How to merg/standardize GatingSets

Usage

groupByTree(x)
checkRedundantNodes(x)
dropRedundantNodes(x,toRemove)
dropRedundantChannels(gs, ...)

Arguments

x
'GatingSet' objects or or list of groups (each group member is a list of 'GatingSet)

toRemove list of the node sets to be removed. its length must equals to the length of argument x

… other arguments

Remove the redudant leaf/terminal nodes

Leaf nodes DNT and DPT are redudant for the analysis and should be removed before merging.

Hide the non-leaf nodes

singlets node is not present in the second tree. But we can't remove it because it will remove all its descendants. We can hide it instead.

invisible(setNode(gs2, "singlets", FALSE))
plot(gs2)
plot(gs3)

Note that even gating trees look the same but singlets still physically exists so we must refer the populations by relative path (path = "auto") instead of full path.

getNodes(gs2)[5]
getNodes(gs3)[5]

## [1] "/not debris/singlets/CD3+/CD4/38- DR+"
## [1] "/not debris/CD3+/CD4/38- DR+"

getNodes(gs2, path = "auto")[5]
getNodes(gs3, path = "auto")[5]

## [1] "CD4/38- DR+"
## [1] "CD4/38- DR+"

Isomorphism

These two trees are not identical due to the different order of CD4 and CD8. However they are still mergable thanks to the reference by gating path instead of by numeric indices

convenient wrapper for merging

To ease the process of merging large number of batches of experiments, here is some internal wrappers to make it semi-automated.

Grouping by tree structures

gslist <- list(gs1, gs2, gs3, gs4, gs5)
gs_groups <- groupByTree(gslist)

## Grouping by Gating tree...

length(gs_groups)

[1] 4

This divides all the GatingSets into different groups, each group shares the same tree structure. Here we have 4 groups,

Check if the discrepancy can be resolved by dropping leaf nodes

res <- try(checkRedundantNodes(gs_groups), silent = TRUE)
print(res[[1]])

[1] “Error in (function (thisNodeSet, thisObj) : \n Can't drop the non-terminal nodes: singlets\n”

Apparently the non-leaf node (singlets) fails this check, and it is up to user to decide whether to hide this node or keep this group separate from further merging.Here we try to hide it.

for(gp in gs_groups)
  plot(gp[[1]])

Based on the tree structure of each group (usually there aren't as many groups as GatingSet objects itself), we will hide singlets for group 2 and group 4.

for(i in c(2,4))
  for(gs in gs_groups[[i]])
    invisible(setNode(gs, "singlets", FALSE))

Now check again with .checkRedundantNodes

toRm <- checkRedundantNodes(gs_groups)
toRm

[[1]] [1] “CCR7+ 45RA+” “CCR7+ 45RA-”

[[2]] [1] “DNT” “DPT”

[[3]] character(0)

[[4]] character(0)

Based on this, these groups can be consolidated by dropping

• CCR7+ 45RA+ and CCR7+ 45RA- from group 1.
• DNT and DPT from group 2.

To proceed the deletion of these nodes, .dropRedundantNodes can be used instead of doing it manually

dropRedundantNodes(gs_groups, toRm)

## Removing CCR7+ 45RA+

## Removing CCR7+ 45RA-

## Removing DNT

## Removing DPT

Now they can be merged into a single GatingSetList.

GatingSetList(gslist)

An GatingSetList with 5 GatingSet containing 5 unique samples.

Remove the redundant channels from GatingSet

Sometime there may be the extra channels in one data set that prevents it from being merged with other. If these channels are not used by any gates, then they can be safely removed.

dropRedundantChannels(gs1)

## drop FSC-H, FSC-W, <G560-A>, <G780-A>, Time

A GatingSet with 1 samples