This function computes and returns the distance matrix determined by using the specified distance metric to compute the distances between the columns of a data matrix.
A numeric matrix or an
A character string defining the distance metric. This
Additional parameters to be passed on to
This function differs from
dist in two ways, both of
which are motivated by common practice in the analysis of microarray
or proteomics data. First, it computes distances between column vectors
instead of between row vectors. In a typical microarray experiment,
the data is organized so the rows represent genes and the columns
represent different biological samples. In many applications,
relations between the biological samples are more interesting than
relationships between genes. Second,
additional distance metrics based on correlation.
pearsonThe most common metric used in the microarray literature is
pearson distance, which can be computed in terms of the
Pearson correlation coefficient as
uncentered correlationThis metric was introduced in
the Cluster and TreeView software from the Eisen lab at
Stanford. It is computed using the formulas for Pearson
correlation, but assuming that both vectors have mean zero.
spearman metric used the same formula, but
substitutes the Spearman rank correlation for the Pearson
absolute pearson metric used the absolute
correlation coefficient; i.e.,
sqrt pearson metric used the square root of the
pearson distance metric; i.e.,
weird metric uses the Euclidean distance between
the vectors of correlation coefficients; i.e., dist(cor(dataset)).
A distance matrix in the form of an object of class
the sort returned by the
dist function or the
It would be good to have a better name for the
Kevin R. Coombes [email protected]
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