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

 Package: DEsubs

-Version: 1.17.0

+Version: 1.17.1

 Date: 2017-07-23

 Title: DEsubs: an R package for flexible identification of

         differentially expressed subpathways using RNA-seq expression

@@ -26,7 +26,7 @@ Date/Publication:

 NeedsCompilation: no

 LazyLoad: yes

 Imports: graph, igraph, RBGL, circlize, limma, edgeR, EBSeq,

-        NBPSeq, DESeq, stats, grDevices, graphics, pheatmap, utils,

+        NBPSeq, stats, grDevices, graphics, pheatmap, utils,

         ggplot2, Matrix, jsonlite, tools, DESeq2, methods

 Suggests: RUnit, BiocGenerics, knitr

 VignetteBuilder: knitr

@@ -1,18 +1,11 @@

 import( 'locfit' )

-importFrom( 'DESeq',

-                'newCountDataSet',

-                'estimateSizeFactors',

-                'estimateDispersions',

-                'nbinomTest',

-                'getVarianceStabilizedData')

+

 importFrom( 'DESeq2',

                 'DESeqDataSetFromMatrix',

                 'estimateSizeFactors',

                 'estimateDispersions',

                 'nbinomWaldTest',

                 'results')

-

-

 importFrom( 'limma', 

                 'voom', 

                 'lmFit', 

@@ -136,7 +129,8 @@ importFrom(  'methods',

                 'setGeneric',

                 'setMethod',

                 'signature',

-                'new')

+                'new', 

+                'is')

 export( 'DEsubs', 

                 'geneVisualization', 

                 'organismVisualization',

@@ -60,8 +60,8 @@ DEsubs <- function( org, mRNAexpr, mRNAnomenclature, pathways,

     if ( missing(classes) )      { message('Please supply the classes.') }

     if ( missing(DEGchoice) )    { message('Please supply a type.') }

-    supportedMethods <- c('edgeR', 'DESeq', 'EBSeq', 'NBPSeq', 

-                            'voom+limma', 'vst+limma', 'TSPM')

+    supportedMethods <- c(

+        'edgeR', 'DESeq2', 'EBSeq', 'NBPSeq', 'voom+limma', 'vst2+limma', 'TSPM')

     if ( DEGchoice == 'edgeR' )

     {

@@ -79,24 +79,6 @@ DEsubs <- function( org, mRNAexpr, mRNAnomenclature, pathways,

         return(adjpvalues)

     }

-    if ( DEGchoice == 'DESeq' )

-    {

-        # run DESeq

-        DESeq.cds <- DESeq::newCountDataSet(countData=count.matrix, 

-                                    conditions=factor(classes))

-        DESeq.cds <- DESeq::estimateSizeFactors(DESeq.cds)

-        DESeq.cds <- DESeq::estimateDispersions(DESeq.cds, 

-                            sharingMode="maximum", method="pooled", 

-                            fitType="local")

-        DESeq.test        <- nbinomTest(DESeq.cds, "1", "2")

-        DESeq.pvalues     <- DESeq.test[['pval']]

-        genes             <- DESeq.test[['id']]

-        DESeq.adjpvalues  <- p.adjust(DESeq.pvalues, method="BH")

-        adjpvalues        <- DESeq.adjpvalues

-        names(adjpvalues) <- genes

-        

-        return(adjpvalues)

-    }

     if ( DEGchoice == 'voom+limma' )

     {

         # voom+limma

@@ -116,34 +98,6 @@ DEsubs <- function( org, mRNAexpr, mRNAnomenclature, pathways,

         return(adjpvalues)

     }

-    # if ( DEGchoice == 'samr' )

-    # {

-    #     # samr

-    #     sink( tempfile() ) 

-    #     SAMseq.test <- suppressMessages(SAMseq(count.matrix, classes, 

-    #                         resp.type="Two class unpaired", 

-    #                         geneid = rownames(count.matrix), 

-    #                         genenames = rownames(count.matrix),

-    #                         nperms = 100, nresamp = 20, fdr.output = 1))

-    #     SAMseq.result.table <- rbind( 

-    #                         SAMseq.test[['siggenes.table']][['genes.up']], 

-    #                         SAMseq.test[['siggenes.table']][['genes.lo']])

