Last updated: 2022-08-29
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Knit directory: scATACseq-topics/
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The aim of this short analysis is to get a better understanding of the Cicero co-accessibility data, and how (and whether) these data can be used to connect chromatin accessibility peaks to genes in order to identify “driving genes” for topics. As an illustration, here I focus on the Cicero data for a single gene, Slc12a1, that was highlighted in the Cusanovich et al (2018) Cell paper in connection with the “loop of henle” cell type (see Fig. 5 of that paper).
Load the packages used in the analysis below.
library(fastTopics)
library(ggplot2)
library(cowplot)
library(ashr)Load the base-pair positions of the genes for the mm9 Mouse Genome Assembly.
load("data/mm9_seq_gene.RData")Load the cicero co-accessibility data for gene Slc12a1.
TO DO: Explain where these data were downloaded from and how they were compiled.
load("data/Cusanovich_2018/processed_data/slc12a1_data.RData")
cicero <- transform(cicero,
Peak1 = as.character(Peak1),
Peak2 = as.character(Peak2))
sessionInfo()
# R version 3.6.2 (2019-12-12)
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