Type:	Package
Title:	A Computational Pipeline for Bulk 'ATAC-Seq' Profiles
Version:	0.2.3
Description:	Differential analyses and Enrichment pipeline for bulk 'ATAC-seq' data analyses. This package combines different packages to have an ultimate package for both data analyses and visualization of 'ATAC-seq' data. Methods are described in 'Karakaslar et al.' (2021) <doi:10.1101/2021.03.05.434143>.
License:	GPL-3
Encoding:	UTF-8
LazyData:	true
URL:	https://github.com/eonurk/cinaR/
BugReports:	https://github.com/eonurk/cinaR/issues/
Depends:	R (≥ 4.0.0)
Imports:	ChIPseeker, DESeq2, dplyr, edgeR, fgsea, GenomicRanges, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, ggplot2, ggrepel, grDevices, limma, utils, pheatmap, preprocessCore, RColorBrewer, sva, writexl
RoxygenNote:	7.1.2
Suggests:	knitr, rmarkdown, markdown
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2022-05-17 08:45:32 UTC; onur-lumc
Author:	Onur Karakaslar [aut, cre], Duygu Ucar [aut]
Maintainer:	Onur Karakaslar <eonurkara@gmail.com>
Repository:	CRAN
Date/Publication:	2022-05-18 14:00:09 UTC

GSEA

Description

Gene set enrichment analyses, runs 'fgsea' package implementation with preset values.

Usage

GSEA(genes, geneset)

Arguments

Value

data.frame, list of pathways and their enrichment (adjusted) p-values.

References

G. Korotkevich, V. Sukhov, A. Sergushichev. Fast gene set enrichment analysis. bioRxiv (2019), doi:10.1101/060012

Examples

library(cinaR)
library(fgsea)
data(examplePathways)
data(exampleRanks)
GSEA(exampleRanks, examplePathways)

HPEA

Description

Hyper-geometric p-value enrichment analyses, looking for over-representation of a set of genes on given pathways.

Usage

HPEA(genes, geneset, background.genes.size)

Arguments

Value

data.frame, list of pathways and their enrichment (adjusted) p-values.

Examples

library(cinaR)

data("VP2008")
genes.to.test <- vp2008[[1]][1:10]
HPEA(genes.to.test,vp2008, background.genes.size = 20e3)

annotatePeaks

Description

Runs DA pipeline and makes it ready for enrichment analyses

Usage

annotatePeaks(cp, reference.genome, show.annotation.pie = FALSE, verbose)

Arguments

Value

DApeaks returns DA peaks

Example peaks from bone marrow of B6 mice

Description

Example peaks from bone marrow of B6 mice

Usage

data(atac_seq_consensus_bm)

Format

An object of class data.frame with 1000 rows and 25 columns.

Examples

data(atac_seq_consensus_bm)

cinaR

Description

Runs differential analyses and enrichment pipelines

Usage

cinaR(
  matrix,
  contrasts,
  experiment.type = "ATAC-Seq",
  DA.choice = 1,
  DA.fdr.threshold = 0.05,
  DA.lfc.threshold = 0,
  comparison.scheme = "OVO",
  save.DA.peaks = FALSE,
  DA.peaks.path = NULL,
  norm.method = "cpm",
  filter.method = "custom",
  library.threshold = 2,
  cpm.threshold = 1,
  TSS.threshold = 50000,
  show.annotation.pie = FALSE,
  reference.genome = NULL,
  batch.correction = FALSE,
  batch.information = NULL,
  additional.covariates = NULL,
  sv.number = NULL,
  run.enrichment = TRUE,
  enrichment.method = NULL,
  enrichment.FDR.cutoff = 1,
  background.genes.size = 20000,
  geneset = NULL,
  verbose = TRUE
)

Arguments

Value

returns differentially accessible peaks

Examples

data(atac_seq_consensus_bm) # calls 'bed'

# a vector for comparing the examples
contrasts <- sapply(strsplit(colnames(bed), split = "-", fixed = TRUE),
                    function(x){x[1]})[4:25]

results <- cinaR(bed, contrasts, reference.genome = "mm10")

color values

Description

color values

Usage

color_values

Format

An object of class character of length 8.

Differential Analyses

Description

Runs differential analyses pipeline of choice on consensus peaks

Usage

differentialAnalyses(
  final.matrix,
  contrasts,
  experiment.type,
  DA.choice,
  DA.fdr.threshold,
  DA.lfc.threshold,
  comparison.scheme,
  save.DA.peaks,
  DA.peaks.path,
  batch.correction,
  batch.information,
  additional.covariates,
  sv.number,
  verbose
)

Arguments

Value

returns consensus peaks (batch corrected version if enabled) and DA peaks

dot_plot

Description

Given the results from 'cinaR' it produces dot plots for enrichment analyses.

