Package 'epiSeeker'

Description

capture name of variable

Usage

.(..., .env = parent.frame())

Arguments

Value

expression

Examples

x <- 1
eval(.(x)[[1]])

Description

Env function for epiSeeker

Usage

.epiSeekerEnv(TxDb, item = "epiSeekerEnv", force = FALSE)

Arguments

Value

Returns 'invisible(NULL)' invisibly. The primary purpose of this function is to manage the TXDB cache through side effects (creating, updating, or removing cached objects), rather than returning a value.

Description

Annotate peaks

Usage

annotateSeq(
  peak,
  tssRegion = c(-3000, 3000),
  TxDb = NULL,
  level = "transcript",
  assignGenomicAnnotation = TRUE,
  genomicAnnotationPriority = c("Promoter", "5UTR", "3UTR", "Exon", "Intron",
    "Downstream", "Intergenic"),
  annoDb = NULL,
  addFlankGeneInfo = FALSE,
  flankDistance = 5000,
  sameStrand = FALSE,
  ignoreOverlap = FALSE,
  ignoreUpstream = FALSE,
  ignoreDownstream = FALSE,
  overlap = "TSS",
  verbose = TRUE,
  columns = c("ENTREZID", "ENSEMBL", "SYMBOL", "GENENAME")
)

Arguments

Value

data.frame or GRanges object with columns of:

all columns provided by input.

annotation: genomic feature of the peak, for instance if the peak is located in 5'UTR, it will annotated by 5'UTR. Possible annotation is Promoter-TSS, Exon, 5' UTR, 3' UTR, Intron, and Intergenic.

geneChr: Chromosome of the nearest gene

geneStart: gene start

geneEnd: gene end

geneLength: gene length

geneStrand: gene strand

geneId: entrezgene ID

distanceToTSS: distance from peak to gene TSS

if annoDb is provided, extra column will be included:

ENSEMBL: ensembl ID of the nearest gene

SYMBOL: gene symbol

GENENAME: full gene name

Author(s)

G Yu

See Also

[plotAnnoBar()] [plotAnnoPie()] [plotDistToTSS()]

Examples

data(peakAnno)
peakAnno

Description

Arrange GRanges object

Usage

## S3 method for class 'GRanges'
arrange(.data, ..., .by_group = FALSE)

Arguments

Value

grange object

Examples

peakfile <- system.file("extdata", "sample_peaks.txt", package = "epiSeeker")
peak <- readPeakFile(peakfile)
dplyr::arrange(peak, seqnames)

Description

convert csAnno object to data.frame

Usage

## S3 method for class 'csAnno'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

Arguments

Value

data.frame

Author(s)

Guangchuang Yu <https://guangchuangyu.github.io>

Description

convert csAnno object to GRanges

Usage

as.GRanges(x)

Arguments

Value

GRanges object

Author(s)

Guangchuang Yu <https://guangchuangyu.github.io>

Examples

data(peakAnno)
as.GRanges(peakAnno)

Description

bin vector function

Usage

bin_vector(vec, nbin = 800)

Arguments

Value

bin list

Description

This is constructor fo bmData objects.

Usage

bmData(
  value1 = NULL,
  value2 = NULL,
  pos = NULL,
  chr = NULL,
  gr = NULL,
  sampleNames = NULL,
  valueNames = NULL,
  ...
)

Arguments

Value

bmData object

Examples

data(demo_bmdata)

Description

This class added extra data to [bsseq::BSseq-class]. Change the assays by storing M/Cov to any value1/2

Value

bmData object

See Also

bmData class inherits [SummarizedExperiment::RangedSummarizedExperiment-class], other slots see [SummarizedExperiment::RangedSummarizedExperiment]

Description

check bin parameter method

Usage

check_bin(nbin, windows, verbose)

Arguments

Value

message or nothing

Description

check upstream and downstream extension

Usage

check_extension(upstream, downstream, type)

Arguments

Value

message or null

Description

check windows function

Usage

check_windows(windows)

Arguments

Value

message or null

Description

Combine csAnno Object

Usage

combine_csAnno(x, ...)

Arguments

Details

https://github.com/YuLab-SMU/ChIPseeker/issues/157

Value

csAnno object

Examples

data(peakAnno)
combine_csAnno(peakAnno, peakAnno)

Description

create regex patterns in negative strand

Usage

create_regex_patterns_negative(motif)

Arguments

Value

regex pattern

Description

create regex patterns in positive strand

Usage

create_regex_patterns_positive(motif)

Arguments

Value

regex pattern

Description

Class "csAnno" This class represents the output of epiSeeker Annotation

Value

annotation object

Slots

anno

annotation

tssRegion

TSS region

level

transcript or gene

hasGenomicAnnotation

logical

detailGenomicAnnotation

Genomic Annotation in detail

annoStat

annotation statistics

peakNum

number of peaks

Author(s)

Guangchuang Yu <https://guangchuangyu.github.io>

See Also

[annotateSeq()]

Description

A small example bmData object representing cytosine methylation measurements from Bisulfite-Seq data. This dataset is intended for demonstrating base-modification visualization, regional methylation profiling, and epiSeeker workflows operating on bmData objects. See data-raw/example_data.R

Format

A bmData object containing one sample.

Value

bmData object

Provenance

The example dataset was constructed from publicly available Bisulfite-Seq data (GEO accession: GSM6940395, genome build: hg38). The raw methylation coverage file (*.bismark.cov.gz) was imported using data.table::fread().

