## ----opts, echo=FALSE-------------------------------------------------------------------------
knitr::opts_chunk$set(fig.width=5, fig.height=5)
oopt <- options(width=96)
.format <- knitr::opts_knit$get("rmarkdown.pandoc.to")
.tag <- function(N, cap ) ifelse(.format == "html",
                                 paste("Figure", N, ":",  cap),
                                 cap)

## ----RPointCloud------------------------------------------------------------------------------
library(RPointCloud)

## ----libpack----------------------------------------------------------------------------------
suppressMessages( library(Mercator) )
library(ClassDiscovery)
library(Polychrome)
data(Dark24)
data(Light24)
suppressMessages( library(igraph) )
suppressMessages( library("ape") )
suppressPackageStartupMessages( library(circlize) )

## ----CLL--------------------------------------------------------------------------------------
data(CLL)
ls()
dim(clinical)
colnames(clinical)

## ----fig01, fig.width = 9, fig.cap = .tag(1, "The Rips barcode diagram from TDA.")------------
diag <- ripdiag[["diagram"]]
opar <- par(mfrow = c(1,2))
plot(diag, barcode = TRUE, main = "Barcode")
plot(diag, main = "Rips Diagram")
par(opar)
rm(opar)

## ----merc-------------------------------------------------------------------------------------
mercury <- Mercator(daisydist, metric = "daisy", method = "hclust", K = 8)
mercury <- addVisualization(mercury, "mds")
mercury <- addVisualization(mercury, "tsne")
mercury <- addVisualization(mercury, "umap")
mercury <- addVisualization(mercury, "som")

## ----fig02, fig.width = 9, fig.height = 12, fig.cap = .tag(2, "Mercator Visualizations of the distance matrix.")----
opar <- par(mfrow = c(3,2), cex = 1.1)
plot(mercury, view = "hclust")
plot(mercury, view = "mds", main = "Mult-Dimensional Scaling")
plot(mercury, view = "tsne", main = "t-SNE")
plot(mercury, view = "umap", main = "UMAP")
barplot(mercury, main = "Silhouette Width")
plot(mercury, view = "som", main = "Self-Organizing Maps")
par(opar)
rm(opar)

## ----fig03, fig.cap = .tag(3, "Hierarchical connections between zero cycles.")----------------
nzero <- sum(diag[, "dimension"] == 0)
cycles <- ripdiag[["cycleLocation"]][2:nzero]
W <- mercury@view[["umap"]]$layout
plot(W, main = "Connected Zero Cycles")
for (cyc in cycles) {
  points(W[cyc[1], , drop = FALSE], pch = 16,col = "red")
  X <- c(W[cyc[1], 1], W[cyc[2],1])
  Y <- c(W[cyc[1], 2], W[cyc[2],2])
  lines(X, Y)
}

## ----igraph-----------------------------------------------------------------------------------
edges <- t(do.call(cbind, cycles)) # this creates an "edgelist"
G <- graph_from_edgelist(edges)
G <- set_vertex_attr(G, "label", value = attr(daisydist, "Labels"))

## ----layouts----------------------------------------------------------------------------------
set.seed(2734)
Lt <- layout_as_tree(G)
L <- layout_with_fr(G)

## ----fig04, fig.cap = .tag(4, "Two igraph depictions of the zero cycle structure."), fig.width = 10----
opar <- par(mfrow = c(1,2), mai = c(0.01, 0.01, 1.02, 0.01))
plot(G, layout = Lt, main = "As Tree")
plot(G, layout = L, main = "Fruchterman-Reingold")
par(opar)

## ----keg--------------------------------------------------------------------------------------
keg <- cluster_edge_betweenness(G) # 20
table(membership(keg)) 
pal <- Dark24[membership(keg)]

## ----fig05, fig.width = 6, fig.height = 6, fig.cap = .tag(5, "Community structure, simplified.")----
is.hierarchical(keg)
H <- as.hclust(keg)
H$labels <- attr(daisydist, "Labels")
K <-  7
colset <- Light24[cutree(H, k=K)]
G2 <- set_vertex_attr(G, "color", value = colset)
e <- 0.01
opar <- par(mai = c(e, e, e, e))
plot(G2, layout = L)
par(opar)

