HDAC_DiffLoop_Distance_Plot

library(diffloop)
library(diffloopdata)
library(ggplot2)
library(GenomicRanges)
library(ggrepel)
library(DESeq2)
library(digest)
library(edgeR)
library(limma)

DiffLoop - from HiChIPPER output – from HiC-Pro output

#Read in loop file ##If not a mango file ## Control_HDAC_SMC3 <- loopsMake("~/Desktop/HDAC_HiChIP/")
Control_HDAC_SMC3 <- loopsMake.mango("~/Desktop/HDAC_HiChIP")  
celltypes <- c("Control_SMC3", "HDAC_SMC3")
Control_HDAC_SMC3 <- updateLDGroups(Control_HDAC_SMC3, celltypes)
dim(Control_HDAC_SMC3)

##   anchors interactions samples colData rowData
## 1   40390       134649       2       2       1

#Subset loops and perform mango correction
Control_HDAC_SMC3 <- subsetLoops(Control_HDAC_SMC3, Control_HDAC_SMC3@rowData$loopWidth >= 5000)
Control_HDAC_SMC3_Mango <- mangoCorrection(Control_HDAC_SMC3, FDR = 0.01)
dim(Control_HDAC_SMC3_Mango)

##   anchors interactions samples colData rowData
## 1   24242        35855       2       2       3

#Filter loops based on width and minumun number of loops per sample
Control_HDAC_SMC3_Filtered <- filterLoops(Control_HDAC_SMC3_Mango, width = 5000, nreplicates = 2, nsamples = 1)
dim(Control_HDAC_SMC3_Filtered)

##   anchors interactions samples colData rowData
## 1   14663        17810       2       2       3

loopMetrics(Control_HDAC_SMC3_Filtered)

##        Control_SMC3 HDAC_SMC3
## unique        18947     78190

Loop Distance Plot

p1 <- loopDistancePlot(Control_HDAC_SMC3_Filtered)
p1

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.