Data structures and object interaction
Compiled: March 20, 2026
Source:vignettes/data_structures.Rmd
data_structures.RmdThe Signac package is an extension of Seurat designed for the analysis of single-cell chromatin assays. This includes assays that generate signal mapped to genomic coordinates, such as scATAC-seq, scCUT&Tag, scNTT-seq, and other methods.
As the analysis of these single-cell chromatin datasets presents some
unique challenges in comparison to the analysis of scRNA-seq data, we
have created an extended Assay5 class to store the
additional information needed, including:
- Genomic ranges associated with the features (eg, peaks or genomic bins)
- Gene annotations
- Genome information
- TF motifs
- Genome-wide signal in a disk-based format (fragment files)
- TF footprinting data
- Tn5 insertion bias data
- Linked genomic regions
A major advantage of the Signac design is its interoperability with existing functions in the Seurat package, and other packages that are able to use the Seurat object. This enables straightforward analysis of multimodal single-cell data through the addition of different assays to the Seurat object.
Here we outline the design of each class defined in the Signac package, and demonstrate methods that can be run on each class.
Differences between Signac v1 and v2
We first introduced Signac with the version 1 release in 2020. Recently we have released Signac v2, with some major updates to the package. The main changes include:
-
Updating the
ChromatinAssayto use the SeuratAssay5class: this update enables storing data in the object using different matrix types, including as a BPCells matrix. BPCells provides efficient on-disk matrix storage and computation methods, and enables scaling analyses to millions of cells with a small memory requirement.
-
More flexible object classes: we now provide
options to use Signac assays without needing each feature to correspond
to a single region of the genome. This is useful for storing different
types of feature, including our recently-released universal feature set
for the human genome, REMO
-
More flexible storage of genomic links: you can now
store multiple sets of genomic links within the same assay. We have also
moved to storing this information using the well-established
GInteractionsformat from the InteractionSet package.
-
New peak calling methods: Signac now uses
macs3inCallPeaks(). We have also enabled parallelization of peak calling across groups of cells, and include an option to use thehmmratacpeak calling method inmacs3.
-
New data visualization methods: we added the
ability to plot GWAS and QTL data alongside single-cell chromatin data
in the
CoveragePlot()function
For a full changelog please see the package release notes.
To update old ChromatinAssay assays you can use the
as.GRangesAssay() function in Signac v2.
The ChromatinAssay5 Class
The ChromatinAssay5 class extends the standard Seurat
Assay5 class and adds several additional slots for data
useful for the analysis of single-cell chromatin datasets. The class
includes all the slots present in a standard Seurat object with the
following additional information:
-
motifs: AMotifobject -
fragments: A list ofFragment2objects -
annotation: AGRangesobject containing gene annotations -
bias: A vector containing Tn5 integration bias information (the frequency of Tn5 integration at different hexamers) -
region.aggregation: A named list ofRegionAggregationobjects containing signal per cell aggregated across a set of genomic regions (for example, motif sites) -
links: A named list ofGInteractionsobjects describing linked genomic regions, such as co-accessible sites or enhancer-gene regulatory relationships.
The GRangesAssay Class
The GRangesAssay is an extended
ChromatinAssay5 object class that adds a requirement that
the features stored in the assay correspond to genomic ranges. It stores
the genomic ranges associated with features in the assay as a GRanges
object. The GRangesAssay enables some additional analyses
that require that the assay features correspond to genomic regions, such
as peak-gene linkage or motif enrichment.
Here we will focus on demonstrating use of the
GRangesAssay, but all functions that run on a
GRangesAssay object can also be run on a
ChromatinAssay5 object. There are some cases where a
ChromatinAssay5 object would be the preferred way to store
your data. For example, you may wish to create an assay that does not
store genomic peak intervals (perhaps quantifying REMO features or
transcription factor activities), but still want the ability to create
genome coverage plots.
Constructing the GRangesAssay
A GRangesAssay object can be constructed using the
CreateGRangesAssay() function.
# get some data to use in the following examples
counts <- LayerData(atac_small, layer = "counts")
# create a standalone GRangesAssay object
gr_assay <- CreateGRangesAssay(counts = counts)To create a Seurat object that contains a GRangesAssay
rather than a standard Assay, we can initialize the object
using the GRangesAssay rather than a count matrix.
