@@ -1,8 +1,8 @@

 Package: OncoSimulR

 Type: Package

 Title: Forward Genetic Simulation of Cancer Progression with Epistasis 

-Version: 2.99.7

-Date: 2020-12-30

+Version: 2.99.9

+Date: 2021-04-22

 Authors@R: c(

 	      person("Ramon", "Diaz-Uriarte", role = c("aut", "cre"),	

  	   		     email = "rdiaz02@gmail.com"),

@@ -57,9 +57,6 @@ oncoSimulSample <- function(Nindiv,

                             max.memory = 2000,

                             max.wall.time.total = 600,

                             max.num.tries.total = 500 * Nindiv,

-                            ## well, obviously they are errors

-                            ## errorHitWallTime = TRUE,

-                            ## errorHitMaxTries = TRUE,

                             typeSample = "whole",

                             thresholdWhole = 0.5,

                             initMutant = NULL,

@@ -458,9 +455,6 @@ oncoSimulIndiv <- function(fp,

                            keepEvery = sampleEvery,

                            minDetectDrvCloneSz = "auto",

                            extraTime = 0,

-                           ## used to be this

-                           ## ifelse(model \%in\% c("Bozic", "Exp"), -9,

-                           ##                     5 * sampleEvery),

                            finalTime = 0.25 * 25 * 365,

                            onlyCancer = TRUE,

                            keepPhylog = FALSE,

@@ -585,84 +579,6 @@ oncoSimulIndiv <- function(fp,

     if(is_null_na(finalTime)) finalTime <- Inf

     if(is_null_na(sampleEvery)) stop("sampleEvery cannot be NULL or NA")

-    

-    ## if(!inherits(fp, "fitnessEffects")) {

-        ## if(any(unlist(lapply(list(fp, 

-        ##                           numPassengers,

-        ##                           s, sh), is.null)))) {

-        ##     m <- paste("You are using the old poset format.",

-        ##                "You must specify all of poset, numPassengers",

-        ##                "s, and sh.")

-        ##     stop(m)

-           

-        ## }

-        ## if(AND_DrvProbExit) {

-        ##     stop("The AND_DrvProbExit = TRUE setting is invalid",

-        ##          " with the old poset format.")

-        ## }

-        ## if(!is.null(muEF))

-        ##     stop("Mutator effects cannot be specified with the old poset format.")

-        ## if( length(initMutant) > 0)  

-        ##     warning("With the old poset format you can no longer use initMutant.",

-        ##             " The initMutant you passed will be ignored.")

-        ## ## stop("With the old poset, initMutant can only take a single value.")

-        ## if(!is_null_na(fixation))

-        ##     stop("'fixation' cannot be specified with the old poset format.")

-        ## ## Seeding C++ is now much better in new version

-        ## if(is.null(seed) || (seed == "auto")) {## passing a null creates a random seed

-        ##     ## name is a legacy. This is really the seed for the C++ generator.

-        ##     ## Nope, we cannot use 2^32, because as.integer will fail.

-        ##     seed <- as.integer(round(runif(1, min = 0, max = 2^16)))

-        ## }

-        ## if(verbosity >= 2)

-        ##     cat(paste("\n Using ", seed, " as seed for C++ generator\n"))

-

-        ## if(!is_null_na(detectionProb)) stop("detectionProb cannot be used in v.1 objects")

-        ## ## if(message.v1)

-        ## ##     message("You are using the old poset format. Consider using the new one.")

-   

-    

-        ## ## A simulation stops if cancer or finalTime appear, the first

-        ## ## one. But if we set onlyCnacer = FALSE, we also accept simuls

-        ## ## without cancer (or without anything)

-        

-        ## op <- try(oncoSimul.internal(poset = fp, ## restrict.table = rt,

-        ##                              ## numGenes = numGenes,

-        ##                              numPassengers = numPassengers,

-        ##                              typeCBN = "CBN",

-        ##                              birth = birth,

-        ##                              s = s,

-        ##                              death = death,  

-        ##                              mu =  mu,  

-        ##                              initSize =  initSize, 

-        ##                              sampleEvery =  sampleEvery,  

-        ##                              detectionSize =  detectionSize, 

-        ##                              finalTime = finalTime, 

-        ##                              initSize_species = 2000, 

-        ##                              initSize_iter = 500, 

-        ##                              seed = seed, 

-        ##                              verbosity = verbosity, 

-        ##                              speciesFS = 10000,  

-        ##                              ratioForce = 2,

-        ##                              typeFitness = typeFitness,

-        ##                              max.memory = max.memory,

-        ##                              mutationPropGrowth = mutationPropGrowth,                                   

