1 ISOMAP without MBP and DAPI channels

emb <- embed(sdat, "Isomap", knn = 7, ndim = 2)
## 2018-02-28 09:16:19: Isomap START
## 2018-02-28 09:16:19: constructing knn graph
## 2018-02-28 09:16:19: calculating geodesic distances
## 2018-02-28 09:16:19: cmdscale
## 2018-02-28 09:16:20: post processing
edat <- emb@data@data

cols <- c("black", "magenta")[gabaID$gaba+1]
acols <- alpha(cols, 0.35)

pairs(emb@data@data, pch = 19, col = acols, cex = 0.2)

2 Results

2.1 1-d Heatmap

2.2 Location meda_plots

2.3 Outliers as given by randomForest

2.4 Correlation Matrix

2.5 Cumulative Variance with Elbows

2.6 Paired Hex-binned plot

2.7 Hierarchical GMM Classifications

2.8 Hierarchical GMM Dendrogram

2.9 Stacked Means

2.10 Cluster Means

3 Restricting hGMM to \(K = 2\)

Here we are restricting hierarchical GMM to only go through on level. We are comparing the cluster results to the gaba labels.

set.seed(3144)
h2 <- hmc(edat, maxDepth = 2, ccol = ccol)
h3 <- hmc(edat, maxDepth = 3, ccol = ccol)
h2lab <- viridis(max(h2$dat$labels$col))
h2col <- h2$dat$labels$col
h3lab <- viridis(max(h3$dat$labels$col))
h3col <- h3$dat$labels$col

3.1 K = 2 stacked means plot

p1 <- stackM(h2, ccol = "black", centered = TRUE, depth = 1)
p12 <- stackM(h3, ccol = "black", centered = TRUE, depth = 3)
L1 <- h3$dat$labels$L1
p2 <- stackMraw(as.data.frame(sdat), L1, ccol = ccol, depth = 3, centered = TRUE)

grid.arrange(p12, p2, nrow = 1)

3.2 Pairs plot colored by true gaba classification

cols <- c("black", "magenta")[gabaID$gaba+1]
acols <- alpha(cols, 0.35)
#pairs(h2$dat$data, pch = 19, cex = 0.7, col = acols)
plot(h2$dat$data, col = acols, pch = c(19,3)[gaba+1], cex = c(0.5,1)[gaba+1])

pairs(sdat, col = acols, pch = c(19,3)[gaba+1], cex = c(0.5,1)[gaba+1])

3.3 Pairs plot colored by hGMM cluster classification

acols2 <- alpha(h2lab[h2$dat$labels$col], 0.45)
par(bg = "gray45")
plot(h2$dat$data, pch = c(3,20)[gaba + 1], cex = 1, col = acols2)

pairs(sdat, pch = 19, cex = 0.7, col = acols2)

dev.off()
## null device 
##           1

4 Permutation test for ARI

permn <- 1.5e4
p0 <- mclust::adjustedRandIndex(pred, gaba)
perms <- foreach(i = 1:permn, .combine = c) %dopar% {
  set.seed(i*2)
  mclust::adjustedRandIndex(sample(pred), gaba)
}
pv0 <- sum(c(perms,p0) >= p0)/length(perms)
hist(perms, xlim = c(min(perms), p0 + 0.25*p0),
     main = "permutation test of ARI values", probability = TRUE)
#hist(perms, probability = TRUE)
abline(v = p0, col = 'red')

t1
##        truth
## pred    FALSE TRUE
##   FALSE   654   25
##   TRUE     54   60

5 Summary Table

measurment value
Misclassification Rate 0.0996217
Accuracy 0.9003783
Sensitivity 0.7058824
Specificity 0.9237288
Precision 0.5263158
Recall 0.7058824
ARI 0.4773253
\(p\)-value for ARI 0.000067
F1-score 0.6030151
TP 60
FP 54
TN 654
FN 25