Core Genome Outlier

Introduction

Looking at the change (delta) in strict core genome size as a function of the genome added.

Inputs

• presence absence tables
• output of find_outlier.pl /data/Segrelab/conlans/Lemon

Outputs

• none, except figures

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Cac PPanGGOLiN-based presence absence

dat<-read.table("Cac_P.txt",header=T,sep="\t")
p1<-ggplot(dat,aes(genome,delta)) +
    geom_boxplot(outlier.shape = NA) +
    geom_jitter(width=0.1, size=2, alpha=0.25) +
    ylim(0,50) +
    theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
p1

Cac GET_HOMOLOGUES-based presence absence

dat<-read.table("Cac_GH.txt",header=T,sep="\t")
p1<-ggplot(dat,aes(genome,delta)) +
    geom_boxplot(outlier.shape = NA) +
    geom_jitter(width=0.1, size=2, alpha=0.25) +
    ylim(0,200) +
    theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
p1

Cps PPanGGOLiN-based presence absence

dat<-read.table("Cps_P.txt",header=T,sep="\t")
p1<-ggplot(dat,aes(genome,delta)) +
    geom_boxplot(outlier.shape = NA) +
    geom_jitter(width=0.1, size=2, alpha=0.25) +
    ylim(0,50) +
    theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
p1

Cps GET_HOMOLOGUES-based presence absence

dat<-read.table("Cps_GH.txt",header=T,sep="\t")
p1<-ggplot(dat,aes(genome,delta)) +
    geom_boxplot(outlier.shape = NA) +
    geom_jitter(width=0.1, size=2, alpha=0.25) +
    ylim(0,200) +
    theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
p1