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June 22, 2010 11:53
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import os | |
import time | |
import re | |
import nose | |
from nose.tools import assert_equals | |
from collections import defaultdict | |
import EUtils | |
from EUtils import HistoryClient, ThinClient | |
import dataset | |
import datasources | |
from datasources import pubmedcentral | |
from datasources import pubmed | |
from datasources import affiliation | |
from datasources import geo | |
from datasources import urlopener | |
import utils | |
from utils.cache import TimedCache | |
import pickle | |
EMAIL_CONTACT = "hpiwowar@gmail.com" | |
VERBOSE = False | |
#base_query = """(GEO[text] OR omnibus[text]) NOT "pmc gds"[filter]""" | |
#base_query = """(GEO[text] OR omnibus[text]) NOT "pmc gds"[filter] AND ("1900"[PubDate] : "2009"[PubDate])""" | |
base_query_reuse = """("1900"[PubDate] : "2009"[PubDate]) NOT "pmc gds"[filter]""" | |
base_query_submit = """("1900"[PubDate] : "2009"[PubDate]) AND "pmc gds"[filter]""" | |
#url_for_gse = """http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=gds&term=GSE[ETYP]&retmax=10000&usehistory=n""" | |
#url_for_gds = """http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=gds&term=GDS[ETYP]&retmax=10000&usehistory=n""" | |
def test_get_accession_in_pmc_fulltext(): | |
response = get_accession_in_pmc_fulltext("GSE", "200008514", base_query_reuse) | |
assert_equals(response, ['2785812', '2620272']) | |
response = get_accession_in_pmc_fulltext("GSE", "200008478", base_query_reuse) | |
assert_equals(response, ['2223695']) | |
response = get_accession_in_pmc_fulltext("GSE", "200007778", base_query_reuse) | |
assert_equals(response, []) | |
response = get_accession_in_pmc_fulltext("GDS", "2643", base_query_reuse) | |
assert_equals(response, ['2715883', '2781753']) | |
def test_get_accession_variants(): | |
response = get_accession_variants("GSE", "200008514") | |
assert_equals(response, ['GSE200008514', '"GSE 200008514"', 'GSE8514', '"GSE 8514"']) | |
def get_accession_variants(id_type, id): | |
variants = [] | |
variants.append(id_type + id) | |
variants.append('"' + id_type + ' ' + id + '"') | |
id_stripped = geo.get_stripped_accession(id) | |
if (id_stripped != id): | |
variants.append(id_type + id_stripped) | |
variants.append('"' + id_type + ' ' + id_stripped + '"') | |
return(variants) | |
def get_accession_in_pmc_fulltext(id_type, id, pmc_query): | |
pmc_ids = [] | |
accession_variants = get_accession_variants(id_type, id) | |
for variant in accession_variants: | |
query = variant + "[text] AND " + pmc_query | |
pmc_ids += pubmedcentral.search(query) | |
return(pmc_ids) | |
def test_get_authors_and_submittors_from_accession(): | |
response = get_authors_and_submittors_from_accession([u'17911395'], [u'Ye P', u'Rainey W']) | |
assert_equals(response, ['Mariniello', 'Shibata', 'Ye', 'Rainey', 'Mantero']) | |
def get_author_last_names(pmids): | |
last_names_list = [] | |
for pmid in pmids: | |
authors = pubmed.authors([pmid]) | |
authors_list = authors[0].split(";") | |
last_names = [author.split(" ")[0] for author in authors_list] | |
last_names_list += last_names | |
return(last_names_list) | |
def get_authors_and_submittors_from_accession(pmids, contributors=None): | |
last_names_list = get_author_last_names(pmids) | |
if contributors: | |
contributor_last_names = [author.split(" ")[0] for author in contributors] | |
last_names_list += contributor_last_names | |
last_names_set = set(last_names_list) | |
return(list(last_names_set)) | |
def test_get_dict_submit_reuse_of_accession_in_pmc_fulltext(): | |
response = get_dict_submit_reuse_of_accession_in_pmc_fulltext("GSE", ["200008514", "200008478"], base_query_reuse) | |
