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.
LLM and agent usage
For model-friendly discovery and tool integration:
-
llms.txtprovides a compact machine-oriented index forcinaR. -
inst/mcp/cinaR-mcp-tools.jsonprovides starter MCP tool definitions with JSON Schemas.
Useful entry points for assistants:
-
cinaR(): differential analysis + enrichment (+ optional TF activity) from consensus data. -
prep_scATAC_cinaR(): pseudobulk 10x scATAC peak-by-cell matrices into cinaR-ready inputs. -
prep_scATAC_seurat(): same preprocessing directly from a Seurat/Signac object.
Use the package site for detailed docs and examples: https://eonurk.github.io/cinaR/.
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")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)
Single-cell ATAC-seq (10x scATAC) preprocessing
Use prep_scATAC_cinaR() to pseudobulk 10x scATAC peak-by-cell matrices into a cinaR-ready consensus matrix.
This preserves biological replicates (sample-level) and avoids inflated significance from per-cell testing.
# counts: peak-by-cell matrix (dense or dgCMatrix)
# meta: data.frame with rownames = cell barcodes
# meta must include biological replicate and condition columns
prep <- prep_scATAC_cinaR(counts, meta,
sample.col = "sample",
group.col = "group")
results <- cinaR(prep$bed, prep$contrasts, reference.genome = "hg38")Per-cell-type (sample × cluster) pseudobulk:
prep_list <- prep_scATAC_cinaR(counts, meta,
sample.col = "sample",
group.col = "group",
cluster.col = "celltype")
results_list <- lapply(prep_list, function(x) {
cinaR(x$bed, x$contrasts, reference.genome = "hg38")
})Seurat/Signac object:
prep <- prep_scATAC_seurat(seurat_obj,
sample.col = "sample",
group.col = "group",
assay = "peaks")
results <- cinaR(prep$bed, prep$contrasts, reference.genome = "hg38")If your peak IDs are not in chr:start-end, chr_start_end, or chr-start-end format, pass a peak.bed data.frame with CHR, START, and STOP columns via peak.bed = ....
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/10.1101/2021.03.05.434143v2},
journal = {bioRxiv}
}