-    #     SAMseq.score        <- rep(0,  nrow(count.matrix))

-    #     idx                 <- match(SAMseq.result.table[,1], 

-    #                                 rownames(count.matrix))

-    #     SAMseq.score[idx]   <- as.numeric(SAMseq.result.table[,3])

-    #     SAMseq.FDR          <- rep(1, nrow(count.matrix))

-    #     idx                 <- match(SAMseq.result.table[,1], 

-    #                                 rownames(count.matrix)) 

-    #     SAMseq.FDR[idx]     <- as.numeric(SAMseq.result.table[,5])/100

-    #     adjpvalues          <- SAMseq.FDR

-    #     genes               <- SAMseq.result.table[, 'Gene ID']

-    #     names(adjpvalues)   <- genes

-

-    #     sink()

-

-    #     return(adjpvalues)

-    # }

     if ( DEGchoice == 'EBSeq' )

     {

         # run EBSeq

@@ -166,26 +120,6 @@ DEsubs <- function( org, mRNAexpr, mRNAnomenclature, pathways,

         return(adjpvalues)

     }

-    if ( DEGchoice == 'vst+limma' )

-    {

-        # vst+limma

-        DESeq.cds  <- newCountDataSet(  countData=count.matrix, 

-                                        conditions=factor(classes))

-        DESeq.cds  <- estimateSizeFactors(DESeq.cds)

-        DESeq.cds  <- estimateDispersions(  DESeq.cds,  

-                                            method="blind", fitType="local")

-        DESeq.vst  <- getVarianceStabilizedData(DESeq.cds)

-        DESeq.vst.fitlimma   <- lmFit(  DESeq.vst,

-                                        design=model.matrix(~factor(classes)))

-        DESeq.vst.fitbayes   <- eBayes(DESeq.vst.fitlimma)

-        DESeq.vst.pvalues    <- DESeq.vst.fitbayes[['p.value']][, 2]

-        genes                <- rownames(DESeq.vst.fitbayes[['p.value']])

-        DESeq.vst.adjpvalues <- p.adjust(DESeq.vst.pvalues, method="BH")

-        adjpvalues           <- DESeq.vst.adjpvalues 

-        names(adjpvalues)    <- genes

-

-        return(adjpvalues)

-    }

     if ( DEGchoice == 'NBPSeq' )

     {

         # NBPSeq

@@ -262,6 +196,7 @@ DEsubs <- function( org, mRNAexpr, mRNAnomenclature, pathways,

         return(adjpvalues)

     }

+

     if  ( !is.null(DEGchoice) )

     {

         unsupportedOptions <- DEGchoice[!DEGchoice %in% supportedMethods]

@@ -16,8 +16,8 @@ or a filename of a text file stored in the 'User' directory.}

     'ensembl_transcript_id', 'ensembl_peptide_id', 'hgnc_id', 'hgnc_symbol',

     'hgnc_transcript_name', 'refseq_mrna', 'refseq_peptide')}

 \item{pathways}{Pathway type ('All', 'Non-Metabolic', 'Metabolic')}

-\item{DEtool}{DEG analysis tool selection for NodeRule ('edgeR', 'DESeq', 

-    'EBSeq', 'NBPSeq', 'voom+limma', 'vst+limma', 'TSPM')}

+\item{DEtool}{DEG analysis tool selection for NodeRule ('edgeR', 'DESeq2',

+    'EBSeq', 'NBPSeq', 'voom+limma', 'vst2+limma', 'TSPM')}

 \item{DEpar}{DE analysis tools Q-value threshold of NodeRule 

     (default: DEGpar = 0.05)}

 \item{CORtool}{ Correlation measure selection for EdgeRule 

@@ -199,9 +199,9 @@ Table: Edge Rule options

 Supported Labels                                    R command

 --------------                                      ----------------

 [@robinson2010edger]                                'edgeR'

-[@anders2010differential]                           'DESeq'

+[@anders2010differential]                           'DESeq2'

 [@leng2013ebseq]                                    'EBSeq'

-[@smyth2004linear]                                  'vst+limma'

+[@smyth2004linear]                                  'vst2+limma'

 [@anders2010differential]; [@smyth2004linear]       'voom+limma'

 [@di2011nbp]                                        'NBPSeq'

 [@auer2011two]                                      'TSPM'