assert_equals(response.items()[0:2], [(('GSE200008514', '2785812'), ('200008514', [u'17911395'], ['2785812', '2620272'], [u'19917117'], ['Mariniello', 'Shibata', 'Ye', 'Rainey', 'Mantero'], ['Kroll', 'Barkema', 'Carlon'], [], ['Algorithms Mol Biol'], ['2009'], [], [], [])), (('GSE200008478', '2223695'), ('200008478', [u'17977147', u'17951393'], ['2223695'], [u'17993534'], ['Moser', 'Fischer', 'Friberg', 'Pessi', 'Lindemann', 'Hauser', 'Rehrauer', 'Hennecke', 'Ahrens'], ['Hacker', 'Aktas', 'Narberhaus', 'Sohlenkamp', 'Geiger'], [], ['J Bacteriol'], ['2008'], [], [], ['deposited', 'accessed', 'published']))] ) | |
def test_get_author_intersect_submit_reuse(): | |
response = get_author_intersect_submit_reuse(['Pessi', 'Ahrens', 'Rehrauer', 'Lindemann', 'Hauser', 'Fischer', 'Hennecke', 'Lindemann', 'Moser', 'Pessi', 'Hauser', 'Friberg', 'Hennecke', 'Fischer', u'Pessi', u'Ahrens', u'Rehrauer', u'Lindemann', u'Hauser', u'Fischer', u'Hennecke'], ['Hacker', 'Sohlenkamp', 'Pessi', 'Aktas', 'Geiger', 'Narberhaus']) | |
assert_equals(response, ['Pessi']) | |
def get_author_intersect_submit_reuse(submit_authors, reuse_authors): | |
intersect = multi_intersection([submit_authors, reuse_authors]) | |
return(intersect) | |
meshes = """"Algorithms"[mesh] | |
"Databases, Genetic"[mesh] | |
"Gene Expression Profiling/methods"[mesh] | |
"Computational Biology/methods"[mesh] | |
"Oligonucleotide Array Sequence Analysis/methods"[mesh] | |
"Genomics/methods"[mesh] | |
"Reproducibility of Results"[mesh] | |
"Software"[mesh] | |
"Computer Simulation"[mesh] | |
"Internet"[mesh] | |
"Data Interpretation, Statistical"[mesh]""".split("\n") | |
metaquery = "(meta-analysis [pt] OR meta-analysis [tw] OR metanalysis [tw]) OR meta-analysis [mh])" | |
words = """submitted | |
deposited | |
user* | |
public | |
accessed | |
downloaded | |
published""".split("\n") | |
def get_dict_submit_reuse_of_accession_in_pmc_fulltext(id_type, ids, pmc_query): | |
response_dict = defaultdict(str) | |
num_ids = len(ids) | |
id_counter = 0 | |
geo_instance = geo.GEO() | |
for accession in ids: | |
id_counter += 1 | |
pmc_reuse_pmcids = get_accession_in_pmc_fulltext(id_type, accession, pmc_query) | |
if not pmc_reuse_pmcids: | |
continue | |
stripped_accession = geo.get_stripped_accession(accession) | |
try: | |
submit_pmids = geo_instance.pmids(id_type + stripped_accession) | |
except Exception: | |
continue | |
submit_contributors = geo_instance.contributors(id_type + stripped_accession) | |
submit_authors = get_authors_and_submittors_from_accession(submit_pmids, submit_contributors) | |
for reuse_pmcid in pmc_reuse_pmcids: | |
reuse_pmids = pubmedcentral.pmcids_to_pmids(reuse_pmcid) | |
reuse_authors = get_authors_and_submittors_from_accession(reuse_pmids) | |
submit_affiliation = affiliation.institution(submit_pmids) if submit_pmids else [] | |
reuse_affiliation = affiliation.institution(reuse_pmids) | |
intersect = get_author_intersect_submit_reuse(submit_authors, reuse_authors) | |
journal = pubmed.journal(reuse_pmids) | |
year = pubmed.year_published(reuse_pmids) | |
medline_status = pubmed.medline_status(reuse_pmids) | |
metaanal = pubmed.filter_pmids(reuse_pmids, metaquery) | |
biolink_filter = pubmedcentral.filter_pmcids([reuse_pmcid], '(geo OR omnibus) AND microarray AND "gene expression" AND accession NOT (databases OR user OR users OR (public AND accessed) OR (downloaded AND published))') | |
basic_filter = pubmedcentral.filter_pmcids([reuse_pmcid], '"gene expression omnibus" AND (submitted OR deposited)') | |
mesh_filters = [term for term in meshes if pubmed.filter_pmids(reuse_pmids, term)] | |
word_filters = [term for term in words if pubmedcentral.filter_pmcids([reuse_pmcid], term + "[text]")] | |
