@@ -2,7 +2,7 @@ Package: evaluomeR

 Type: Package

 Title: Evaluation of Bioinformatics Metrics

 URL: http://sele.inf.um.es/evaluome/index.html

-Version: 0.99.3

+Version: 0.99.4

 Author: José Antonio Bernabé-Díaz [aut, cre], Manuel Franco [aut], Juana-María Vivo [aut], Manuel Quesada-Martínez [aut], Astrid Duque-Ramos [aut], Jesualdo Tomás Fernández-Breis [aut].

 Maintainer: José Antonio Bernabé Díaz <[email protected]>

 Description: Evaluating the reliability of your own metrics 

@@ -1,5 +1,9 @@

+Changes in version 0.99.4 (2019-04-16)

+  + The `correlations` method also uses `SummarizedExperiment` as output object.

+

+

 Changes in version 0.99.3 (2019-04-16)

-+ All methods depend on `SummarizedExperiment` as input/output object.

+  + All methods depend on `SummarizedExperiment` as input/output object.

 Changes in version 0.99.2 (2019-04-15)

 + SummarizedExperiment can be now processed via `seToDataFrame` method. Adding SummarizedExperiment and airway dependencies.

@@ -10,7 +10,8 @@

 #' @inheritParams stability

 #' @param margins See \code{\link{par}}.

 #'

-#' @return The Pearson correlation matrix.

+#' @return The Pearson correlation matrix as an assay

+#' in a \code{\link{SummarizedExperiment}} object.

 #'

 #' @examples

 #' # Using example data from our package

@@ -36,8 +37,8 @@ correlations <- function(data, margins=c(0,10,9,11), getImages=TRUE,

     }

     runMetricsCorrelationIMG(data, MatCorr, margins, label, path)

   }

-

-  return(MatCorr)

+  se <- createSE(MatCorr)

+  return(se)

 }

 runMetricsCorrelationIMG <- function(data, correlations, margins, label, path) {

@@ -25,7 +25,8 @@ The rows contains the measurements of the metrics for each instance in the datas

 \item{path}{String. Path to a valid directory where plots are saved.}

 }

 \value{

-The Pearson correlation matrix.

+The Pearson correlation matrix as an assay

+in a \code{\link{SummarizedExperiment}} object.

 }

 \description{

 Calculation of Pearson correlation coefficient between every pair of

@@ -110,7 +110,9 @@ We provide the `correlations` function to evaluate the correlations among the me

 ```{r correlations-1, echo=TRUE}

 library(evaluomeR)

 rnaMetrics <- loadSample("rna-metrics")

-correlationMatrix <- correlations(rnaMetrics, margins =  c(4,4,12,10))

+correlationSE <- correlations(rnaMetrics, margins =  c(4,4,12,10))

+# Access the correlation matrix via its first assay:

+# assay(correlationSE,1)

 ```

 ## Stability analysis ##