A small genomic window on chromosome 22 ([10525991, 10526342]) was selected to create a lightweight example dataset. The data were processed as follows:

1. Filter records where chrom == 22 and positions fall within the chosen window.

2. Convert chromosome name to UCSC style ("chr22").

3. Compute total coverage as: Cov = methylated + unmethylated.

4. Extract columns: chromosome, position, coverage, and methylation percentage.

5. Convert methylation percentage to a fraction.

Data structure

A bmData S4 object containing one sample ("acinar_methyl"). Each entry stores:

chr

Chromosome in UCSC format (e.g. "chr22").

pos

Genomic coordinate of the cytosine.

Cov

Total read coverage at the site.

Methylation

Methylation level as a fraction (0–1).

Description

Peak in Grange object. See data-raw/example_data.R

Format

A GRanges object with 200 rows and 5 metadata columns.

Value

Grange object

Provenance

The demo peaks were extracted from GSM6418464 in the GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM6418464).

Data structure

A GRanges object with 220 genomic ranges and the following metadata columns:

seqnames

chr name

ranges

Peak ranges

strand

strand information

mcol

output from MACS2

Description

Download all BED files of a particular genome version

Usage

downloadGEObedFiles(genome, destDir = getwd())

Arguments

Value

GEO files

Author(s)

G Yu

Examples

gse <- "GSE11431"

Description

Download BED supplementary files of a list of GSM accession numbers

Usage

downloadGSMbedFiles(GSM, destDir = getwd())

Arguments

Value

GEO data

Author(s)

G Yu

Examples

gsm <- "GSM288348"

Description

dropAnno

Usage

dropAnno(csAnno, distanceToTSS_cutoff = 10000)

Arguments

Details

drop annotation exceeding distanceToTSS_cutoff

Value

csAnno object

Author(s)

Guangchuang Yu

Examples

data(peakAnno)
dropAnno(peakAnno)

Description

Calculate overlap significance of ChIP experiments based on their nearest gene annotation

Usage

enrichAnnoOverlap(
  queryPeak,
  targetPeak,
  TxDb = NULL,
  pAdjustMethod = "BH",
  chainFile = NULL,
  distanceToTSS_cutoff = NULL
)

Arguments

Value

data.frame

Author(s)

G Yu

Examples

if (interactive()) {
    require(TxDb.Hsapiens.UCSC.hg38.knownGene)
    txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
    peakfile <- system.file("extdata", "demo_peak.txt", package = "epiSeeker")
    enrichAnnoOverlap(peakfile, peakfile, txdb)
}

Description

calculate overlap significant of ChIP experiments based on the genome coordinations

Usage

enrichPeakOverlap(
  queryPeak,
  targetPeak,
  TxDb = NULL,
  pAdjustMethod = "BH",
  nShuffle = 1000,
  chainFile = NULL,
  pool = TRUE,
  mc.cores = detectCores() - 1,
  verbose = TRUE
)

Arguments

Value

data.frame

Author(s)

G Yu

Examples

require(TxDb.Hsapiens.UCSC.hg38.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
peakfile <- system.file("extdata", "demo_peak.txt", package = "epiSeeker")
peak <- readPeakFile(peakfile)[1:10]
enrichPeakOverlap(peak, peakfile, txdb, mc.cores = 1, nShuffle = 20)

Description

Name of the epiSeeker cache environment (internal static variable)

Usage

epiSeekerCache

Format

character vector

Description

Extend regions functions

Usage

extend_gr(regions, upstream, downstream, by, type)

Arguments

Value

GRanges object

Description

Extend filter to Peak (GRanges class object)

Usage

## S3 method for class 'GRanges'
filter(.data, ..., .by = NULL, .preserve = FALSE)

Arguments

Value

A filtered GRanges object containing only rows that meet the specified criteria

grange object

Examples

peakfile <- system.file("extdata", "sample_peaks.txt", package = "epiSeeker")
peak <- readPeakFile(peakfile)
dplyr::filter(peak, fold_enrichment > 20)

Description

getting status of annotation

Usage

getAnnoStat(x)

Arguments

Value

data frame

Examples

data(peakAnno)
getAnnoStat(peakAnno)

Description

Prepare a bioregion of selected feature

Usage

getBioRegion(
  TxDb = NULL,
  upstream = 1000,
  downstream = 1000,
  by = "gene",
  type = "start_site"
)

Arguments

Details

this function combined previous functions getPromoters(), getBioRegion() and getGeneBody() in order to solve the following issues.

(1) <https://github.com/GuangchuangYu/ChIPseeker/issues/16>

(2) <https://github.com/GuangchuangYu/ChIPseeker/issues/87>

1. function can provide a region of interest from txdb object. 2. function can make region from granges object. txdb object do not contain insulator or enhancer regions. Users can provide these regions through self-made granges object https://github.com/YuLab-SMU/ChIPseeker/issues/189.

There are three kinds of way to extend regions: start_site, end_site and body. We take transcript region to expain the differences of these three regions (tx: chr1 1000 1400).

(1) body region refers to the 1000 ~ 1400 bp.

(2) start_site region with (upstream = upstream = 100) refers to 900-1100bp.

(3) end_site region with (upstream = upstream = 100) refers to 1300-1500bp.

