@@ -17,7 +17,7 @@ Encoding: UTF-8

 biocViews: ImmunoOncology, BiologicalQuestion, StatisticalMethod, 

     mRNAMicroarray, Microarray, Genetics, RNASeq

 Suggests: BiocStyle, knitr, testthat

-Imports: Rcpp, Biobase, stats, biwt, gtools, MASS, tools

+Imports: Rcpp, Biobase, stats, biwt, gtools, MASS, tools, dplyr, methods

 License: GPL (>= 2)

 URL: https://github.com/siskac/discordant

 NeedsCompilation: yes

@@ -12,4 +12,5 @@ import(stats)

 import(tools)

 importFrom(Rcpp,sourceCpp)

 importFrom(dplyr,case_when)

+importFrom(methods,is)

 useDynLib(discordant, .registration = TRUE)

@@ -53,22 +53,16 @@ createVectors <- function(x, y = NULL, groups,

                                          "sparcc")) {

     cor.method <- match.arg(cor.method)

     .checkCreateVectorsInputs(x, y, groups, cor.method)

-    #print(x)

-    

-    index1 <- which(groups == 1)

-    index2 <- which(groups == 2)

-    x <- exprs(x)

     if(is.null(y)) {

-        data <- x

+        data <- exprs(x)

     } else {

-        y <- exprs(y)

-        data <- rbind(x, y)

-        featureSize <- dim(x)[1]

+        data <- rbind(exprs(x), exprs(y))

+        featureSize <- dim(exprs(x))[1]

     }

-    data1 <- data[,index1]

-    data2 <- data[,index2]

+    data1 <- data[, which(groups == 1)]

+    data2 <- data[, which(groups == 2)]

     if (cor.method == "spearman" || cor.method == "pearson") {

         statMatrix1 <- cor(t(data1), method = cor.method)

@@ -82,8 +76,6 @@ createVectors <- function(x, y = NULL, groups,

     }

     if (is.null(y)) {

-        statVector1 <- as.vector(statMatrix1)

-        statVector2 <- as.vector(statMatrix2)

         diag <- lower.tri(statMatrix1, diag = FALSE)

         statVector1 <- statMatrix1[diag]

         statVector2 <- statMatrix2[diag]

@@ -96,7 +88,7 @@ createVectors <- function(x, y = NULL, groups,

         statVector2 <- as.vector(statMatrix2)

     }

-    vector_names <- getNames(x, y)

+    vector_names <- .getNames(exprs(x), y)

     names(statVector1) <- vector_names

     names(statVector2) <- vector_names

     return(list(v1 = statVector1, v2 = statVector2))

@@ -160,7 +160,7 @@ discordantRun <- function(v1, v2, x, y = NULL, transform = TRUE,

       tau <- total_tau / iter

       pi <- total_pi / iter

-      finalResult <- subSampleData(pdata, class, mu, sigma, nu, tau, pi, 

+      finalResult <- .subSampleData(pdata, class, mu, sigma, nu, tau, pi, 

                                    components)

       zTable <- finalResult$z

       classVector <- finalResult$class

@@ -182,7 +182,7 @@ discordantRun <- function(v1, v2, x, y = NULL, transform = TRUE,

         colnames(discordPPMatrix) <- rownames(x)

         rownames(classMatrix) <- rownames(x)

         colnames(classMatrix) <- rownames(x)

-        vector_names <- getNames(x)

+        vector_names <- .getNames(x)

         names(discordPPV) <- vector_names

         names(classVector) <- vector_names

     } else {

@@ -194,7 +194,7 @@ discordantRun <- function(v1, v2, x, y = NULL, transform = TRUE,

         rownames(classMatrix) <- rownames(x)

         colnames(classMatrix) <- rownames(y)

-        vector_names <- getNames(x,y)

+        vector_names <- .getNames(x,y)

         names(discordPPV) <- vector_names

         names(classVector) <- vector_names

     }

@@ -219,8 +219,8 @@ em.normal.partial.concordant <- function(data, class, components) {

         return( c(zx[k,] %o% zy[k,]) )

     }

-    zx <- unmap(class[,1], components = components)

-    zy <- unmap(class[,2], components = components)

+    zx <- .unmap(class[,1], components = components)

