| 528aa60b |
# .cCCalcLL = function(mCPByS, nGByCP, s, z, K, nS, nG, alpha, beta)
.cCSplitZ <- function(counts,
mCPByS,
nGByCP,
nCP,
s,
z,
K,
nS,
nG,
alpha,
beta,
zProb,
maxClustersToTry = 10,
minCell = 3) {
## Identify clusters to split
zTa <- tabulate(z, K)
zToSplit <- which(zTa >= minCell)
zNonEmpty <- which(zTa > 0)
if (length(zToSplit) == 0) {
m <- paste0(date(),
" .... Cluster sizes too small. No additional splitting was",
" performed.")
return(list(z = z,
mCPByS,
nGByCP,
nCP = nCP,
message = m))
}
|
| a90d4f7a |
|
| 528aa60b |
## Loop through each split-able Z and perform split
clustSplit <- vector("list", K)
for (i in zToSplit) {
clustLabel <- .celda_C(
counts[, z == i],
K = 2,
zInitialize = "random",
maxIter = 5,
splitOnIter = -1,
splitOnLast = FALSE,
verbose = FALSE
)
|
| 06b0c870 |
clustSplit[[i]] <- clusters(clustLabel)$z
|
| 528aa60b |
}
## Find second best assignment give current assignments for each cell
zProb[cbind(seq(nrow(zProb)), z)] <- NA
zSecond <- apply(zProb, 1, which.max)
## Set up initial variables
zSplit <- matrix(NA,
nrow = length(z),
ncol = length(zToSplit) * maxClustersToTry)
zSplitLl <- rep(NA, times = length(zToSplit) * maxClustersToTry)
zSplitLl[1] <- .cCCalcLL(mCPByS, nGByCP, s, z, K, nS, nG, alpha, beta)
zSplit[, 1] <- z
## Select worst clusters to test for reshuffling
previousZ <- z
llShuffle <- rep(NA, K)
for (i in zNonEmpty) {
ix <- z == i
newZ <- z
newZ[ix] <- zSecond[ix]
p <- .cCReDecomposeCounts(counts, s, newZ, previousZ, nGByCP, K)
nGByCP <- p$nGByCP
mCPByS <- p$mCPByS
llShuffle[i] <- .cCCalcLL(mCPByS, nGByCP, s, z, K, nS, nG, alpha, beta)
previousZ <- newZ
}
zToShuffle <- utils::head(order(llShuffle, decreasing = TRUE, na.last = NA),
n = maxClustersToTry)
pairs <- c(NA, NA)
splitIx <- 2
for (i in zToShuffle) {
otherClusters <- setdiff(zToSplit, i)
for (j in otherClusters) {
newZ <- z
## Assign cluster i to the next most similar cluster (excluding
## cluster j)
## as defined above by the correlation
ixToMove <- z == i
newZ[ixToMove] <- zSecond[ixToMove]
## Split cluster j according to the clustering defined above
ixToSplit <- z == j
newZ[ixToSplit] <- ifelse(clustSplit[[j]] == 1, j, i)
p <- .cCReDecomposeCounts(counts, s, newZ, previousZ, nGByCP, K)
nGByCP <- p$nGByCP
mCPByS <- p$mCPByS
## Calculate likelihood of split
zSplitLl[splitIx] <- .cCCalcLL(mCPByS,
nGByCP,
s,
z,
K,
nS,
nG,
alpha,
beta)
zSplit[, splitIx] <- newZ
splitIx <- splitIx + 1L
previousZ <- newZ
pairs <- rbind(pairs, c(i, j))
}
}
|
| 8343e975 |
|
| 528aa60b |
select <- which.max(zSplitLl)
|
| 8343e975 |
|
| 528aa60b |
if (select == 1) {
m <- paste0(date(), " .... No additional splitting was performed.")
} else {
m <- paste0(date(),
" .... Cluster ",
pairs[select, 1],
" was reassigned and cluster ",
pairs[select, 2],
" was split in two."