# create a Seurat object containing a GRangesAssay
object <- CreateSeuratObject(counts = gr_assay)Adding a GRangesAssay to a Seurat
object
To add a new GRangesAssay object to an existing Seurat
object, we can use the standard assignment operation used for adding
standard Assay objects and other data types to the Seurat
object.
object[["peaks"]] <- CreateGRangesAssay(counts = counts)Getting and setting GRangesAssay data
We can get/set data for the GRangesAssay in much the
same way we do for a standard Assay object: using the
LayerData function defined in Seurat. For
example:
## Getting
# access the data slot, found in standard Assays and GRangesAssays
data <- LayerData(atac_small, layer = "data")
# access the bias slot, unique to the GRangesAssay
bias <- LayerData(atac_small, layer = "bias")## Warning: Layer 'bias' is empty
## Setting
# set the data slot
LayerData(atac_small, layer = "data") <- dataWe also have a variety of convenience functions defined for
getting/setting data in specific slots. This includes the
Fragments(), Motifs(), Links(),
Annotation(), RegionAggr(), and
Bias() functions. For example, to get or set gene
annotation data we can use the Annotation() getter and
Annotation<- setter functions:
# first get some gene annotations
library(AnnotationHub)
ah <- AnnotationHub()
ensdb_v98 <- ah[["AH75011"]]
# convert EnsDb to GRanges
gene.ranges <- GetGRangesFromEnsDb(ensdb = ensdb_v98)
# set gene annotations
Annotation(atac_small) <- gene.ranges
# get gene annotation information
Annotation(atac_small)## GRanges object with 3200207 ranges and 5 metadata columns:
## seqnames ranges strand | tx_id gene_name
## <Rle> <IRanges> <Rle> | <character> <character>
## ENSE00001489430 X 276322-276394 + | ENST00000399012 PLCXD1
## ENSE00001536003 X 276324-276394 + | ENST00000484611 PLCXD1
## ENSE00002160563 X 276353-276394 + | ENST00000430923 PLCXD1
## ENSE00001750899 X 281055-281121 + | ENST00000445062 PLCXD1
## ENSE00001719251 X 281194-281256 + | ENST00000429181 PLCXD1
## ... ... ... ... . ... ...
## ENST00000361739 MT 7586-8269 + | ENST00000361739 MT-CO2
## ENST00000361789 MT 14747-15887 + | ENST00000361789 MT-CYB
## ENST00000361851 MT 8366-8572 + | ENST00000361851 MT-ATP8
## ENST00000361899 MT 8527-9207 + | ENST00000361899 MT-ATP6
## ENST00000362079 MT 9207-9990 + | ENST00000362079 MT-CO3
## gene_id gene_biotype type
## <character> <character> <factor>
## ENSE00001489430 ENSG00000182378 protein_coding exon
## ENSE00001536003 ENSG00000182378 protein_coding exon
## ENSE00002160563 ENSG00000182378 protein_coding exon
## ENSE00001750899 ENSG00000182378 protein_coding exon
## ENSE00001719251 ENSG00000182378 protein_coding exon
## ... ... ... ...
## ENST00000361739 ENSG00000198712 protein_coding cds
## ENST00000361789 ENSG00000198727 protein_coding cds
## ENST00000361851 ENSG00000228253 protein_coding cds
## ENST00000361899 ENSG00000198899 protein_coding cds
## ENST00000362079 ENSG00000198938 protein_coding cds
## -------
## seqinfo: 25 sequences (1 circular) from GRCh38 genomeThe Fragments(), Motifs(), and
Links() functions are demonstrated in other sections
below.
Other GRangesAssay methods
As the GRangesAssay object uses the Bioconductor GRanges
class to store information, we can also call standard Bioconductor
functions defined in the IRanges,
GenomicRanges, and Seqinfo packages on the
GRangesAssay object (or a Seurat object with a
GRangesAssay as the default assay).
The following methods use the genomic ranges stored in a
GRangesAssay object.