Value

GRanges object

Author(s)

Guangchuang Yu

Examples

require(TxDb.Hsapiens.UCSC.hg38.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
getBioRegion(txdb)

Description

getBmMatrix method for [bsseq::BSseq]

getBmMatrix method for bmData

Usage

getBmMatrix(
  region,
  input,
  BSgenome,
  base = NULL,
  motif = NULL,
  position_bias = NULL,
  ...
)

## S4 method for signature 'ANY,BSseq'
getBmMatrix(
  region,
  input,
  BSgenome,
  base = NULL,
  motif = NULL,
  position_bias = NULL,
  cover_depth = TRUE,
  ...
)

## S4 method for signature 'ANY,bmData'
getBmMatrix(
  region,
  input,
  BSgenome,
  base = NULL,
  motif = NULL,
  position_bias = NULL,
  ...
)

Arguments

Value

data.frame

dataframe

Examples

require(BSgenome.Hsapiens.UCSC.hg38)
data(demo_bmdata)
bmMatrix <- getBmMatrix(
    region = data.frame(chr = "chr22", start = 10525991, end = 10526342),
    BSgenome = BSgenome.Hsapiens.UCSC.hg38,
    input = demo_bmdata,
    base = "C",
    motif = c("CG")
)

Description

get the information of base modification

Usage

getBmMatrix.bmData(
  region,
  input,
  BSgenome,
  base = NULL,
  motif = NULL,
  position_bias = NULL
)

Arguments

Details

This function retrieve the information of each base, requiring bmData object as input. Then organized it to dataframe.

Value

dataframe

Description

Get the information of base modification

Usage

getBmMatrix.BSseq(
  region,
  input,
  BSgenome,
  cover_depth = TRUE,
  base = NULL,
  motif = NULL,
  position_bias = NULL
)

Arguments

Details

This function retrieve the information of each base, requiring [bsseq::BSseq] object as input. Then organized it to data.frame.

Value

data.frame

Description

Get gene annotation, symbol, gene name etc.

Usage

getGeneAnno(annoDb, geneID, type, columns)

Arguments

Value

data.frame

Author(s)

G Yu

Description

Get Genomic Annotation of peaks

Usage

getGenomicAnnotation(
  peaks,
  distance,
  tssRegion = c(-3000, 3000),
  TxDb,
  level,
  genomicAnnotationPriority,
  sameStrand = FALSE
)

Arguments

Value

character vector

Author(s)

G Yu

Description

Get genome version statistics collecting from GEO ChIPseq data

Usage

getGEOgenomeVersion()

Value

data.frame

Author(s)

G Yu

Examples

getGEOgenomeVersion()

Description

Get subset of GEO information by genome version keyword

Usage

getGEOInfo(genome, simplify = TRUE)

Arguments

Value

data.frame

Author(s)

G Yu

Examples

hg19 <- getGEOInfo(genome = "hg19", simplify = TRUE)

Description

Accessing species statistics collecting from GEO database

Usage

getGEOspecies()

Value

data.frame

Author(s)

G Yu

Examples

getGEOspecies()

Description

Get the information of motif in a range

Usage

getMotifMatrix(region, pwm, ref_obj, by = "name")

Arguments

Value

score matrix

Examples

require(BSgenome.Hsapiens.UCSC.hg38)
data(pwm_obj)

region_oi <- GRanges(
    seqnames = "chr22",
    ranges = IRanges(start = 10525891, end = 10525991)
)
motifMatrix <- getMotifMatrix(
    region = region_oi,
    pwm = pwm_obj[c(45, 120, 170)],
    ref_obj = BSgenome.Hsapiens.UCSC.hg38
)

Description

Get index of features that closest to peak and calculate distance

Usage

getNearestFeatureIndicesAndDistances(
  peaks,
  features,
  sameStrand = FALSE,
  ignoreOverlap = FALSE,
  ignoreUpstream = FALSE,
  ignoreDownstream = FALSE,
  overlap = "TSS"
)

Arguments

Value

list

Author(s)

G Yu

Description

Get promoter region in GRanges format

Usage

getPromoters(TxDb = NULL, upstream = 1000, downstream = 1000, by = "gene")

Arguments

Value

GRanges object

Author(s)

Guangchuang Yu

Examples

require(TxDb.Hsapiens.UCSC.hg38.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
promoters <- getPromoters(TxDb = txdb, upstream = 1000, downstream = 1000)

Description

get filenames of sample files

Usage

getSampleFiles()

Value

list of file names

Author(s)

G Yu

Examples

files <- getSampleFiles()

Description

getTagMatrix

Usage

getTagMatrix(
  peak,
  upstream = 0,
  downstream = 0,
  windows = NULL,
  type = NULL,
  by = NULL,
  TxDb = NULL,
  weightCol = NULL,
  nbin = NULL,
  verbose = TRUE,
  ignore_strand = FALSE
)

Arguments

Details

getTagMatrix() function can produce the matrix for visualization. Matrix represents the peak count in a windows and there are two ways to specify the 'windows':

(1) use getPromoters and getBioRegion to get 'windows' and put it into windows parameter in getTagMatrix().

(2) use getTagMatrix() to call getPromoters()/getBioRegion(). In this way users do not need to input 'windows' parameter but need to input 'TxDb' parameter. 'TxDb' can accept a set of packages contained annotation of regions of different genomes(e.g. TxDb.Hsapiens.UCSC.hg38.knownGene). Users can get the regions of interest through specific functions. These specific functions are built in getPromoters()/getBioRegion().

