Package 'cinaR' reference manual

Title:	A Computational Pipeline for Bulk 'ATAC-Seq' Profiles
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>.
Authors:	Onur Karakaslar [aut, cre] , Duygu Ucar [aut]
Maintainer:	Onur Karakaslar <[email protected]>
License:	GPL-3
Version:	0.2.4
Built:	2025-02-20 05:49:53 UTC
Source:	https://github.com/eonurk/cinar

annotatePeaks

Description

Runs DA pipeline and makes it ready for enrichment analyses

Usage

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

Arguments

`cp`	bed formatted consensus peak matrix: CHR, START, STOP and raw peak counts (peaks by 3+samples)
`reference.genome`	genome of interested species. It should be 'hg38', 'hg19' or 'mm10'.
`show.annotation.pie`	shows the annotation pie chart produced with ChipSeeker
`verbose`	prints messages through running the pipeline

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)
data(atac_seq_consensus_bm)

Format

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

Examples

data(atac_seq_consensus_bm)
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
)
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

`matrix`	either bed formatted consensus peak matrix (peaks by 3+samples) CHR, START, STOP and raw peak counts OR count matrix (genes by 1+samples).
`contrasts`	user-defined contrasts for comparing samples
`experiment.type`	The type of experiment either set to "ATAC-Seq" or "RNA-Seq"
`DA.choice`	determines which pipeline to run: (1) edgeR, (2) limma-voom, (3) limma-trend, (4) DEseq2. Note: Use limma-trend if consensus peaks are already normalized, otherwise use other methods.
`DA.fdr.threshold`	fdr cut-off for differential analyses
`DA.lfc.threshold`	log-fold change cutoff for differential analyses
`comparison.scheme`	either one-vs-one (OVO) or one-vs-all (OVA) comparisons.
`save.DA.peaks`	saves differentially accessible peaks to an excel file
`DA.peaks.path`	the path which the excel file of the DA peaks will be saved, if not set it will be saved to current directory.
`norm.method`	normalization method for consensus peaks
`filter.method`	filtering method for low expressed peaks
`library.threshold`	number of libraries a peak occurs so that it is not filtered default set to 2
`cpm.threshold`	count per million threshold for not to filter a peak
`TSS.threshold`	Distance to transcription start site in base-pairs. Default set to 50,000.
`show.annotation.pie`	shows the annotation pie chart produced with ChipSeeker
`reference.genome`	genome of interested species. It should be 'hg38', 'hg19' or 'mm10'.
`batch.correction`	logical, if set will run unsupervised batch correction via sva (default) or if the batch information is known 'batch.information' argument should be provided by user.
`batch.information`	character vector, given by user.
`additional.covariates`	vector or data.frame, this parameter will be directly added to design matrix before running the differential analyses, therefore won't affect the batch corrections but adjust the results in down-stream analyses.
`sv.number`	number of surrogate variables to be calculated using SVA, best left untouched.
`run.enrichment`	logical, turns off enrichment pipeline
`enrichment.method`	There are two methodologies for enrichment analyses, Hyper-geometric p-value (HPEA) or Geneset Enrichment Analyses (GSEA).
`enrichment.FDR.cutoff`	FDR cut-off for enriched terms, p-values are corrected by Benjamini-Hochberg procedure
`background.genes.size`	number of background genes for hyper-geometric p-value calculations. Default is 20,000.
`geneset`	Pathways to be used in enrichment analyses. If not set vp2008 (Chaussabel, 2008) immune modules will be used. This can be set to any geneset using 'read.gmt' function from 'qusage' package. Different modules are available: https://www.gsea-msigdb.org/gsea/downloads.jsp.
`verbose`	prints messages through running the pipeline

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")



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
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
)
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

`final.matrix`	Annotated Consensus peaks
`contrasts`	user-defined contrasts for comparing samples
`experiment.type`	The type of experiment either set to "ATAC-Seq" or "RNA-Seq"
`DA.choice`	determines which pipeline to run: (1) edgeR, (2) limma-voom, (3) limma-trend, (4) DEseq2
`DA.fdr.threshold`	fdr cut-off for differential analyses
`DA.lfc.threshold`	log-fold change cutoff for differential analyses
`comparison.scheme`	either one-vs-one (OVO) or one-vs-all (OVA) comparisons.
`save.DA.peaks`	logical, saves differentially accessible peaks to an excel file
`DA.peaks.path`	the path which the excel file of the DA peaks will be saved, if not set it will be saved to current directory.
`batch.correction`	logical, if set will run unsupervised batch correction via sva (default) or if the batch information is known 'batch.information' argument should be provided by user.
`batch.information`	character vector, given by user.
`additional.covariates`	vector or data.frame, this parameter will be directly added to design matrix before running the differential analyses, therefore won't affect the batch corrections but adjust the results in down-stream analyses.
`sv.number`	number of surrogate variables to be calculated using SVA, best left untouched.
`verbose`	prints messages through running the pipeline

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

Arguments

`results`	cinaR result object
`fdr.cutoff`	Pathways with smaller fdr values than the cut-off will be shown as dots.
`filter.pathways`	logical, it will filter the pathways from dot plot with fdr values less than 'fdr.cutoff'.