# extract the genomic ranges associated with each feature in the data matrix
granges(atac_small)## GRanges object with 100 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 9772-10660 *
## [2] chr1 180712-181178 *
## [3] chr1 181200-181607 *
## [4] chr1 191183-192084 *
## [5] chr1 267576-268461 *
## ... ... ... ...
## [96] chr1 1291564-1292473 *
## [97] chr1 1299784-1300670 *
## [98] chr1 1301689-1302543 *
## [99] chr1 1305198-1306109 *
## [100] chr1 1307720-1308738 *
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
# find the nearest range
nearest(atac_small, subject = Annotation(atac_small))## [1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [51] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [76] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
# distance to the nearest range
distanceToNearest(atac_small, subject = Annotation(atac_small))## Hits object with 0 hits and 1 metadata column:
## queryHits subjectHits | distance
## <integer> <integer> | <integer>
## -------
## queryLength: 100 / subjectLength: 3200207
# find overlaps with another set of genomic ranges
findOverlaps(atac_small, subject = Annotation(atac_small))## Hits object with 0 hits and 0 metadata columns:
## queryHits subjectHits
## <integer> <integer>
## -------
## queryLength: 100 / subjectLength: 3200207Many other methods are defined, see the documentation for
nearest-methods, findOverlaps-methods,
inter-range-methods, and coverage in Signac
for a full list.
For a full list of methods for the GRangesAssay class
run:
methods(class = "GRangesAssay")## [1] [[<- [<- AddMotifs coerce
## [5] coerce<- colMeans colSums ConvertMotifID
## [9] countOverlaps coverage disjoin disjointBins
## [13] distance distanceToNearest findOverlaps follow
## [17] gaps genome GetAssayData granges
## [21] isCircular isDisjoint merge nearest
## [25] precede range reduce RegionStats
## [29] rowMeans rowSums RunChromVAR seqinfo
## [33] seqlengths seqlevels seqnames SetAssayData
## [37] show split subset
## see '?methods' for accessing help and source codeSubsetting a GRangesAssay
We can use the standard subset() function or the
[ operator to subset Seurat object containing
GRangesAssays. This works the same way as for standard
Assay objects.
# subset using the subset() function
# this is meant for interactive use
subset.obj <- subset(atac_small, subset = nCount_peaks > 10)
# subset using the [ extract operator
# this can be used programmatically
subset.obj <- atac_small[, atac_small$nCount_peaks > 10]Converting between Assay and GRangesAssay
or ChromatinAssay5
To convert from a GRangesAssay to a standard
Assay use the as() function
# convert a ChromatinAssay to an Assay
assay <- as(object = atac_small[["peaks"]], Class = "Assay5")
assay## Assay (v5) data with 100 features for 100 cells
## First 10 features:
## chr1:9772-10660, chr1:180712-181178, chr1:181200-181607,
## chr1:191183-192084, chr1:267576-268461, chr1:270850-271755,
## chr1:273946-274792, chr1:585753-586648, chr1:605079-605959,
## chr1:629538-630397
## Layers:
## counts, dataTo convert from a standard Assay to a
GRangesAssay we use the as.GRangesAssay()
function. This takes a standard assay object, as well as information to
fill the additional slots in the GRangesAssay class.
# convert an Assay to a ChromatinAssay
gr_assay <- as.GRangesAssay(assay)
gr_assay## GRangesAssay data with 100 features for 100 cells
## Variable features: 0
## Annotation present: FALSE
## Fragment files: 0
## Motifs present: FALSE
## Links present: 0
## Region aggregation matrices: 0Similarly, we can use as.ChromatinAssay5() to convert to
a ChromatinAssay5 object:
chr_assay <- as.ChromatinAssay5(assay)
chr_assay## ChromatinAssay5 data with 100 features for 100 cells
## Variable features: 0
## Annotation present: FALSE
## Fragment files: 0
## Motifs present: FALSE
## Links present: 0
## Region aggregation matrices: 0The Fragment2 Class
The Fragment2 class is designed for storing and
interacting with a fragment
file commonly used for single-cell chromatin data. It contains: *
The path to an indexed fragment file on disk
* The path to the fragment file index * An MD5 hash for the fragment
file and the fragment file index * A vector of cell names contained in
the fragment file. Importantly, this is a named vector where the
elements of the vector are the cell names as they appear in the fragment
file, and the name of each element is the cell name as it appears in the
assay object storing the Fragment2 object. This allows a
mapping of cell names on disk to cell names in R, and avoids the need to
alter fragment files on disk. This path can also be a remote file
accessible by http or ftp. * A vector of
sequence names contained in the fragment file. This is a named vector of
sequence levels (eg, chromosome name) where each element is the sequence
name as it appears in the fragment file, and then name of each element
is the corresponding sequence name as stored in the assay. This enables
renaming sequence names in your assay object without needing to change
your fragment file.
