Chromstar               Wrapper function for the 'chromstaR' package
binReads                Convert aligned reads from various file formats
                        into read counts in equidistant bins
binned.data             Binned read counts
callPeaksMultivariate   Fit a Hidden Markov Model to multiple ChIP-seq
                        samples
callPeaksReplicates     Fit a multivariate Hidden Markov Model to
                        multiple ChIP-seq replicates
callPeaksUnivariate     Fit a Hidden Markov Model to a ChIP-seq sample.
callPeaksUnivariateAllChr
                        Fit a Hidden Markov Model to a ChIP-seq sample.
changeMaxPostCutoff     Adjust sensitivity of peak detection
changePostCutoff        Change the posterior cutoff of a Hidden Markov
                        Model
chromstaR-objects       chromstaR objects
chromstaR-package       Combinatorial and differential chromatin state
                        analysis for ChIP-seq data
collapseBins            Collapse consecutive bins
combinatorialStates     Get the (decimal) combinatorial states of a
                        list of univariate HMM models
combineMultivariates    Combine combinatorial states from several
                        Multivariates
combinedMultiHMM        Combined multivariate HMM object
conversion              Conversion of decimal and binary states
enrichmentAtAnnotation
                        Enrichment of (combinatorial) states for
                        genomic annotations
enrichment_analysis     Enrichment analysis
experiment.table        Experiment data table
exportFiles             Export genome browser uploadable files
exportGRangesAsBedFile
                        Export genome browser viewable files
fixedWidthBins          Make fixed-width bins
genes_rn4               Gene coordinates for rn4
genomicFrequencies      Frequencies of combinatorial states
getCombinations         Get combinations
getDistinctColors       Get distinct colors
getStateColors          Get state colors
heatmapCombinations     Plot a heatmap of combinatorial states
heatmapCountCorrelation
                        Read count correlation heatmap
heatmapTransitionProbs
                        Heatmap of transition probabilities
loadHmmsFromFiles       Load 'chromstaR' objects from file
mergeChroms             Merge several 'multiHMM's into one object
model.combined          Combined multivariate HMM for demonstration
                        purposes
model.multivariate      Multivariate HMM for demonstration purposes
model.univariate        Univariate HMM for demonstration purposes
multiHMM                Multivariate HMM object
multivariateSegmentation
                        Multivariate segmentation
plotExpression          Overlap with expression data
plotGenomeBrowser       #' Plot a genome browser view #' #' Plot a
                        simple genome browser view. This is useful for
                        scripted genome browser snapshots. #' #' @param
                        counts A 'GRanges-class' object with meta-data
                        column 'counts'. #' @param peaklist A named
                        list() of 'GRanges-class' objects containing
                        peak coordinates. #' @param chr,start,end
                        Chromosome, start and end coordinates for the
                        plot. #' @param countcol A character giving the
                        color for the counts. #' @param peakcols A
                        character vector with colors for the peaks in
                        'peaklist'. #' @param style One of 'c('peaks',
                        'density')'. #' @param peakTrackHeight Relative
                        height of the tracks given in 'peaklist'
                        compared to the 'counts'. #' @return A 'ggplot'
                        object. #' @examples #'## Get an example
                        multiHMM ## #'file <-
                        system.file("data","multivariate_mode-combinatorial_condition-SHR.RData",
                        #' package="chromstaR") #'model <-
                        get(load(file)) #'## Plot genome browser
                        snapshot #'bins <- model$bins #'bins$counts <-
                        model$bins$counts.rpkm[,1]
                        #'plotGenomeBrowser(counts=bins,
                        peaklist=model$peaks, #' chr='chr12', start=1,
                        end=1e6) #' plotGenomeBrowser2 <-
                        function(counts, peaklist=NULL, chr, start,
                        end, countcol='black', peakcols=NULL,
                        style='peaks', peakTrackHeight=5) ## Select