-        ##                              initMutant = -1, 

-        ##                              max.wall.time = max.wall.time,

-        ##                              max.num.tries = max.num.tries,

-        ##                              keepEvery = keepEvery,  

-        ##                              ## alpha = 0.0015,  

-        ##                              sh = sh,

-        ##                              K = K, 

-        ##                              minDetectDrvCloneSz = minDetectDrvCloneSz,

-        ##                              extraTime = extraTime,

-        ##                              detectionDrivers = detectionDrivers,

-        ##                              onlyCancer = onlyCancer,

-        ##                              errorHitWallTime = errorHitWallTime,

-        ##                              errorHitMaxTries = errorHitMaxTries),

-        ##           silent = !verbosity)

-        ## objClass <- "oncosimul"

-    ## } else {

         s <- sh <- NULL ## force it.

         if(numPassengers != 0)

             warning(paste("Specifying numPassengers has no effect",

@@ -934,88 +850,6 @@ plot.oncosimulpop <- function(x, ask = TRUE,

 }

-## plot.oncosimul <- function(x, col = c(8, "orange", 6:1),

-##                            log = "y",

-##                            ltyClone = 2:6,

-##                            lwdClone = 0.9,

-##                            ltyDrivers = 1,

-##                            lwdDrivers = 3,

-##                            xlab = "Time units",

-##                            ylab = "Number of cells",

-##                            plotClones = TRUE,

-##                            plotDrivers = TRUE,

-##                            addtot = FALSE,

-##                            addtotlwd = 0.5,

-##                            yl = NULL,

-##                            thinData = FALSE,

-##                            thinData.keep = 0.1,

-##                            thinData.min = 2,

-##                            plotDiversity = FALSE,

-##                            ...

-##                            ) {

-

-##     if(thinData)

-##         x <- thin.pop.data(x, keep = thinData.keep, min.keep = thinData.min)

-

-##     ## uvx

-##     if(!inherits(x, "oncosimul2"))

-##         ndr <- colSums(x$Genotypes[1:x$NumDrivers, , drop = FALSE])

-##     else {

-##         ndr <- colSums(x$Genotypes[x$Drivers, , drop = FALSE])

-##     }

-    

-##     if(is.null(yl)) {

-##         if(log %in% c("y", "xy", "yx") )

-##             yl <- c(1, max(apply(x$pops.by.time[, -1, drop = FALSE], 1, sum)))

-##         else

-##             yl <- c(0, max(apply(x$pops.by.time[, -1, drop = FALSE], 1, sum)))

-##     }

-##     if(plotDiversity) {

-##         par(fig = c(0, 1, 0.8, 1))

-##         m1 <- par()$mar

-##         m <- m1

-##         m[c(1, 3)] <- c(0, 0.7)

-##         op <- par(mar = m )

-##         plotShannon(x)

-##         par(op)

-##         m1[c(3)] <- 0.2

-##         op <- par(mar = m1)

-##         par(fig = c(0, 1, 0, 0.8), new = TRUE)  

-##     }

-##     if(plotClones) {

-##         plotClones(x,

-##                    ndr = ndr, 

-##                    xlab = xlab,

-##                    ylab = ylab,

-##                    lty = ltyClone,

-##                    col = col, 

-##                    ylim = yl,

-##                    lwd = lwdClone,

-##                    axes = FALSE,

-##                    log = log,

-##                    ...)

-##     }

-

-##     if(plotClones && plotDrivers)

-##         par(new = TRUE)

-    

-##     if(plotDrivers){

-##         plotDrivers0(x,

-##                      ndr,

-##                      timescale = 1,

-##                      trim.no.drivers = FALSE,

-##                      xlab = "", ylab = "",

-##                      lwd = lwdDrivers,

-##                      lty = ltyDrivers,

-##                      col = col, 

-##                      addtot = addtot,

-##                      addtotlwd = addtotlwd,

-##                      log = log, ylim = yl,

-##                      ...)

-##     }

-    

-## }

-

 plot.oncosimul <- function(x,

                            show = "drivers", 

@@ -1511,10 +1345,6 @@ plotPoset <- function(x, names = NULL, addroot = FALSE,

         box()

 }

-## this function seems to never be used

-## plotAdjMat <- function(adjmat) {

-##     plot(as(adjmat, "graphNEL"))

-## }

@@ -1591,12 +1421,6 @@ plotClonePhylog <- function(x, N = 1, t = "last",

              "very fast, before any clones beyond the initial were ",

              "generated.")

     pc <- phylogClone(x, N, t, keepEvents)

-    ## if(is.na(pc)) {

-    ##     ## This should not be reachable, as caught before

-    ##     ## where we check for nrow of PhylogDF   

-    ##     warning("No clone phylogeny available. Exiting without plotting.")