Constructing the Fragment2 class
A Fragment2 object can be constructed using the
CreateFragmentObject() function.
frag.path <- system.file(
"extdata", "fragments_header.tsv.gz", package = "Signac"
)
fragments <- CreateFragmentObject(
path = frag.path,
cells = colnames(atac_small),
validate.fragments = TRUE
)## Computing hashThe validate.fragments parameter controls whether the
file is inspected to check whether the expected cell names are present.
This can help avoid assigning the wrong fragment file to the object. If
you’re sure that the file is correct, you can set this value to
FALSE to skip this step and save some time. This check is
typically only run once when the Fragment2 object is
created, and is not normally run on existing Fragment2
files.
Inspecting the fragment file
To extract the first few lines of a fragment file on-disk, we can use
the head() method defined for Fragment2
objects. This is useful for quickly checking the chromosome naming style
in our fragment file, or checking how the cell barcodes are named:
head(fragments)## chrom start end barcode readCount
## 1 chr1 10168 10216 AAACTGCTCTTCCACG-1 2
## 2 chr1 77156 77221 AAAGATGCACGCTGTG-1 1
## 3 chr1 181068 181131 AAACTGCCAGTAGGCA-1 1
## 4 chr1 181348 181514 AAACTGCAGGCAAGCT-1 1
## 5 chr1 190824 191033 AAAGATGAGACTAGGC-1 3
## 6 chr1 267990 268023 AAACTCGAGAGGCCTA-1 1Some fragment files also contain header lines with additional
information. This can be viewed using the header()
function:
header(fragments)## [1] "# id=10k_pbmc_ATACv2_nextgem_Chromium_Controller"
## [2] "# description=Human PBMCs"
## [3] "#"
## [4] "# pipeline_name=cellranger-atac"
## [5] "# pipeline_version=cellranger-atac-2.1.0"
## [6] "#"
## [7] "# reference_path=/mnt/scratch2/cellranger-atac-2.1.0/reference/refdata-cellranger-arc-GRCh38-2020-A-2.0.0"
## [8] "# reference_fasta_hash=b6f131840f9f337e7b858c3d1e89d7ce0321b243"
## [9] "# reference_gtf_hash=3b4c36ca3bade222a5b53394e8c07a18db7ebb11"
## [10] "# reference_version=2020-A"
## [11] "# mkref_version=cellranger-arc-2.0.0"
## [12] "#"
## [13] "# primary_contig=chr1"
## [14] "# primary_contig=chr10"
## [15] "# primary_contig=chr11"
## [16] "# primary_contig=chr12"
## [17] "# primary_contig=chr13"
## [18] "# primary_contig=chr14"
## [19] "# primary_contig=chr15"
## [20] "# primary_contig=chr16"
## [21] "# primary_contig=chr17"
## [22] "# primary_contig=chr18"
## [23] "# primary_contig=chr19"
## [24] "# primary_contig=chr2"
## [25] "# primary_contig=chr20"
## [26] "# primary_contig=chr21"
## [27] "# primary_contig=chr22"
## [28] "# primary_contig=chr3"
## [29] "# primary_contig=chr4"
## [30] "# primary_contig=chr5"
## [31] "# primary_contig=chr6"
## [32] "# primary_contig=chr7"
## [33] "# primary_contig=chr8"
## [34] "# primary_contig=chr9"
## [35] "# primary_contig=chrX"
## [36] "# primary_contig=chrY"
## [37] "# primary_contig=KI270728.1"
## [38] "# primary_contig=KI270727.1"
## [39] "# primary_contig=GL000009.2"
## [40] "# primary_contig=GL000194.1"
## [41] "# primary_contig=GL000205.2"
## [42] "# primary_contig=GL000195.1"
## [43] "# primary_contig=GL000219.1"
## [44] "# primary_contig=KI270734.1"
## [45] "# primary_contig=GL000213.1"
## [46] "# primary_contig=GL000218.1"
## [47] "# primary_contig=KI270731.1"
## [48] "# primary_contig=KI270721.1"
## [49] "# primary_contig=KI270726.1"
## [50] "# primary_contig=KI270711.1"
## [51] "# primary_contig=KI270713.1"Adding a Fragment2 object to the
GRangesAssay