)
}
p <- .cCReDecomposeCounts(counts, s, zSplit[, select], previousZ, nGByCP, K)
return(list(z = zSplit[, select],
mCPByS = p$mCPByS,
nGByCP = p$nGByCP,
nCP = p$nCP,
message = m))
|
| 8343e975 |
}
|
| 528aa60b |
# .cCGCalcLL = function(K, L, mCPByS, nTSByCP, nByG, nByTS, nGByTS,
# nS, nG, alpha, beta, delta, gamma)
.cCGSplitZ <- function(counts,
mCPByS,
nTSByC,
nTSByCP,
nByG,
nByTS,
nGByTS,
nCP,
s,
z,
K,
L,
nS,
nG,
alpha,
beta,
delta,
gamma,
zProb,
maxClustersToTry = 10,
minCell = 3) {
## Identify clusters to split
zTa <- tabulate(z, K)
zToSplit <- which(zTa >= minCell)
zNonEmpty <- which(zTa > 0)
if (length(zToSplit) == 0) {
m <- paste0(date(),
" .... Cluster sizes too small. No additional splitting was",
" performed.")
return(list(z = z,
mCPByS = mCPByS,
nTSByCP = nTSByCP,
nCP = nCP,
message = m))
}
|
| 8343e975 |
|
| 528aa60b |
## Loop through each split-able Z and perform split
clustSplit <- vector("list", K)
for (i in zToSplit) {
clustLabel <- .celda_C(counts[, z == i],
K = 2,
zInitialize = "random",
maxIter = 5,
splitOnIter = -1,
splitOnLast = FALSE,
verbose = FALSE)
|
| 06b0c870 |
clustSplit[[i]] <- clusters(clustLabel)$z
|
| 528aa60b |
}
## Find second best assignment give current assignments for each cell
zProb[cbind(seq(nrow(zProb)), z)] <- NA
zSecond <- apply(zProb, 1, which.max)
## Set up initial variables
zSplit <- matrix(NA,
nrow = length(z),
ncol = length(zToSplit) * maxClustersToTry)
|
| b6cf56ae |
zSplitLl <- rep(NA, ncol = length(zToSplit) * maxClustersToTry)
|
| 528aa60b |
zSplitLl[1] <- .cCGCalcLL(K,
L,
mCPByS,
nTSByCP,
nByG,
nByTS,
nGByTS,
nS,
nG,
alpha,
beta,
delta,
gamma)
zSplit[, 1] <- z
## Select worst clusters to test for reshuffling
previousZ <- z
llShuffle <- rep(NA, K)
for (i in zNonEmpty) {
ix <- z == i
newZ <- z
newZ[ix] <- zSecond[ix]
p <- .cCReDecomposeCounts(nTSByC, s, newZ, previousZ, nTSByCP, K)
nTSByCP <- p$nGByCP
mCPByS <- p$mCPByS
llShuffle[i] <- .cCGCalcLL(K,
L,
mCPByS,
nTSByCP,
nByG,
nByTS,
nGByTS,
nS,
nG,
alpha,
beta,
delta,
gamma)
previousZ <- newZ
}
zToShuffle <- utils::head(order(llShuffle, decreasing = TRUE, na.last = NA),
n = maxClustersToTry)
pairs <- c(NA, NA)
splitIx <- 2
for (i in zToShuffle) {
otherClusters <- setdiff(zToSplit, i)
for (j in otherClusters) {
newZ <- z
## Assign cluster i to the next most similar cluster (excluding
## cluster j)
## as defined above by the correlation
ixToMove <- z == i
newZ[ixToMove] <- zSecond[ixToMove]
## Split cluster j according to the clustering defined above
ixToSplit <- z == j
newZ[ixToSplit] <- ifelse(clustSplit[[j]] == 1, j, i)
p <- .cCReDecomposeCounts(nTSByC, s, newZ, previousZ, nTSByCP, K)
nTSByCP <- p$nGByCP
mCPByS <- p$mCPByS
## Calculate likelihood of split
zSplitLl[splitIx] <- .cCGCalcLL(K,
L,
mCPByS,
nTSByCP,
nByG,
nByTS,
nGByTS,
nS,
nG,
alpha,
beta,
delta,
gamma)
zSplit[, splitIx] <- newZ
splitIx <- splitIx + 1L
previousZ <- newZ
pairs <- rbind(pairs, c(i, j))
}
}
select <- which.max(zSplitLl)
|
| 8343e975 |
|
| 528aa60b |
if (select == 1) {
m <- paste0(date(), " .... No additional splitting was performed.")