Usage

dot_plot(results, fdr.cutoff = 0.1, filter.pathways = FALSE)

Arguments

Value

ggplot object

Examples

library(cinaR)
data(atac_seq_consensus_bm) # calls 'bed'

# a vector for comparing the examples
contrasts <- sapply(strsplit(colnames(bed), split = "-", fixed = TRUE),
                    function(x){x[1]})[4:25]

results <- cinaR(bed, contrasts, reference.genome = "mm10")

dot_plot(results)

filterConsensus

Description

Filters lowly expressed peaks from down-stream analyses

Usage

filterConsensus(
  cp,
  filter.method = "custom",
  library.threshold = 2,
  cpm.threshold = 1
)

Arguments

Value

returns differentially accessible peaks

Examples

set.seed(123)
cp <- matrix(rexp(200, rate=.1), ncol=20)

## using cpm function from `edgeR` package
cp.filtered <- filterConsensus(cp)

Grch37

Description

Grch37

Usage

data(grch37)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 66978 rows and 3 columns.

Grch38

Description

Grch38

Usage

data(grch38)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 67495 rows and 3 columns.

Grcm38

Description

Grcm38

Usage

data(grcm38)

Format

An object of class data.frame with 25350 rows and 4 columns.

heatmap_differential

Description

plot differentially accessible peaks for a given comparison

Usage

heatmap_differential(results, comparison = NULL, ...)

Arguments

Value

ggplot object

Examples

library(cinaR)
data(atac_seq_consensus_bm) # calls 'bed'

# a vector for comparing the examples
contrasts <- sapply(strsplit(colnames(bed), split = "-", fixed = TRUE),
                    function(x){x[1]})[4:25]

results <- cinaR(bed, contrasts, reference.genome = "mm10")

heatmap_differential(results)

heatmap_var_peaks

Description

plot most variable k peaks (default k = 100) among all samples

Usage

heatmap_var_peaks(results, heatmap.peak.count = 100, ...)

Arguments

Value

ggplot object

Examples

library(cinaR)
data(atac_seq_consensus_bm) # calls 'bed'

# creating dummy results
results <- NULL
results[["cp"]] <- bed[,c(4:25)]

heatmap_var_peaks(results)

normalizeConsensus

Description

Normalizes consensus peak using different methods

Usage

normalizeConsensus(cp, norm.method = "cpm", log.option = FALSE)

Arguments

Value

Normalized consensus peaks

Examples

set.seed(123)
cp <- matrix(rexp(200, rate=.1), ncol=20)

## using cpm function from `edgeR` package
cp.normalized <- normalizeConsensus(cp)

## quantile normalization option
cp.normalized <- normalizeConsensus(cp, norm.method = "quantile")

pca_plot

Description

pca_plot

Usage

pca_plot(results, overlaid.info, sample.names = NULL, show.names = TRUE)

Arguments

Value

ggplot object

Examples

#' library(cinaR)
data(atac_seq_consensus_bm) # calls 'bed'

# creating dummy results
results <- NULL
results[["cp"]] <- bed[,c(4:25)]

# a vector for comparing the examples
contrasts <- sapply(strsplit(colnames(bed), split = "-", fixed = TRUE),
                    function(x){x[1]})[4:25]

## overlays the contrasts info onto PCA plots
pca_plot(results, contrasts)

## you can overlay other information as well,
## as long as it is the same length with the
## number of samples.

sample.info <- c(rep("Group A", 11), rep("Group B", 11))
pca_plot(results, sample.info, show.names = FALSE)

run_enrichment

Description

This function is run, if the enrichment pipeline wants to be called afterwards. Setting reference genome to the same genome which cinaR was run should be given to this function!

Usage

run_enrichment(
  results,
  geneset = NULL,
  experiment.type = "ATAC-Seq",
  reference.genome = NULL,
  enrichment.method = NULL,
  enrichment.FDR.cutoff = 1,
  background.genes.size = 20000,
  verbose = TRUE
)

Arguments

Value

list, enrichment analyses results along with corresponding differential analyses outcomes

Examples

library(cinaR)
data(atac_seq_consensus_bm) # calls 'bed'

# a vector for comparing the examples
contrasts <- sapply(strsplit(colnames(bed), split = "-", fixed = TRUE),
                    function(x){x[1]})[4:25]

results <- cinaR(bed, contrasts, reference.genome = "mm10", run.enrichment = FALSE)

results_with_enrichment <- run_enrichment(results, reference.genome = "mm10")

scale_rows

Description

Normalize (z-score) rows of a matrix

Usage

scale_rows(x)

Arguments

Value

Row-normalized matrix

Examples

library(cinaR)
data(atac_seq_consensus_bm) # calls 'bed'
bed.row.normalized <- scale_rows(bed[,c(4:25)])
head(bed.row.normalized)

show_comparisons

Description

returns the names of the created comparisons

Usage

show_comparisons(results)

Arguments

Value

comparisons created

verboseFn

Description

returns a printing function to be used with in the script

Usage

verboseFn(verbose)

Arguments

Value

print function

Immune modules

Description

Immune modules

Usage

data(VP2008)

Format

An object of class GMT; see read.gmt from qusage package.

References

Chaussabel et al. (2008) Immunity 29:150-164 (PubMed)