library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(ggforce)
library(cocoframer)
library(reshape2)
library(tidyr)##
## Attaching package: 'tidyr'## The following object is masked from 'package:reshape2':
##
## smithslibrary(purrr)
options(stringsAsFactors = FALSE)
ccf_anno <- get_ccf_annotation() %>% melt()
ccf_anno <- ccf_anno %>%
filter(value != 0)
names(ccf_anno) <- c("x","y","z","id")
ont <- get_mba_ontology() %>% flatten_mba_ontology()
ont <- generate_mba_taxons(ont)
region_groups <- data.frame(
region_group = c(rep("RHP", 5),
"ENT",
rep("HIP",4),
"ACA",
rep("TEa-PERI-ECT", 3),
"RSP",
rep("VIS", 7),
"VISp",
"PTLp",
"AUD",
"SSp",
rep("SSs-GU-VISC-AIp", 4),
rep("AI", 2),
"CLA",
rep("MOs-FRP", 2),
rep("MOp",1),
rep("PL-ILA-ORB", 3)),
acronym = c("PAR","POST","PRE","SUB","ProS",
"ENT",
"CA","DG","FC","IG",
"ACA",
"TEa","PERI","ECT",
"RSP",
"VISal","VISam","VISl","VISpl","VISpm","VISli","VISpor",
"VISp",
"PTLp",
"AUD",
"SSp",
"SSs","GU","VISC","AIp",
"AId","AIv",
"CLA",
"MOs","FRP",
"MOp",
"PL","ILA","ORB")
)
region_ont <- map_dfr(1:nrow(region_groups),
function(x) {
parent_acronym <- region_groups$acronym[x]
ontology_children <- filter_mba_ontology_children(ont,
parent_acronym,
include_parent = TRUE)
ontology_children$region_group <- region_groups$region_group[x]
ontology_children
})
region_vox <- ccf_anno %>%
filter(id %in% region_ont$id) %>%
left_join(region_ont)## Joining with `by = join_by(id)`
region_centroids <- region_vox %>%
filter(z < 229) %>% # one hemisphere
group_by(region_group) %>%
summarise(vox = n(),
x = mean(x),
y = mean(y),
z = mean(z))