-    ##     return(NULL)

-    ## }

     l0 <- igraph::layout.reingold.tilford(pc$g)

     if(!timeEvents) {

@@ -1724,198 +1548,6 @@ get.mut.vector <- function(x, timeSample, typeSample,

 }

-## get.mut.vector <- function(x, timeSample, typeSample,

-##                            thresholdWhole, popSizeSample) {

-##     if(is.null(x$TotalPopSize)) {

-##         warning(paste("It looks like this simulation never completed.",

-##                       " Maybe it reached maximum allowed limits.",

-##                       " You will get NAs"))

-##         return(rep(NA, length(x$geneNames)))

-##     }

-##     the.time <- get.the.time.for.sample(x, timeSample, popSizeSample)

-##     if(the.time < 0) { 

-##         return(rep(NA, nrow(x$Genotypes)))

-##     } 

-##     pop <- x$pops.by.time[the.time, -1]

-    

-##     if(all(pop == 0)) {

-##         stop("You found a bug: this should never happen")

-##     }

-    

-##     if(typeSample %in% c("wholeTumor", "whole")) {

-##         popSize <- x$PerSampleStats[the.time, 1]

-##         return( as.numeric((tcrossprod(pop,

-##                                        x$Genotypes)/popSize) >= thresholdWhole) )

-##     } else if (typeSample %in%  c("singleCell", "single")) {

-

-##         return(x$Genotypes[, sample(seq_along(pop), 1, prob = pop)])

-##     } else {

-##         stop("Unknown typeSample option")

-##     }

-## }

-

-

-

-

-

-

-

-

-

-

-

-

-## oncoSimul.internal <- function(poset, ## restrict.table,

-##                                numPassengers, 

-##                                ## numGenes,

-##                                typeCBN,

-##                                birth, 

-##                                s,

-##                                death,

-##                                mu,

-##                                initSize,

-##                                sampleEvery,

-##                                detectionSize,

-##                                finalTime,

-##                                initSize_species,

-##                                initSize_iter,

-##                                seed,

-##                                verbosity,

-##                                speciesFS,

-##                                ratioForce,

-##                                typeFitness,

-##                                max.memory,

-##                                mutationPropGrowth, ## make it explicit

-##                                initMutant,

-##                                max.wall.time,

-##                                keepEvery,

-##                                alpha,

-##                                sh,                               

-##                                K,

-##                                ## endTimeEvery,

-##                                detectionDrivers,

-##                                onlyCancer,

-##                                errorHitWallTime,

-##                                max.num.tries,

-##                                errorHitMaxTries,

-##                                minDetectDrvCloneSz,

-##                                extraTime) {

-

-##     ## the value of 20000, in megabytes, for max.memory sets a limit of ~ 20 GB

-  

-

-##     ## if(keepEvery < sampleEvery)

-##     ##     warning("setting keepEvery to sampleEvery")

-

-##     ## a backdoor to allow passing restrictionTables directly

-##     if(inherits(poset, "restrictionTable"))

-##         restrict.table <- poset

-##     else

-##         restrict.table <- poset.to.restrictTable(poset)

-##     numDrivers <- nrow(restrict.table)

-##     numGenes <- (numDrivers + numPassengers)

-##     if(numGenes < 2)

-##         stop("There must be at least two genes (loci) in the fitness effects.",

-##              "If you only care about a degenerate case with just one,",

-##              "you can enter a second gene",

-##              "with fitness effect of zero.")

-##     if(numGenes > 64)

-##         stop("Largest possible number of genes (loci) is 64 for version 1.",

-##              "You are strongly encouraged to use the new specification",

-##              "as in version 2.")

-

-##     ## These can never be set by the user

-##     ## if(initSize_species < 10) {

-##     ##     warning("initSize_species too small?")

-##     ## }

-##     ## if(initSize_iter < 100) {

-##     ##     warning("initSize_iter too small?")

