[BioC] allen brain atlas
Martin Morgan
mtmorgan at fhcrc.org
Fri Aug 22 21:15:19 CEST 2008
Hi Radek --
Radek Blatny <blatny at img.cas.cz> writes:
> Hi, is there a way to access the expression data from the Allen Brain
> Atlas and use them for annotation of a topTable? I am particularly
> interested in being able to download expression profiles for gene
> symbols (or other suitable identifiers), which is possible to query
> in semiquantitative graphical format for all brain anatomic regions
> at the ABA webpage - for instance here:
>
> <http://mouse.brain-map.org/brain/Myst4.html?ispopup=1>
Not sure of a package, but it might be easy enough to create something
useful yourself. Here's a quite fun first attempt:
library(XML)
## some utitlity functions
abaUrl <- function(type, id = "") {
if (!missing(id))
id <- paste("/",id, ".xml", sep="")
paste("http://www.brain-map.org/aba/api/", type, id, sep="")
}
xq <- function(xml, query) {
unlist(xpathApply(xml, query, xmlValue))
}
## retrieve information about a gene; separate some of the info into
## two data frames
abaGene <- function(symbol) {
url <- abaUrl("gene", symbol)
xml <- xmlTreeParse(url, useInternal=TRUE)
res <- list(gene_expressions=data.frame(
structurename=xq(xml, "//structurename"),
avgdensity=as.numeric(xq(xml, "//avgdensity")),
avglevel=as.numeric(xq(xml, "//avglevel")),
row.names=1),
image_series=data.frame(
imageseriesdisplayname=xq(
xml, "//imageseriesdisplayname"),
age=as.integer(xq(xml, "//age")),
imageseriesid=as.integer(xq(
xml, "//imageseriesid")),
sex=factor(xq(xml, "//sex"))))
free(xml)
res
}
With this one can
> coch <- abaGene("Coch")
> coch
$gene_expressions
avgdensity avglevel
Cerebellum 7.888339 11.977408
Cerebral cortex 52.086498 46.967388
Hippocampal region 2.939453 5.846055
Hippocampal formation 4.755450 6.896857
Hypothalamus 8.690369 9.506371
Lateral septal complex 8.783522 7.551568
Midbrain 9.267348 11.813993
Medulla 7.031233 12.667056
Olfactory bulb 26.625715 34.037346
Pons 3.897534 5.504243
Pallidum 8.595852 9.827189
Retrohippocampal region 7.256083 7.999830
Striatum-like amygdalar nuclei 7.874489 9.268417
Striatum 74.447227 79.294968
Striatum dorsal region 100.000000 100.000000
Striatum ventral region 6.137324 4.482482
Thalamus 27.540497 36.667355
$image_series
imageseriesdisplayname age imageseriesid sex
1 Coch-Sagittal-06-0609 55 75990683 M
2 Coch-Coronal-05-2779 55 71717614 M
For more fun I used the EBImage package and a little more exploration
## Retrieve an image
library(EBImage)
abaImageSeries <- function(imageseriesid) {
url <- abaUrl("imageseries", imageseriesid)
xml <- xmlTreeParse(url, useInternal=TRUE)
res <- list(images=data.frame(
imagedisplayname=xq(xml, "//imagedisplayname"),
downloadImagePath=xq(xml, "//downloadImagePath"),
stringsAsFactors=FALSE))
free(xml)
res
}
abaImage <- function(downloadImagePath, zoom=1) {
url <- paste(abaUrl("image"),
"?zoom=", zoom,
"&path=", downloadImagePath, sep="")
readImage(url)
}
and then
> series <- abaImageSeries(coch$image_series[1,"imageseriesid"])
> series
$images
imagedisplayname
1 Coch_2
2 Coch_10
3 Coch_18
4 Coch_26
5 Coch_34
6 Coch_42
7 Coch_50
8 Coch_58
9 Coch_74
10 Coch_82
11 Coch_90
12 Coch_98
13 Coch_106
14 Coch_114
15 Coch_122
16 Coch_130
17 Coch_138
18 Coch_146
19 Coch_154
downloadImagePath
1 production18/Coch_06-0609_38068/zoomify/primary/0203016726/Coch_2_0203016726_A.aff