response_dict[(id_type+stripped_accession, reuse_pmcid)] = (id_type+stripped_accession, submit_pmids, reuse_pmcid, reuse_pmids, submit_authors, reuse_authors, submit_affiliation, reuse_affiliation, intersect, journal, year, medline_status, metaanal, biolink_filter, basic_filter, mesh_filters, word_filters) | |
print response_dict[(id_type+stripped_accession, reuse_pmcid)] | |
print id_counter, "of", num_ids, ":", stripped_accession, "--", (submit_pmids), "; ", len(pmc_reuse_pmcids) | |
return(response_dict) | |
def authors_in_common_from_pmids(): | |
pmids = ["20349403", "18998887", "18767901"] | |
response = authors_in_common_from_pmids(pmids) | |
assert_equals(response, ["Piwowar"]) | |
def multi_intersection(xs): | |
inter = reduce(set.intersection, [set(x) for x in xs]) | |
return list(inter) | |
def authors_in_common_from_pmids(pmids): | |
last_names_list = [] | |
for pmid in pmids: | |
authors = pubmed.authors([pmid]) | |
authors_list = authors[0].split(";") | |
last_names = [author.split(" ")[0] for author in authors_list] | |
last_names_list.append(last_names) | |
authors_intersection = multi_intersection(last_names_list) | |
return(authors_intersection) | |
def get_from_query_gds_in_pmc_fulltext_dict(id_type, pmc_query, geo_year=None): | |
ids = geo.get_ids_by_year(id_type, geo_year) | |
(response_dict) = get_dict_submit_reuse_of_accession_in_pmc_fulltext(id_type, ids, pmc_query) | |
return(response_dict) | |
def print_accession_pmcids(prefix, accession_dict): | |
for accession in accession_dict: | |
pmcid_list = accession_dict[accession] | |
if pmcid_list: | |
for pmcid in pmcid_list: | |
print "%s\t%s%s\t%s" %(prefix, prefix, accession, pmcid) | |
else: | |
print "%s\t%s%s\t%s" %(prefix, prefix, accession, "") | |
#print_accession_pmcids("GDS", gds_dict) | |
#print_accession_pmcids("GSE", gse_dict) | |
def test_estimate_pmc_coverage(): | |
response = estimate_pmc_coverage("arrayexpress[title]") | |
assert_equals(response, (6, 14, 0.42857142857142855)) | |
response = estimate_pmc_coverage('"gene expression profiling"[mesh]') | |
assert_equals(response, (11038, 47384, 0.23294783049130507)) | |
def test_estimate_pmc_coverage_given_years(): | |
response = estimate_pmc_coverage('"gene expression profiling"[mesh]', "2007", "2009") | |
assert_equals(response, (6311, 21569, 0.29259585516250175)) | |
def estimate_pmc_coverage(query, start_year="1800", end_year="3000"): | |
pubmed_query = query + ' AND ("' + start_year + '"[pdat] : "' + end_year + '"[pdat])' | |
pubmed_ids = pubmed.search(pubmed_query) | |
num_pubmed = len(pubmed_ids) | |
pmc_query = query + ' AND ("' + start_year + '"[PubDate] : "' + end_year + '"[PubDate])' | |
pmc_ids = pubmedcentral.search(pmc_query) | |
num_pmc = len(pmc_ids) | |
ratio = num_pmc / (num_pubmed + 0.0) | |
return(num_pmc, num_pubmed, ratio) | |
def run_stats(): | |
if False: | |
(num_pmc, num_pubmed, ratio) = estimate_pmc_coverage('"gene expression profiling"[mesh]', "2007", "2009") | |
geo_year = "2007" | |
id_types = ["GDS", "GSE"] | |
response_dict = {} | |
for id_type in id_types: | |
response_dict[(id_type, geo_year)] = get_from_query_gds_in_pmc_fulltext_dict(id_type, base_query_reuse, geo_year) | |
pkl_file = open("scienceplot/results/" + id_type + "_dict" + geo_year + ".pkl", "wb") | |
pickle.dump(response_dict[(id_type, geo_year)], pkl_file) | |
pkl_file.close() | |
fh = open("scienceplot/results/" + id_type + geo_year + ".csv", "w") | |
header = "id_type+stripped_accession, submit_pmids, reuse_pmcid, reuse_pmids, submit_authors, reuse_authors, submit_affiliation, reuse_affiliation, intersect, journal, year, medline_status, metaanal, biolink_filter, basic_filter, mesh_filters, word_filters" | |
fh.write(header + "\n") | |
dataset.csv_write_to_file(fh, response_dict[(id_type, geo_year)].values()) | |
fh.close() | |
return(response_dict) |
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