-##     ## }

-

-##     ## numDrivers <- nrow(restrict.table)

-##     if(length(unique(restrict.table[, 1])) != numDrivers)

-##         stop("BAIL OUT NOW: length(unique(restrict.table[, 1])) != numDrivers)")

-##     ddr <- restrict.table[, 1]

-##     if(any(diff(ddr) != 1))

-##         stop("BAIL OUT NOW:  any(diff(ddr) != 1")

-##     ## sanity checks

-##     if(max(restrict.table[, 1]) != numDrivers)

-##         stop("BAIL OUT NOW: max(restrict.table[, 1]) != numDrivers")

-##     if(numDrivers > numGenes)

-##         stop("BAIL OUT NOW: numDrivers > numGenes")

-    

-##     non.dep.drivers <- restrict.table[which(restrict.table[, 2] == 0), 1]

-

-

-

-

-##     ## if( (is.null(endTimeEvery) || (endTimeEvery > 0)) &&

-##     ##    (typeFitness %in% c("bozic1", "exp") )) {

-##     ##     warning(paste("endTimeEvery will take a positive value. ",

-##     ##                   "This will make simulations not stop until the next ",

-##     ##                   "endTimeEvery has been reached. Thus, in simulations ",

-##     ##                   "with very fast growth, simulations can take a long ",

-##     ##                   "time to finish, or can hit the wall time limit. "))

-##     ## }

-##     ## if(is.null(endTimeEvery))

-##     ##     endTimeEvery <- keepEvery

-##     ## if( (endTimeEvery > 0) && (endTimeEvery %% keepEvery) )

-##     ##     warning("!(endTimeEvery %% keepEvery)")

-##     ## a sanity check in restricTable, so no neg. indices for the positive deps

-##     neg.deps <- function(x) {

-##         ## checks a row of restrict.table

-##         numdeps <- x[2]

-##         if(numdeps) {

-##             deps <- x[3:(3+numdeps - 1)]

-##             return(any(deps < 0))

-##         } else FALSE

-##     }

-##     if(any(apply(restrict.table, 1, neg.deps)))

-##         stop("BAIL OUT NOW: Negative dependencies in restriction table")

-

-##     ## transpose the table

-##     rtC <- convertRestrictTable(restrict.table)

-

-    

-##     return(c(

-##         BNB_Algo5(restrictTable = rtC,

-##         numDrivers = numDrivers,

-##         numGenes = numGenes,

-##         typeCBN_= typeCBN,

-##         s = s, 

-##         death = death,

-##         mu = mu,

-##         initSize = initSize,

-##         sampleEvery = sampleEvery,

-##         detectionSize = detectionSize,

-##         finalTime = finalTime,

-##         initSp = initSize_species,

-##         initIt = initSize_iter,

-##         seed = seed,

-##         verbosity = verbosity,

-##         speciesFS = speciesFS,

-##         ratioForce = ratioForce,

-##         typeFitness_ = typeFitness,

-##         maxram = max.memory,

-##         mutationPropGrowth = as.integer(mutationPropGrowth),

-##         initMutant = initMutant,

-##         maxWallTime = max.wall.time,

-##         keepEvery = keepEvery,

-##         sh = sh,

-##         K = K,

-##         detectionDrivers = detectionDrivers,

-##         onlyCancer = onlyCancer,

-##         errorHitWallTime = errorHitWallTime,

-##         maxNumTries = max.num.tries,

-##         errorHitMaxTries = errorHitMaxTries,

-##         minDetectDrvCloneSz = minDetectDrvCloneSz,

-##         extraTime = extraTime

-##     ),

-##     NumDrivers = numDrivers

-##              ))

-

-## }

-

 OncoSimulWide2Long <- function(x) {

     ## Put data in long format, for ggplot et al

@@ -1947,23 +1579,6 @@ OncoSimulWide2Long <- function(x) {

-## We are not using this anymore

-## create.muts.by.time <- function(tmp) { ## tmp is the output from Algorithm5

-##     if(tmp$NumClones > 1) {

-##         NumMutations <- apply(tmp$Genotypes, 2, sum)

-##         muts.by.time <- cbind(tmp$pops.by.time[, c(1), drop = FALSE],

-##                               t(apply(tmp$pops.by.time[, -c(1),

-##                                                        drop = FALSE], 1,

-##                                       function(x) tapply(x,

-##                                                          NumMutations, sum))))

-##         colnames(muts.by.time)[c(1)] <- "Time"

-##     } else {

-##         muts.by.time <- tmp$pops.by.time

-##     }

-##     return(muts.by.time)

