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scATAC-seq/scripts/10xgenomics_PBMC_5k_motifs_classification_5/classification_sp1_motif.R
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classification_sp1_motif.R
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setwd(file.path("/", "local", "groux", "scATAC-seq"))
# libraries
library(RColorBrewer)
library(seqLogo)
# functions
source(file.path("scripts", "functions.R"))
# the minimum number of classes searched
k.min = 1
# the maximum number of classes searched
k.max = 10
# path to the images for the logo
path.a = file.path("res/A.png")
path.c = file.path("res/C.png")
path.g = file.path("res/G.png")
path.t = file.path("res/T.png")
################## sequence patterns around sp1 motifs ##################
for(k in k.min:k.max)
{
# sequence
data = read.sequence.models(file.path("results", "10xgenomics_PBMC_5k_motifs_classification_5",
sprintf("sp1_motifs_10e-7_sequences_%dclass_model.mat", k)))
model.seq = data$models
model.prob = data$prob
data = NULL
# plot classes
col = brewer.pal(3, "Set1")
# X11(width=17, height=10)
png(filename=file.path("results", "10xgenomics_PBMC_5k_motifs_classification_5",
sprintf("sp1_motifs_10e-7_classification_sequences_%dclass.png", k)),
units="in", res=720, width=18, height=12)
m = matrix(1:10, nrow=5, ncol=2, byrow=F)
layout(m)
# order from most to least probable class
ord = order(model.prob, decreasing=T)
ref.seq = model.seq[,,ord, drop=F]
prob = model.prob[ord]
class = c(1:dim(ref.seq)[3])[ord]
for(i in 1:(dim(ref.seq)[3]))
{ # plot logo
plot.logo(ref.seq[,,i], path.a, path.c, path.g, path.t,
main=sprintf("class %d (p=%.2f)", class[i], prob[i]))
# x-axis
x.lab = seq(-floor(ncol(ref.seq)/2), floor(ncol(ref.seq)/2), length.out=3)
x.at = seq(1, ncol(ref.seq), length.out=3)
axis(1, at=x.at, labels=x.lab)
# y-axis is [0,1] for min/max signal
x.at = seq(0, 1, 0.5)
axis(2, at=x.at, labels=x.at)
}
dev.off()
}
################## sequence patterns around sp1 motifs repeat masked ##################
for(k in k.min:k.max)
{
# sequence
data = read.sequence.models(file.path("results", "10xgenomics_PBMC_5k_motifs_classification_5",
sprintf("sp1_motifs_10e-7_sequences_rmsk_%dclass_model.mat", k)))
model.seq = data$models
model.prob = data$prob
data = NULL
# plot classes
col = brewer.pal(3, "Set1")
# X11(width=17, height=10)
png(filename=file.path("results", "10xgenomics_PBMC_5k_motifs_classification_5",
sprintf("sp1_motifs_10e-7_classification_sequences_rmsk_%dclass.png", k)),
units="in", res=720, width=18, height=12)
m = matrix(1:10, nrow=5, ncol=2, byrow=F)
layout(m)
# order from most to least probable class
ord = order(model.prob, decreasing=T)
ref.seq = model.seq[,,ord, drop=F]
prob = model.prob[ord]
class = c(1:dim(ref.seq)[3])[ord]
for(i in 1:(dim(ref.seq)[3]))
{ # plot logo
plot.logo(ref.seq[,,i], path.a, path.c, path.g, path.t,
main=sprintf("class %d (p=%.2f)", class[i], prob[i]))
# x-axis
x.lab = seq(-floor(ncol(ref.seq)/2), floor(ncol(ref.seq)/2), length.out=3)
x.at = seq(1, ncol(ref.seq), length.out=3)
axis(1, at=x.at, labels=x.lab)
# y-axis is [0,1] for min/max signal
x.at = seq(0, 1, 0.5)
axis(2, at=x.at, labels=x.at)
}
dev.off()
}
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