Nothing
## ----setup, echo=FALSE, results="hide"----------------------------------------
knitr::opts_chunk$set(tidy = FALSE,
cache = FALSE,
dev = "png",
message = FALSE, error = FALSE, warning = TRUE,
fig.dpi = 96)
## -----------------------------------------------------------------------------
library(cinaR)
data("atac_seq_consensus_bm")
## -----------------------------------------------------------------------------
dim(bed)
## -----------------------------------------------------------------------------
# bed formatted file
head(bed[,1:4])
## -----------------------------------------------------------------------------
# create contrast vector which will be compared.
contrasts<- c("B6", "B6", "B6", "B6", "B6", "NZO", "NZO", "NZO", "NZO", "NZO", "NZO",
"B6", "B6", "B6", "B6", "B6", "NZO", "NZO", "NZO", "NZO", "NZO", "NZO")
## -----------------------------------------------------------------------------
# If reference genome is not set hg38 will be used!
results <- cinaR(bed, contrasts, reference.genome = "mm10")
## -----------------------------------------------------------------------------
names(results)
## -----------------------------------------------------------------------------
names(results$DA.results)
## -----------------------------------------------------------------------------
colnames(results$DA.results$DA.peaks$B6_NZO)
## -----------------------------------------------------------------------------
head(results$DA.results$DA.peaks$B6_NZO[,1:5])
## -----------------------------------------------------------------------------
head(results$Enrichment.Results$B6_NZO[,c("module.name","overlapping.genes", "adj.p")])
## -----------------------------------------------------------------------------
pca_plot(results, contrasts, show.names = F)
## -----------------------------------------------------------------------------
# Overlaid information
overlaid.info <- c("B6-18mo", "B6-18mo", "B6-18mo", "B6-18mo", "B6-18mo",
"NZO-18mo", "NZO-18mo", "NZO-18mo", "NZO-18mo", "NZO-18mo", "NZO-18mo",
"B6-3mo", "B6-3mo", "B6-3mo", "B6-3mo", "B6-3mo",
"NZO-3mo", "NZO-3mo", "NZO-3mo", "NZO-3mo", "NZO-3mo", "NZO-3mo")
# Sample IDs
sample.names <- c("S01783", "S01780", "S01781", "S01778", "S01779",
"S03804", "S03805", "S03806", "S03807", "S03808",
"S03809", "S04678", "S04679", "S04680", "S04681",
"S04682", "S10918", "S10916", "S10919", "S10921",
"S10917", "S10920")
## -----------------------------------------------------------------------------
pca_plot(results, overlaid.info, sample.names)
## -----------------------------------------------------------------------------
show_comparisons(results)
## -----------------------------------------------------------------------------
heatmap_differential(results, comparison = "B6_NZO")
## -----------------------------------------------------------------------------
heatmap_differential(results, comparison = "B6_NZO", show_colnames = FALSE)
## -----------------------------------------------------------------------------
heatmap_var_peaks(results)
## -----------------------------------------------------------------------------
heatmap_var_peaks(results, heatmap.peak.count = 200, cluster_cols = F)
## -----------------------------------------------------------------------------
dot_plot(results)
## -----------------------------------------------------------------------------
dot_plot(results, filter.pathways = T)
## -----------------------------------------------------------------------------
contrasts <- sapply(strsplit(colnames(bed), split = "-", fixed = T),
function(x){paste(x[1:4], collapse = ".")})[4:25]
unique(contrasts)
## ----eval=FALSE---------------------------------------------------------------
# cinaR(matrix = count.matrix, ..., experiment.type = "RNA-Seq")
## ----eval=FALSE---------------------------------------------------------------
# cinaR(..., geneset = new_geneset)
## ----eval=FALSE---------------------------------------------------------------
# # default geneset to be used
# data("VP2008")
## ----eval=FALSE---------------------------------------------------------------
# cinaR(..., batch.correction = T)
## ----eval=FALSE---------------------------------------------------------------
# # runs SVA
# cinaR(..., batch.correction = T)
#
# # runs SVA with 2 surrogate variables
# cinaR(..., batch.correction = T, sv.number = 2)
#
# # adds only batch information to the design matrix! (does not run SVA)
# # batch.information should be number a vector where
# # the length of it equals to the number of samples.
# cinaR(..., batch.correction = T, batch.information = c(rep(0, 11), rep(1,11)))
## ----eval=FALSE---------------------------------------------------------------
# # Ages of the samples could be not in biological interests but should be accounted for!
# cinaR(..., additional.covariates = c((18, 11), (3, 11)))
#
# # More than one covariate for instance, sex and age
# sex.info <- c("M", "F", "M", "F", "F", "F", "F", "F", "M", "M", "M",
# "F", "F", "M", "M", "M", "F", "F", "M", "M", "F", "M")
#
# age.info <- c((18, 11), (3, 11)
# covs <- data.frame(Sex = sex.info, Age = age.info)
#
# cinaR(..., additional.covariates = covs)
## ----eval=FALSE---------------------------------------------------------------
# results <- cinaR(..., save.DA.peaks = T, DA.peaks.path = "./Peaks_mice.xlsx")
## ----eval=FALSE---------------------------------------------------------------
# # new FDR threshold for DA peaks
# results <- cinaR(..., DA.fdr.threshold = 0.1)
#
# # filters out pathways
# results <- cinaR(..., enrichment.FDR.cutoff = 0.1)
#
# # does not run enrichment pipeline
# results <- cinaR(..., run.enrichment = FALSE)
#
# # creates the piechart from chIpSeeker package
# results <- cinaR(..., show.annotation.pie = TRUE)
#
# # change cut-off value for dot plots
# dot_plot(..., fdr.cutoff = 0.05)
## ----session Info-------------------------------------------------------------
sessionInfo()
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