However, many regions can not be gain through txdb(e.g. insulator and enhancer regions), Users can provide these regions in the form of granges object. These self-made granges object will be passed to 'TxDb' and they will be passed to makeBioRegionFromGranges() to produce the 'windows'.

In a word, 'TxDb' parameter getTagMatrix() is a reference information. Users can pass txdb object or self-made granges into it.

Value

tagMatrix

Author(s)

G Yu

Examples

if (interactive()) {
    require(TxDb.Hsapiens.UCSC.hg38.knownGene)
    txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
    data(demo_peak)
    tagMatrix <- getTagMatrix(demo_peak,
        type = "start_site", by = "gene",
        upstream = 500, downstream = 500,
        TxDb = txdb, weightCol = "V7"
    )
}

Description

getTagMatrix function for region of body

Usage

getTagMatrix_body(
  peak.cov,
  windows,
  nbin,
  verbose = TRUE,
  ignore_strand = FALSE
)

Arguments

Value

tagMatrix

Description

get tagmatrix internal function

Usage

getTagMatrix_body_internal(peak.cov, windows, nbin, chr.idx)

Arguments

Value

matrix

Description

getTagMatrix function for region of site

Usage

getTagMatrix_site(
  peak.cov,
  windows,
  chr.idx,
  nbin = NULL,
  verbose = TRUE,
  ignore_strand = FALSE
)

Arguments

Value

tagMatrix

Description

getTagMatrix internal function

Usage

getTagMatrix.internal(
  peak,
  upstream = 0,
  downstream = 0,
  windows = NULL,
  type = NULL,
  by = NULL,
  TxDb = NULL,
  weightCol = NULL,
  nbin = NULL,
  verbose = TRUE,
  ignore_strand = FALSE
)

Arguments

Value

matrix

Description

change a list grange object to matrix

Usage

grange2mt(gr_list, weightCol = NULL)

Arguments

Value

matrix

Examples

data(demo_peak)
grange2mt(list(a = demo_peak, b = demo_peak), "V5")

Description

ucsc genome version, precalculated data and gsm information

Format

A data frame with 'n' rows (GSM samples) and 14 columns.

Value

data frame

Provenance

The 'gsminfo' dataset was constructed programmatically from public resources in the NCBI GEO and UCSC Genome Browser databases. The data generation pipeline is implemented in 'data-raw/' (see 'prepareGSMInfo()' in the package source).

Briefly, GEO metadata were retrieved using the 'GEOmetadb' SQLite database and 'GEOquery'. The latest GEOmetadb SQLite file was downloaded via 'getSQLiteFile()' or, if unavailable, directly from <http://starbuck1.s3.amazonaws.com/sradb/GEOmetadb.sqlite.gz>. Platform (GPL) records were queried to identify platforms associated with high-throughput sequencing experiments. For each sequencing platform, the corresponding GSM records were obtained using 'Meta(getGEO())'. Supplementary BED-like files for each GSM were collected using 'getGSMsuppFile()' and 'batchGetGSMsuppFile()'.

Additional metadata fields (title, organism, extract protocol, characteristics, data processing description, submission date, and supplementary file URLs) were extracted from GSM SOFT files downloaded using 'GEOquery'. Genome assembly versions for each GSM were inferred using the function 'getGenomicVersion()', which matches UCSC genome labels to either the data processing description or the supplementary file names, using the reference table provided in the internal dataset 'ucsc_release'.

PubMed IDs associated with each GEO series (GSE) were obtained from the 'gse' table in GEOmetadb. All GSM-level metadata were merged, cleaned, and converted to ASCII using 'iconv()' to remove non-ASCII characters.

Finally, newly processed GSM entries were appended to any preexisting 'gsminfo' object stored in the package, deduplicated, and saved as 'gsminfo.rda' with 'compress="xz"'.

Thus, 'gsminfo' represents a curated, reproducibly constructed metadata table summarizing GEO high-throughput sequencing samples, including organism, platform, experimental descriptions, processing information, genome versions, supplementary BED file locations, and associated PubMed IDs.

Data structure

A data frame with one row per GSM sample and the following columns:

'series_id'

GEO series accession (GSE).

'gsm'

GEO sample accession (GSM).

'gpl'

GEO platform accession (GPL).

'organism'

Organism name (e.g., *Mus musculus*).

'title'

Sample title as provided in GEO.

'characteristics'

Experiment-specific metadata such as cell type, treatment, or antibody.

'source_name'

Source material for sequencing, typically cell or tissue type.

'extract_protocol'

Detailed wet-lab protocol for chromatin extraction, immunoprecipitation, and library preparation as reported in GEO.

'description'

Antibody information or additional sample description.

'data_processing'

Bioinformatics processing description including aligner, genome build, peak calling method, and filtering steps.

'submission_date'

Date when the sample was submitted to GEO.

'supplementary_file'

URL to supplementary processed files (e.g., BED).

'genomeVersion'

Genome assembly used in the processed data (e.g., mm8, hg19).

'pubmed_id'

PMID of the reference publication associated with the dataset.

