Nothing

```
adjacency.splineReg = function(datExpr, df = 6-(nrow(datExpr)<100)-(nrow(datExpr)<30), symmetrizationMethod = "mean", ...) {
if (!is.element(symmetrizationMethod, c("none", "min" ,"max", "mean"))) {
stop("Unrecognized symmetrization method.")
}
datExpr = matrix(as.numeric(as.matrix(datExpr)), nrow(datExpr), ncol(datExpr))
n = ncol(datExpr)
splineRsquare = matrix(NA, n,n)
for (i in 2:n) {
for (j in 1:(i-1)) {
del = is.na(datExpr[, i]+datExpr[,j])
if (sum(del)>=(n-1) | var(datExpr[, i], na.rm=T)==0 | var(datExpr[, j], na.rm=T)==0) {
splineRsquare[i, j] = splineRsquare[j, i]=NA
}else{
dati = datExpr[!del, i]; datj = datExpr[!del, j];
lmSij=glm( dati ~ ns( datj, df = df, ...))
splineRsquare[i, j] = cor( dati, predict(lmSij))^2
lmSji=glm( datj ~ ns( dati, df = df, ...))
splineRsquare[j, i] = cor( datj, predict(lmSji))^2
rm(dati, datj, lmSij, lmSji)
}
}
}
diag(splineRsquare) = rep(1,n)
if (symmetrizationMethod =="none") {adj= splineRsquare} else
{ adj = switch(symmetrizationMethod,
min = pmin(splineRsquare, t(splineRsquare)),
max = pmax(splineRsquare, t(splineRsquare)),
mean = (splineRsquare + t(splineRsquare))/2)}
adj
}
```

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