knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/", out.width = "100%", fig.dpi = 96 )
cinaR
is a single wrapper function for end-to-end computational analyses of
bulk ATAC-seq (or RNA-seq) profiles. Starting from a consensus peak file, it outputs
differentially accessible peaks, enrichment results, and provides users with various configurable visualization options. For more details, please see the preprint.
# CRAN mirror install.packages("cinaR")
To get bug fix and use a feature from the development version:
# install.packages("devtools") devtools::install_github("eonurk/cinaR")
Sometimes bioconductor related packages may not be installed automatically.
Therefore, you may need to install them manually:
BiocManager::install(c("ChIPseeker", "DESeq2", "edgeR", "fgsea","GenomicRanges", "limma", "preprocessCore", "sva", "TxDb.Hsapiens.UCSC.hg38.knownGene", "TxDb.Hsapiens.UCSC.hg19.knownGene", "TxDb.Mmusculus.UCSC.mm10.knownGene"))
knitr::opts_chunk$set(dpi=300,fig.width=6)
library(cinaR) # 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") pca_plot(results, contrasts, show.names = F)
For more details please go to our site from here!
@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} }
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