A GRangesAssay object can contain a list of
Fragment2 objects. This avoids the need to merge fragment
files on disk and simplifies processes of merging or integrating
different Seurat objects containing GRangesAssays. To add a
new Fragment2 object to a GRangesAssay, or a
Seurat object containing a GRangesAssay, we can use the
Fragments<- assignment function. This will do a few
things:
- Re-compute the MD5 hash for the fragment file and index and verify
that it matches the hash computed when the
Fragment2object was created. - Check that none of the cells contained in the
Fragment2object being added are already contained in anotherFragment2object stored in theGRangesAssay. All fragments from a cell must be present in only one fragment file. - Check that the same fragment file is not already stored in the
assay.
- Append the
Fragment2object to the list ofFragment2objects stored in theGRangesAssay.
Fragments(atac_small) <- fragmentsThe show() method for Fragment2-class
objects prints the number of cells that the Fragment2
object contains data for.
fragments## A Fragment v2 object for 100 cells
## Unknown seqlevelsAlternatively, we can initialize the GRangesAssay with a
Fragment2 object in a couple of ways. We can either pass a
vector of Fragment2 objects to the fragments
parameter in CreateGRangesAssay(), or pass the path to a
single fragment file. If we pass the path to a fragment file we assume
that the file contains fragments for all cells in the
GRangesAssay and that the cell names are the same in the
fragment file on disk and in the GRangesAssay. For
example:
chrom_assay <- CreateGRangesAssay(
counts = counts,
fragments = frag.path
)## Computing hash
object <- CreateSeuratObject(
counts = chrom_assay,
assay = "peaks"
)This will create a Seurat object containing a
GRangesAssay, with a single Fragment2
object.
Removing a Fragment2 object from the
GRangesAssay
All the Fragment2 objects associated with a
GRangesAssay can be removed by assigning NULL
using the Fragment<- assignment function. For
example:
## list()Changing the fragment file path in an existing
Fragment2 object
The path to the fragment file can be updated using the
UpdatePath() function. This can be useful if you move the
fragment file to a new directory, or if you copy a stored Seurat object
containing a GRangesAssay to a different server.
fragments <- UpdatePath(fragments, new.path = frag.path)To change the path to fragment files in an object, you will need to remove the fragment objects, update the paths, and then add the fragment objects back to the object. For example:
frags <- Fragments(object) # get list of fragment objects
Fragments(object) <- NULL # remove fragment information from assay
# create a vector with all the new paths, in the correct order for your
# list of fragment objects
# In this case we only have 1
new.paths <- list(frag.path)
for (i in seq_along(frags)) {
frags[[i]] <- UpdatePath(frags[[i]], new.path = new.paths[[i]]) # update path
}
Fragments(object) <- frags # assign updated list back to the object
Fragments(object)## [[1]]
## A Fragment v2 object for 100 cells
## Unknown seqlevelsUsing remote fragment files
Fragment files hosted on remote servers accessible via http or ftp
can also be added to the GRangesAssay in the same way as
for locally-hosted fragment files. This can enable the exploration of
large single-cell datasets without the need for downloading large files.