Description

load defaulst txdb

Usage

loadTxDb(TxDb)

Arguments

Value

txdb object

Description

makeBmDataFromData method generics

makeBmDataFromData method for 'CompressedGRangesList' objects

makeBmDataFromData method for 'GRanges' objects

makeBmDataFromData method for 'list' objects

makeBmDataFromData method for data.frame objects

Usage

makeBmDataFromData(data, sampleNames = NULL)

## S4 method for signature 'CompressedGRangesList'
makeBmDataFromData(data, sampleNames = NULL)

## S4 method for signature 'GRanges'
makeBmDataFromData(data, sampleNames = NULL)

## S4 method for signature 'list'
makeBmDataFromData(data, sampleNames = NULL)

## S4 method for signature 'data.frame'
makeBmDataFromData(data, sampleNames = NULL)

Arguments

Details

The objects in 'data' must have specific forms. Colunms should be features, which should be organized in the order of "chr", "pos", "value1", "value2(optional)". chr stands for chromosome. pos stands for position on chromosome, also known as coordinates. value1/2 stands for the value on each base. The colnames can be any character but must be in the order. Rows stands for each observation.

The objects in data must have specific forms. Colunms should be features, which should be organized in the order of "chr", "pos", "value1", "value2(optional)". chr stands for chromosome. pos stands for position on chromosome, also known as coordinates. value1/2 stands for the value on each base. The colnames can be any character but must be in the order. Rows stands for each observation.

Value

bmData

Examples

demo_bisseq_file <- system.file("extdata", "demo_bisseq.txt",
    package = "epiSeeker"
)
demo_bisseq <- read.table(demo_bisseq_file, header = TRUE)
demo_bmdata <- makeBmDataFromData(
    data = list(acinar_methyl = demo_bisseq),
    sampleNames = "acinar_methyl"
)

Description

make dmData object from data

Usage

makeBmDataFromData.internal(data, sampleNames = NULL)

Arguments

Details

This internal function was inspired by DSS::makeBSseqData.

The objects in data must have specific forms. Colunms should be features, which should be organized in the order of "chr", "pos", "value1", "value2(optional)". chr stands for chromosome. pos stands for position on chromosome, also known as coordinates. value1/2 stands for the value on each base. The colnames can be any character but must be in the order. Rows stands for each observation.

Value

dmData object

Description

This function makes bmData object from files. Users can input the name of a file or a file folder.

Usage

makeBmDataFromFiles(name, sampleNames = NULL, variablesNames = NULL)

Arguments

Details

bed files and txt files are supported. Bed files can only contain no more than two metadata, as it stands for value1/2. Txt files should organize the columns as chr, pos, value1, value2(optional).

Value

bmData

Examples

demo_bisseq_file <- system.file("extdata", "demo_bisseq.txt", package = "epiSeeker")
data <- makeBmDataFromFiles(demo_bisseq_file,
    sampleNames = "acinar_methyl",
    variablesNames = c("Cov", "Methylation")
)

Description

Extend mutate to Peak (GRanges class object)

Usage

## S3 method for class 'GRanges'
mutate(
  .data,
  ...,
  .by = NULL,
  .keep = c("all", "used", "unused", "none"),
  .before = NULL,
  .after = NULL
)

Arguments

Value

A processed GRanges object containing the added or modified columns

Examples

peakfile <- system.file("extdata", "sample_peaks.txt", package = "epiSeeker")
peak <- readPeakFile(peakfile)
dplyr::mutate(peak, score = tags)

Description

calculate the overlap matrix, which is useful for vennplot

Usage

overlap(Sets)

Arguments

Value

data.frame

Author(s)

G Yu

Description

parse peak str

Usage

parse_peak(peak_str)

Arguments

Value

data frame

Examples

parse_peak("chr1:150235946-150236624")

Description

A 'csAnno' object representing the annotation result of the example peak set 'demo_peak'. Peaks were annotated using the function 'annotateSeq()' in 'epiSeeker'.

Format

A 'csAnno' object containing 220 annotated peaks.

Value

csAnno object

Provenance

Input peaks were taken from the example dataset 'demo_peak'. Annotation was generated using 'epiSeeker::annotateSeq()'.

Data structure

A 'csAnno' S4 object with the following slots:

'anno'

A 'GRanges' object containing the annotated peaks, including peak coordinates, basic peak metrics, and gene-based annotation fields.

'tssRegion'

Numeric vector of length two defining the upstream and downstream window used for TSS annotation.

'level'

Character string indicating whether annotation was performed at the '"transcript"' or '"gene"' level.

'hasGenomicAnnotation'

Logical value indicating whether detailed genomic annotation (promoter, exon, intron, etc.) was computed.

'detailGenomicAnnotation'

A data frame providing per-peak binary indicators for genomic categories.

'annoStat'

A data frame summarizing annotation category frequencies across the annotated peak set.

'peakNum'

Total number of annotated peaks.

Description

A list of csAnno objects obtained by annotating multiple peak files using epiSeeker::annotateSeq(). See data-raw/example_data.R

Format

A a list of csAnno objects.

Value

list of csAnno object

Provenance

The example peak annotation list was generated using several example peak files returned by getSampleFiles(). Each peak file was annotated using epiSeeker::annotateSeq().

Data structure

A named list where each element is a csAnno S4 object produced by annotateSeq().

Description

plotAnnoBar method for 'csAnno' instance

Usage

plotAnnoBar(
  x,
  xlab = "",
  ylab = "Percentage(%)",
  title = "Feature Distribution",
  ...
)

## S4 method for signature 'list'
plotAnnoBar(
  x,
  xlab = "",
  ylab = "Percentage(%)",
  title = "Feature Distribution",
  ...
)

plotAnnoBar(x, xlab="", ylab='Percentage(%)',title="Feature Distribution", ...)

