{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:24Z","timestamp":1772138064842,"version":"3.50.1"},"reference-count":68,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T00:00:00Z","timestamp":1704758400000},"content-version":"vor","delay-in-days":8,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Swedish Foundation for Strategic Research","award":["BD15-0043 to L.K"],"award-info":[{"award-number":["BD15-0043 to L.K"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>In pathway analysis, we aim to establish a connection between the activity of a particular biological pathway and a difference in phenotype. There are many available methods to perform pathway analysis, many of them rely on an upstream differential expression analysis, and many model the relations between the abundances of the analytes in a pathway as linear relationships.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Here, we propose a new method for pathway analysis, MIPath, that relies on information theoretical principles and, therefore, does not model the association between pathway activity and phenotype, resulting in relatively few assumptions. For this, we construct a graph of the data points for each pathway using a nearest-neighbor approach and score the association between the structure of this graph and the phenotype of these same samples using Mutual Information while adjusting for the effects of random chance in each score. The initial nearest neighbor approach evades individual gene-level comparisons, hence making the method scalable and less vulnerable to missing values. These properties make our method particularly useful for single-cell data. We benchmarked our method on several single-cell datasets, comparing it to established and new methods, and found that it produces robust, reproducible, and meaningful scores.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Source code is available at https:\/\/github.com\/statisticalbiotechnology\/mipath, or through Python Package Index as \u201cmipathway.\u201d<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad776","type":"journal-article","created":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T16:25:05Z","timestamp":1704731105000},"source":"Crossref","is-referenced-by-count":5,"title":["Pathway analysis through mutual information"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4438-2325","authenticated-orcid":false,"given":"Gustavo S","family":"Jeuken","sequence":"first","affiliation":[{"name":"Science for Life Laboratory, KTH \u2013 Royal Institute of Technology , Stockholm 171 65, Sweden"},{"name":"Computer Science Department, Vrije Universiteit Amsterdam , Amsterdam 1081 HV, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5689-9797","authenticated-orcid":false,"given":"Lukas","family":"K\u00e4ll","sequence":"additional","affiliation":[{"name":"Science for Life Laboratory, KTH \u2013 Royal Institute of Technology , Stockholm 171 65, Sweden"}]}],"member":"286","published-online":{"date-parts":[[2024,1,9]]},"reference":[{"key":"2024011122050593600_btad776-B1","doi-asserted-by":"crossref","first-page":"1295","DOI":"10.1038\/s41591-020-0939-8","article-title":"Distinct synovial tissue macrophage subsets regulate inflammation and remission in rheumatoid arthritis","volume":"26","author":"Alivernini","year":"2020","journal-title":"Nat Med"},{"key":"2024011122050593600_btad776-B2","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1152\/physrev.00026.2007","article-title":"Telomeres and aging","volume":"88","author":"Aubert","year":"2008","journal-title":"Physiol Rev"},{"key":"2024011122050593600_btad776-B3","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.arr.2016.06.004","article-title":"The emerging role of notch pathway in ageing: focus on the related mechanisms in age-related diseases","volume":"29","author":"Balistreri","year":"2016","journal-title":"Ageing Res Rev"},{"key":"2024011122050593600_btad776-B4","volume-title":"Network Science","author":"Barab\u00e1si","year":"2016"},{"key":"2024011122050593600_btad776-B5","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1038\/nature08460","article-title":"Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1","volume":"462","author":"Barbie","year":"2009","journal-title":"Nature"},{"key":"2024011122050593600_btad776-B6","doi-asserted-by":"crossref","first-page":"111697","DOI":"10.1016\/j.celrep.2022.111697","article-title":"Systematic single-cell pathway analysis to characterize early T cell activation","volume":"41","author":"Bibby","year":"2022","journal-title":"Cell Rep"},{"key":"2024011122050593600_btad776-B7","doi-asserted-by":"crossref","first-page":"P10008","DOI":"10.1088\/1742-5468\/2008\/10\/P10008","article-title":"Fast unfolding of communities in large networks","volume":"2008","author":"Blondel","year":"2008","journal-title":"J Stat Mech"},{"key":"2024011122050593600_btad776-B8","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1038\/nbt.4096","article-title":"Integrating single-cell transcriptomic data across different conditions, technologies, and species","volume":"36","author":"Butler","year":"2018","journal-title":"Nat Biotechnol"},{"key":"2024011122050593600_btad776-B9","doi-asserted-by":"crossref","first-page":"627","DOI":"10.3390\/e22060627","article-title":"Information theory in computational biology: where we stand today","volume":"22","author":"Chanda","year":"2020","journal-title":"Entropy"},{"key":"2024011122050593600_btad776-B10","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1038\/nature10983","article-title":"The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups","volume":"486","author":"Curtis","year":"2012","journal-title":"Nature"},{"key":"2024011122050593600_btad776-B11","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.jare.2018.02.004","article-title":"Ageing: is there a role for arachidonic acid and other bioactive lipids? 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