cinaR

Overview

cinaR is a single wrapper function for end-to-end computational analyses of bulk ATAC-seq (or RNA-seq) profiles. Starting from a consensus peak file, it outputs differentially accessible peaks, enrichment results, and provides users with various configurable visualization options. For more details, please see the preprint.

Installation

# CRAN mirror
install.packages("cinaR")

Development version

To get bug fix and use a feature from the development version:

# install.packages("devtools")
devtools::install_github("eonurk/cinaR")

Known Installation Issues

Sometimes bioconductor related packages may not be installed automatically.
Therefore, you may need to install them manually:

BiocManager::install(c("ChIPseeker", "DESeq2", "edgeR", "fgsea","GenomicRanges", "limma", "preprocessCore", "sva", "TxDb.Hsapiens.UCSC.hg38.knownGene", "TxDb.Hsapiens.UCSC.hg19.knownGene", "TxDb.Mmusculus.UCSC.mm10.knownGene"))

Usage

library(cinaR)
#> Checking for required Bioconductor packages...
#> All required Bioconductor packages are already installed.

# create contrast vector which will be compared.
contrasts<- c("B6", "B6", "B6", "B6", "B6", "NZO", "NZO", "NZO", "NZO", "NZO", "NZO", 
              "B6", "B6", "B6", "B6", "B6", "NZO", "NZO", "NZO", "NZO", "NZO", "NZO")

# If reference genome is not set hg38 will be used!
results <- cinaR(bed, contrasts, reference.genome = "mm10")
#> >> Experiment type: ATAC-Seq
#> >> Matrix is filtered!
#> 
#> >> preparing features information...      2024-05-22 12:38:01 
#> >> identifying nearest features...        2024-05-22 12:38:02 
#> >> calculating distance from peak to TSS...   2024-05-22 12:38:02 
#> >> assigning genomic annotation...        2024-05-22 12:38:02 
#> >> assigning chromosome lengths           2024-05-22 12:38:11 
#> >> done...                    2024-05-22 12:38:11
#> >> Method: edgeR
#>  FDR:0.05& abs(logFC)<0
#> >> Estimating dispersion...
#> >> Fitting GLM...
#> >> DA peaks are found!
#> >> No `geneset` is specified so immune modules (Chaussabel, 2008) will be used!
#> >> enrichment.method` is not selected. Hyper-geometric p-value (HPEA) will be used!
#> >> Mice gene symbols are converted to human symbols!
#> >> Enrichment results are ready...
#> >> Done!

pca_plot(results, contrasts, show.names = F)

For more details please go to our site from here!

Citation

@article {Karakaslar2021.03.05.434143,
    author = {Karakaslar, E Onur and Ucar, Duygu},
    title = {cinaR: A comprehensive R package for the differential analyses and 
    functional interpretation of ATAC-seq data},
    year = {2021},
    doi = {10.1101/2021.03.05.434143},
    publisher = {Cold Spring Harbor Laboratory},
    URL = {https://www.biorxiv.org/content/early/2021/03/08/2021.03.05.434143.1},
    journal = {bioRxiv}
}

Contribution

You can send pull requests to make your contributions.

License

• GNU General Public License v3.0