UMI4Cats

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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.

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 cutpos is 0, and the resenz "GATC".

hg19dpnii <- digestGenome( cutpos = 0, resenz = "GATC", nameRE = "DpnII", refgen = BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19, outpath = file.path(tempdir(), "digested_genome/") )

2) Process UMI-4C fastq files --------------------------

raw_dir <- file.path(path, "CIITA", "fastq")

contactsUMI4C( fastqdir = rawdir, wkdir = file.path(path, "CIITA"), baitseq = "GGACAAGCTCCCTGCAACTCA", baitpad = "GGACTTGCA", resenz = "GATC", cutpos = 0, digestedgenome = hg19dpnii, bowtieindex = file.path(path, "refgenome", "ucsc.hg19.chr16"), refgen = BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19, threads = 5 )

3) Get filtering and alignment stats -------------------

statsUMI4C(wk_dir = file.path(path, "CIITA")) ```

``` 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) ) ```

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.