cinaR

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# cinaR <a href='https://eonurk.github.io/cinaR/'><img src='man/figures/cinaR.png' align="right" alt="" width="139" /></a>

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## 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](https://www.biorxiv.org/content/10.1101/2021.03.05.434143v2).

![](man/figures/overview@5x.png)

## Installation

``` r
# CRAN mirror
install.packages("cinaR")
```

### Development version

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

``` r
# 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:

``` r
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

``` r
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)
```

<img src="man/figures/unnamed-chunk-5-1.png" width="100%" />

## 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.

``` r
# 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:

``` r
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:

``` r
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!](https://eonurk.github.io/cinaR/articles/cinaR.html)

## 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}
    }

## Contribution

You can send pull requests to make your contributions.

## License

- GNU General Public License v3.0