RORB Data: pseudo-centroids from annotation polygons using Gaussian weighted average over 11x11x11 cubes ≈ 0.61 microns^3 followed by step-wise regression.
2018-03-01
## Start: AIC=217.42
## gaba ~ MBP + TdTomato + DAPI1 + DAPI2 + DAPI3 + GluN1 + PSD95 +
## synapsin + VGlut1 + GABA + GAD2 + Gephyrin## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Df Deviance AIC
## - DAPI1 1 191.45 215.45
## - DAPI2 1 191.52 215.52
## - MBP 1 193.09 217.09
## - DAPI3 1 193.24 217.24
## <none> 191.42 217.42
## - GluN1 1 194.28 218.28
## - VGlut1 1 195.50 219.50
## - synapsin 1 196.82 220.82
## - GAD2 1 197.46 221.46
## - TdTomato 1 197.79 221.79
## - Gephyrin 1 206.31 230.31
## - GABA 1 208.68 232.68
## - PSD95 1 244.35 268.35
##
## Step: AIC=215.45
## gaba ~ MBP + TdTomato + DAPI2 + DAPI3 + GluN1 + PSD95 + synapsin +
## VGlut1 + GABA + GAD2 + Gephyrin## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Df Deviance AIC
## - DAPI2 1 191.54 213.54
## - MBP 1 193.21 215.21
## <none> 191.45 215.45
## - DAPI3 1 193.50 215.50
## - GluN1 1 194.29 216.29
## + DAPI1 1 191.42 217.42
## - VGlut1 1 195.52 217.52
## - synapsin 1 196.82 218.82
## - GAD2 1 197.61 219.61
## - TdTomato 1 197.91 219.91
## - Gephyrin 1 206.98 228.98
## - GABA 1 209.12 231.12
## - PSD95 1 244.69 266.69## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred##
## Step: AIC=213.54
## gaba ~ MBP + TdTomato + DAPI3 + GluN1 + PSD95 + synapsin + VGlut1 +
## GABA + GAD2 + Gephyrin## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Df Deviance AIC
## - MBP 1 193.24 213.24
## <none> 191.54 213.54
## - DAPI3 1 193.70 213.70
## - GluN1 1 194.81 214.81
## + DAPI2 1 191.45 215.45
## + DAPI1 1 191.52 215.52
## - VGlut1 1 196.01 216.01
## - synapsin 1 197.10 217.10
## - GAD2 1 197.73 217.73
## - TdTomato 1 202.69 222.69
## - Gephyrin 1 207.10 227.10
## - GABA 1 210.70 230.70
## - PSD95 1 245.77 265.77
##
## Step: AIC=213.24
## gaba ~ TdTomato + DAPI3 + GluN1 + PSD95 + synapsin + VGlut1 +
## GABA + GAD2 + Gephyrin## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Df Deviance AIC
## - DAPI3 1 193.81 211.81
## <none> 193.24 213.24
## + MBP 1 191.54 213.54
## - GluN1 1 195.84 213.84
## - VGlut1 1 197.03 215.03
## + DAPI1 1 193.11 215.11
## + DAPI2 1 193.21 215.21
## - synapsin 1 199.92 217.92
## - GAD2 1 200.03 218.03
## - TdTomato 1 204.75 222.75
## - Gephyrin 1 209.69 227.69
## - GABA 1 218.72 236.72
## - PSD95 1 246.18 264.18
##
## Step: AIC=211.81
## gaba ~ TdTomato + GluN1 + PSD95 + synapsin + VGlut1 + GABA +
## GAD2 + Gephyrin## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Df Deviance AIC
## <none> 193.81 211.81
## - GluN1 1 195.92 211.92
## + DAPI3 1 193.24 213.24
## + DAPI1 1 193.50 213.50
## - VGlut1 1 197.59 213.59
## + DAPI2 1 193.62 213.62
## + MBP 1 193.70 213.70
## - GAD2 1 200.19 216.19
## - synapsin 1 200.92 216.92
## - TdTomato 1 206.17 222.17
## - Gephyrin 1 210.19 226.19
## - GABA 1 227.36 243.36
## - PSD95 1 246.20 262.20##
## Call:
## glm(formula = gaba ~ TdTomato + GluN1 + PSD95 + synapsin + VGlut1 +
## GABA + GAD2 + Gephyrin, family = "binomial", data = sdat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -4.2827 -0.1847 -0.0263 -0.0020 3.1709
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.9390 1.1058 -6.275 3.49e-10 ***
## TdTomato -0.9247 0.3088 -2.994 0.00275 **
## GluN1 -0.4248 0.2990 -1.420 0.15548
## PSD95 -4.7646 1.1946 -3.988 6.65e-05 ***
## synapsin -1.2382 0.5178 -2.391 0.01678 *
## VGlut1 -0.8295 0.4565 -1.817 0.06921 .
## GABA 1.2649 0.2707 4.672 2.98e-06 ***
## GAD2 0.6805 0.2934 2.320 0.02035 *
## Gephyrin 0.7470 0.1821 4.103 4.08e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 540.18 on 792 degrees of freedom
## Residual deviance: 193.81 on 784 degrees of freedom
## AIC: 211.81
##
## Number of Fisher Scoring iterations: 91 Results
1.1 1-d Heatmap
1.2 Location meda_plots
1.3 Outliers as given by randomForest
1.4 Correlation Matrix
1.5 Cumulative Variance with Elbows
1.6 Paired Hex-binned plot
1.7 Hierarchical GMM Classifications
1.8 Hierarchical GMM Dendrogram
1.9 Stacked Means
1.10 Cluster Means
2 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(sdat[, -1], maxDepth = 2, ccol = ccol, model = c("VVV"))
h2lab <- viridis(max(h2$dat$labels$col))
h2col <- h2$dat$labels$col2.1 K = 2 stacked means plot
p1 <- stackM(h2, ccol = ccol, centered = TRUE, depth = 1)
p1
2.2 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$data, col = acols, pch = c(19,3)[gaba+1], cex = c(0.5,1)[gaba+1])
pairs(sdat[,-1], col = acols, pch = c(19,3)[gaba+1], cex = c(0.5,1)[gaba+1])
2.3 Pairs plot colored by hGMM cluster classification
acols2 <- alpha(h2lab[h2$dat$labels$col], 0.45)
par(bg = "gray45")
#plot(h2$dat$data, pch = c(3,20)[gaba + 1], cex = 1, col = acols2)
pairs(sdat[,-1], pch = 19, cex = 0.7, col = acols2)
dev.off()## null device
## 13 Permutation test for ARI
p0 <- mclust::adjustedRandIndex(pred, gaba)
perms <- foreach(i = 1:1.5e4, .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 590 9
## TRUE 118 764 Summary Table
| measurment | value |
|---|---|
| Misclassification Rate | 0.1601513 |
| Accuracy | 0.8398487 |
| Sensitivity | 0.8941176 |
| Specificity | 0.8333333 |
| Precision | 0.3917526 |
| Recall | 0.8941176 |
| ARI | 0.3584828 |
| \(p\)-value for ARI | 0.000067 |
| F1-score | 0.5448029 |
| TP | 76 |
| FP | 118 |
| TN | 590 |
| FN | 9 |