Last updated: 2023-08-30
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Knit directory: lab-notes/
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The goal for this section is to describe how to download ‘sample tables’ from GEO. Basically the core problem is that sometimes authors will upload raw data to supplement files in GEO and place the processed results as a table per sample. For example this sort of reporting is done for both the MESA and GENOA datasets.
The GENOA and MESA datasets are found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE138914 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE56045 (respectively). You can navigate to a particular samples’ processed results by clicking on the sample in the Series and then selecting ‘view full table’. Our goal is to download these such tables. You can see an example here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM1352002
We will actaully make urls from sample IDs and then just loop over this accross all samples. Generally the flow and how this works is like so: sample id > make url > connect to internet > read html > import to BS4 lib > extract table w/ BS4 > pandas dataframe > save
This code is meant to run inside of a jupyter notebook on Midway3. In order to run this locally change the tqdm module (shows progress bar, time, iterations) to: “import tqdm”.
import sys, os
import io
import requests
import pandas
import urllib
from bs4 import BeautifulSoup
from tqdm import tqdm_notebook as tqdm
from pathlib import Path
import time
# GEO SITE :: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE56045
# series ID : 56045
# GEO BROWSER SERIES LINK :: https://www.ncbi.nlm.nih.gov/geo/browse/?view=samples&series=56045
# series browser gives geo series sample IDs in the samples tab -> csv/tsv table
# upload or copy/paste the data to server/machine
geo_browser_samples_export = '.../MESA_signal_tables/mesa_GEO_samples.tsv'
# now read (table from above) in
sampledf = pandas.read_csv(geo_browser_samples_export, sep = '\t')
# now we need to extract just the GSM id (Accession) codes for each sample in the series
gsm_ids = sampledf['Accession'].tolist()
print('Accession codes loaded for {} samples'.format(len(gsm_ids))) # this will correspond EXACTLY to the # of samples in GEO series
# main loop outs
out_dir = '../MESA_signal_tables/sample_tables'
errors = []
# main loop
for GSM in tqdm(gsm_ids):
#print('Fetching table for {}'.format(GSM))
save_path = out_dir+'/{}_gc_rma_signal_table.tsv'.format(GSM)
if not Path(save_path).exists(): # skip if its already downloaded
try:
time.sleep(1)
target_url = 'https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?view=data&acc={}'.format(GSM)
response = urllib.request.urlopen(target_url)
code = response.getcode()
except:
print('Error w/ ' + GSM ) # this catches 502 errors (likely connection issue from urlopen() call; re-try later)
errors.append(GSM)
if code == 200:
html = response.read() # read the response into html
soup = BeautifulSoup(html)
response.close()
table_search = soup.find("pre")
if not table_search:
print('Table-Search Error w/ ' + GSM )
errors.append(GSM)
for e in table_search: # if we find genes in one of the elements [out of 6]
#print(type(e)) # print the element type: either a BS4 tag or BS4 navigatable str
if 'ENSG' in e:
textdata = e.get_text() # or str(child.encode('utf-8')) # extract text / encode the bytes w/ utf8
#print(textdata)
df = pandas.read_csv(io.StringIO(textdata), header=None, sep="\t") # feed str to io then to pandas
df.columns = ['ID_REF','log2_GC-RMA_signal'] # set the correct column names (also present in table_search[0?] + table_search[1?] in theory)
#print(save_path)
df.to_csv(save_path, sep = '\t')
else:
print('Error w/ ' + GSM + ' eCode: '+code) # mainly a catch for 404 errors (likely bad sample URL / or sample itself)
errors.append(GSM)
edf = pandas.DataFrame(errors) # save the errors to a seperate file for review
edf.columns = ['GSM_dl_error']
edf.to_csv(out_dir+'/_dl_error_table.tsv', sep = '\t')