## ----fig06, fig.width=7, fig.height = 7, fig.cap = .tag(6, "Cladogram realization, from the ape package.")----
P <- as.phylo(H)
opar <- par(mai = c(0.01, 0.01, 1.0, 0.01))
plot(P, type = "u", tip.color = colset, cex = 0.8, main = "Ape/Cladogram")
par(opar)
rm(opar)

## ----views------------------------------------------------------------------------------------
U <- mercury@view[["mds"]]
V <- mercury@view[["tsne"]]$Y
W <- mercury@view[["umap"]]$layout

## ----fig07, fig.width = 9, fig.cap = .tag(7, "UMAP visualizations with clinical features.")----
featMU <- Feature(clinical[,"mutation.status"], "Mutation Status", c("cyan", "red"),
                  c("Mutated", "Unmutated"))
featRai <- Feature(clinical[,"CatRAI"], "Rai Stage", c("green", "magenta"), c("High", "Low"))
opar <- par(mfrow = c(1,2))
plot(W, main = "UMAP; Mutation Status", xlab = "U1", ylab = "U2")
points(featMU, W, pch = 16, cex = 1.4)
plot(W, main = "UMAP; Rai Stage", xlab = "U1", ylab = "U2")
points(featRai, W, pch = 16, cex = 1.4)
par(opar)
rm(opar)

## ----persistence------------------------------------------------------------------------------
persistence <- diag[, "Death"] - diag[, "Birth"]

## ----d0---------------------------------------------------------------------------------------
d0 <- persistence[diag[, "dimension"] == 0]
d0 <- d0[d0 < 1]
summary(d0)
mu <- mean(d0)
nu <- median(d0)
sigma <- sd(d0)
shape <- mu^2/sigma^2
rate <- mu/sigma^2
xx <- seq(0, 0.23, length = 100)
yy <- dgamma(xx, shape = shape, rate = rate)

## ----fig08, fig.cap = .tag(8, "Histogram of the duration of zero cycles, with an overlaid gammm distibution.")----
hist(d0, breaks = 123, freq = FALSE)
lines(xx, yy, col = "purple", lwd = 2)

## ----fig09, fig.width=12, fig.cap = .tag(9, "Empirical Bayes detection of significant one-cycles.")----
d1 <- persistence[diag[, "dimension"] == 1]
ef <- ExpoFit(d1) # should be close to log(2)/median? 
eb <- EBexpo(d1, 200)
opar <- par(mfrow = c(1,3))
plot(ef)
hist(eb)
plot(eb, prior = 0.56)
par(opar)
rm(opar)
sum(d1 > cutoff(0.8, 0.56, eb)) # posterior 80%, prior 0.56
sum(d1 > 0.065) # post 90%
which(d1 > 0.047)
which(d1 > 0.065)

## ---------------------------------------------------------------------------------------------
cyc1 <- Cycle(ripdiag, 1, 236, "forestgreen")
cyc1@index

## ----fig10, fig.width = 12, fig.cap = .tag(10, "Three views of three one-cycles.")------------
cyc2 <- Cycle(ripdiag, 1, 123, "red")
cyc3 <- Cycle(ripdiag, 1, 214, "purple")

opar <- par(mfrow = c(1, 3))
plot(cyc1, W, lwd = 2, pch = 16, col = "gray", xlab = "U1", ylab = "U2", main = "UMAP")
lines(cyc2, W, lwd=2)
lines(cyc3, W, lwd=2)

plot(U, pch = 16, col = "gray", main = "MDS")
lines(cyc1, U, lwd = 2)
lines(cyc2, U, lwd = 2)
lines(cyc3, U, lwd = 2)

plot(V, pch = 16, col = "gray", main = "t-SNE")
lines(cyc1, V, lwd = 2)
lines(cyc2, V, lwd = 2)
lines(cyc3, V, lwd = 2)
par(opar)
rm(opar)