|
| 8343e975 |
} else {
|
| 528aa60b |
m <- paste0(date(),
" .... Cluster ",
pairs[select, 1],
" was reassigned and cluster ",
pairs[select, 2],
" was split in two.")
|
| 8343e975 |
}
|
| 528aa60b |
p <- .cCReDecomposeCounts(nTSByC,
s,
zSplit[, select],
previousZ,
nTSByCP,
K)
return(list(z = zSplit[, select],
mCPByS = p$mCPByS,
nTSByCP = p$nGByCP,
nCP = p$nCP,
message = m))
|
| 8343e975 |
}
|
| 528aa60b |
.cCGSplitY <- function(counts,
y,
mCPByS,
nGByCP,
nTSByC,
nTSByCP,
nByG,
nByTS,
nGByTS,
nCP,
s,
z,
K,
L,
nS,
nG,
alpha,
beta,
delta,
gamma,
yProb,
maxClustersToTry = 10,
KSubclusters = 10,
minCell = 3) {
#########################
## First, the cell dimension of the original matrix will be reduced by
## splitting each z cluster into 'KSubclusters'.
#########################
## This will not be as big as the original matrix (which can take a lot of
## time to process with large number of cells), but not as small as the
## 'nGByCP' with current z assignments
zTa <- tabulate(z, K)
zNonEmpty <- which(zTa > 0)
tempZ <- rep(0, length(z))
currentTopZ <- 0
for (i in zNonEmpty) {
ix <- z == i
if (zTa[i] <= KSubclusters) {
tempZ[ix] <- seq(currentTopZ + 1, currentTopZ + zTa[i])
} else {
clustLabel <- .celda_C(counts[, z == i],
K = KSubclusters,
zInitialize = "random",
maxIter = 5,
splitOnIter = -1,
splitOnLast = FALSE,
verbose = FALSE)
|
| 06b0c870 |
tempZ[ix] <- clusters(clustLabel)$z + currentTopZ
|
| 528aa60b |
}
currentTopZ <- max(tempZ, na.rm = TRUE)
}
|
| 8343e975 |
|
| 528aa60b |
## Decompose counts according to new/temp z labels
|
| 2e877ffe |
tempNGByCP <- .colSumByGroup(counts, group = tempZ, K = currentTopZ)
|
| 528aa60b |
#########################
## Second, different y splits will be estimated and tested
#########################
## Identify clusters to split
yTa <- tabulate(y, L)
yToSplit <- which(yTa >= minCell)
yNonEmpty <- which(yTa > 0)
if (length(yToSplit) == 0) {
m <- paste0(date(),
" .... Cluster sizes too small. No additional splitting was",
" performed.")
