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fix issue when df_pw is a vector #29

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fix issue when df_pw is a vector #29

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d44ea0b
fix issue when df_pw is a vector
guokai8 Feb 27, 2019
0242f43
fix issue when grouping_column is not a factor
guokai8 Feb 27, 2019
3573c81
Update plot_anova_diversity.R
guokai8 Mar 30, 2019
0a32e79
Update plot_anova_diversity.R
guokai8 Aug 2, 2019
bfd9851
Create biplot.R
guokai8 Aug 2, 2019
bc22d84
Update DESCRIPTION
guokai8 Sep 23, 2020
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2 changes: 2 additions & 0 deletions DESCRIPTION
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Expand Up @@ -18,6 +18,8 @@ Imports: igraph (>= 1.1.2),
reshape2 (>= 1.4.2),
gtable (>= 0.2.0),
ggplot2 (>= 2.2.1),
impute,
adespatial,
gridExtra (>= 2.2.1)
Suggests: R.rsp
VignetteBuilder: R.rsp
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35 changes: 35 additions & 0 deletions R/biplot.R
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@@ -0,0 +1,35 @@
biplot<-function(phy,color=NULL,shape=NULL,top=10,pointsize=5,alpha=0.7,taxa="Phylum",ellipse=FALSE,biplot=TRUE,show=TRUE){
###c("edgernorm", "varstab", "randomsubsample", "proportion", "relative", "log-relative", "scale")
#plog<-normalise_data(phy,norm.method = "relative")
plog=transform_sample_counts(phy,function(x)x/sum(x))
#plog=transform_sample_counts(phy,function(x)log(x+1))
pord<- ordinate(plog, method = "MDS", distance = "bray")
p<-plot_ordination(plog, pord, color = color,type="samples",shape=shape) +geom_point(size=pointsize,alpha=alpha,aes_string(shape=shape))+theme_light(base_size = 15)+
scale_color_brewer(type="qual", palette="Set1")
p1<-plot_ordination(plog, pord,type="taxa",shape=taxa) +geom_point(size=pointsize,alpha=alpha,aes_string(shape=shape))+theme_light(base_size = 15)+
scale_color_brewer(type="qual", palette="Set2")
pp<-p1$data
ll=gsub('.*;','',gsub(';NA','',apply(pp[3:ncol(pp)],1,function(x)paste(x,collapse =";"))))
if(show==FALSE){
lx=rownames(pp)
}else{
lx<-paste(rownames(pp),ll,sep="\n")
}
pp$labels=lx
pp$dist=p1$data[,1]^2+p1$data[,2]^2
pp=pp[order(pp$dist,decreasing = T),]
pp=pp[1:top,]
arrowhead = arrow(length = unit(0.02, "npc"))
p2<-p+geom_segment(aes(xend=1.3*Axis.1,yend=1.3*Axis.2,x=0,y=0),size=0.5,color="darkgray",arrow=arrowhead,data=pp)+
geom_text_repel(aes(x=1.3*Axis.1,y=1.3*Axis.2,label=labels),color="black",data=pp,show.legend = FALSE)+scale_color_brewer(type="qual", palette="Set1")
if(ellipse==TRUE){
p2<-p2+stat_ellipse()
}
if(biplot==FALSE){
p3<-p
}else{
p3<-p2
}
p3$layers<-p3$layers[-1]
p3
}
3 changes: 3 additions & 0 deletions R/perform_anova.R
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Expand Up @@ -70,6 +70,9 @@ perform_anova <- function(df,meta_table,grouping_column,pValueCutoff){
}
}
if(!is.null(df_pw)){
if(is.null(dim(df_pw)[1])){
df_pw<-rbind(df_pw)
}
df_pw<-data.frame(row.names=NULL,df_pw)
names(df_pw)<-c("measure","from","to","y","p")
}
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76 changes: 49 additions & 27 deletions R/plot_anova_diversity.R
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Expand Up @@ -26,38 +26,60 @@
#' @export plot_anova_diversity
#'

plot_anova_diversity <- function(physeq, method, grouping_column,pValueCutoff=0.05)
plot_anova_diversity<-function (physeq, method, grouping_column, color = NULL, pValueCutoff = 0.05,
fontsize.x = 10, fontsize.y = 10, fill = NULL)
{
#enforce orientation
if(taxa_are_rows(physeq)){
if (taxa_are_rows(physeq)) {
physeq <- t(physeq)
}
abund_table <- otu_table(physeq)
meta_table <- sample_data(physeq)

#get diversity measure using selected methods
div.df <- alpha_div(physeq,method)

#=add grouping information to alpha diversity measures
df<-data.frame(div.df,(meta_table[,grouping_column])[as.character(div.df$sample),])

