Description: Calculate the colocalization index, NSInC, in two different ways as described in the paper (Liu et al., 2019. Manuscript submitted for publication.) for multiple-species spatial data which contain the precise locations and membership of each spatial point. The two main functions are nsinc.d() and nsinc.z(). They provide the Pearson’s correlation coefficients of signal proportions in different memberships within a concerned proximity of every signal (or every base signal if single direction colocalization is considered) across all (base) signals using two different ways of normalization. The proximity sizes could be an individual value or a range of values, where the default ranges of values are different for the two functions.

Author: Jiahui Xu, Xueyan Liu, Lin Li, Cheng Cheng, Hui Zhang

Title: Clusters of Colocalized Sequences
Authors@R: person("Stefan", "Golas", email = "stefanmgolas@gmail.com", role = c("cre","aut"))
Description: Finds clusters of colocalized sequences in .bed annotation files
up to a specified cut-off distance. Two sequences are colocalized if they
are within the cut-off distance of each other, and clusters are sets of
sequences where each sequence is colocalized to at least one other sequence
in the cluster. For a set of .bed annotation tables provided in a list
along with a cut-off distance, the program will output a file containing
the locations of each cluster. Annotated .bed files are from
the 'pwmscan' application at <https://ccg.epfl.ch/pwmtools/pwmscan.php>.
Personal machines might crash or take excessively long depending
on the number of annotated sequences in each file and whether chromsearch()
Imports: foreach, doParallel, purrr, utils

Description: Also abbreviates to "CCSeq". Finds clusters of colocalized sequences in .bed annotation files

Title: Conduct Co-Localization Analysis of Fluorescence Microscopy
Description: Automate the co-localization analysis of fluorescence microscopy
images. Selecting regions of interest, extract pixel intensities from
the image channels and calculate different co-localization statistics. The
methods implemented in this package are based on Dunn et al. (2011)
Suggests: testthat, shinytest, covr, knitr, rmarkdown, devtools, purrr,
Imports: imager, magick, shiny, scales, magrittr