dist_mat | R Documentation |
This utility function calculates distance based on a user defined measure and optionally filters probes by leading log fold change.
dist_mat(dat, dist, p, top, filter_method, center, robust = FALSE)
dat |
Omic data matrix. |
dist |
Distance measure to be used. |
p |
Power of the Minkowski distance. |
top |
Optional number (if > 1) or proportion (if < 1) of top probes to be used for distance calculations. |
filter_method |
String specifying whether to apply a |
center |
Center each probe prior to computing distances? |
robust |
Use robust probe centering? |
Data are optionally centered by probe and samplewise distance calculated using one of the following methods:
"euclidean"
, "maximum"
, "manhattan"
, and
"minkowski"
are all documented in the dist
function. bioplotr
relies on a lower level implementation via
Rfast::Dist
to speed up computations.
"cosine"
and "canberra"
are implemented and documented
in wordspace::dist.matrix
.
"pearson"
, "kendall"
, and "spearman"
correspond
to various forms of correlation distance, generally defined as 1 – the
absolute value of the correlation coefficient. See
cor
for more details.
"bray"
, "kulczynski"
, "jaccard"
, "gower"
,
"altGower"
, "morisita"
, "horn"
, "mountford"
,
"raup"
, "binomial"
, "chao"
, "cao"
, and
"mahalanobis"
are all available and documented in the
vegan::vegdist
function. These are designed for use with
ecological data, e.g. a matrix of microbial OTU counts.
"bhattacharyya"
, "hellinger"
, "kullback_leibler"
,
and "MI"
are information theoretic distance metrics. The former
three are implemented in Rfast::Dist
. See
bioDist::MIdist
for details on the latter.
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