#perform anova of diversity measure between groups
anova_res <- perform_anova(df,meta_table,grouping_column,pValueCutoff)
df_pw <- anova_res$df_pw #get pairwise p-values

#Draw the boxplots
p<-ggplot(aes_string(x=grouping_column,y="value",color=grouping_column),data=df)
p<-p+geom_boxplot()+geom_jitter(position = position_jitter(height = 0, width=0))
p<-p+theme_bw()
p<-p+theme(axis.text.x = element_text(angle = 90, hjust = 1))
p<-p+facet_wrap(~measure,scales="free_y",nrow=1)+ylab("Observed Values")+xlab("Samples")
p<-p+theme(strip.background = element_rect(fill = "white"))+xlab("Groups")

#This loop will generate the lines and signficances
if(!is.null(df_pw)){ #this only happens when we have significant pairwise anova results
for(i in 1:dim(df_pw)[1]){
p<-p+geom_path(inherit.aes=F,aes(x,y),data = data.frame(x = c(which(levels(df[,grouping_column])==as.character(df_pw[i,"from"])),which(levels(df[,grouping_column])==as.character(df_pw[i,"to"]))), y = c(as.numeric(as.character(df_pw[i,"y"])),as.numeric(as.character(df_pw[i,"y"]))), measure=c(as.character(df_pw[i,"measure"]),as.character(df_pw[i,"measure"]))), color="black",lineend = "butt",arrow = arrow(angle = 90, ends = "both", length = unit(0.1, "inches")))
p<-p+geom_text(inherit.aes=F,aes(x=x,y=y,label=label),data=data.frame(x=(which(levels(df[,grouping_column])==as.character(df_pw[i,"from"]))+which(levels(df[,grouping_column])==as.character(df_pw[i,"to"])))/2,y=as.numeric(as.character(df_pw[i,"y"])),measure=as.character(df_pw[i,"measure"]),label=as.character(cut(as.numeric(as.character(df_pw[i,"p"])),breaks=c(-Inf, 0.001, 0.01, 0.05, Inf),label=c("***", "**", "*", "")))))
div.df <- alpha_div(physeq, method)
df <- data.frame(div.df, (meta_table[, grouping_column])[as.character(div.df$sample),
])
df[, grouping_column] <- as.factor(df[, grouping_column])
anova_res <- perform_anova(df, meta_table, grouping_column,
pValueCutoff)
df_pw <- anova_res$df_pw
if (is.null(color)) {
color = grouping_column
}
p <- ggplot(aes_string(x = grouping_column, y = "value",
color = color), data = df)
if (!is.null(fill)) {
p <- p + geom_boxplot(aes_string(fill = fill)) + geom_point(aes_string(shape=fill))
}
else {
p <- p + geom_boxplot() + geom_point(aes_string(shape=color))
}
p <- p + theme_bw()
p <- p + theme(axis.text.x = element_text(angle = 90, hjust = 1,
size = fontsize.x))
p <- p + theme(axis.text.y = element_text(size = fontsize.y))
p <- p + facet_wrap(~measure, scales = "free_y", nrow = 1) +
ylab("Observed Values") + xlab("Samples")
p <- p + theme(strip.background = element_rect(fill = "white")) +
xlab("Groups")
if (!is.null(df_pw)) {
for (i in 1:dim(df_pw)[1]) {
p <- p + geom_path(inherit.aes = F, aes(x, y), data = data.frame(x = c(which(levels(df[,
grouping_column]) == as.character(df_pw[i, "from"])),
which(levels(df[, grouping_column]) == as.character(df_pw[i,
"to"]))), y = c(as.numeric(as.character(df_pw[i,
"y"])), as.numeric(as.character(df_pw[i, "y"]))),
measure = c(as.character(df_pw[i, "measure"]),
as.character(df_pw[i, "measure"]))), color = "black",
lineend = "butt", arrow = arrow(angle = 90, ends = "both",
length = unit(0.1, "inches")))
p <- p + geom_text(inherit.aes = F, aes(x = x, y = y,
label = label), data = data.frame(x = (which(levels(df[,
grouping_column]) == as.character(df_pw[i, "from"])) +
which(levels(df[, grouping_column]) == as.character(df_pw[i,
"to"])))/2, y = as.numeric(as.character(df_pw[i,
"y"])), measure = as.character(df_pw[i, "measure"]),
label = as.character(cut(as.numeric(as.character(df_pw[i,
"p"])), breaks = c(-Inf, 0.001, 0.01, 0.05,
Inf), label = c("***", "**", "*", "")))))
}
}
return(p)
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