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

 Package: OncoSimulR

 Type: Package

 Title: Forward Genetic Simulation of Cancer Progression with Epistasis 

-Version: 2.5.7

-Date: 2016-12-15

+Version: 2.5.8

+Date: 2016-12-17

 Authors@R: c(person("Ramon", "Diaz-Uriarte", role = c("aut", "cre"),

 		     email = "rdiaz02@gmail.com"),

 	      person("Mark", "Taylor", role = "ctb", email = "ningkiling@gmail.com"))

@@ -99,12 +99,49 @@ to_Fitness_Matrix <- function(x, max_num_genotypes) {

 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))

@@ -120,17 +157,21 @@ from_genotype_fitness <- function(x) {

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

             ## genes. Otherwise, it is not manageable.

         }

-        if(emarker) {

-            x <- x[, c(1, 2)]

+        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"))) {

                 message("Column names of object not Genotype and Fitness.",

                         " Renaming them assuming that is what you wanted")

                 colnames(x) <- c("Genotype", "Fitness")

             }

+            if((!omarker) && (!emarker) && (!nogoodepi)) {

+                message("All single-gene genotypes as input to from_genotype_fitness")

+            }

             ## Yes, we need to do this to  scale the fitness and put the "-"

             return(genot_fitness_to_epistasis(allGenotypes_to_matrix(x)))

         }

-        

     }

 }

@@ -161,7 +202,7 @@ genot_fitness_to_epistasis <- function(x) {

     ## Why should I stop?

     if(any(f < 0))

         message("Negative fitnesses. Watch out if you divide by the wildtype")

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

+    x <- x[, -ncol(x), drop = FALSE]

     wt <- which(rowSums(x) == 0)

     fwt <- 1

     if(length(wt) == 1)

@@ -209,6 +250,7 @@ allGenotypes_to_matrix <- function(x) {

     if(length(anywt) == 1) {

         fwt <- x[anywt, 2]

         x <- x[-anywt, ]

+        ## Trivial case of passing just a WT?

     } else {

         fwt <- 1

     }

@@ -230,8 +272,8 @@ allGenotypes_to_matrix <- function(x) {

                m)

     ## Ensure sorted

     m <- data.frame(m)

-    rs <- rowSums(m[, -ncol(m)])

-    m <- m[order(rs), ]

+    rs <- rowSums(m[, -ncol(m), drop = FALSE])

+    m <- m[order(rs), , drop = FALSE]

     ## m <- m[do.call(order, as.list(cbind(rs, m[, -ncol(m)]))), ]

     return(m)

 }

@@ -1,3 +1,6 @@

+Changes in version 2.5.8 (2016-12-17):

+	- Handle trivial cases in genotFitness

+

 Changes in version 2.5.7 (2016-12-15):

 	- Clarified McFarland parameterization.

@@ -127,12 +127,14 @@ allMutatorEffects(epistasis = NULL, noIntGenes = NULL,

   Genotypes can be specified in two ways:

   \itemize{

-    \item As a matrix (or data frame) with g + 1 columns. Each of the

-    first g columns contains a 1 or a 0 indicating that the gene of that

-    column is mutated or not. Column g+ 1 contains the fitness

-    values. This is, for instance, the output you will get from

+    \item As a matrix (or data frame) with g + 1 columns (where g >

+    1). Each of the first g columns contains a 1 or a 0 indicating that

+    the gene of that column is mutated or not. Column g+ 1 contains the

+    fitness values. This is, for instance, the output you will get from

     \code{\link{rfitness}}. If the matrix has all columns named, those

-    will be used for the names of the genes.

+    will be used for the names of the genes. Of course, except for

+    column or row names, all entries in this matrix or data frame must

+    be numeric.

     \item As a two column data frame. The second column is fitness, and

     the first column are genotypes, given as a character vector. For

@@ -1190,5 +1190,147 @@ test_that("not all genes named", {

                  fixed = TRUE)

 })

+

+test_that("We can deal with single-gene genotypes and trivial cases" ,{

+

+    ## we get the message

+    expect_message(allFitnessEffects(

+        genotFitness = data.frame(g = c("A", "B"),

+                                  y = c(1, 2))), "All single-gene genotypes",

+        fixed = TRUE)

+

+    

+    expect_true(identical(

+        data.frame(Genotype = c("WT", "A", "B", "A, B"),

+                   Fitness = c(1.0, 1.0, 2.0, 1.0),

+                   stringsAsFactors = FALSE),

+        as.data.frame(evalAllGenotypes(

+            allFitnessEffects(genotFitness = data.frame(g = c("A", "B"),

+                                                        y = c(1, 2))),

+            addwt = TRUE))

+    ))

+

+    expect_true(identical(

+        data.frame(Genotype = c("WT", "A", "B", "A, B"),

+                   Fitness = c(1.0, 1.5, 2.9, 1.0),

+                   stringsAsFactors = FALSE),

+        as.data.frame(evalAllGenotypes(

+            allFitnessEffects(genotFitness = data.frame(g = c("A", "B"),

+                                                        y = c(1.5, 2.9))),

+            addwt = TRUE))

