@@ -343,6 +343,7 @@ combineMultivariates <- function(hmms, mode) {

     hmm$segments.per.condition <- segments.per.condition

     hmm$peaks <- peaks

     hmm$frequencies <- freqs$table

+    hmm$mode <- mode

     return(hmm)

 }

@@ -77,13 +77,14 @@ NULL

 #' Univariate HMM object

 #'

-#' The univariate HMM object is output of the function \code{\link{callPeaksUnivariate}} and is a \code{list()} with various entries. The class() attribute of this list was set to "uniHMM". For a given hmm, the entries can be accessed with the list operators 'hmm[[]]' or 'hmm$'.

+#' The univariate HMM object is output of the function \code{\link{callPeaksUnivariate}} and is a \code{list()} with various entries. The \code{class()} attribute of this list was set to "uniHMM". For a given hmm, the entries can be accessed with the list operators 'hmm[[]]' or 'hmm$'.

 #'

 #' @return

 #' A \code{list()} with the following entries:

-#' \item{ID}{An identifier that is used in various \pkg{\link{chromstaR}} functions.}

+#' \item{info}{Experiment table for this object.}

+#' \item{bincounts}{A \code{\link[GenomicRanges]{GRanges}} object containing the genomic bin coordinates and original binned read count values for different offsets.}

 #' \item{bins}{A \code{\link[GenomicRanges]{GRanges}} object containing the genomic bin coordinates, their read count, (optional) posteriors and state classification.}

-#' \item{segments}{Same as \code{bins}, but consecutive bins with the same state are collapsed into segments.}

+#' \item{peaks}{A \code{list()} with \code{\link[GenomicRanges]{GRanges}} containing peak coordinates for each ID in \code{info}.}

 #' \item{weights}{Weight for each component. Same as \code{apply(hmm$posteriors,2,mean)}.}

 #' \item{transitionProbs}{Matrix of transition probabilities from each state (row) into each state (column).}

 #' \item{transitionProbs.initial}{Initial \code{transitionProbs} at the beginning of the Baum-Welch.}

@@ -91,7 +92,7 @@ NULL

 #' \item{startProbs.initial}{Initial \code{startProbs} at the beginning of the Baum-Welch.}

 #' \item{distributions}{Estimated parameters of the emission distributions.}

 #' \item{distributions.initial}{Distribution parameters at the beginning of the Baum-Welch.}

-#' \item{post.cutoff}{Cutoff for posterior probabilities to call peaks (default=0.5).}

+#' \item{post.cutoff}{Cutoff for posterior probabilities to call peaks.}

 #' \item{convergenceInfo}{Contains information about the convergence of the Baum-Welch algorithm.}

 #' \item{convergenceInfo$eps}{Convergence threshold for the Baum-Welch.}

 #' \item{convergenceInfo$loglik}{Final loglikelihood after the last iteration.}

@@ -107,13 +108,15 @@ NULL

 #' Multivariate HMM object

 #'

-#' The multivariate HMM object is output of the function \code{\link{callPeaksMultivariate}} and is a \code{list()} with various entries. The class() attribute of this list was set to "multiHMM". For a given hmm, the entries can be accessed with the list operators 'hmm[[]]' or 'hmm$'.

+#' The multivariate HMM object is output of the function \code{\link{callPeaksMultivariate}} and is a \code{list()} with various entries. The \code{class()} attribute of this list was set to "multiHMM". For a given hmm, the entries can be accessed with the list operators 'hmm[[]]' or 'hmm$'.

 #' 

 #' @return

 #' A \code{list()} with the following entries:

-#' \item{IDs}{IDs of the input univariate HMMs.}

+#' \item{info}{Experiment table for this object.}

+#' \item{bincounts}{A \code{\link[GenomicRanges]{GRanges}} object containing the genomic bin coordinates and original binned read count values for different offsets.}

 #' \item{bins}{A \code{\link[GenomicRanges]{GRanges}} object containing the genomic bin coordinates, their read count, (optional) posteriors and state classification.}

 #' \item{segments}{Same as \code{bins}, but consecutive bins with the same state are collapsed into segments.}

+#' \item{peaks}{A \code{list()} with \code{\link[GenomicRanges]{GRanges}} containing peak coordinates for each ID in \code{info}.}

 #' \item{mapping}{A named vector giving the mapping from decimal combinatorial states to human readable combinations.}

 #' \item{weights}{Weight for each component. Same as \code{apply(hmm$posteriors,2,mean)}.}

