README.md

In oganm/allenBrain: Acquiring puctures from allen brain atlas

Table of Contents

• allenBrain
• Example usage
  • Image acquisition
  • Region expression data
  • Incomplete stuff

allenBrain

This R package acquires pictures from Allen Brain Atlas.

Install allenBrain to R

devtools::install_github('oganm/allenBrain')

Example usage

Image acquisition

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)

Adding a scale to the image

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)

Image syncronization

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 data

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.

Incomplete stuff

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")

oganm/allenBrain documentation built on March 28, 2024, 11:16 p.m.