In aertslab/cisTopic: cisTopic: Probabilistic modelling of cis-regulatory topics from single cell epigenomics data

Vignette built on r format(Sys.time(), "%b %d, %Y") with cisTopic version r packageVersion("cisTopic").

This vignette provides the code to run rGREAT with the hg38 assembly and RcisTarget with hg38 and mm10 based on liftover.

Installation

R < 3.5

If your R version is below 3.5, you will need to install manually the following packages:

devtools::install_github("aertslab/AUCell")
devtools::install_github("aertslab/RcisTarget")

source("https://bioconductor.org/biocLite.R")
biocLite('GenomicRanges')

cisTopic package

For installing cisTopic run:

devtools::install_github("aertslab/cisTopic")

Running rGREAT and RcisTarget with mm10

# GREAT can work directly on mm10 coordinates
cisTopicObject <- GREAT(cisTopicObject, genome='mm10', fold_enrichment=2, geneHits=1, sign=0.05, request_interval=10)

# For using RcisTarget, we need to liftover the coordinates to the mm9 assemble
library(R.utils)
url <- "http://hgdownload.soe.ucsc.edu/goldenPath/mm10/liftOver/mm10ToMm9.over.chain.gz"
mm10Tomm9.chain <- "mm10Tomm9.over.chain"
download.file(url, destfile = paste0(mm10Tomm9.chain, ".gz"))
gunzip(paste0(mm10Tomm9.chain, ".gz"))

# Import chain file
mm10Tomm9.chain  <- import.chain(mm10Tomm9.chain)

# Obtain liftOver dictionary (as list)
mm10_coord <- [e-mail]
mm10_to_mm9_list <- liftOver(mm10_coord, mm10Tomm9.chain)

# Run GREAT based on liftover to mm9 coordinates
cisTopicObject <- binarizedcisTopicsToCtx(cisTopicObject, liftOver=mm10_to_mm9_list, genome='mm9')
cisTopicObject <- scoredRegionsToCtx(cisTopicObject, liftOver=mm10_to_mm9_list, genome='mm9')
pathToFeather <- 'mm9-DHS_enh_prom_pennstate-9species.all_regions.mc9nr.feather'
cisTopicObject <- topicsRcisTarget(cisTopicObject, genome='mm9', pathToFeather, reduced_database=FALSE, nesThreshold=3, rocthr=0.005, maxRank=20000, nCores=4)
cisTopicObject<- getCistromes(cisTopicObject, annotation = 'Both', nCores=5)

Running rGREAT and RcisTarget with hg38

# url and file name for a chain file
library(R.utils)
url <- "http://hgdownload.soe.ucsc.edu/goldenPath/hg38/liftOver/hg38ToHg19.over.chain.gz"
hg38ToHg19.chain <- "data/hg38ToHg19.over.chain"
download.file(url, destfile = paste0(hg38ToHg19.chain, ".gz"))
gunzip(paste0(hg38ToHg19.chain, ".gz"))

# Import chain file
hg38ToHg19.chain <- import.chain(hg38ToHg19.chain)

# Obtain liftOver dictionary (as list)
hg38_coord <- [e-mail]
hg38_to_hg19_list <- liftOver(hg38_coord, hg38ToHg19.chain)

# Run GREAT based on liftover to hg19 coordinates
cisTopicObject <- GREAT(cisTopicObject, genome='hg19', liftOver=hg38_to_hg19_list, fold_enrichment=2, geneHits=1, sign=0.05, request_interval=10)

# Run GREAT based on liftover to hg19 coordinates
cisTopicObject <- binarizedcisTopicsToCtx(cisTopicObject, liftOver=hg38_to_hg19_list, genome="hg19")
cisTopicObject <- scoredRegionsToCtx(cisTopicObject, liftOver=hg38_to_hg19_list, genome="hg19")
pathToFeather <- "hg19-regions-1M-9species.all_regions.mc9nr.feather"
cisTopicObject <- topicsRcisTarget(cisTopicObject, genome='hg19', pathToFeather, reduced_database=FALSE, nesThreshold=3, rocthr=0.005, maxRank=20000, nCores=1)
cisTopicObject<- getCistromes(cisTopicObject, annotation = 'Both', nCores=5)

aertslab/cisTopic documentation built on April 6, 2024, 9:31 p.m.