 #' \item{weights.univariate}{Weights of the univariate HMMs.}

@@ -122,7 +125,6 @@ NULL

 #' \item{startProbs}{Probabilities for the first bin. Same as \code{hmm$posteriors[1,]}.}

 #' \item{startProbs.initial}{Initial \code{startProbs} at the beginning of the Baum-Welch.}

 #' \item{distributions}{Emission distributions used for this model.}

-#' \item{post.cutoff}{False discovery rate. NULL means that the state with maximum posterior probability was chosen, irrespective of its absolute probability (default=NULL).}

 #' \item{convergenceInfo}{Contains information about the convergence of the Baum-Welch algorithm.}

 #' \item{convergenceInfo$eps}{Convergence threshold for the Baum-Welch.}

 #' \item{convergenceInfo$loglik}{Final loglikelihood after the last iteration.}

@@ -143,13 +145,17 @@ NULL

 #' Combined multivariate HMM object

 #'

-#' The multivariate HMM object is output of the function \code{\link{combineMultivariates}} and is a \code{list()} with various entries. The class() attribute of this list was set to "combinedMultiHMM". For a given hmm, the entries can be accessed with the list operators 'hmm[[]]' or 'hmm$'.

+#' The combined multivariate HMM object is output of the function \code{\link{combineMultivariates}} and is a \code{list()} with various entries. The \code{class()} attribute of this list was set to "combinedMultiHMM". For a given hmm, the entries can be accessed with the list operators 'hmm[[]]' or 'hmm$'.

 #' 

 #' @return

 #' A \code{list()} with the following entries:

+#' \item{info}{Experiment table for this object.}

 #' \item{bins}{A \code{\link[GenomicRanges]{GRanges}} object containing genomic bin coordinates and human readable combinations for the combined \code{\link{multiHMM}} objects.}

 #' \item{segments}{Same as \code{bins}, but consecutive bins with the same state are collapsed into segments.}

-#' \item{segments.per.condition}{A \code{list} with segments for each condition separately.}

+#' \item{segments.per.condition}{A \code{list()} with segments for each condition separately.}

+#' \item{peaks}{A \code{list()} with \code{\link[GenomicRanges]{GRanges}} containing peak coordinates for each ID in \code{info}.}

+#' \item{frequencies}{Genomic frequencies of combinations.}

+#' \item{mode}{Mode of analysis.}

 #' @seealso \code{\link{combineMultivariates}}, \code{\link{uniHMM}}, \code{\link{multiHMM}}

 #' @name combinedMultiHMM

 #' @aliases combinedHMM

@@ -260,7 +260,7 @@ plotFoldEnrichHeatmap <- function(hmm, annotations, what="combinations", combina

 #' @importFrom reshape2 melt

 #' @importFrom IRanges subsetByOverlaps

 #' @export

-plotEnrichCountHeatmap <- function(hmm, annotation, bp.around.annotation=10000, max.rows=1000, combinations=NULL, colorByCombinations=TRUE, sortByCombinations=TRUE, sortByColumns=NULL) {

+plotEnrichCountHeatmap <- function(hmm, annotation, bp.around.annotation=10000, max.rows=1000, combinations=NULL, colorByCombinations=sortByCombinations, sortByCombinations=is.null(sortByColumns), sortByColumns=NULL) {

     if (!is.null(sortByColumns)) {

         sortByCombinations <- FALSE

@@ -391,7 +391,13 @@ plotEnrichCountHeatmap <- function(hmm, annotation, bp.around.annotation=10000,

     ggplt <- ggplt + scale_fill_continuous(trans='log1p', low='white', high='black')

     if (sortByCombinations) {

         # Insert horizontal lines

-        y.lines <- sapply(split(df$id, df$combination), function(x) { max(as.integer(x)) })

+        y.lines <- sapply(split(df$id, df$combination), function(x) { 

+          y <- -Inf

+          if (length(x)>0) {

+            y <- max(as.integer(x))

+          }

+          return(y)

+        })

         df.lines <- data.frame(y=sort(y.lines[-1]) + 0.5)

         ggplt <- ggplt + geom_hline(data=df.lines, mapping=aes_string(yintercept='y'), linetype=2)

     }

@@ -591,7 +597,7 @@ enrichmentAtAnnotation <- function(bins, info, annotation, bp.around.annotation=

     seqlevels.only.in.bins <- setdiff(seqlevels(bins), seqlevels(annotation))

     seqlevels.only.in.annotation <- setdiff(seqlevels(annotation), seqlevels(bins))

     if (length(seqlevels.only.in.bins) > 0 | length(seqlevels.only.in.annotation) > 0) {

