:mouse: Acquiring images from allen brain atlas, mouse ISH experiments in R.
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 idsIDs = 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 regiongranuleID = 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 regionimageID = structureToImage(datasetID = datasetID, regionIDs = granuleID)# get the closest atlas image.atlasID = imageToAtlas(imageID$section.image.id,imageID$x,imageID$y,planeOfSection ='sagittal')# decide how much to you wish to downsampledownsample = 2# download the slidedownloadImage(imageID = imageID$section.image.id,view = 'projection',outputFile = 'README_files/image.jpg',downsample = downsample)
# download the atlasdownloadAtlas(imageID = atlasID$section.image.id,outputFile = 'README_files/atlas.jpg',downsample = downsample)
Images can be centered by providing center coordinates of a brain
region. Input is either a file path or a magick-image object
# crop the slide so that the desired brain region is in the centercenterImage(image = 'README_files/image.jpg',x = imageID$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 experimentallImages = listImages(datasetID) %>% arrange(as.numeric(`section.number`))# get coordinates that are closest to the center of the brain regioncloseSections = imageToImage2D(imageID$section.image.id,imageID$x,imageID$y,allImages$id)# download and crop them allcroppedImage = closeSections %>% apply(1,function(x){# download and crop the imagesimage = 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 magickanimation = 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")