+    ))

+

+    expect_true(identical(

+        data.frame(Genotype = c("WT", "A", "B", "E", "A, B", "A, E", "B, E", "A, B, E"),

+                   Fitness = c(1.0, 1.3, 2.4, 3.2, rep(1.0, 4)),

+                   stringsAsFactors = FALSE),

+        as.data.frame(evalAllGenotypes(

+            allFitnessEffects(genotFitness = data.frame(g = c("A", "B", "E"),

+                                                        y = c(1.3, 2.4, 3.2))),

+            addwt = TRUE))

+    ))

+

+

+    expect_true(identical(

+        data.frame(Genotype = c("WT", "A"),

+                   Fitness = c(1.0, 1.0),

+                   stringsAsFactors = FALSE),

+        as.data.frame(evalAllGenotypes(

+            allFitnessEffects(genotFitness = data.frame(g = c("A"),

+                                                        y = c(1))),

+            addwt = TRUE))

+    ))

+    

+    expect_true(identical(

+        data.frame(Genotype = c("WT", "A"),

+                   Fitness = c(1.0, 0.6),

+                   stringsAsFactors = FALSE),

+        as.data.frame(evalAllGenotypes(

+            allFitnessEffects(genotFitness = data.frame(g = c("A"),

+                                                        y = c(0.6))),

+            addwt = TRUE))

+    ))

+

+    expect_true(identical(

+        data.frame(Genotype = c("WT", "A", "D", "F", "A, D", "A, F", "D, F", "A, D, F"),

+                   Fitness = c(rep(1, 7), 1.7),

+                   stringsAsFactors = FALSE),

+        as.data.frame(evalAllGenotypes(

+            allFitnessEffects(genotFitness = data.frame(g = c("A, D, F"),

+                                                        y = c(1.7))),

+            addwt = TRUE))

+    ))    

+

+

+    m <- rbind(c(1, 0, 1.2),

+               c(0, 1, 2.4))

+

+    expect_true(identical(

+        data.frame(Genotype = c("WT", "A", "B", "A, B"),

+                   Fitness = c(1.0, 1.2, 2.4, 1.0),

+                   stringsAsFactors = FALSE),

+        as.data.frame(evalAllGenotypes(

+            allFitnessEffects(genotFitness = m),

+            addwt = TRUE))

+    ))    

+

+    m2 <- rbind(c(1, 0, 1.2),

+               c(0, 1, 2.4))

+    colnames(m2) <- c("U", "M", "Fitness")

+    expect_true(identical(

+        data.frame(Genotype = c("WT", "M", "U", "M, U"),

+                   Fitness = c(1.0, 2.4, 1.2, 1.0),

+                   stringsAsFactors = FALSE),

+        as.data.frame(evalAllGenotypes(

+            allFitnessEffects(genotFitness = m2),

+            addwt = TRUE))

+    ))

+    

+    m2df <- data.frame(rbind(c(1, 0, 1.2),

+               c(0, 1, 2.4)))

+    colnames(m2df) <- c("U", "M", "Fitness")

+    expect_true(identical(

+        data.frame(Genotype = c("WT", "M", "U", "M, U"),

+                   Fitness = c(1.0, 2.4, 1.2, 1.0),

+                   stringsAsFactors = FALSE),

+        as.data.frame(evalAllGenotypes(

+            allFitnessEffects(genotFitness = m2df),

+            addwt = TRUE))

+    ))   

+

+    m3 <- matrix(c(1, 1.2), ncol = 2)

+    colnames(m3) <- c("U", "Fitness")

+    expect_error(

+        allFitnessEffects(genotFitness = m3),

+        "genotFitness: if two-column must be data frame",

+        fixed = TRUE)

+

+    ## Stupid

+    m5 <- data.frame(x = 1, y = 2, stringsAsFactors= FALSE)

+    expect_error(evalAllGenotypes(allFitnessEffects(genotFitness = m5)),

+                 "genotFitness: first column of data frame is numeric.",

+                 fixed = TRUE)

+

+    m6 <- matrix(letters[1:4], ncol = 4)

+    expect_error(allFitnessEffects(genotFitness = m6),

+                 "A genotype fitness matrix/data.frame must be numeric",

+                 fixed = TRUE)

+

+    m7 <- as.data.frame(matrix(letters[1:4], ncol = 4))

+    expect_error(allFitnessEffects(genotFitness = m7),

+                 "A genotype fitness matrix/data.frame must be numeric",

+                 fixed = TRUE)

+

+    m8 <- 1:9

+    expect_error(allFitnessEffects(genotFitness = m8),

+                 "Input must inherit from matrix or data.frame",

+                 fixed = TRUE)

+

+

+    

+

+})

+

+

 cat(paste("\n Ending all-fitness at", date()))