-## } 

-

-

 create.drivers.by.time <- function(tmp, ndr) {

     ## CountNumDrivers <- apply(tmp$Genotypes[1:numDrivers, ,drop = FALSE], 2, sum)

     CountNumDrivers <- ndr

@@ -2084,248 +1699,9 @@ is_null_na <- function(x) {

-

-

-

-

-

-

-

-

-

 ## simpsonI <- function(x) {

 ##     sx <- sum(x)

 ##     p <- x/sx

 ##     p <- p[p > 0]

 ##     return(sum(p^2)))

 ## }

-

-## plotSimpson <- function(z) {

-    

-##     h <- apply(z$pops.by.time[, 2:ncol(z$pops.by.time), drop = FALSE],

-##                1, shannonI)

-##     plot(x = z$pops.by.time[, 1],

-##          y = h, lty = "l", xlab = "", ylab = "H")

-## }

-

-

-## plotClones <- function(z, ndr = NULL, na.subs = TRUE,

-##                        log = "y", type = "l",

-##                        lty = 1:8, col = 1:9, ...) {

-

-##     ## if given ndr, we order columns based on ndr, so clones with more

-##     ## drivers are plotted last

-

-##     y <- z$pops.by.time[, 2:ncol(z$pops.by.time), drop = FALSE]

-    

-##     if(na.subs){

-##         y[y == 0] <- NA

-##     }

-##     if(!is.null(ndr)) {

-##         ## could be done above, to avoid creating

-##         ## more copies

-##         oo <- order(ndr)

-##         y <- y[, oo, drop = FALSE]

-##         ndr <- ndr[oo]

-##         col <- col[ndr + 1]

-##     }

-##     matplot(x = z$pops.by.time[, 1],

-##             y = y,

-##             log = log, type = type,

-##             col = col, lty = lty,

-##             ...)

-##     box()

-## }

-

-

-

-

-

-## No longer used

-## rtNoDep <- function(numdrivers) {

-##     ## create a restriction table with no dependencies

-##     x <- matrix(nrow = numdrivers, ncol = 3)

-##     x[, 1] <- 1:numdrivers

-##     x[, 2] <- 0

-##     x[, 3] <- -9

-##     return(x)

-## }

-

-

-## Simulate from generative model. This is a quick function, and is most

-## likely wrong! Never used for anything.

-

-## simposet <- function(poset, p) {

-##     ## if (length(parent.nodes) != length (child.nodes)){

-##     ##     print("An Error Occurred")

-##     ## }

-##     ##    else {

-##     num.genes <- max(poset) - 1 ## as root is not a gene

-##     genotype <-t(c(1, rep(NA, num.genes)))

-##     colnames(genotype) <- as.character(0:num.genes)

-    

-    

-##     poset$runif <- runif(nrow(poset))

-##     ## this.relation.prob.OK could be done outside, but having it inside

-##     ## the loop would allow to use different thresholds for different

-##     ## relationships

-##     for (i in (1:nrow(poset))) {

-##         child <- poset[i, 2]

-##         this.relation.prob.OK <- as.numeric(poset[i, "runif"] > p)

-##         the.parent <- genotype[ poset[i, 1] ] ## it's the value of parent in genotype. 

-##         if (is.na(genotype[child])){

-##             genotype[child] <- this.relation.prob.OK * the.parent  

-##         }

-##         else

-##             genotype[child] <- genotype[child]*(this.relation.prob.OK * the.parent)

-##     }

-##     ##    }

-    

-##     return(genotype)

-## }

-

-

-## to plot and adjacency matrix in this context can do

-## plotPoset(intAdjMatToPoset(adjMat))

-## where intAdjMatToPoset is from best oncotree code: generate-random-trees.

-## No! the above is simpler

-

-

-

-

-## get.mut.vector.whole <- function(tmp, timeSample = "last", threshold = 0.5) {

-##     ## Obtain, from  results from a simulation run, the vector

-##     ## of 0/1 corresponding to each gene.

-    

-##     ## threshold is the min. proportion for a mutation to be detected

-##     ## We are doing whole tumor sampling here, as in Sprouffske

-

-##     ## timeSample: do we sample at end, or at a time point, chosen

-##     ## randomly, from all those with at least one driver?

