GetReadsInRegion()when no fragments present that overlap the region (@rockweiler; #1348)
RegionMatrix()where regions on minus strand were not handled correctly (#1368)
FeatureMatrix()where cell names would not be converted correctly when running with
CoverageTrackto enable reproducible coverage plots (#1206)
Motifclass definition to allow any
CsparseMatrixin the data slot
FindMotifs(), and new
p.adjustcolumn in output dataframe
FindMotifs()to control multiple testing correction method used
CallPeaks()when project name contained whitespace (#981)
InsertionBias()that set the coordinates beyond the end of some chromosomes (#986)
BigwigTrack()when supplying a single bigwig file (#1053)
GeneActivity()when specifying biotypes (#1058)
GeneActivity()when gene name is an empty string (#1055)
FeatureMatrix()when using list of Fragment objects (#1056)
RegionMatrix()when running on objects containing renamed cells (#1076)
Footprint()when using a FASTA file (#1092)
Footprint()when using list of genomic regions (#1098)
CoveragePlot()by providing a list of assay names
CreateChromatinAssay()to retain cells with
FeatureMatrix()when cells information not present in Fragment object (#803)
LinkPeaks()using Ensembl IDs (#858)
GeneActivity()when gene names are
FeatureMatrix()when only one region supplied
ExpressionPlot()when using scaled data (#893)
assayparameter can now be a list of assays to plot data from, with signal colored by assay of origin.
FindMotifs()when using only one region as input (#732)
ClosestFeature()when query contained regions on contigs not present in gene annotation (#758)
TSSEnrichment()when using multiple fragment files (#783)
CallPeaks()when multiple fragment files used as input
CallPeaks()to account for 0-based starts in called peaks
LinkPeaks()function when running on a single gene (#629)
CallPeaks()to enable setting directory that split fragment files are written to during peak calling (#579)
RenameCells()when cell information not present in Fragment object (#704)
BigwigTrack()function to plot data from bigWig files
CoveragePlot()to include bigWig files in
GeneActivity()to be incorrect (#521)
ChromatinAssay-specific functions on non-
Biostringsto suggested packages
PeakPlot()to allow coloring plotted genomic ranges by metadata variables.
CoveragePlot()to allow coloring plotted genomic ranges in
CoveragePlot()to be colored by metadata variables.
CreateChromatinAssay()when setting both
PlotFootprint()when only one cell in an identity class (#406)
CallPeaks()function to call peaks using MACS2. Peaks can be called for different groups of cells separately by setting the
LinkPeaks()function to link peaks to correlated genes.
AddMotifs()function to add motif information to a Seurat object or ChromatinAssay.
AggregateTiles()function to combine adjacent genome tiles
CoveragePlot()to plot addition sets of genomic ranges
CoveragePlot()to plot accessibility of all cells combined
Fragmentobjects and modify the file path for existing fragment objects (#206)
AlleleFreq()(#196 and #260)
TSSEnrichment()when cell information not set for fragment files (#203)
TSSEnrichment()when no fragments present in TSS region (#244)
SetAssayData()when setting the
This release includes major updates to the Signac package, including new functionality, performance improvements, and new data structures.
The entire package has been updated to use the new
ChromatinAssay class for the storage of single-cell chromatin data. This is an extension of the standard Seurat
Assay that adds additional slots needed for the analysis of chromatin data, including genomic ranges, genome information, fragment file information, motifs, gene annotations, and genomic links.
In addition, we have defined a new
Fragment class to store information relating to a fragment file. This makes use of the fragment files within Signac more robust, as checks are now performed to verify that the expected cells are present in the fragment file, and that the fragment file or index are not modified on disk.
Key new functionality:
PlotFootprint()functions for TF footprinting analysis.
seqinfo(), and other Bioconductor generic functions directly on the
NucleosomeSignal(): we have greatly improved the scalability of
NucleosomeSignal(), and fixed a bug present in previous versions. The score computed by
NucleosomeSignal()in 1.0.0 will be different to that computed by previous versions of Signac.
CountFragments()function: a fast, memory-efficient function implemented in C++ that counts the total number of fragments for each cell barcode present in a fragment file.
fastoption in the
TSSEnrichment()function. Setting this to
TRUEwill compute the TSS enrichment score per cell without storing the entire cell by TSS position matrix. This can significantly reduce memory requirements for large datasets, but does not allow subsequent plotting of the TSS signal for different groups of cells.
CoveragePlot()to plot Tn5 integration events in a genomic region for individual cells.
FRiP()function to use total fragment counts per cell stored in object metadata.
DepthCorfunction to compute the correlation between sequencing depth and reduced dimension components.
CoveragePlot. Use GRanges instead.
nchunkwas greater than the number of features used.
CoveragePlotthat would prevent plotting multiple regions when using
CoveragePlotthat would prevent plotting when a different assay was active.
SubsetMatrixfunction to subset a matrix based on number of non-zero elements in the rows or columns.
RunSVD: previously, scaling was applied to each cell rather than each component. Now, mean centering and SD scaling are applied to the cell embeddings within a component.
RunSVDto control whether embeddings are scaled and centered.
SingleCoveragePlotfrom exported functions
TSSPlotfunctions for TSS enrichment scoring
CoveragePlotfor scaling tracks
CoveragePlot: now plots a Tn5 integration score per base, rather than the whole fragment.
GetIntersectingFeaturesfunction to find overlapping peaks between objects
MergeWithRegionsfunction to perform region-aware Seurat object merging
RunChromVARfunction to run chromVAR through Signac
RegionStatsfunction to add statistics about peak sequences to the feature metadata
FindMotifs: now selects a set of background peaks matching the sequence characteristics of the input