-        warning("Sequence levels in 'bins' but not in 'annotation': ", paste0(seqlevels.only.in.bins, collapse = ', '), "\n  Sequence levels in 'annotation' but not in 'bins': ", paste0(seqlevels.only.in.annotation, collapse = ''))

+        warning("Sequence levels in 'bins' but not in 'annotation': ", paste0(seqlevels.only.in.bins, collapse = ', '), "\n  Sequence levels in 'annotation' but not in 'bins': ", paste0(seqlevels.only.in.annotation, collapse = ', '))

     }

     ## Variables

new file mode 100644

@@ -0,0 +1,12 @@

+opt <- list()

+opt$chromstarObject <- '~/bioinformatics/galaxy/tools/chromstaR-galaxy/test-data/output_chromstaR-result.rdata'

+opt$annotationBed6 <- '~/bioinformatics/galaxy/tools/chromstaR-galaxy/test-data/rn4_protein_coding_genes.bed'

+opt$bpAroundAnnotation <- 10000

+opt$numIntervals <- 20

+opt$statistic <- "fold"

+opt$numLoci <- 1000

+

+opt <- list()

+opt$chromstarObject <- '~/bioinformatics/galaxy/tools/chromstaR-galaxy/test-data/output_chromstaR-result.rdata'

+opt$changeWhat <- 'changePostCutoff'

+opt$cutoff <- 0.9999999

@@ -6,12 +6,16 @@

 \title{Combined multivariate HMM object}

 \value{

 A \code{list()} with the following entries:

+\item{info}{Experiment table for this object.}

 \item{bins}{A \code{\link[GenomicRanges]{GRanges}} object containing genomic bin coordinates and human readable combinations for the combined \code{\link{multiHMM}} objects.}

 \item{segments}{Same as \code{bins}, but consecutive bins with the same state are collapsed into segments.}

-\item{segments.per.condition}{A \code{list} with segments for each condition separately.}

+\item{segments.per.condition}{A \code{list()} with segments for each condition separately.}

+\item{peaks}{A \code{list()} with \code{\link[GenomicRanges]{GRanges}} containing peak coordinates for each ID in \code{info}.}

+\item{frequencies}{Genomic frequencies of combinations.}

+\item{mode}{Mode of analysis.}

 }

 \description{

-The multivariate HMM object is output of the function \code{\link{combineMultivariates}} and is a \code{list()} with various entries. The class() attribute of this list was set to "combinedMultiHMM". For a given hmm, the entries can be accessed with the list operators 'hmm[[]]' or 'hmm$'.

+The combined multivariate HMM object is output of the function \code{\link{combineMultivariates}} and is a \code{list()} with various entries. The \code{class()} attribute of this list was set to "combinedMultiHMM". For a given hmm, the entries can be accessed with the list operators 'hmm[[]]' or 'hmm$'.

 }

 \seealso{

 \code{\link{combineMultivariates}}, \code{\link{uniHMM}}, \code{\link{multiHMM}}

@@ -11,8 +11,9 @@ plotFoldEnrichHeatmap(hmm, annotations, what = "combinations",

   combinations = NULL, marks = NULL, plot = TRUE, logscale = TRUE)

 plotEnrichCountHeatmap(hmm, annotation, bp.around.annotation = 10000,

-  max.rows = 1000, combinations = NULL, colorByCombinations = TRUE,

-  sortByCombinations = TRUE, sortByColumns = NULL)

+  max.rows = 1000, combinations = NULL,

+  colorByCombinations = sortByCombinations,

+  sortByCombinations = is.null(sortByColumns), sortByColumns = NULL)

 plotEnrichment(hmm, annotation, bp.around.annotation = 10000,

   region = c("start", "inside", "end"), num.intervals = 20,

@@ -6,9 +6,11 @@

 \title{Multivariate HMM object}

 \value{

 A \code{list()} with the following entries:

-\item{IDs}{IDs of the input univariate HMMs.}

+\item{info}{Experiment table for this object.}

+\item{bincounts}{A \code{\link[GenomicRanges]{GRanges}} object containing the genomic bin coordinates and original binned read count values for different offsets.}

 \item{bins}{A \code{\link[GenomicRanges]{GRanges}} object containing the genomic bin coordinates, their read count, (optional) posteriors and state classification.}

 \item{segments}{Same as \code{bins}, but consecutive bins with the same state are collapsed into segments.}

+\item{peaks}{A \code{list()} with \code{\link[GenomicRanges]{GRanges}} containing peak coordinates for each ID in \code{info}.}