2 production18/Coch_06-0609_38068/zoomify/primary/0203016726/Coch_10_0203016726_B.aff
3 production18/Coch_06-0609_38068/zoomify/primary/0203016726/Coch_18_0203016726_C.aff
4 production18/Coch_06-0609_38068/zoomify/primary/0203016726/Coch_26_0203016726_D.aff
5 production18/Coch_06-0609_38068/zoomify/primary/0203026750/Coch_34_0203026750_A.aff
6 production18/Coch_06-0609_38068/zoomify/primary/0203026750/Coch_42_0203026750_B.aff
7 production18/Coch_06-0609_38068/zoomify/primary/0203026750/Coch_50_0203026750_C.aff
8 production18/Coch_06-0609_38068/zoomify/primary/0203026750/Coch_58_0203026750_D.aff
9 production18/Coch_06-0609_38068/zoomify/primary/0203034952/Coch_74_0203034952_B.aff
10 production18/Coch_06-0609_38068/zoomify/primary/0203034952/Coch_82_0203034952_C.aff
11 production18/Coch_06-0609_38068/zoomify/primary/0203034952/Coch_90_0203034952_D.aff
12 production18/Coch_06-0609_38068/zoomify/primary/0203044879/Coch_98_0203044879_A.aff
13 production18/Coch_06-0609_38068/zoomify/primary/0203044879/Coch_106_0203044879_B.aff
14 production18/Coch_06-0609_38068/zoomify/primary/0203044879/Coch_114_0203044879_C.aff
15 production18/Coch_06-0609_38068/zoomify/primary/0203044879/Coch_122_0203044879_D.aff
16 production18/Coch_06-0609_38068/zoomify/primary/0203054806/Coch_130_0203054806_A.aff
17 production18/Coch_06-0609_38068/zoomify/primary/0203054806/Coch_138_0203054806_B.aff
18 production18/Coch_06-0609_38068/zoomify/primary/0203054806/Coch_146_0203054806_C.aff
19 production18/Coch_06-0609_38068/zoomify/primary/0203054806/Coch_154_0203054806_D.aff
> img <- abaImage(series$images[1,"downloadImagePath"], 2)
> img
'Image'
colorMode() : Grayscale
storage class : numeric 3D array, writable images in range [0..1]
dim() : 513x414
fileName() : /tmp/magick-XXxlP9B8
compression() : JPEG
resolution() : dx = 72.0, dy = 72.0
image 1/1:
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 0.9803922 0.9803922 0.9803922 0.9803922 0.9803922 0.9803922
[2,] 0.9803922 0.9803922 0.9803922 0.9803922 0.9803922 0.9803922
[3,] 0.9803922 0.9803922 0.9803922 0.9803922 0.9803922 0.9803922
[4,] 0.9803922 0.9803922 0.9803922 0.9803922 0.9803922 0.9803922
[5,] 0.9803922 0.9803922 0.9803922 0.9803922 0.9803922 0.9803922
...
> display(img)
This displays an image at a sort of reasonable resolution; it's then
possible to use EBImage to do all kinds of fun manipulations. There's
an incredible amount of scope for programmatic manipulation here.
The way to construct the queries above, and the structure of the
return data, are described on the 'API' page, e.g., following the link
to 'API documetation' on this page:
http://community.brain-map.org/confluence/display/DataAPI/Home
Martin
> Regards, Radek
>
>
> Radek Blatny, MSc.
> Institute of Molecular Genetics
> Department of Mouse Molecular Genetics (C/O Jiri Forejt)
> Czech Academy of Sciences
> Videnska 1083
> 142 20, Prague
> Czech Republic
> Tel. (+420) 241 062 260
> Fax (+420) 241 062 154
> http://www.img.cas.cz/mmg
> email: blatny at img.cas.cz
> Skype name: blatny
>
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--
Martin Morgan
Computational Biology / Fred Hutchinson Cancer Research Center
1100 Fairview Ave. N.
PO Box 19024 Seattle, WA 98109
Location: Arnold Building M2 B169
Phone: (206) 667-2793
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