Add files via upload

Code_gate_design.R

@@ -0,0 +1,90 @@
+setwd("your_path")
+
+##source functions for Gate design
+source('Functions_gate_design.R', echo=TRUE)
+
+##load gateID training dataset (rows= single cells & columns=ClusterID followed by index data in all available channels)
+data <- as.data.frame(read.table("gateID_training_dataset_test.csv", header = T, sep = "\t", stringsAsFactors = F))
+
+##define the desired cell type 
+desired.cluster <- c(1)
+
+##define parameter for gate design
+datastart <- 2  ##index data start at column 2 since colunm 1 is ClusterID
+tgc <-t(combn(seq(datastart,dim(data)[2]), m = 2)) 
+combs <- t(combn(seq(datastart,dim(data)[2], 1), m=2)) 
+bool.desired <- data$ClusterID %in% desired.cluster
+seed <- 23
+thresh <- 0.3
+nverts <- 4  ##define number of vertices for gates
+
+##create dataframe for GateID results
+gate_sol <- data.frame(matrix(0, ncol = 20 , nrow = nrow(tgc)))
+
+##the for loop runs the gate design for each gate combination (runs in 5 minutes) 
+##for gate design on a full dataset, run on an hpc and submit as array job
+##gate_sol contains solutions
+for(j in c(1:nrow(tgc))){
+  gc <- tgc[j,]
+  gs <- as.vector(t(combs[gc, ]))
+  dimensions <- gs
+  dataset <- data
+  numgates <- length(gs)/2
+  d2.desired <- dataset[bool.desired,dimensions]
+  d2.nondesired <- dataset[!bool.desired,dimensions]
+  jumpindex <- seq(1,ncol(d2.desired), 2)
+  d2.desired <- lapply(jumpindex, function(x) as.matrix(d2.desired[, c(x,(x+1))]))
+  d2.nondesired <- lapply(jumpindex, function(x) as.matrix(d2.nondesired[, c(x,(x+1))]))
+  l = lapply(1:length(d2.desired), function(x) t(apply(d2.desired[[x]], 2, function(y) quantile(y, probs = c(0.01, 0.99)))))
+  quart <- as.matrix(l[[1]])
+  for (i in 2:length(l)) {quart <- rbind(quart, l[[i]])}
+  initials <- lapply(l, function(y) orderCW(as.matrix(expand.grid(y[1,], y[2,])), 4))
+  initials <- unlist(lapply(initials, function(x) as.numeric(x)))
+  numpoints.thresh <- round(dim(d2.desired[[1]])[1] * thresh)
+  nd <- length(initials)/numgates
+  ji <- seq(1,length(initials),length(initials)/numgates)
+  lb <-  as.numeric(unlist(lapply(quart[,1], function(y) rep(y,4))))
+  ub <-  as.numeric(unlist(lapply(quart[,2], function(y) rep(y,4))))
+  lb2 <-  rep(0, length(lb))
+
+  ##malsChains-based optimization
+  initialpop <- initials
+  cmares <- malschains(li, lower = lb, upper = ub, maxEvals = 20000, seed = seed, initialpop = initialpop, verbosity = 0,
+                      control = malschains.control(optimum = 0, istep = 100, ls = "sw", effort = 0.55, alpha = 0.5, popsize = 50))
+  impurity <- cmares$fitness
+  initialpop <- cmares$sol
+  cmares2  <- malschains(my, lower = lb2, upper = ub, maxEvals = 10000, initialpop = initialpop, seed = seed, verbosity = 0,
+                       control = malschains.control(istep = 300, ls = "cmaes", effort = 0.55, alpha = 0.5, popsize = 50))
+  yield.cma <- abs(cmares2$fitness)
+  temp = sum(bool.desired)*yield.cma
+  impurity.cma <- temp/(impurity+temp)
+  spec = c(gc, yield.cma, impurity.cma, cmares2$sol)
+
+  gate_sol[j,] <- spec
+}
+
+##order gates based on yield (column 3) and purity (column 4)
+gate_sol <- gate_sol[rev(order(gate_sol$X4, gate_sol$X3)), ]
+
+##picks the vertex coordinates (16 values) for the best gate combination
+##change the rowgate parameter to visualize different gates solutions
+rowgate <- 1
+bestgate <- as.numeric(gate_sol[rowgate, c(5:ncol(gate_sol))]) 
+
+###this tells you the start of the coordinates of each gate
+numgates <- 2
+ji <- seq(1,length(bestgate),length(bestgate)/numgates)
+
+###gate.ori = list of both gate coordinates
+nd <- length(bestgate)/numgates
+gate.ori <- list()
+gate.ori <- lapply(ji, function(y) as.matrix(orderCW(bestgate[y:(y+(nd-1))],4)))
+gate.ori <- lapply(gate.ori, function(y) as.matrix(rbind(y, y[1, ])))
+
+####dims = channels in which the gates are designed (refers to the columns of the training which contain clusterID)
+h.index <- as.numeric(gate_sol[rowgate, c(1,2)])
+channel.comb <- t(combn(seq(datastart,dim(data)[2]), m = 2))
+dims <- channel.comb[h.index, ]
+
+# Plot gate and its parameters
+cal.gate.efficacy(trainingdata = data, bool.desired = bool.desired, gates = gate.ori, dims = dims, plot = T, verbose = T)