For example, we can create a Fragment object using a file hosted on the
10x Genomics website:
fragments <- CreateFragmentObject(
path = "http://cf.10xgenomics.com/samples/cell-atac/1.1.0/atac_v1_pbmc_10k/atac_v1_pbmc_10k_fragments.tsv.gz"
)## Fragment file is on a remote server## Warning in readLines(con = con, n = 10000): incomplete final line found on
## 'gzcon(http://cf.10xgenomics.com/samples/cell-atac/1.1.0/atac_v1_pbmc_10k/atac_v1_pbmc_10k_fragments.tsv.gz)'## Computing hash
fragments## A Fragment v2 object for 0 cells
## Unknown seqlevelsWhen files are hosted remotely, the checks described in the section above (MD5 hash and expected cells) are not performed.
Getting and setting Fragment2 data
To access the cell names stored in a Fragment2 object,
we can use the Cells() function.
Importantly, this returns the cell names as they appear
in the GRangesAssay, rather than as they appear in the
fragment file itself.
fragments <- CreateFragmentObject(
path = frag.path,
cells = colnames(atac_small),
validate.fragments = TRUE
)## Computing hash## [1] "AAACGAAAGAGAGGTA-1" "AAACGAAAGCAGGAGG-1" "AAACGAAAGGAAGAAC-1"
## [4] "AAACGAAAGTCGACCC-1" "AAACGAACAAGCACTT-1" "AAACGAACAAGCGGTA-1"Similarly, we can set the cell name information in a
Fragment2 object using the Cells<-
assignment function. This will set the named vector of cells stored in
the Fragment2 object. Here we must supply a named
vector.
To extract any of the data stored in a Fragment2 object
we can also use the GetFragmentData() function. For
example, we can find the path to the fragment file on disk:
GetFragmentData(object = fragments, slot = "file.path")## [1] "/charonfs/scratch/users/astar/gis/stuartt/mytmp/Rtmp1aKDCv/temp_libpath2d9a8f69f36444/Signac/extdata/fragments_header.tsv.gz"For a full list of methods for the Fragment2 class
run:
methods(class = "Fragment2")## [1] CallPeaks Cells Cells<- head
## [5] RenameCells renameSeqlevels seqlevels seqlevels<-
## [9] seqlevelsStyle seqlevelsStyle<- show subset
## see '?methods' for accessing help and source codeThe Motif Class
The Motif class stores information needed for DNA
sequence motif analysis, and has the following slots:
-
data: a sparse feature by motif matrix, where entries are 1 if the feature contains the motif, and 0 otherwise -
pwm: A named list of position weight or position frequency matrices -
motif.names: a list of motif IDs and their common names -
positions: AGRangesListobject containing the exact positions of each motif -
meta.data: Additional information about the motifs
Many of these slots are optional and do not need to be filled, but
are only required when running certain functions. For example, the
positions slot will be needed if running TF
footprinting.
Constructing the Motif class
A Motif object can be constructed using the
CreateMotifObject() function. Much of the data needed for
constructing a Motif object can be generated using
functions from the TFBSTools
and motifmatchr
packages. Position frequency matrices for motifs can be loaded using the
JASPAR packages on Bioconductor
or the chromVARmotifs
package. For example:
library(JASPAR2020)
library(TFBSTools)
library(motifmatchr)
# Get a list of motif position frequency matrices from the JASPAR database
pfm <- getMatrixSet(
x = JASPAR2020,
opts = list(species = 9606) # 9606 is the species code for human
)
# Scan the DNA sequence of each peak for the presence of each motif
motif.matrix <- CreateMotifMatrix(
features = granges(atac_small),
pwm = pfm,
genome = "hg38"
)
# Create a new Mofif object to store the results
motif <- CreateMotifObject(
data = motif.matrix,
pwm = pfm
)The show() method for the Motif class
prints the total number of motifs and regions included in the
object:
motif## A Motif object containing 633 motifs in 100 regionsAdding a Motif object to the
GRangesAssay
We can add a Motif object to the
GRangesAssay, or a Seurat object containing a
GRangesAssay using the Motifs<- assignment
operator.
Motifs(atac_small) <- motifGetting and setting Motif data
Data stored in a Motif object can be accessed using the
GetMotifData() and SetMotifData()
functions.
