README.md

In Pasquali-lab/UMI4Cats: UMI4Cats: Processing, analysis and visualization of UMI-4C chromatin contact data

UMI4Cats

Bioconductor release status

| Branch | R CMD check | Last updated | |:----------------:|:----------------:|:------------:| | devel | | | | release | | |

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.

library(UMI4Cats)

## 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"))

## 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)
)

Code of Conduct

Please note that the UMI4Cats project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Pasquali-lab/UMI4Cats documentation built on March 23, 2024, 9:42 p.m.