## ----setup, include = FALSE----------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ------------------------------------------------------------------------
library(Bioi)
# Generate two data sets to simulate 3D PALM data.
set.seed(10)
mOne <- as.matrix(data.frame(
x = rnorm(10),
y = rbinom(10, 100, 0.5),
z = runif(10)
))
mTwo <- as.matrix(data.frame(
x = rnorm(20),
y = rbinom(20, 100, 0.5),
z = runif(20)
))
# Get separation distances.
find_min_dists(mOne, mTwo)
## ------------------------------------------------------------------------
# Generate random data.
set.seed(10)
input <- as.matrix(data.frame(x=rnorm(10),y=rnorm(10)))
# Perform clustering.
groups <- euclidean_linker(input, 0.8)
print(groups)
## ----eval=FALSE----------------------------------------------------------
# library(ggplot2)
# input <- as.data.frame(input)
# input$group <- as.character(groups)
# ggplot(input, aes(x, y, colour = group)) + geom_point(size = 3)
## ------------------------------------------------------------------------
# Generate a random matrix.
set.seed(10)
mat <- matrix(runif(70), nrow = 7)
# Arbitrarily say that everything below 0.8 is background.
logical_mat <- mat > 0.8
# Row names and column names are preserved in the output of find_blobs
rownames(logical_mat) <- letters[1:7]
colnames(logical_mat) <- 1:10
# Find blobs
find_blobs(logical_mat)
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