                        ranges to plot ranges2plot <-
                        reduce(counts[counts@seqnames == chr &
                        start(counts) >= start & start(counts) <= end])
                        ## Counts counts <- subsetByOverlaps(counts,
                        ranges2plot) if (style == 'peaks') df <-
                        data.frame(x=(start(counts)+end(counts))/2,
                        counts=counts$counts) # plot triangles centered
                        at middle of the bin ggplt <- ggplot(df) +
                        geom_area(aes_string(x='x', y='counts')) +
                        theme(panel.grid = element_blank(),
                        panel.background = element_blank(), axis.text.x
                        = element_blank(), axis.title =
                        element_blank(), axis.ticks.x =
                        element_blank(), axis.line = element_blank())
                        maxcounts <- max(counts$counts) ggplt <- ggplt
                        + scale_y_continuous(breaks=c(0, maxcounts))
                        else if (style == 'density') df <-
                        data.frame(xmin=start(counts),
                        xmax=end(counts), counts=counts$counts) ggplt
                        <- ggplot(df) +
                        geom_rect(aes_string(xmin='xmin', xmax='xmax',
                        ymin=0, ymax=4, alpha='counts')) +
                        theme(panel.grid = element_blank(),
                        panel.background = element_blank(), axis.text =
                        element_blank(), axis.title = element_blank(),
                        axis.ticks = element_blank(), axis.line =
                        element_blank()) else stop("Unknown value '",
                        style, "' for parameter 'style'. Must be one of
                        c('peaks', 'density').") ## Peaks if
                        (!is.null(peaklist)) if (is.null(peakcols))
                        peakcols <- getDistinctColors(length(peaklist))
                        for (i1 in 1:length(peaklist)) p <-
                        peakTrackHeight peaks <-
                        subsetByOverlaps(peaklist[[i1]], ranges2plot)
                        if (length(peaks) > 0) df <-
                        data.frame(start=start(peaks), end=end(peaks),
                        ymin=-p*i1, ymax=-p*i1+0.9*p) ggplt <- ggplt +
                        geom_rect(data=df,
                        mapping=aes_string(xmin='start', xmax='end',
                        ymin='ymin', ymax='ymax'), col=peakcols[i1],
                        fill=peakcols[i1]) trackname <-
                        names(peaklist)[i1] df <-
                        data.frame(x=start(counts)[1], y=-p*i1+0.5*p,
                        label=trackname) ggplt <- ggplt +
                        geom_text(data=df, mapping=aes_string(x='x',
                        y='y', label='label'), vjust=0.5, hjust=0.5,
                        col=peakcols[i1]) return(ggplt) Plot a genome
                        browser view
plotHistogram           Histogram of binned read counts with fitted
                        mixture distribution
plotHistograms          Histograms of binned read counts with fitted
                        mixture distribution
plotting                chromstaR plotting functions
print.combinedMultiHMM
                        Print combinedMultiHMM object
print.multiHMM          Print multiHMM object
print.uniHMM            Print uniHMM object
readBamFileAsGRanges    Import BAM file into GRanges
readBedFileAsGRanges    Import BED file into GRanges
readConfig              Read chromstaR configuration file
readCustomBedFile       Read bed-file into GRanges
removeCondition         Remove condition from model
scanBinsizes            Find the best bin size for a given dataset
scores                  chromstaR scores
simulateMultivariate    Simulate multivariate data
simulateReadsFromCounts
                        Simulate read coordinates
simulateUnivariate      Simulate univariate data
state.brewer            Obtain combinatorial states from specification
stateBrewer             Obtain combinatorial states from experiment
                        table
subsample               Normalize read counts
transitionFrequencies   Transition frequencies of combinatorial states
uniHMM                  Univariate HMM object
unis2pseudomulti        Combine univariate HMMs to a multivariate HMM
variableWidthBins       Make variable-width bins
writeConfig             Write chromstaR configuration file
zinbinom                The Zero-inflated Negative Binomial
                        Distribution
