This R package acquires pictures from Allen Brain Atlas.
Install allenBrain to R
devtools::install_github('oganm/allenBrain')You can use downloadImage and downloadAtlas functions to get images. Output of these functions are magick-image objects
library(dplyr)
# get a list of structure names and ids
IDs = getStructureIDs()
IDs %>% head## id name acronyms parent
## 1 997 root root <NA>
## 2 8 Basic cell groups and regions grey 997
## 3 567 Cerebrum CH 8
## 4 688 Cerebral cortex CTX 567
## 5 695 Cortical plate CTXpl 688
## 6 315 Isocortex Isocortex 695
# get the id of the desired region
granuleID = IDs['Dentate gyrus, granule cell layer' == IDs$name,]$id
# get the dataset for the desired gene (the first saggital experiment that did not fail)
datasetID = getGeneDatasets(gene = 'Prox1',
planeOfSection = 'sagittal',
probeOrientation = 'antisense')[1]
# get the slide that has the desired brain region and coordinates of the center of the region
imageID = structureToImage(datasetID = datasetID, regionIDs = granuleID)
# get the closest atlas image.
atlasID = imageToAtlas(imageID$section.image.id,imageID$x,imageID$y,planeOfSection ='sagittal')
# decide how much to you wish to downsample
downsample = 2
# download the slide
downloadImage(imageID = imageID$section.image.id,
view = 'projection',
outputFile = 'README_files/image.jpg',
downsample = downsample)
# download the atlas
downloadAtlas(imageID = atlasID$section.image.id,
outputFile = 'README_files/atlas.jpg',
downsample = downsample)
Images can be centered by providing center coordinates of a brain region. Input is either a file path or a magick-image object
# crop the slide so that the desired brain region is in the center
centerImage(image = 'README_files/image.jpg',
x = imageID$x,
y= imageID$y,
xProportions = c(.1,.1),
yProportions =c(.1,.1),
outputFile = 'README_files/cropped.jpg',
downsample = downsample)
centerImage(image = 'README_files/atlas.jpg',
x = atlasID['x'],
y= atlasID['y'],
xProportions = c(.1,.1),
yProportions =c(.1,.1),
outputFile = 'README_files/croppedAtlas.jpg',
downsample = downsample)
You can get closest points of other slides from the same dataset to get other slides depicting the region
# gel all images for Prox1 experiment
allImages = listImages(datasetID) %>% arrange(as.numeric(`section.number`))
# get coordinates that are closest to the center of the brain region
closeSections = imageToImage2D(imageID$section.image.id,imageID$x,imageID$y,allImages$id)
# download and crop them all
croppedImage = closeSections %>% apply(1,function(x){
# download and crop the images
image = downloadImage(imageID = x['section.image.id'],
view = 'projection',
# outputFile = file.path('README_files/allProx1',x['section.image.id']),
downsample = downsample)
centerImage(image = image,
x = x['x'],
y= x['y'],
xProportions = c(.1,.1),
yProportions =c(.1,.1),
# outputFile = file.path('README_files/allProx1',x['section.image.id']),
downsample = downsample)
}) %>% do.call(c,.)
# some magick
animation = magick::image_animate(croppedImage, fps = 1)
magick::image_write(animation, "README_files/Prox1.gif")
Region expression can be acquired by datasetID. Data displayed in ABA web portals is expression.energy.
head(getStructureExpressions(datasetID))## expression.density expression.energy id section.data.set.id
## 1 0.00877632 1.256040 417549802 69289763
## 2 0.00877632 1.256040 417549811 69289763
## 3 0.00977504 1.435310 417549818 69289763
## 4 0.00829527 1.240160 417549825 69289763
## 5 0.00490790 0.625739 417549832 69289763
## 6 0.00435703 0.550791 417549837 69289763
## structure.id sum.expressing.pixel.intensity sum.expressing.pixels
## 1 15564 990364000 6919940.0
## 2 15565 990364000 6919940.0
## 3 15566 788831000 5372270.0
## 4 15567 612042000 4093880.0
## 5 15568 9924780 77843.7
## 6 15569 2830360 22389.6
## sum.pixel.intensity sum.pixels voxel.energy.cv voxel.energy.mean
## 1 38839100000 788478000 2.29688 1.247900
## 2 38839100000 788478000 2.29688 1.247900
## 3 27367800000 549591000 2.34411 1.429890
## 4 24459100000 493519000 2.66998 1.231220
## 5 682957000 15860900 1.23383 0.633086
## 6 235064000 5138730 1.48268 0.558472
## structure.acronym structure.color.hex.triplet structure.graph.order
## 1 mouse C86A39 0
## 2 NP C86A39 1
## 3 F C86A39 2
## 4 SP A84D10 3
## 5 RSP A84D10 4
## 6 POTel A84D10 5
## structure.hemisphere.id structure.id.1 structure.name
## 1 3 15564 Mus musculus
## 2 3 15565 neural plate
## 3 3 15566 forebrain
## 4 3 15567 secondary prosencephalon
## 5 3 15568 rostral secondary prosencephalon
## 6 3 15569 preoptic telencephalon
## structure.ontology.id structure.safe.name
## 1 12 Mus musculus
## 2 12 neural plate
## 3 12 forebrain
## 4 12 secondary prosencephalon
## 5 12 rostral secondary prosencephalon
## 6 12 preoptic telencephalon
## structure.st.level structure.structure.id.path
## 1 -1 /15564/
## 2 0 /15564/15565/
## 3 1 /15564/15565/15566/
## 4 2 /15564/15565/15566/15567/
## 5 3 /15564/15565/15566/15567/15568/
## 6 4 /15564/15565/15566/15567/15568/15569/
## structure.structure.name.facet structure.weight
## 1 3843147059 8390
## 2 3041346888 8390
## 3 2526114016 8390
## 4 333870831 8390
## 5 2675393843 8390
## 6 2617066679 8390
If you want to get all genes, use listGenes to get all available genes for the species. Then do getGeneDatasets.
genes = listGenes()
geneDatasets = genes$acronym[1:10] %>% lapply(getGeneDatasets)You may want to limit your search space as getting the data for all genes is a slow process.
Grid data of a dataset can be downloaded by gridData function
gridData(datasetID = datasetID,
outputFile ='README_files/Prox1_data.zip',
include = c('energy','density','intensity'))
unzip(zipfile = 'README_files/Prox1_data.zip',exdir = "README_files")