downsample | R Documentation |
The downsample
function implements a structured reduction
of data points with a parent Mercator
distance visualization to
improve visualization and computational time, especially for the
implementation of the iGraph visualization.
downsample(target, distanceMat, cutoff)
target |
An |
distanceMat |
An object of class |
cutoff |
An inclusion cutoff for selected points based on the local density within the parent data. |
Mercator can be used to visualize complex networks using
iGraph. To improve clarity of the visualization and computational
time, we implement the downsample
function to reduce the
number of data points to be linked and visualized. The conceptual
grounding for downsample
draws on Peng Qiu's implementation of the
SPADE clustering algorithm for mass cytometry data. The
downsample
function
under-samples the densest regions of the data space to make it more
likely that rarer clusters will still be adequately sampled.
downsample
returns an object of class Mercator
containing
a structured subset of items from the parent Mercator
object.
Kevin R. Coombes <krc@silicovore.com>, Caitlin E. Coombes
Qiu, P., et. al. (2011). Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nature biotechnology, 29(10), 886.
#Form a BinaryMatrix
data("iris")
my.data <- as.matrix(iris[,c(1:4)])
my.rows <- as.data.frame(c(1:length(my.data[,1])))
my.binmat <- BinaryMatrix(my.data, , my.rows)
my.binmat <- t(my.binmat)
summary(my.binmat)
# Form and plot Mercator object
# Set K to the known number of species in the dataset
my.vis <- Mercator(my.binmat, "euclid", "tsne", K=3)
summary(my.vis)
plot(my.vis, view = "tsne", main="t-SNE plot of all data points")
#Downsample the Mercator object
M <- as.matrix(my.vis@distance)
set.seed(21340)
DS <- downsample(50, M, 0.1)
red.vis <- my.vis[DS]
#Visualize the down sampled t-SNE plot
plot(red.vis, view = "tsne", main="Down sampled t-SNE Plot")
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