Arguments

Value

plot

Author(s)

Guangchuang Yu <https://guangchuangyu.github.io>

Examples

data(peakAnno)
plotAnnoBar(peakAnno)

Description

Plot feature distribution based on their chromosome region

Usage

plotAnnoBar.data.frame(
  anno.df,
  xlab = "",
  ylab = "Percentage(%)",
  title = "Feature Distribution",
  categoryColumn
)

Arguments

Details

plot chromosome region features

Value

bar plot that summarize genomic features of peaks

Author(s)

Guangchuang Yu <https://yulab-smu.top>

See Also

[annotateSeq()] [plotAnnoPie()]

Description

plotAnnoPie method for 'csAnno' instance

Usage

plotAnnoPie(
  x,
  ndigit = 2,
  cex = 0.9,
  col = NA,
  legend.position = "rightside",
  pie3D = FALSE,
  radius = 0.8,
  ...
)

plotAnnoPie(x, ndigit = 2, cex = 0.9, col = NA,
    legend.position = "rightside", pie3D = FALSE,
    radius = 0.8, ...)

Arguments

Value

plot

Author(s)

Guangchuang Yu <https://guangchuangyu.github.io>

Examples

data(peakAnno)
plotAnnoPie(peakAnno)

Description

pieplot from peak genomic annotation

Usage

plotAnnoPie.csAnno(
  x,
  ndigit = 2,
  cex = 0.8,
  col = NA,
  legend.position = "rightside",
  pie3D = FALSE,
  radius = 0.8,
  ...
)

Arguments

Value

pie plot of peak genomic feature annotation

Author(s)

Guangchuang Yu <https://yulab-smu.top>

See Also

[annotateSeq()] [plotAnnoBar()]

Examples

data(peakAnno)
plotAnnoPie(peakAnno)

Description

Plot base modification profile

Usage

plotBmProf(
  df,
  motif_color = NULL,
  title = NULL,
  xlim = NULL,
  interactive = FALSE,
  width_svg = 10,
  height_svg = 6,
  highlight = NULL,
  highlight_color = "#c6c3c3",
  highlight_alpha = 0.2,
  xlab = "Genomic Region(5'->3')",
  ylab = NULL,
  second_ylab = NULL,
  switch_y_value = TRUE,
  legend_lab_motif = NULL,
  legend_lab_value2 = NULL,
  strip_placement = "outside",
  angle_of_facet_label = 360,
  alpha = 0.6,
  y_ticks_length = 0.25,
  x_ticks_length = 0.25,
  auto_x_axis = TRUE,
  strip_border = FALSE,
  facet_label_text_size = 10,
  axis_title_text_size = 17,
  title_text_size = 20,
  right_y_axis_text_size = 10,
  left_y_axis_text_size = 10,
  x_axis_text_size = 10,
  depth_heatmap = TRUE,
  nrow = NULL,
  ncol = NULL,
  panel_spacing = 1,
  legend_box_spacing = 3,
  legend_position = "right"
)

Arguments

Value

ggplot object

Examples

require(BSgenome.Hsapiens.UCSC.hg38)
data(demo_bmdata)
bmMatrix <- getBmMatrix(
    region = data.frame(chr = "chr22", start = 10525991, end = 10526342),
    BSgenome = BSgenome.Hsapiens.UCSC.hg38,
    input = demo_bmdata,
    #                          base = "C",
    motif = c("CG")
)
plotBmProf(bmMatrix)

Description

plotCov

Usage

plotCov(
  peak,
  weightCol = NULL,
  facet_level = NULL,
  highlight = NULL,
  highlight_color = "#c6c3c3",
  highlight_alpha = 0.2,
  xlab = "Chromosome Size (bp)",
  ylab = "",
  interactive = FALSE,
  width_svg = 10,
  height_svg = 6,
  title = "ChIP Peaks over Chromosomes",
  x_text_size = 10,
  y_text_size = 10,
  facet_label_text_size = 10,
  chrs = NULL,
  xlim = NULL,
  facet_var = NULL,
  facet_scales = "free",
  lower = 1,
  fill_color = "black",
  add_cluster_tree = FALSE,
  cluster_dist_method = "euclidean",
  cluster_hclust_methond = "complete",
  legend_position = NULL,
  add_coaccess = FALSE,
  curvature = 0.3,
  coaccess_top_n = NULL,
  coaccess_cor_threshold = NULL,
  design = NULL,
  coaccess_legend_pos = c(0.9, 0.5),
  coaccess_legend_text_size = 10,
  coaccess_legend_title_size = 12
)

Arguments

Details

Plot peak coverage

Value

ggplot2 object

Author(s)

G Yu

Examples

peakfile <- system.file("extdata", "sample_peaks.txt", package = "epiSeeker")
peak <- readPeakFile(peakfile)
plotCov(peak)

Description

plotDistToTSS method for 'csAnno' instance

Usage

plotDistToTSS(
  x,
  distanceColumn = "distanceToTSS",
  xlab = "",
  ylab = "Binding sites (%) (5'->3')",
  title = "Distribution of transcription factor-binding loci relative to TSS",
  ...
)

## S4 method for signature 'list'
plotDistToTSS(
  x,
  distanceColumn = "distanceToTSS",
  xlab = "",
  ylab = "Binding sites (%) (5'->3')",
  title = "Distribution of transcription factor-binding loci relative to TSS",
  distanceBreaks = c(0, 1000, 3000, 5000, 10000, 1e+05),
  palette = NULL,
  ...
)

plotDistToTSS(x,distanceColumn="distanceToTSS", xlab="",
ylab="Binding sites (%) (5'->3')",
title="Distribution of transcription factor-binding loci relative to TSS",...)

