parse winlist cascade gating xml protocol

[mikejiang]

Here is the preliminary parsing result, the count difference is probably due to the incorrect hyperlog transformation, which is yet to be done.

> stats
    flowCore.freq   xml.freq     xml.count  flowCore.count          node
 1:    1.00000000  1.00000000       100000         119531          root
 2:    0.77520476  0.77532000        77532          92661        debris
 3:    0.86631916  0.86676469        67202          80274      singlets
 4:    0.63539876  0.63457933        42645          51006           CD3
 5:    0.26053798  0.29185133        12446          13289           CD8
 6:    0.01033212  0.01031774          440            527           DPT
 7:    0.02513430  0.04087232         1743           1282            DN
 8:    0.29933733  0.65695861        28016          15268           CD4
 9:    0.32473146  0.37093090        10392           4958           45+
10:    0.05730941  0.04865077         1363            875 activated CD4

[mikejiang]

Here is the feedback from WL engineer regarding to the gate falling on the edge of the plot, which is exactly what Cytobank does (so called boundary transform). In this case, we will do as usual: extend the gates instead of touching the data

On 03/02/2017 06:22 AM, Ben Hunsberger wrote:
What the program is doing is adjusting any events that fall below the transform’s defined low value. The events with actual values of less than the low value of the transform are set to the low value.
Would that explain the issue? I suspect that FlowJo does the same thing. It’s a long-standing practice in flow to pile events up on the boundary (high and low) if they would naturally fall outside the boundaries.

[mikejiang]

After fixing the gate at the edge, we now have consistent counts and quadgate

> getPopStats(gs[[1]])
    openCyto.freq   xml.freq openCyto.count xml.count          node
 1:    1.00000000 1.00000000         119531    119531          root
 2:    0.77520476 0.77520476          92661     92661        debris
 3:    0.86631916 0.86631916          80274     80274      singlets
 4:    0.63542367 0.63483818          51008     50961           CD3
 5:    0.29189539 0.29171327          14889     14866           CD8
 6:    0.01033171 0.01032162            527       526           DPT
 7:    0.04060147 0.04061930           2071      2070            DN
 8:    0.65717142 0.65734581          33521     33499           CD4
 9:    0.36911190 0.36893639          12373     12359           45+
10:    0.05092330 0.04871787           1707      1632 activated CD4