lineup (version 0.34-1)

summary.lineupdist: Summarize inter-individual distances

Description

Summarize the results of distee or disteg, with inter-individual distances between two sets of gene expression data.

Usage

## S3 method for class 'lineupdist':
summary(object, cutoff, dropmatches=TRUE,
                  reorder=c("bydistance", "alignmatches", "no"), ...)

Arguments

object

Output of distee or disteg.

cutoff

(Optional) Cutoff on correlation/distance, with rows in the results only being kept if the best distance/correlation is above this cutoff or the self-self result is not missing and is above this cutoff.

dropmatches

If TRUE, omit rows for which an individual's best match is itself.

reorder

If "bydistance", reorder rows by increasing distance (or decreasing correlation) to the best match and then by decreasing distance (or decreasing correlation) to self; if "alignmatches", group related errors together; if

...

Passed to print.data.frame.

Value

• A list with two components: the distances summarized by row and the distances summarized by column.

  For each individual, we calculate the minimum distance to others, next-smallest distance, the self-self distance, the mean and SD of the distances to others, and finally indicate the individual (or individuals) that is closest.

See Also

pulldiag, omitdiag, distee, disteg, plot2dist, plot.lineupdist

Examples

# simulate MVN, 100 individuals, 40 measurements (of which 20 are just noise)
V <- matrix(0.3, ncol=20, nrow=20) + diag(rep(0.5, 20)) 
D <- chol(V)
z <- matrix(rnorm(20*100), ncol=20)
# create two data matrices as z + noise
x <- cbind(z + rnorm(20*100, 0, 0.2), matrix(rnorm(20*100), ncol=20))
y <- cbind(z + rnorm(20*100, 0, 0.2), matrix(rnorm(20*100), ncol=20))

# permute some rows
x[51:53,] <- x[c(52,53,51),]
y[41:42,] <- y[42:41,]

# add column and row names
dimnames(x) <- dimnames(y) <- list(paste("ind", 1:100, sep=""),
                                   paste("gene", 1:40, sep=""))

# calculate correlations between cols of x and cols of y
thecor <- corbetw2mat(x, y)

# subset x and y, taking only columns with corr > 0.75
xs <- x[,thecor > 0.8]
ys <- y[,thecor > 0.8]

# calculate distance (using "RMS difference" as a measure)
d1 <- distee(xs, ys, d.method="rmsd", labels=c("x","y"))

# calculate distance (using "correlation" as a measure...really similarity)
d2 <- distee(xs, ys, d.method="cor", labels=c("x", "y"))

# pull out the smallest 8 self-self correlations
sort(pulldiag(d2))[1:8]

# summary of results
summary(d1)
summary(d2)

# order to put matches together
summary(d2, reorder="alignmatches")

# plot histograms of RMS distances
plot(d1)

# plot histograms of correlations
plot(d2)

# plot distances against one another
plot2dist(d1, d2)