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$y, xProportions = c(.1,.1), yProportions =c(.1,.1), outputFile = 'README_files/cropped.jpg', downsample = downsample)
centerImage(image = 'README_files/atlas.jpg', x = atlasID['x'], y= atlasID['y'], xProportions = c(.1,.1), yProportions =c(.1,.1), outputFile = 'README_files/croppedAtlas.jpg', downsample = downsample)
getSectionImage function returns all available information about an
image. We can use this to access the resolution information which lists
micron/pixel ratio in a given image.
(image_res = getSectionImage(imageID$section.image.id)$resolution)
## [1] "1.049"
We can then use the add_scale function to add a scale
downloadImage(imageID = imageID$section.image.id, view = 'projection', downsample = downsample) %>% centerImage(x = imageID$x, y= imageID$y, xProportions = c(.1,.1), yProportions =c(.1,.1), outputFile = 'README_files/cropped.jpg', downsample = downsample) %>% add_scale(image_res,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 structure.st.level
## 1 12 Mus musculus -1
## 2 12 neural plate 0
## 3 12 forebrain 1
## 4 12 secondary prosencephalon 2
## 5 12 rostral secondary prosencephalon 3
## 6 12 preoptic telencephalon 4
## structure.structure.id.path structure.structure.name.facet
## 1 /15564/ 3843147059
## 2 /15564/15565/ 3041346888
## 3 /15564/15565/15566/ 2526114016
## 4 /15564/15565/15566/15567/ 333870831
## 5 /15564/15565/15566/15567/15568/ 2675393843
## 6 /15564/15565/15566/15567/15568/15569/ 2617066679
## structure.weight
## 1 8390
## 2 8390
## 3 8390
## 4 8390
## 5 8390
## 6 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")