatingSet(gs, objId = objId)