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)

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

Arguments

`cp`	consensus peak matrix, with unique ids at rownames.
`filter.method`	filtering method for low expressed peaks
`library.threshold`	number of libraries a peak occurs so that it is not filtered default set to 2
`cpm.threshold`	count per million threshold for not to filter a peak

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)

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)
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)
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)
data(grcm38)

Format

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

GSEA

Description

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

Usage

GSEA(genes, geneset)
GSEA(genes, geneset)

Arguments

`genes`	DA gene names to be checked if they are over-represented or not.
`geneset`	Pathways to be used in enrichment analyses. If not set vp2008 (Chaussabel, 2008) immune modules will be used. This can be set to any geneset using 'read.gmt' function from 'qusage' package. Different modules are available: https://www.gsea-msigdb.org/gsea/downloads.jsp.

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)

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

heatmap_differential

Description

plot differentially accessible peaks for a given comparison

Usage

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

Arguments

`results`	cinaR result object
`comparison`	these are created by cinaR from 'contrasts' user provided. If not selected the first comparison will be shown!
`...`	additional arguments for heatmap function, for more info '?pheatmap'

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)

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

Arguments

`results`	cinaR result object
`heatmap.peak.count`	number of peaks to be plotted. If number of peaks are less than k then all peaks will be used.
`...`	additional arguments for heatmap function, for more info '?pheatmap'

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)

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

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

heatmap_var_peaks(results)

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

Arguments

`genes`	DA gene names to be checked if they are over-represented or not.
`geneset`	Pathways to be used in enrichment analyses. If not set vp2008 (Chaussabel, 2008) immune modules will be used. This can be set to any geneset using 'read.gmt' function from 'qusage' package. Different modules are available: https://www.gsea-msigdb.org/gsea/downloads.jsp.
`background.genes.size`	number of background genes for hyper-geometric p-value calculations. Default is 20,000.

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)


library(cinaR)

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

normalizeConsensus

Description

Normalizes consensus peak using different methods

Usage

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

Arguments

`cp`	bed formatted consensus peak matrix: CHR, START, STOP and raw peak counts (peaks by 3+samples)
`norm.method`	normalization method for consensus peaks
`log.option`	logical, log option for cpm function in edgeR

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

Arguments

`results`	cinaR result object
`overlaid.info`	overlaid information onto the samples
`sample.names`	names of the samples shown on pca plot
`show.names`	logical, if set FALSE sample names will be hidden

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)

#' 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
)
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

`results`	list, DA peaks list for different contrasts
`geneset`	Pathways to be used in enrichment analyses. If not set vp2008 (Chaussabel, 2008) immune modules will be used. This can be set to any geneset using 'read.gmt' function from 'qusage' package. Different modules are available: https://www.gsea-msigdb.org/gsea/downloads.jsp.
`experiment.type`	The type of experiment either set to "ATAC-Seq" or "RNA-Seq"
`reference.genome`	genome of interested species. It should be 'hg38', 'hg19' or 'mm10'.
`enrichment.method`	There are two methodologies for enrichment analyses, Hyper-geometric p-value (HPEA) or Geneset Enrichment Analyses (GSEA).
`enrichment.FDR.cutoff`	FDR cut-off for enriched terms, p-values are corrected by Benjamini-Hochberg procedure
`background.genes.size`	number of background genes for hyper-geometric p-value calculations. Default is 20,000.
`verbose`	prints messages through running the pipeline

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")

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)
scale_rows(x)

Arguments

`x`	a matrix, possibly containing gene by samples

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)

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)
show_comparisons(results)

Arguments

results

output of the cinaR

Value

comparisons created

verboseFn

Description

returns a printing function to be used with in the script

Usage

verboseFn(verbose)
verboseFn(verbose)

Arguments

verbose

boolean, determines whether the output going be printed or not

Value

print function

Immune modules

Description

Immune modules

Usage

data(VP2008)
data(VP2008)

Format

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

References

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

Package 'cinaR'

Help Index

annotatePeaks

Description

Usage

Arguments

Value

Example peaks from bone marrow of B6 mice

Description

Usage

Format

Examples

cinaR

Description

Usage

Arguments

Value

Examples

color values

Description

Usage

Format

Differential Analyses

Description

Usage

Arguments

Value

dot_plot

Description

Usage

Arguments

Value

Examples

filterConsensus

Description

Usage

Arguments

Value

Examples

Grch37

Description

Usage

Format

Grch38

Description

Usage

Format

Grcm38

Description

Usage

Format

GSEA

Description

Usage

Arguments

Value

References

Examples

heatmap_differential

Description

Usage

Arguments

Value

Examples

heatmap_var_peaks

Description

Usage

Arguments

Value

Examples

HPEA

Description

Usage

Arguments

Value

Examples

normalizeConsensus

Description

Usage

Arguments