-    

-##     if(timeSample == "last") {

-##         if(tmp$TotalPopSize == 0)

-##             warning(paste("Final population size is 0.",

-##                           "Thus, there is nothing to sample with ",

-##                           "sampling last. You will get NAs"))

-##         return(as.numeric(

-##             (tcrossprod(tmp$pops.by.time[nrow(tmp$pops.by.time), -1],

-##                         tmp$Genotypes)/tmp$TotalPopSize) > threshold))

-##     } else if (timeSample %in% c("uniform", "unif")) {

-##           candidate.time <- which(tmp$PerSampleStats[, 4] > 0)

-          

-##           if (length(candidate.time) == 0) {

-##               warning(paste("There is not a single sampled time",

-##                             "at which there are any mutants.",

-##                             "Thus, no uniform sampling possible.",

-##                             "You will get NAs"))

-##               return(rep(NA, nrow(tmp$Genotypes)))

-##           } else if (length(candidate.time) == 1) {

-##                 the.time <- candidate.time

-##             } else {

-##                   the.time <- sample(candidate.time, 1)

-##               }

-##           pop <- tmp$pops.by.time[the.time, -1]

-##           popSize <- tmp$PerSampleStats[the.time, 1]

-##           ## if(popSize == 0)

-##           ##     warning(paste("Population size at this time is 0.",

-##           ##                   "Thus, there is nothing to sample at this time point.",

-##           ##                   "You will get NAs"))

-##           return( as.numeric((tcrossprod(pop,

-##                                        tmp$Genotypes)/popSize) > threshold) )

-##       }

-## }

-

-

-

-##           the.time <- sample(which(tmp$PerSampleStats[, 4] > 0), 1)

-##           if(length(the.time) == 0) {

-##               warning(paste("There are no clones with drivers at any time point.",

-##                             "No uniform sampling possible.",

-##                             "You will get a vector of NAs."))

-##             return(rep(NA, nrow(tmp$Genotypes)))  

-##           }

-## get.mut.vector.singlecell <- function(tmp, timeSample = "last") {

-##     ## No threshold, as single cell.

-

-##     ## timeSample: do we sample at end, or at a time point, chosen

-##     ## randomly, from all those with at least one driver?

-    

-##     if(timeSample == "last") {

-##         the.time <- nrow(tmp$pops.by.time)

-##     } else if (timeSample %in% c("uniform", "unif")) {

-##          candidate.time <- which(tmp$PerSampleStats[, 4] > 0)

-         

-##          if (length(candidate.time) == 0) {

-##              warning(paste("There is not a single sampled time",

-##                            "at which there are any mutants.",

-##                            "Thus, no uniform sampling possible.",

-##                            "You will get NAs"))

-##              return(rep(NA, nrow(tmp$Genotypes)))

-##          } else if (length(candidate.time) == 1) {

-##                the.time <- candidate.time

-##            } else {

-##                  the.time <- sample(candidate.time, 1)

-##              }

-

-##      }

-##     pop <- tmp$pops.by.time[the.time, -1]

-##     ##       popSize <- tmp$PerSampleStats[the.time, 1]

-##     ## genot <- sample(seq_along(pop), 1, prob = pop)

-##     if(all(pop == 0)) {

-##         warning(paste("All clones have a population size of 0",

-##                       "at the chosen time. Nothing to sample.",

-##                       "You will get NAs"))

-##         return(rep(NA, nrow(tmp$Genotypes)))

-##     } else {

-##           return(tmp$Genotypes[, sample(seq_along(pop), 1, prob = pop)])

-##       }

-## }

-

-

-## get.mut.vector <- function(x, timeSample = "whole", typeSample = "last",

-##                            thresholdWhole = 0.5) {

-##     if(typeSample %in% c("wholeTumor", "whole")) {

-##         get.mut.vector.whole(x, timeSample = timeSample,

-##                              threshold = thresholdWhole)

-##     } else if(typeSample %in%  c("singleCell", "single")) {

-##         get.mut.vector.singlecell(x, timeSample = timeSample)

-##     }

-## }

-

-

-

-

-

-## plotClonePhylog <- function(x, timeEvent = FALSE,

-##                             showEvents = TRUE,

-##                             fixOverlap = TRUE) {

-##     if(!inherits(x, "oncosimul2"))

-##         stop("Phylogenetic information is only stored with v >=2")

-##     if(nrow(x$other$PhylogDF) == 0)

-##         stop("It seems you run the simulation with keepPhylog= FALSE")

-##     ## requireNamespace("igraph")

-##     df <- x$other$PhylogDF

-##     if(!showEvents) {

-##         df <- df[!duplicated(df[, c(1, 2)]), ]

-##     }

-##     g <- igraph::graph.data.frame(df)

-##     l0 <- igraph::layout.reingold.tilford(g)

-##     if(!timeEvent) {

-##         plot(g, layout = l0)