Arguments

Value

plot

Author(s)

Guangchuang Yu <https://guangchuangyu.github.io>

Examples

data(peakAnno)
plotDistToTSS(peakAnno)

Description

Plot feature distribution based on the distances to the TSS

Usage

plotDistToTSS.data.frame(
  peakDist,
  distanceColumn = "distanceToTSS",
  distanceBreaks = c(0, 1000, 3000, 5000, 10000, 1e+05),
  palette = NULL,
  xlab = "",
  ylab = "Binding sites (%) (5'->3')",
  title = "Distribution of transcription factor-binding loci relative to TSS",
  categoryColumn = ".id"
)

Arguments

Value

bar plot that summarize distance from peak to TSS of the nearest gene.

Author(s)

Guangchuang Yu https://guangchuangyu.github.io

See Also

annotateSeq

Description

Plot gene track

Usage

plotGeneTrack(
  txdb,
  chr,
  start_pos,
  end_pos,
  xlab = "",
  ylab = "",
  x_text_size = 10,
  y_text_size = 10,
  select_gene = "all",
  palette = NULL,
  fromType = "ENTREZID",
  highlight = NULL,
  highlight_color = "#c6c3c3",
  highlight_alpha = 0.2,
  OrgDb = NULL,
  show_legend = FALSE,
  auto_x_axis = TRUE
)

Arguments

Value

ggplot object

Examples

require(TxDb.Hsapiens.UCSC.hg38.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
plotGeneTrack(txdb = txdb, chr = "chr8", start_pos = 126712193, end_pos = 126712293)

Description

Plot the profile of motif of specific peak

Usage

plotMotifProf(
  df,
  legend_lab = "motif",
  y_lab = "motif score",
  x_lab = NULL,
  interactive = FALSE,
  width_svg = 10,
  height_svg = 6
)

Arguments

Value

ggplot object

Examples

require(BSgenome.Hsapiens.UCSC.hg38)
data(pwm_obj)
region_oi <- GRanges(
    seqnames = "chr22",
    ranges = IRanges(start = 10525891, end = 10525991)
)
motifMatrix <- getMotifMatrix(
    region = region_oi,
    pwm = pwm_obj[c(45, 120, 170)],
    ref_obj = BSgenome.Hsapiens.UCSC.hg38
)
plotMotifProf(motifMatrix)

Description

plotPeakHeatmap function

Usage

plotPeakHeatmap(
  tagMatrix,
  plot_prof = TRUE,
  xlab = "",
  ylab = "",
  palette = NULL,
  title = NULL,
  facet_label_text_size = 12,
  nrow = NULL,
  ncol = NULL,
  conf = NULL,
  statistic_method = "mean",
  missingDataAsZero = TRUE,
  facet = "none",
  free_y = TRUE,
  height_proportion = 4,
  ...
)

Arguments

Value

ggplot object

Examples

data(tagMatrix)
plotPeakHeatmap(tagMatrix)

Description

Plot peak heatmap sub function

Usage

plotPeakHeatmap_sub(
  tagMatrix,
  xlab = "",
  ylab = "",
  palette = NULL,
  title = NULL,
  facet_label_text_size = 12,
  nrow = NULL,
  ncol = NULL
)

Arguments

Value

ggplot object

Description

internal function of plotPeakHeatmap

Usage

plotPeakHeatmap_sub.internal(
  tagMatrix,
  xlab = "",
  ylab = "",
  palette = NULL,
  title = NULL,
  facet_label_text_size = 12
)

Arguments

Value

ggplot object

Description

plot peak profile

Usage

plotPeakProf(
  tagMatrix,
  xlab = "Genomic Region (5'->3')",
  ylab = "Peak Count Frequency",
  conf = NULL,
  title = "",
  facet = "none",
  free_y = TRUE,
  statistic_method = "mean",
  missingDataAsZero = TRUE,
  ...
)

Arguments

Value

ggplot object

Author(s)

G Yu; Y Yan

Examples

data(tagMatrix)
plotPeakProf(tagMatrix)

Description

A collection of transcription factor position weight matrices (PWMs) retrieved from the JASPAR 2024 database. This dataset is used to demonstrate motif enrichment, motif scanning, and peak–motif association analyses in epiSeeker. See data-raw/example_data.R

Format

A PFMatrixList object containing PWMs for multiple human transcription factors from the JASPAR 2024 CORE collection.

Value

pwm_obj

Provenance

The PWM set was obtained using the JASPAR 2024 SQLite database bundled in the JASPAR2024 package. Matrices were retrieved using TFBSTools with the following parameters:

• collection = "CORE"

• all_versions = FALSE

• species = "Homo sapiens"

• tax_group = "vertebrates"

Data structure

A TFBSTools::PWMatrixList (or PFMatrixList) object containing one PWM per transcription factor. Each matrix stores nucleotide position weights across the TF binding motif, with rows representing A, C, G, T and columns representing motif positions.