# extract data from the Motif object
pfm <- GetMotifData(object = motif, slot = "pwm")
# set data in the Motif object
motif <- SetMotifData(object = motif, slot = "pwm", new.data = pfm)We can access the set of motifs and set of features used in the
Motif object using the colnames() and
rownames() functions:
## [1] "MA0030.1" "MA0031.1" "MA0051.1" "MA0057.1" "MA0059.1" "MA0066.1"## [1] "chr1:9772-10660" "chr1:180712-181178" "chr1:181200-181607"
## [4] "chr1:191183-192084" "chr1:267576-268461" "chr1:270850-271755"To quickly convert between motif IDs (like MA0497.1) and
motif common names (like MEF2C), we can use the
ConvertMotifID() function. For example:
# convert ID to common name
ids <- c("MA0030.1", "MA0031.1", "MA0051.1", "MA0057.1")
names <- ConvertMotifID(object = motif, id = ids)
names## [1] "FOXF2" "FOXD1" "IRF2" "MZF1(var.2)"
# convert names to IDs
ConvertMotifID(object = motif, name = names)## [1] "MA0030.1" "MA0031.1" "MA0051.1" "MA0057.1"For a full list of methods for the Motif class run:
methods(class = "Motif")## [1] [ coerce ConvertMotifID dim dimnames
## [6] GetMotifData SetMotifData show subset
## see '?methods' for accessing help and source codeSession Info
## R version 4.5.0 (2025-04-11)
## Platform: x86_64-pc-linux-gnu
## Running under: Red Hat Enterprise Linux 8.10 (Ootpa)
##
## Matrix products: default
## BLAS: /charonfs/home/users/astar/gis/stuartt/R-4.5.0/lib/libRblas.so
## LAPACK: /charonfs/home/users/astar/gis/stuartt/R-4.5.0/lib/libRlapack.so; LAPACK version 3.12.1
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Asia/Singapore
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] BSgenome.Hsapiens.UCSC.hg38_1.4.5 BSgenome_1.78.0
## [3] rtracklayer_1.70.1 BiocIO_1.20.0
## [5] Biostrings_2.78.0 XVector_0.50.0
## [7] GenomeInfoDb_1.46.2 motifmatchr_1.32.0
## [9] TFBSTools_1.48.0 JASPAR2020_0.99.10
## [11] ensembldb_2.34.0 AnnotationFilter_1.34.0
## [13] GenomicFeatures_1.62.0 AnnotationDbi_1.72.0
## [15] Biobase_2.70.0 GenomicRanges_1.62.1
## [17] Seqinfo_1.0.0 IRanges_2.44.0
## [19] S4Vectors_0.48.0 AnnotationHub_4.0.0
## [21] BiocFileCache_3.0.0 dbplyr_2.5.2
## [23] BiocGenerics_0.56.0 generics_0.1.4
## [25] Signac_1.9999.0 Seurat_5.4.0
## [27] SeuratObject_5.3.0 sp_2.2-1
##
## loaded via a namespace (and not attached):
## [1] fs_1.6.7 ProtGenerics_1.42.0
## [3] matrixStats_1.5.0 spatstat.sparse_3.1-0
## [5] bitops_1.0-9 DirichletMultinomial_1.52.0
## [7] httr_1.4.8 RColorBrewer_1.1-3
## [9] InteractionSet_1.38.0 tools_4.5.0
## [11] sctransform_0.4.3 backports_1.5.0
## [13] R6_2.6.1 lazyeval_0.2.2
## [15] uwot_0.2.4 withr_3.0.2
## [17] gridExtra_2.3 progressr_0.18.0
## [19] cli_3.6.5 textshaping_1.0.4
## [21] spatstat.explore_3.7-0 fastDummies_1.7.5
## [23] sass_0.4.10 S7_0.2.1
## [25] spatstat.data_3.1-9 