-##     } else {

-##         l1 <- l0

-##         indexAppear <- match(V(g)$name, as.character(df[, 2]))

-##         firstAppear <- df$time[indexAppear]

-##         firstAppear[1] <- 0

-##         l1[, 2] <- (max(firstAppear) - firstAppear)

-##         if(fixOverlap) {

-##             dx <- which(duplicated(l1[, 1]))

-##             if(length(dx)) {

-##                 ra <- range(l1[, 1])

-##                 l1[dx, 1] <- runif(length(dx), ra[1], ra[2])

-##             }

-##         }

-##         plot(g, layout = l1)         

-##     }

-## }

@@ -30,6 +30,3 @@ accessibleGenotypes_former <- function(y, f, numMut, th) {

     .Call('OncoSimulR_accessibleGenotypes_former', PACKAGE = 'OncoSimulR', y, f, numMut, th)

 }

-## readFitnessEffects <- function(rFE, echo) {

-##     invisible(.Call('OncoSimulR_readFitnessEffects', PACKAGE = 'OncoSimulR', rFE, echo))

-## }

@@ -319,65 +319,6 @@ faster_accessible_genotypes_R <- function(x, th) {

 }

-## ## This uses slam, but that is actually slower because

-## ## of the assignment

-## faster_accessible_genots_slam <- function(x, th = 0) {

-

-##     ## Given a genotype matrix, return the genotypes that are accessible

-##     ## via creating a directed adjacency matrix between genotypes

-##     ## connected (i.e., those that differ by gaining one mutation). 0

-##     ## means not connected, 1 means connected.

-    

-##     ## There is a more general function in OncoSimulR that will give the

-##     ## fitness difference. But not doing the difference is faster than

-##     ## just setting a value, say 1, if all we want is to keep track of

-##     ## accessible ones. And by using only 0/1 we can store only an

-##     ## integer. And no na.omits, etc. Is too restricted? Yes. But for

-##     ## simulations and computing just accessible genotypes, probably a

-##     ## hell of a lot faster.

-

-##     ## Well, this is not incredibly fast either.

-    

-##     ## Make sure sorted, so ancestors always before descendants

-##     rs0 <- rowSums(x[, -ncol(x)])

-##     x <- x[order(rs0), ]

-##     rm(rs0)

-    

-##     y <- x[, -ncol(x)]

-##     f <- x[, ncol(x)]

-##     rs <- rowSums(y)

-

-##     ## If 0, not accessible

-##     adm <- slam::simple_triplet_zero_matrix(nrow = length(rs), ncol = length(rs),

-##                                       mode = "integer")

-##     for(i in 1:length(rs)) { ## i is the current genotype

-##         candidates <- which(rs == (rs[i] + 1))

-##         for(j in candidates) {

-##             ## sumdiff <- sum(abs(y[j, ] - y[i, ]))

-##             ## if(sumdiff == 1)

-##             if( (sum(abs(y[j, ] - y[i, ])) == 1) &&

-##                 ( (f[j] - f[i]) >= th ) )

-##                 adm[i, j] <- 1L

-##         }

-##     }

-

-##     colnames(adm) <- rownames(adm) <- 1:ncol(adm)

-##     admtmp <- adm[, -1, drop = FALSE] ## we do not want the root column.

-##     while(TRUE) {

-##         ## We remove inaccessible cols (genotypes) and the corresponding

-##         ## rows repeatedly until nothing left to remove; any column left

-##         ## is therefore accessible throw at least one path.

-

-##         ## inacc_col <- which(slam::colapply_simple_triplet_matrix(admtmp, FUN = sum) == 0L)

-##         inacc_col <- which(slam::col_sums(admtmp) == 0L)

-##         if(length(inacc_col) == 0) break;

-##         inacc_row <- inacc_col + 1 ## recall root row is left

-##         admtmp <- admtmp[-inacc_row, -inacc_col, drop = FALSE]

-##     }

-##     return(as.numeric(c(colnames(adm)[1], colnames(admtmp))))

-## }

-

-

 generate_matrix_genotypes <- function(g) {

     ## FIXME future: do this for order too? Only if rfitness for order.

     ## Given a number of genes, generate all possible genotypes.

@@ -470,25 +411,6 @@ genot_to_adj_mat <- function(x) {

 }

-## ## to move above to C++ note that loop can be

-## for(i in 1:length(rs)) { ## i is the current genotype

-##     for(j in (i:length(rs))) {

-##         if(rs[j] > (rs[i] + 1)) break;

-##         else if(rs[j] == (rs[i] + 1)) {

-##             ## and use here my HammingDistance function

-##             ## sumdiff <- sum(abs(y[j, ] - y[i, ]))

-##             ## if(sumdiff == 1) adm[i, j] <- (f[j] - f[i])

-##             if(HammingDistance(y[j, ], y[i, ]) == 1) adm[i, j] = (f[j] - f[i]);

-##             }

-##     }

-## }

-

-## actually, all that is already in accessibleGenotypes except for the

-## filling up of adm.

