Check for redundant parent regions included in a list of acronyms in a plate. For example, if all the the subregions for
the hypothalamus are represented, the HY should not be included in the list.
Check if there are enough mice per analysis subgroup across all regions.
if the normalized counts data sets are split by specified grouping variables.
This function also automatically keeps only the common regions that are found across all comparison groups.
Detect, log, and remove outlier counts. This function
removes any normalized regions counts that are more than n_sd standard deviations (default = 2) higher
than their cohort mean.
Get colabelled cells data table. This is designed specifically to create a segmentation object from the imported raw files that are outputs from the batch_3D_MultiColocalization.ijm macro.
#' Modification of Marcos' combine regions function
#'
#' @param normalized_counts list with length = No. channels., each channel element is df of normalized counts
#' @param keywords a vector of keywords with which to simplify the region names
#'
#' @return a list with length = No. channels. Each channel df has simplified parent acronyms and names is summed from original df based on them.
#'
#' @examples
simplify.regions <- function(normalized_counts, keywords = c("layer","part","stratum","division"))
make.filter(
data,
params = c("Vol..unit.", "Moment1", "Moment2", "Moment3", "Moment4", "Sigma"),
ranges = list(c(200, 12000), c(3, 50), c(0, 600), c(0, 2000), c(0, 5), c(20, Inf))
)
#' Modification of Marcos' combine regions function
#'
#' @param normalized_counts list with length = No. channels., each channel element is df of normalized counts
#' @param keywords a vector of keywords with which to simplify the region names
#'
#' @return a list with length = No. channels. Each channel df has simplified parent acronyms and names is summed from original df based on them.
#'
#' @examples
simplify.regions <- function(normalized_counts, keywords = c("layer","part","stratum","division"))
Initialize empty list vector to store the simplified counts
simplified_counts <- vector(mode = "list", length = length(normalized_counts))
names(simplified_counts) <- names(normalized_counts)
Internal algorithm for maslov-sneppen rewiring
This algorithm is not appropriate if you would like to take weights into account
See Maslov & Sneppen (2002)"Specificity and stability in topology of protein networks"
This function allows for plotting of normalized cell counts by area across specific regions to plot. Two different mouse attributes can be used as categorical variables to map to either the color or
pattern aesthetics of the bar plot, e.g. sex and experimental group.
The color aesthetic takes precedence over the pattern aesthetic so if you only want to use one mouse attribute, for plotting
set it to the color_mapping parameter and set the pattern_mapping parameter to NULL.
This function allows for plotting of colabelled cells over either the "cfos" or "eyfp" channels. And allows for specification
of specific brain regions to plot. Two different mouse attributes can be used as categorical variables to map to either the color or
pattern aesthetics of the bar plot, e.g. sex and experimental group.
The color aesthetic takes precedence over the pattern aesthetic so if you only want to use one mouse attribute, for plotting
set it to the color_mapping parameter and set the pattern_mapping parameter to NULL.
Implement rewiring algorithms to current empirical networks to randomize certain network properties, while
keeping other characterisitics constant (such as preserved degree sequence). These null networks can them be used to compare
against and normalize the empirical networks.