# UMI4Cats <img src="man/figures/logo.png" width="121px" height="140px" align="right" style="padding-left:10px;background-color:white;" />
<!-- badges: start -->
[](https://github.com/Pasquali-lab/UMI4Cats/issues)
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
[](https://github.com/Pasquali-lab/UMI4Cats/actions)
<!-- badges: end -->
## Bioconductor release status
| Branch | R CMD check | Last updated |
|:----------------:|:----------------:|:------------:|
| [_devel_](http://bioconductor.org/packages/devel/bioc/html/UMI4Cats.html) | [](http://bioconductor.org/checkResults/devel/bioc-LATEST/UMI4Cats) |  |
| [_release_](http://bioconductor.org/packages/release/bioc/html/UMI4Cats.html) | [](http://bioconductor.org/checkResults/release/bioc-LATEST/UMI4Cats) |  |
The goal of UMI4Cats is to provide and easy-to-use package to analyze
UMI-4C contact data.
## Installation
You can install the latest release of `UMI4Cats` from Bioconductor:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("UMI4Cats")
If you want to test the development version, you can install it from the github repository:
BiocManager::install("Pasquali-lab/UMI4Cats")
Now you can load the package using `library(UMI4Cats)`.
## Basic usage
For detailed instructions on how to use UMI4Cats, please see the [vignette](https://pasquali-lab.github.io/UMI4Cats/articles/UMI4Cats.html).
``` r
library(UMI4Cats)
```
``` r
## 0) Download example data -------------------------------
path <- downloadUMI4CexampleData()
## 1) Generate Digested genome ----------------------------
# The selected RE in this case is DpnII (|GATC), so the cut_pos is 0, and the res_enz "GATC".
hg19_dpnii <- digestGenome(
cut_pos = 0,
res_enz = "GATC",
name_RE = "DpnII",
ref_gen = BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19,
out_path = file.path(tempdir(), "digested_genome/")
)
## 2) Process UMI-4C fastq files --------------------------
raw_dir <- file.path(path, "CIITA", "fastq")
contactsUMI4C(
fastq_dir = raw_dir,
wk_dir = file.path(path, "CIITA"),
bait_seq = "GGACAAGCTCCCTGCAACTCA",
bait_pad = "GGACTTGCA",
res_enz = "GATC",
cut_pos = 0,
digested_genome = hg19_dpnii,
bowtie_index = file.path(path, "ref_genome", "ucsc.hg19.chr16"),
ref_gen = BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19,
threads = 5
)
## 3) Get filtering and alignment stats -------------------
statsUMI4C(wk_dir = file.path(path, "CIITA"))
```
<img src="man/figures/README-analysis-plot-1.png" width="100%" />
``` r
## 4) Analyze UMI-4C results ------------------------------
# Load sample processed file paths
files <- list.files(file.path(path, "CIITA", "count"),
pattern = "*_counts.tsv",
full.names = TRUE
)
# Create colData including all relevant information
colData <- data.frame(
sampleID = gsub("_counts.tsv.gz", "", basename(files)),
file = files,
stringsAsFactors = FALSE
)
library(tidyr)
colData <- colData %>%
separate(sampleID,
into = c("condition", "replicate", "viewpoint"),
remove = FALSE
)
# Load UMI-4C data and generate UMI4C object
umi <- makeUMI4C(
colData = colData,
viewpoint_name = "CIITA",
grouping = "condition"
)
## 5) Perform differential test ---------------------------
umi <- fisherUMI4C(umi,
grouping = "condition",
filter_low = 20
)
## 6) Plot results ----------------------------------------
plotUMI4C(umi,
grouping = "condition",
ylim = c(0, 15),
xlim = c(10.75e6, 11.25e6)
)
```
<img src="man/figures/README-analysis-plot-2.png" width="100%" />
## Code of Conduct
Please note that the UMI4Cats project is released with a [Contributor
Code of
Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html).
By contributing to this project, you agree to abide by its terms.