+    zy <- .unmap(class[,2], components = components)

     zxy <- sapply(1:dim(zx)[1], yl.outer, zx, zy)

     pi <- double(g*g)

@@ -267,6 +267,7 @@ em.normal.partial.concordant <- function(data, class, components) {

 }

 # Internal function to validate user inputs for discordantRun()

+#' @importFrom methods is

 .checkDiscordantInputs <- function(v1, v2, x, y, transform, 

                                    subsampling, subSize, iter, 

                                    components) {

@@ -28,7 +28,7 @@ fishersTrans <- function(rho) {

     return(z)

 }

-subSampleData <- function(pdata, class, mu, sigma, nu, tau, pi, components) {

+.subSampleData <- function(pdata, class, mu, sigma, nu, tau, pi, components) {

     n <- as.integer(dim(pdata)[1])

     g <- as.integer(nlevels(as.factor(class)))

@@ -54,7 +54,7 @@ subSampleData <- function(pdata, class, mu, sigma, nu, tau, pi, components) {

 } 

 # modified from package mclust

-unmap <- function(classification, components){

+.unmap <- function(classification, components){

     n <- length(classification)

     # u <- sort(unique(classification)) # OG Code

     u <- 0:(components - 1) # Max's potential fix

@@ -66,8 +66,9 @@ unmap <- function(classification, components){

     return(z)

 }

-getNames <- function(x, y = NULL) {

+.getNames <- function(x, y = NULL) {

     if(is.null(y) == FALSE) {

+        y <- exprs(y)

         namesMatrix <- NULL

         for(i in 1:nrow(x)) {

             tempMatrix <- cbind(rep(rownames(x)[i], nrow(y)), rownames(y))

@@ -317,10 +317,10 @@ from x and the column names are features from y.

 ```{r}

 # Within -omics

-wthn_result$discordPPMatrix[1:4, 1:4]

+wthn_result$discordPPMatrix[1:5, 1:4]

 # Between -omics

-btwn_result$discordPPMatrix[1:4, 1:4]

+btwn_result$discordPPMatrix[1:5, 1:4]

 ```

 __discordPPVector__

@@ -339,22 +339,22 @@ head(btwn_result$discordPPVector)

 __classMatrix__

 Matrix of classes with the highest posterior probability for each pair. 

-Row and column names are the same as in discordPPMatrix and determined by 

+Row and column names are the same as in \code{discordPPMatrix} and determined by 

 whether only x is inputted or both x and y.

 ```{r}

 # Within -omics

-wthn_result$classMatrix[1:4,1:4]

+wthn_result$classMatrix[1:5,1:4]

 # Between -omics

-btwn_result$classMatrix[1:4,1:4]

+btwn_result$classMatrix[1:5,1:4]

 ```

 __classVector__

 Vector of class with the highest posterior probability for each pair. The 

 length is the number of feature pairs. Names of vector correspond to the 

-feature pairs, similar to discordPPVector.

+feature pairs, similar to \code{discordPPVector}.

 ```{r}

 # Within -omics

@@ -370,9 +370,9 @@ Matrix of all posterior probabilities, where the number of rows is

 the number of feature pairs and the columns represent the class within the 

 class matrix. The number of columns can be 9 or 25, depending on how many 

 mixture components are chosen (discussed later). The values across each row 

-add up to 1. Posterior probabilities in discordPPMatrix and discordPPVector 

-are the summation of columns that correspond to differential correlation 

-classes (Table 1).

+add up to 1. Posterior probabilities in \code{discordPPMatrix} and 

+\code{discordPPVector} are the summation of columns that correspond to 

+differential correlation classes (Table 1).

 ```{r}

 # Within -omics

@@ -460,13 +460,11 @@ data(TCGA_GBM_transcript_microarray)

 groups <- c(rep(1,10), rep(2,10))

 #Within -Omics

-

 wthn_vectors <- createVectors(TCGA_GBM_transcript_microarray, groups = groups)

 wthn_result <- discordantRun(wthn_vectors$v1, wthn_vectors$v2, 

                              TCGA_GBM_transcript_microarray)

 #Between -Omics

-

 btwn_vectors <- createVectors(TCGA_GBM_miRNA_microarray, 

                               TCGA_GBM_transcript_microarray, groups = groups)

 btwn_result <- discordantRun(btwn_vectors$v1, btwn_vectors$v2,