ggridges_0.5.7
## [27] pbapply_1.7-4 pkgdown_2.2.0
## [29] Rsamtools_2.26.0 systemfonts_1.3.2
## [31] foreign_0.8-91 dichromat_2.0-0.1
## [33] parallelly_1.46.1 rstudioapi_0.18.0
## [35] RSQLite_2.4.6 gtools_3.9.5
## [37] ica_1.0-3 spatstat.random_3.4-4
## [39] dplyr_1.2.0 Matrix_1.7-4
## [41] abind_1.4-8 lifecycle_1.0.5
## [43] yaml_2.3.12 SummarizedExperiment_1.40.0
## [45] SparseArray_1.10.8 Rtsne_0.17
## [47] grid_4.5.0 blob_1.3.0
## [49] promises_1.5.0 pwalign_1.6.0
## [51] crayon_1.5.3 miniUI_0.1.2
## [53] lattice_0.22-9 cowplot_1.2.0
## [55] cigarillo_1.0.0 KEGGREST_1.50.0
## [57] pillar_1.11.1 knitr_1.51
## [59] rjson_0.2.23 future.apply_1.20.2
## [61] codetools_0.2-20 fastmatch_1.1-8
## [63] glue_1.8.0 spatstat.univar_3.1-6
## [65] data.table_1.18.2.1 vctrs_0.7.1
## [67] png_0.1-9 spam_2.11-3
## [69] gtable_0.3.6 cachem_1.1.0
## [71] xfun_0.56 S4Arrays_1.10.1
## [73] mime_0.13 survival_3.8-6
## [75] RcppRoll_0.3.1 fitdistrplus_1.2-6
## [77] ROCR_1.0-12 nlme_3.1-168
## [79] bit64_4.6.0-1 filelock_1.0.3
## [81] RcppAnnoy_0.0.23 bslib_0.10.0
## [83] irlba_2.3.7 KernSmooth_2.23-26
## [85] otel_0.2.0 rpart_4.1.24
## [87] colorspace_2.1-2 seqLogo_1.76.0
## [89] DBI_1.3.0 Hmisc_5.2-5
## [91] nnet_7.3-20 tidyselect_1.2.1
## [93] bit_4.6.0 compiler_4.5.0
## [95] curl_7.0.0 httr2_1.2.2
## [97] htmlTable_2.4.3 desc_1.4.3
## [99] DelayedArray_0.36.0 plotly_4.12.0
## [101] checkmate_2.3.4 scales_1.4.0
## [103] caTools_1.18.3 lmtest_0.9-40
## [105] rappdirs_0.3.4 stringr_1.6.0
## [107] digest_0.6.39 goftest_1.2-3
## [109] spatstat.utils_3.2-1 rmarkdown_2.30
## [111] htmltools_0.5.9 pkgconfig_2.0.3
## [113] base64enc_0.1-6 sparseMatrixStats_1.22.0
## [115] MatrixGenerics_1.22.0 fastmap_1.2.0
## [117] rlang_1.1.7 htmlwidgets_1.6.4
## [119] UCSC.utils_1.6.1 shiny_1.13.0
## [121] farver_2.1.2 jquerylib_0.1.4
## [123] zoo_1.8-15 jsonlite_2.0.0
## [125] BiocParallel_1.44.0 VariantAnnotation_1.56.0
## [127] RCurl_1.98-1.17 magrittr_2.0.4
## [129] Formula_1.2-5 dotCall64_1.2
## [131] patchwork_1.3.2 Rcpp_1.1.1
## [133] reticulate_1.45.0 stringi_1.8.7
## [135] MASS_7.3-65 plyr_1.8.9
## [137] parallel_4.5.0 listenv_0.10.1
## [139] ggrepel_0.9.7 deldir_2.0-4
## [141] splines_4.5.0 tensor_1.5.1
## [143] igraph_2.2.2 spatstat.geom_3.7-0
## [145] RcppHNSW_0.6.0 reshape2_1.4.5
## [147] TFMPvalue_1.0.0 BiocVersion_3.22.0
## [149] XML_3.99-0.23 evaluate_1.0.5
## [151] biovizBase_1.58.0 BiocManager_1.30.27
## [153] httpuv_1.6.16 RANN_2.6.2
## [155] tidyr_1.3.2 purrr_1.2.1
## [157] polyclip_1.10-7 future_1.70.0
## [159] scattermore_1.2 ggplot2_4.0.2
## [161] xtable_1.8-8 restfulr_0.0.16
## [163] RSpectra_0.16-2 later_1.4.8
## [165] viridisLite_0.4.3 ragg_1.5.0
## [167] tibble_3.3.1 memoise_2.0.1
## [169] GenomicAlignments_1.46.0 cluster_2.1.8.2
## [171] globals_0.19.1