## ----fig.width = 8, fig.height = 8, fig.cap = .tag(11, "Circos plot of features varying around the most persistent cycle.")----
poof <- angleMeans(W, ripdiag, cyc1, clinical)
poof[is.na(poof)] <- 0
angle.df <- poof[, c("mutation.status", "CatB2M", "CatRAI", "CatCD38",
                     "Massive.Splenomegaly", "Hypogammaglobulinemia")]
colorScheme <- list(c(M = "green", U = "magenta"),
                    c(Hi = "cyan", Lo ="red"),
                    c(Hi = "blue", Lo = "yellow"),
                    c(Hi = "#dddddd", Lo = "#111111"),
                    c(No = "#dddddd", Yes = "brown"),
                    c(No = "#dddddd", Yes = "purple"))
annote <- LoopCircos(cyc1, angle.df, colorScheme)
image(annote)

## ----fig12, fig.width=12, fig.cap = .tag(12, "Empirical Bayes detection of significant two-cycles.")----
d2 <- persistence[diag[, "dimension"] == 2]
ef <- ExpoFit(d2) # should be close to log(2)/median? 
eb <- EBexpo(d2, 200)
opar <- par(mfrow = c(1, 3))
plot(ef)
hist(eb)
plot(eb, prior = 0.75)
par(opar)
rm(opar)
sum(d2 > cutoff(0.8, 0.75, eb)) # posterior 80%, prior 0.56
sum(d2 > cutoff(0.95, 0.75, eb)) # posterior 90%, prior 0.56
cutoff(0.95, 0.75, eb)
sum(d2 > 0.032) # post 90%
which(d2 > 0.034)

## ----as.is = TRUE-----------------------------------------------------------------------------
vd <- Cycle(ripdiag, 2, 95, "purple")
mds <- cmdscale(daisydist, k = 3)
voidPlot(vd, mds)
voidFeature(featMU, mds, radius = 0.011) # need to increase radius when you overlay one sphere on another
rgl::rglwidget()
#htmlwidgets::saveWidget(rglwidget(), "mywidget.html")

## ----eval = FALSE, echo = FALSE---------------------------------------------------------------
#  mixed <- clinical[,"mutation.status"] + 2*clinical[,"CatB2M"] + 4*clinical[,"Matutes"]
#  table(mixed)
#  scheme <- c("cyan", "#cccccc", "blue", "magenta", "green", "orange", "black", "red")
#  terp <- c("MHH", "UHH", "MLH", "ULH", "MHL", "UHL", "MLL", "ULL")
#  names(scheme) <- terp
#  swatchHue(scheme)
#  mixedFeatures <- Feature(mixed, "Mixed", c("white", "black"), terp)
#  mixedFeatures@colRamp <-  colorRamp2(0:7, scheme)
#  plot(mixedFeatures, W, pch = 16, cex = 1.2)
#  ag <- aggregate(mds, list(mixedFeatures@values), mean, na.rm = TRUE)[, 2:4]
#  colnames(ag)  <- c("x", "y", "z")
#  voidPlot(vd, mds, mixedFeatures)
#  voidPlot(vd, mds)
#  rgl::spheres3d(ag, color = scheme, radius = 0.05, alpha = 0.5)
#  
#  
#  featMatutes <- Feature(clinical[,"Matutes"], "Matutes Score", c("blue", "orange"), c("Abnormal", "Normal"))
#  featB2m <- Feature(clinical[,"CatB2M"], "Beta-2 microglobulin", c("green", "magenta"), c("High", "Low"))

## ----eval = FALSE, echo = FALSE---------------------------------------------------------------
#  ob <- Projection(vd, mds, mixedFeatures, span = 0.15)
#  opar <- par(mfrow = c(1,2))
#  plot(ob, cex = 1.5)
#  image(ob, col = scheme)
#  par(opar)
#  rm(opar)

## ----fig13, fig.width = 9, fig.cap = .tag(13, "Planar projection of mutation status around void.")----
ob <- Projection(vd, mds, featMU, span = 0.2)
opar <- par(mfrow = c(1,2))
plot(ob)
image(ob, col = colorRampPalette(c("cyan", "gray", "red"))(64))
par(opar)
rm(opar)

## ----cleanup------------------------------------------------------------------
options(oopt)
#rm(list = ls())

