| 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.
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)
.
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)
)
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.
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