return(list(y = y,
mCPByS = mCPByS,
nTSByCP = nTSByCP,
nCP = nCP,
message = m))
}
|
| 8343e975 |
|
| 528aa60b |
## Loop through each split-able Z and perform split
clustSplit <- vector("list", L)
for (i in yToSplit) {
|
| b6cf56ae |
clustLabel <- .celda_G(tempNGByCP[y == i, ],
|
| 528aa60b |
L = 2,
yInitialize = "random",
maxIter = 5,
splitOnIter = -1,
splitOnLast = FALSE,
verbose = FALSE)
|
| 06b0c870 |
clustSplit[[i]] <- clusters(clustLabel)$y
|
| 528aa60b |
}
|
| 8343e975 |
|
| 528aa60b |
## Find second best assignment give current assignments for each cell
yProb[cbind(seq(nrow(yProb)), y)] <- NA
ySecond <- apply(yProb, 1, which.max)
## Set up initial variables
ySplit <- matrix(NA,
nrow = length(y),
ncol = length(yToSplit) * maxClustersToTry)
|
| b6cf56ae |
ySplitLl <- rep(NA, ncol = length(yToSplit) * maxClustersToTry)
|
| 528aa60b |
ySplitLl[1] <- .cCGCalcLL(K,
L,
mCPByS,
nTSByCP,
nByG,
nByTS,
nGByTS,
nS,
nG,
alpha,
beta,
delta,
gamma)
ySplit[, 1] <- y
## Select worst clusters to test for reshuffling
previousY <- y
llShuffle <- rep(NA, L)
for (i in yNonEmpty) {
ix <- y == i
newY <- y
newY[ix] <- ySecond[ix]
p <- .cGReDecomposeCounts(nGByCP, newY, previousY, nTSByCP, nByG, L)
nTSByCP <- p$nTSByC
nByTS <- p$nByTS
nGByTS <- p$nGByTS
llShuffle[i] <- .cCGCalcLL(K,
L,
mCPByS,
nTSByCP,
nByG,
nByTS,
nGByTS,
nS,
nG,
alpha,
beta,
delta,
gamma)
previousY <- newY
|
| 8343e975 |
}
|
| 528aa60b |
yToShuffle <- utils::head(order(llShuffle, decreasing = TRUE, na.last = NA),
n = maxClustersToTry)
pairs <- c(NA, NA)
splitIx <- 2
for (i in yToShuffle) {
otherClusters <- setdiff(yToSplit, i)
for (j in otherClusters) {
newY <- y
## Assign cluster i to the next most similar cluster (excluding
## cluster j)
## as defined above by the correlation
ixToMove <- y == i
newY[ixToMove] <- ySecond[ixToMove]
## Split cluster j according to the clustering defined above
ixToSplit <- y == j
newY[ixToSplit] <- ifelse(clustSplit[[j]] == 1, j, i)
p <- .cGReDecomposeCounts(nGByCP, newY, previousY, nTSByCP, nByG, L)
nTSByCP <- p$nTSByC
nByTS <- p$nByTS
nGByTS <- p$nGByTS
## Calculate likelihood of split
ySplitLl[splitIx] <- .cCGCalcLL(
K,
L,
mCPByS,
nTSByCP,
nByG,
nByTS,
nGByTS,
nS,
nG,
alpha,
beta,
delta,
gamma)
ySplit[, splitIx] <- newY
splitIx <- splitIx + 1L
previousY <- newY
pairs <- rbind(pairs, c(i, j))
}
}
select <- which.max(ySplitLl)
if (select == 1) {
m <- paste0(date(), " .... No additional splitting was performed.")
} else {
m <- paste0(date(),
" .... Cluster ",
pairs[select, 1],
" was reassigned and cluster ",
pairs[select, 2],
" was split in two.")
}
p <- .cGReDecomposeCounts(nGByCP,
ySplit[, select],
previousY,
nTSByCP,
nByG,
L)
return(list(y = ySplit[, select],
nTSByCP = p$nTSByC,
nByTS = p$nByTS,
nGByTS = p$nGByTS,
message = m))
|
| 8343e975 |
}
|
| 528aa60b |
# .cGCalcLL = function(nTSByC, nByTS, nByG, nGByTS, nM, nG, L, beta, delta,
# gamma) {
.cGSplitY <- function(counts,
y,
nTSByC,
nByTS,
nByG,
nGByTS,
nM,
nG,
L,
beta,
delta,
gamma,
yProb,
minFeature = 3,
maxClustersToTry = 10) {
## Identify clusters to split
yTa <- table(factor(y, levels = seq(L)))
yToSplit <- which(yTa >= minFeature)
yNonEmpty <- which(yTa > 0)
if (length(yToSplit) == 0) {
m <- paste0(date(),
" .... Cluster sizes too small. No additional splitting was",
" performed.")
