How to download images from the Ivy Glioblastoma Atlas Project

Technical
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1

I’m trying to download all the images on IvyGAP (ISH, H&E, TumorFeature). I was able to locate the APIs page (http://help.brain-map.org/display/glioblastoma/API), but it seems that this either has to be done through website GUI or there are some id’s that I could not associate to the proper images. Is there any direct way (such as FTP) to download all of these images?

2

There is no built-in functionality to download all the images for the Ivy Glioblastoma Atlas project. However, with a bit of programming effort, the generic image download functionality of the Allen Brain Atlas API can be leveraged to do so.

The Glioblastoma API page ( http://help.brain-map.org/display/glioblastoma/API ) lists general API calls in the “Experimental Overview and Metadata” section to get information about specimens (Note that you can change the ‘xml’ to ‘json’ if that is easier for you to work with).

Using the example for “All ISH SectionDataSets in sub-block Specimen “W32-1-1-K.01””, you can collect the ids of “sub image” records that are of interest to you.

http://api.brain-map.org/api/v2/data/query.xml?criteria=

model::SectionDataSet

,rma::criteria,specimen[external_specimen_name$eq’W32-1-1-K.01’],treatments[name$eq’ISH’]

,rma::include,genes,sub_images

sub_image_id = msg.2.sub_images.0.id = 278272303

This value can be used in an additional call to download the image:

http://api.brain-map.org/api/v2/image_download/278272303

This variation will get the annotated version of the same image:

http://api.brain-map.org/api/v2/image_download/278272303?view=tumor_feature_annotation

This variation will get the boundary version of the same image:

http://api.brain-map.org/api/v2/image_download/278272303?view=tumor_feature_boundary

This help page describes the general API functions available to download Allen Brain Atlas images: http://help.brain-map.org/display/api/Downloading+an+Image

3

http://api.brain-map.org/api/v2/data/query.xml?criteria=model::SectionDataSet,rma::criteria,specimen[external_specimen_name$eq%27W32-1-1-K.01%27],treatments[name$eq%27ISH%27],rma::include,genes,sub_images

1 Like
4

Here is a small Python snippet to get a table that contains all URLs for one sub-block.

import pandas as pd
import requests

def get_urls_sub_block(sub_block_name):
    url = f"https://api.brain-map.org/api/v2/data/query.json?criteria=model::SectionDataSet,rma::criteria,specimen[external_specimen_name$eq'{sub_block_name}'],treatments[name$eq'ISH'],rma::include,genes,sub_images"
    response = requests.get(url)
    data = response.json()["msg"]
    image_ids = []
    gene_symbols = []
    for image in data:
        try:
            image_id = image["sub_images"][0]["id"]
            image_ids.append(image_id)
        except:
            print(f"Error in {sub_block_name}")
            image_ids.append("error")
        try:
            gene_symbol = image["genes"][0]["acronym"]
            gene_symbols.append(gene_symbol)
        except:
            print(f"Error in {sub_block_name}")
            gene_symbols.append("error")
    images_df = pd.DataFrame({"image_id": image_ids, "gene_symbol": gene_symbols})
    images_df["sub_block_name"] = sub_block_name
    images_df["url"] = images_df.image_id.apply(lambda x: f"https://api.brain-map.org/api/v2/image_download/{x}")
    return images_df

sub_block_name = "W32-1-1-K.01"
get_urls_sub_block(sub_block_name)

Result:

image_id	gene_symbol	sub_block_name	url
278269506	MECOM	W32-1-1-K.01	https://api.brain-map.org/api/v2/image_download/278269506
278272201	PDGFRA	W32-1-1-K.01	https://api.brain-map.org/api/v2/image_download/278272201
278272354	ITGA6	W32-1-1-K.01	https://api.brain-map.org/api/v2/image_download/278272354
278272252	ID1	W32-1-1-K.01	https://api.brain-map.org/api/v2/image_download/278272252
278269352	TNFAIP3	W32-1-1-K.01	https://api.brain-map.org/api/v2/image_download/278269352
278272303	OLIG2	W32-1-1-K.01	https://api.brain-map.org/api/v2/image_download/278272303
278269301	HIF1A	W32-1-1-K.01	https://api.brain-map.org/api/v2/image_download/278269301
278271742	MYC	W32-1-1-K.01	https://api.brain-map.org/api/v2/image_download/278271742
278271691	ID2	W32-1-1-K.01	https://api.brain-map.org/api/v2/image_download/278271691
278269455	TGFBR2	W32-1-1-K.01	https://api.brain-map.org/api/v2/image_download/278269455
278271793	MET	W32-1-1-K.01	https://api.brain-map.org/api/v2/image_download/278271793
278272099	PROM1	W32-1-1-K.01	https://api.brain-map.org/api/v2/image_download/278272099
278272048	EZH2	W32-1-1-K.01	https://api.brain-map.org/api/v2/image_download/278272048
278271844	IGFBP2	W32-1-1-K.01	https://api.brain-map.org/api/v2/image_download/278271844
278271895	CD44	W32-1-1-K.01	https://api.brain-map.org/api/v2/image_download/278271895
278271997	DANCR	W32-1-1-K.01	https://api.brain-map.org/api/v2/image_download/278271997
278272150	POSTN	W32-1-1-K.01	https://api.brain-map.org/api/v2/image_download/278272150
278271946	NOS2	W32-1-1-K.01	https://api.brain-map.org/api/v2/image_download/278271946
278271640	PDPN	W32-1-1-K.01	https://api.brain-map.org/api/v2/image_download/278271640
278271589	PI3	W32-1-1-K.01	https://api.brain-map.org/api/v2/image_download/278271589
278269404	BIRC5	W32-1-1-K.01	https://api.brain-map.org/api/v2/image_download/278269404