-

-

-

-

 peak_valley <- function(x) {

     ## FIXME: when there are no identical entries, all this

@@ -307,90 +307,6 @@ to_genotFitness_std <- function(x,

     return(x)

 }

-## Deprecated after flfast

-## to_genotFitness_std is faster and has better error checking

-## and is very similar and does not use

-## the genot_fitness_to_epistasis, which is not reasonable anymore.

-

-## from_genotype_fitness <- function(x) {

-##     ## Would break with output from allFitnessEffects and

-##     ## output from allGenotypeAndMut

-

-##     ## For the very special and weird case of

-##     ## a matrix but only a single gene so with a 0 and 1

-##     ## No, this is a silly and meaningless case.

-##     ## if( ( ncol(x) == 2 ) && (nrow(x) == 1) && (x[1, 1] == 1) ) {

-

-##     ## } else  blabla:

-

-##     if(! (inherits(x, "matrix") || inherits(x, "data.frame")) )

-##         stop("Input must inherit from matrix or data.frame.")

-

-##     ## if((ncol(x) > 2) && !(inherits(x, "matrix"))

-##     ##     stop(paste0("Genotype fitness input either two-column data frame",

-##     ##          " or a numeric matrix with > 2 columns."))

-##     ## if( (ncol(x) > 2) && (nrow(x) == 1) )

-##     ##     stop(paste0("It looks like you have a matrix for a single genotype",

-##     ##                 " of a single gene. For this degenerate cases use",

-##     ##                 " a data frame specification."))

-

-##     if(ncol(x) > 2) {

-##         if(inherits(x, "matrix")) {

-##             if(!is.numeric(x))

-##                 stop("A genotype fitness matrix/data.frame must be numeric.")

-##         } else if(inherits(x, "data.frame")) {

-##             if(!all(unlist(lapply(x, is.numeric))))

-##                 stop("A genotype fitness matrix/data.frame must be numeric.")

-##         }

-

-##         ## We are expecting here a matrix of 0/1 where columns are genes

-##         ## except for the last column, that is Fitness

-##         ## Of course, can ONLY work with epistastis, NOT order

-##         return(genot_fitness_to_epistasis(x))

-##     } else {

-##         if(!inherits(x, "data.frame"))

-##             stop("genotFitness: if two-column must be data frame")

-##         ## Make sure no factors

-##         if(is.factor(x[, 1])) x[, 1] <- as.character(x[, 1])

-##         ## Make sure no numbers

-##         if(any(is.numeric(x[, 1])))

-##             stop(paste0("genotFitness: first column of data frame is numeric.",

-##                         " Ambiguous and suggests possible error. If sure,",

-##                         " enter that column as character"))

-

-##         omarker <- any(grepl(">", x[, 1], fixed = TRUE))

-##         emarker <- any(grepl(",", x[, 1], fixed = TRUE))

-##         nogoodepi <- any(grepl(":", x[, 1], fixed = TRUE))

-##         ## if(omarker && emarker) stop("Specify only epistasis or order, not both.")

-##         if(nogoodepi && emarker) stop("Specify the genotypes separated by a ',', not ':'.")

-##         if(nogoodepi && !emarker) stop("Specify the genotypes separated by a ',', not ':'.")

-##         ## if(nogoodepi && omarker) stop("If you want order, use '>' and if epistasis ','.")

-##         ## if(!omarker && !emarker) stop("You specified neither epistasis nor order")

-##         if(omarker) {

-##             ## do something. To be completed

-##             stop("This code not yet ready")

-##             ## You can pass to allFitnessEffects genotype -> fitness mappings that

-##             ## involve epistasis and order. But they must have different

-##             ## genes. Otherwise, it is not manageable.

-##         }

-##         if( emarker || ( (!omarker) && (!emarker) && (!nogoodepi)) ) {

-##             ## the second case above corresponds to passing just single letter genotypes

-##             ## as there is not a single marker

-##             x <- x[, c(1, 2), drop = FALSE]

-##             if(!all(colnames(x) == c("Genotype", "Fitness"))) {