return(list(y = y,
nTSByC = nTSByC,
nByTS = nByTS,
nGByTS = nGByTS,
message = m))
}
## Loop through each split-able y and find best split
clustSplit <- vector("list", L)
for (i in yToSplit) {
|
| b6cf56ae |
clustLabel <- .celda_G(counts[y == i, ],
|
| 528aa60b |
L = 2,
yInitialize = "random",
maxIter = 5,
splitOnIter = -1,
splitOnLast = FALSE,
verbose = FALSE)
|
| 06b0c870 |
clustSplit[[i]] <- clusters(clustLabel)$y
|
| 528aa60b |
}
## Find second best assignment give current assignments for each cell
yProb[cbind(seq(nrow(yProb)), y)] <- NA
ySecond <- apply(yProb, 1, which.max)
## Set up initial variables
ySplit <- matrix(NA,
nrow = length(y),
ncol = length(yToSplit) * maxClustersToTry)
ySplitLl <- rep(NA, ncol = length(yToSplit) * maxClustersToTry)
ySplitLl[1] <- .cGCalcLL(nTSByC,
nByTS,
nByG,
nGByTS,
nM,
nG,
L,
beta,
delta,
gamma)
ySplit[, 1] <- y
## Select worst clusters to test for reshuffling
llShuffle <- rep(NA, L)
previousY <- y
for (i in yNonEmpty) {
ix <- y == i
newY <- y
newY[ix] <- ySecond[ix]
p <- .cGReDecomposeCounts(counts, newY, previousY, nTSByC, nByG, L)
llShuffle[i] <- .cGCalcLL(p$nTSByC,
p$nByTS,
nByG,
p$nGByTS,
nM,
nG,
L,
beta,
delta,
gamma)
previousY <- newY
}
yToShuffle <- utils::head(order(llShuffle, decreasing = TRUE, na.last = NA),
n = maxClustersToTry)
pairs <- c(NA, NA)
splitIx <- 2
for (i in yToShuffle) {
otherClusters <- setdiff(yToSplit, i)
for (j in otherClusters) {
newY <- y
## Assign cluster i to the next most similar cluster (excluding
## cluster j)
## as defined above by the spearman correlation
ixToMove <- y == i
newY[ixToMove] <- ySecond[ixToMove]
## Split cluster j according to the clustering defined above
ixToSplit <- y == j
newY[ixToSplit] <- ifelse(clustSplit[[j]] == 1, j, i)
## Calculate likelihood of split
p <- .cGReDecomposeCounts(counts, newY, previousY, nTSByC, nByG, L)
ySplitLl[splitIx] <- .cGCalcLL(p$nTSByC,
p$nByTS,
nByG,
p$nGByTS,
nM,
nG,
L,
beta,
delta,
gamma)
ySplit[, splitIx] <- newY
splitIx <- splitIx + 1L
previousY <- newY
pairs <- rbind(pairs, c(i, j))
}
}
select <- which.max(ySplitLl)
if (select == 1) {
m <- paste0(date(), " .... No additional splitting was performed.")
} else {
m <- paste0(date(),
" .... Cluster ",
pairs[select, 1],
" was reassigned and cluster ",
pairs[select, 2],
" was split in two.")
}
p <- .cGReDecomposeCounts(counts,
ySplit[, select],
previousY,
nTSByC,
nByG,
L)
return(list(y = ySplit[, select],
nTSByC = p$nTSByC,
nByTS = p$nByTS,
nGByTS = p$nGByTS,
message = m))
|
| 61565ebc |
}
|