Description

Read peak file and store in data.frame or GRanges object

Usage

readPeakFile(peakfile, as = "GRanges", ...)

Arguments

Value

peak information, in GRanges or data.frame object

Author(s)

G Yu

Examples

peakfile <- system.file("extdata", "sample_peaks.txt", package = "epiSeeker")
peak.gr <- readPeakFile(peakfile, as = "GRanges")
peak.gr

Description

Rename columns of a GRanges object

Usage

## S3 method for class 'GRanges'
rename(.data, ...)

Arguments

Value

A GRanges object with renamed metadata columns.

Examples

peakfile <- system.file("extdata", "sample_peaks.txt", package = "epiSeeker")
peak <- readPeakFile(peakfile)
dplyr::rename(peak, tag = tags)

Description

Annotate genomic regions to genes in many-to-many mapping

Usage

seq2gene(seq, tssRegion, flankDistance, TxDb, sameStrand = FALSE)

Arguments

Details

This function associates genomic regions with coding genes in a many-to-many mapping. It first maps genomic regions to host genes (either located in exon or intron), proximal genes (located in promoter regions) and flanking genes (located in upstream and downstream within user-specified distance).

Value

gene vector

Author(s)

Guangchuang Yu

Examples

data(seq2gene_result)
seq2gene_result

Description

A character vector of gene IDs returned by seq2gene(), representing genes associated with a subset of peaks. This dataset is used to illustrate peak-to-gene mapping and regulatory region annotation workflows in epiSeeker. See data-raw/example_data.R

Format

A character vector of gene IDs generated by seq2gene() from the subset of peaks derived from demo_peak.

Value

vector of gene names

Data structure

A character vector of gene identifiers (ENTREZ IDs) representing genes linked to the example peak set via TSS proximity or flanking-gene search.

Provenance

The example peak set demo_peak was constructed by sampling up to 10 peaks per autosome (chr1–chr22) from the ChIP-seq dataset GSM6418464. Peaks were imported using readPeakFile(), subset by chromosome, and combined into a single GRanges object.

The gene-level associations were then computed directly using:

seq2gene_result <- seq2gene(
    demo_peak,
    tssRegion = c(-1000, 1000),
    flankDistance = 3000,
    txdb
)

The resulting character vector of gene IDs was saved via data-raw/example_data.R.

Description

show method for 'csAnno' instance

Usage

show(object)

Arguments

Value

message

Author(s)

Guangchuang Yu <https://guangchuangyu.github.io>

Examples

peakfile <- system.file("extdata", "sample_peaks.txt", package = "epiSeeker")
show(peakfile)

Description

shuffle the position of peak

Usage

shuffle(peak.gr, TxDb)

Arguments

Value

GRanges object

Author(s)

G Yu

Examples

require(TxDb.Hsapiens.UCSC.hg38.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
p <- GRanges(
    seqnames = c("chr1", "chr3"),
    ranges = IRanges(start = c(1, 100), end = c(50, 130))
)
shuffle(p, TxDb = txdb)

Description

tagMatrix result used to demonstrate TSS enrichment visualization and tag distribution plotting functions in epiSeeker. See data-raw/example_data.R

Format

A numeric matrix with n genes × 500 bins.

Value

matrix

Provenance

The tag matrix was generated using a sample peak file obtained from getSampleFiles()[[4]]. Peaks were imported via readPeakFile() and processed using epiSeeker::getTagMatrix() with the following settings:

• Transcript database: TxDb.Hsapiens.UCSC.hg19.knownGene

• Annotation mode: type = "start_site", by = "gene"

• TSS window: upstream 3000 bp, downstream 3000 bp

• Peak weight: column "V5" of the peak file

• Number of bins: nbin = 500

Data structure

A numeric matrix in which:

Rows

Represent individual genes contributing tags around their TSS.

Columns

Represent evenly spaced bins across the TSS window from -3000 bp to +3000 bp (500 bins total).

Description

upsetplot method generics

Usage

upsetplot(x, ...)

Arguments

Value

plot

Author(s)

Guangchuang Yu <https://guangchuangyu.github.io>

Examples

data(peakAnno)
upsetplot(peakAnno)

Description

vennpie method generics

Usage

vennpie(x, r = 0.2, cex = 1.2, ...)

vennpie(x, r = 0.2, cex=1.2, ...)

Arguments

Value

plot

Author(s)

Guangchuang Yu <https://guangchuangyu.github.io>

Examples

data(peakAnno)
vennpie(peakAnno)

Description

Plot the overlap of a list of object

Usage

vennplot(Sets, ...)

Arguments

Details

venn plot produced through this way has colors which can be defined by users using ggplot2 grammar e.g.(scale_fill_distiller()). And users can specify any details, like digital number, text size and showing percentage or not, by inputting '...' extra parameters.

Value

venn plot that summarize the overlap of peaks from different experiments or gene annotation from different peak files.

Author(s)

G Yu

Examples

data(peakAnnoList)
genes <- lapply(peakAnnoList, function(i) as.data.frame(i)$geneId)
vennplot(genes)

Description

Vennplot for peak files

Usage

vennplot.peakfile(files, labels = NULL)

Arguments

Value

figure

Author(s)

G Yu

Examples

files <- list(
    system.file("extdata", "sample_peaks.txt", package = "epiSeeker"),
    system.file("extdata", "sample_peaks.txt", package = "epiSeeker")
)
vennplot.peakfile(files)