R/vis.R In covRNA: Multivariate Analysis of Transcriptomic Data

Documented in vis

```# simultaneous visualisation of stat and ord: vis

# input:
# [Stat] object of stat function;
# [Ord] object of ord function;
# [alpha] defines significance level of pvalues (default:0.05);
# [xaxis] and [yaxis] define axes of yination (default:1 and 2, resp.);
# [col] three cols: first col defines non-significant variables, second/third col
# define significant positive/negative asscoiations, respectively
# (default: "gray",transblue,transred);
# [alphatrans] defines degree of transparency of the second and third col;
# [cex] defines text size (default=1);
# [rangex, rangey] define ize of the plot (default=2);

vis <- function(Stat, Ord=NULL, alpha=0.05, xaxis=1, yaxis=2,
col=c("gray", transblue, transred),
alphatrans=0.5, cex=1, rangex=2, rangey=2, ...) {

if (missing(Stat)) {
print("Only ordination will be plotted.")
}
else if (!inherits(Stat, "stat")) {
stop("object of class stat is required here")
}

if (!inherits(Ord, "ord")) {
stop("object of class ord is required here")
}
if (length(Ord\$eig) < 2) stop("The fourthcorner matrix should be factorized
into at least two eigenvectors.")

# col: neutral, positive, negative; alphatrans: level of transparency
transblue=rgb(0, 0, 1, alpha=alphatrans)
transred=rgb(1, 0, 0, alpha=alphatrans)

# size of text
cex <- par("cex") * cex

# save coordinates of rows and columns of Ord
rowcoor <- Ord\$l1[,c(xaxis,yaxis)]
colcoor <- Ord\$c1[,c(xaxis,yaxis)]

# span the coordinate system
plot(rangex * range(min(rowcoor[,1], colcoor[,1]), max(rowcoor[,1], colcoor[,1])),
rangey * range(min(rowcoor[,2], colcoor[,2]), max(rowcoor[,2], colcoor[,2])),
xlab=paste("axis", xaxis),
ylab=paste("axis", yaxis),
type='n',
bty='l')

# order rows of rowcoor and colcoor alphabetically
rowcoor <- rowcoor[order(rownames(rowcoor)),]
colcoor <- colcoor[order(rownames(colcoor)),]

# determine positive and negative significant associations
if (missing(Stat)) {
text(rowcoor[,1], rowcoor[,2], row.names(rowcoor), cex=cex)
text(colcoor[,1], colcoor[,2], row.names(colcoor), cex=cex)
}
else {
possig <- which(Stat\$adj.pvalue <= alpha & Stat\$stat > 0, arr.ind=TRUE)
negsig <- which(Stat\$adj.pvalue <= alpha & Stat\$stat < 0, arr.ind=TRUE)
# save all significant associations
sig <- list(unique(c(possig[,1], negsig[,1])),
unique(c(possig[,2], negsig[,2])))
# if there are significant associations:
# visualise segments between significant variables in cols[2,3]
if (length(sig[[1]]) > 0) {
if (nrow(possig) > 0) {
segments(rowcoor[possig[,1],1], rowcoor[possig[,1],2],
colcoor[possig[,2],1], colcoor[possig[,2],2], lty=1, lwd=2,
col=col[2])
}
if (nrow(negsig) > 0) {
segments(rowcoor[negsig[,1],1], rowcoor[negsig[,1],2],
colcoor[negsig[,2],1], colcoor[negsig[,2],2], lty=1, lwd=2,
col=col[3])
}

# visualize variables without significant associations in col[1]
if (length(row.names(rowcoor)[-sig[[1]]]) > 0) {
text(rowcoor[-sig[[1]],1], rowcoor[-sig[[1]],2],
row.names(rowcoor)[-sig[[1]]], cex=cex, col=col[1])
}
if (length(row.names(colcoor)[-sig[[2]]]) > 0) {
text(colcoor[-sig[[2]],1], colcoor[-sig[[2]],2],
row.names(colcoor)[-sig[[2]]], cex=cex, col=col[1])
}

# visualize significant variables in black
if (length(row.names(rowcoor)[sig[[1]]]) > 0) {
text(rowcoor[sig[[1]],1], rowcoor[sig[[1]],2],
row.names(rowcoor)[sig[[1]]], cex=cex)
}
if (length(row.names(colcoor)[sig[[2]]]) > 0) {
text(colcoor[sig[[2]],1], colcoor[sig[[2]],2],
row.names(colcoor)[sig[[2]]], cex=cex)
}

# if there are no significant associations, show all in col[1]
} else {
text(rowcoor[,1], rowcoor[,2], row.names(rowcoor),
cex=cex, col=col[1])
text(colcoor[,1], colcoor[,2], row.names(colcoor),
cex=cex, col=col[1])
}
}
}
```

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covRNA documentation built on Nov. 1, 2018, 3:38 a.m.