@@ -16,14 +16,14 @@

 #' \code{resultNamePrefix_CDF}. If this parameter is set to \code{NULL}, then

 #' \code{"VAM_geneSetCollectionName_"} will be used. Default \code{NULL}.

 #' @param center Boolean. If \code{TRUE}, values will be mean centered when

-#' computing the Mahalanobis statistic. Default \code{TRUE}.

+#' computing the Mahalanobis statistic. Default \code{FALSE}.

 #' @param gamma Boolean. If \code{TRUE}, a gamma distribution will be fit to

 #' the non-zero squared Mahalanobis distances computed from a row-permuted

 #' version of the gene expression matrix. The estimated gamma distribution will

 #' be used to compute a one-sided p-value for each cell. If \code{FALSE}, the

 #' p-value will be computed using the standard chi-square approximation for the

 #' squared Mahalanobis distance (or non-central if \code{center = FALSE}).

-#' Default \code{FALSE}.

+#' Default \code{TRUE}.

 #' @importFrom methods slot

 #' @return A \linkS4class{SingleCellExperiment} object with VAM metrics stored

 #' in \code{reducedDim} as \code{VAM_NameOfTheGeneset_Distance} and

@@ -48,7 +48,7 @@

 #'               geneSetCollectionName = "GeneSetCollection",

 #'               useAssay = "logcounts")

 runVAM <- function(inSCE, geneSetCollectionName, useAssay,

-                   resultNamePrefix = NULL, center = TRUE, gamma = FALSE) {

+                   resultNamePrefix = NULL, center = FALSE, gamma = TRUE) {

   ###################################################

   ###  create gene set collection

   ###################################################

@@ -63,7 +63,8 @@ pcaParams <- list(

   inSCE = data,

   nPCs = pc.count,

   verbose = FALSE)

-data <- do.call("runSeuratPCA", pcaParams)

+#data <- do.call("runSeuratPCA", pcaParams)

+data <- runSeuratPCA(inSCE = data, nPCs = pc.count, verbose = FALSE)

 ```

 ```{r, echo=plotJackStraw, warning=FALSE, eval = plotJackStraw}

@@ -74,7 +75,8 @@ jackStrawParams <- list(

   numReplicate = 100,

   propFreq = 0.025

 )

-data <- do.call("runSeuratJackStraw", jackStrawParams)

+#data <- do.call("runSeuratJackStraw", jackStrawParams)

+data <- runSeuratJackStraw(data, useAssay = "seuratScaledData", dims = pc.count)

 ```

 ```{r, echo=FALSE, results='asis', include=plotElbowPlot, eval= plotElbowPlot}

@@ -152,7 +154,8 @@ pcHeatmapParams <- list(

   ncol = 4,

   fast = FALSE

 )

-heatmap <- do.call("runSeuratHeatmap", pcHeatmapParams)  

+#heatmap <- do.call("runSeuratHeatmap", pcHeatmapParams)  

+heatmap <- runSeuratHeatmap(inSCE = data, useAssay = "seuratScaledData", useReduction = "pca", dims = pc.count, balanced = TRUE, ncol = 4, fast = FALSE)

 heatmap

 ```

@@ -9,8 +9,8 @@ runVAM(

   geneSetCollectionName,

   useAssay,

   resultNamePrefix = NULL,

-  center = TRUE,

-  gamma = FALSE

+  center = FALSE,

+  gamma = TRUE

 )

 }

 \arguments{

@@ -29,7 +29,7 @@ output matrices will be \code{resultNamePrefix_Distance} and

 \code{"VAM_geneSetCollectionName_"} will be used. Default \code{NULL}.}

 \item{center}{Boolean. If \code{TRUE}, values will be mean centered when

-computing the Mahalanobis statistic. Default \code{TRUE}.}

+computing the Mahalanobis statistic. Default \code{FALSE}.}

 \item{gamma}{Boolean. If \code{TRUE}, a gamma distribution will be fit to

 the non-zero squared Mahalanobis distances computed from a row-permuted

@@ -37,7 +37,7 @@ version of the gene expression matrix. The estimated gamma distribution will

 be used to compute a one-sided p-value for each cell. If \code{FALSE}, the

 p-value will be computed using the standard chi-square approximation for the

 squared Mahalanobis distance (or non-central if \code{center = FALSE}).

-Default \code{FALSE}.}

+Default \code{TRUE}.}

 }

 \value{

 A \linkS4class{SingleCellExperiment} object with VAM metrics stored