 \item{mapping}{A named vector giving the mapping from decimal combinatorial states to human readable combinations.}

 \item{weights}{Weight for each component. Same as \code{apply(hmm$posteriors,2,mean)}.}

 \item{weights.univariate}{Weights of the univariate HMMs.}

@@ -17,7 +19,6 @@ A \code{list()} with the following entries:

 \item{startProbs}{Probabilities for the first bin. Same as \code{hmm$posteriors[1,]}.}

 \item{startProbs.initial}{Initial \code{startProbs} at the beginning of the Baum-Welch.}

 \item{distributions}{Emission distributions used for this model.}

-\item{post.cutoff}{False discovery rate. NULL means that the state with maximum posterior probability was chosen, irrespective of its absolute probability (default=NULL).}

 \item{convergenceInfo}{Contains information about the convergence of the Baum-Welch algorithm.}

 \item{convergenceInfo$eps}{Convergence threshold for the Baum-Welch.}

 \item{convergenceInfo$loglik}{Final loglikelihood after the last iteration.}

@@ -27,7 +28,7 @@ A \code{list()} with the following entries:

 \item{correlation.matrix}{Correlation matrix of transformed reads.}

 }

 \description{

-The multivariate HMM object is output of the function \code{\link{callPeaksMultivariate}} and is a \code{list()} with various entries. The class() attribute of this list was set to "multiHMM". For a given hmm, the entries can be accessed with the list operators 'hmm[[]]' or 'hmm$'.

+The multivariate HMM object is output of the function \code{\link{callPeaksMultivariate}} and is a \code{list()} with various entries. The \code{class()} attribute of this list was set to "multiHMM". For a given hmm, the entries can be accessed with the list operators 'hmm[[]]' or 'hmm$'.

 }

 \examples{

 ## Get an example multiHMM

@@ -6,9 +6,10 @@

 \title{Univariate HMM object}

 \value{

 A \code{list()} with the following entries:

-\item{ID}{An identifier that is used in various \pkg{\link{chromstaR}} functions.}

+\item{info}{Experiment table for this object.}

+\item{bincounts}{A \code{\link[GenomicRanges]{GRanges}} object containing the genomic bin coordinates and original binned read count values for different offsets.}

 \item{bins}{A \code{\link[GenomicRanges]{GRanges}} object containing the genomic bin coordinates, their read count, (optional) posteriors and state classification.}

-\item{segments}{Same as \code{bins}, but consecutive bins with the same state are collapsed into segments.}

+\item{peaks}{A \code{list()} with \code{\link[GenomicRanges]{GRanges}} containing peak coordinates for each ID in \code{info}.}

 \item{weights}{Weight for each component. Same as \code{apply(hmm$posteriors,2,mean)}.}

 \item{transitionProbs}{Matrix of transition probabilities from each state (row) into each state (column).}

 \item{transitionProbs.initial}{Initial \code{transitionProbs} at the beginning of the Baum-Welch.}

@@ -16,7 +17,7 @@ A \code{list()} with the following entries:

 \item{startProbs.initial}{Initial \code{startProbs} at the beginning of the Baum-Welch.}

 \item{distributions}{Estimated parameters of the emission distributions.}

 \item{distributions.initial}{Distribution parameters at the beginning of the Baum-Welch.}

-\item{post.cutoff}{Cutoff for posterior probabilities to call peaks (default=0.5).}

+\item{post.cutoff}{Cutoff for posterior probabilities to call peaks.}

 \item{convergenceInfo}{Contains information about the convergence of the Baum-Welch algorithm.}

 \item{convergenceInfo$eps}{Convergence threshold for the Baum-Welch.}

 \item{convergenceInfo$loglik}{Final loglikelihood after the last iteration.}

@@ -27,7 +28,7 @@ A \code{list()} with the following entries:

 \item{convergenceInfo$read.cutoff}{Cutoff value for read counts.}

 }

 \description{

-The univariate HMM object is output of the function \code{\link{callPeaksUnivariate}} and is a \code{list()} with various entries. The class() attribute of this list was set to "uniHMM". For a given hmm, the entries can be accessed with the list operators 'hmm[[]]' or 'hmm$'.

+The univariate HMM object is output of the function \code{\link{callPeaksUnivariate}} and is a \code{list()} with various entries. The \code{class()} attribute of this list was set to "uniHMM". For a given hmm, the entries can be accessed with the list operators 'hmm[[]]' or 'hmm$'.

 }

 \seealso{

 \code{\link{callPeaksUnivariate}}, \code{\link{multiHMM}}, \code{\link{combinedMultiHMM}}