{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:09Z","timestamp":1772138049796,"version":"3.50.1"},"reference-count":60,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T00:00:00Z","timestamp":1639958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012390","name":"SystemsX.ch","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012390","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012390","name":"Swiss Initiative in Systems Biology","doi-asserted-by":"publisher","award":["RTD 2013\/152"],"award-info":[{"award-number":["RTD 2013\/152"]}],"id":[{"id":"10.13039\/501100012390","id-type":"DOI","asserted-by":"publisher"}]},{"name":"TargetInfectX\u2014Multi-Pronged Perturbation of Pathogen Infection in Human Cells"},{"DOI":"10.13039\/501100001711","name":"Swiss National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001711","id-type":"DOI","asserted-by":"publisher"}]},{"name":"ERC Synergy Grant","award":["609883"],"award-info":[{"award-number":["609883"]}]},{"name":"European Research Council","award":["786461"],"award-info":[{"award-number":["786461"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Signaling pathways control cellular behavior. Dysregulated pathways, for example, due to mutations that cause genes and proteins to be expressed abnormally, can lead to diseases, such as cancer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We introduce a novel computational approach, called Differential Causal Effects (dce), which compares normal to cancerous cells using the statistical framework of causality. The method allows to detect individual edges in a signaling pathway that are dysregulated in cancer cells, while accounting for confounding. Hence, technical artifacts have less influence on the results and dce is more likely to detect the true biological signals. We extend the approach to handle unobserved dense confounding, where each latent variable, such as, for example, batch effects or cell cycle states, affects many covariates. We show that dce outperforms competing methods on synthetic datasets and on CRISPR knockout screens. We validate its latent confounding adjustment properties on a GTEx (Genotype\u2013Tissue Expression) dataset. Finally, in an exploratory analysis on breast cancer data from TCGA (The Cancer Genome Atlas), we recover known and discover new genes involved in breast cancer progression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The method dce is freely available as an R package on Bioconductor (https:\/\/bioconductor.org\/packages\/release\/bioc\/html\/dce.html) as well as on https:\/\/github.com\/cbg-ethz\/dce. The GitHub repository also contains the Snakemake workflows needed to reproduce all results presented here.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab847","type":"journal-article","created":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T15:24:25Z","timestamp":1639495465000},"page":"1550-1559","source":"Crossref","is-referenced-by-count":11,"title":["Identifying cancer pathway dysregulations using differential causal effects"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4166-4343","authenticated-orcid":false,"given":"Kim Philipp","family":"Jablonski","sequence":"first","affiliation":[{"name":"Department of Biosystems Science and Engineering, ETH Zurich , 4058 Basel, Switzerland"},{"name":"SIB Swiss Institute of Bioinformatics , 4058 Basel, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6986-0813","authenticated-orcid":false,"given":"Martin","family":"Pirkl","sequence":"additional","affiliation":[{"name":"Department of Biosystems Science and Engineering, ETH Zurich , 4058 Basel, Switzerland"},{"name":"SIB Swiss Institute of Bioinformatics , 4058 Basel, Switzerland"}]},{"given":"Domagoj","family":"\u0106evid","sequence":"additional","affiliation":[{"name":"Seminar for Statistics, ETH Z\u00fcrich , 8092 Z\u00fcrich, Switzerland"}]},{"given":"Peter","family":"B\u00fchlmann","sequence":"additional","affiliation":[{"name":"Seminar for Statistics, ETH Z\u00fcrich , 8092 Z\u00fcrich, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0573-6119","authenticated-orcid":false,"given":"Niko","family":"Beerenwinkel","sequence":"additional","affiliation":[{"name":"Department of Biosystems Science and Engineering, ETH Zurich , 4058 Basel, Switzerland"},{"name":"SIB Swiss Institute of Bioinformatics , 4058 Basel, Switzerland"}]}],"member":"286","published-online":{"date-parts":[[2021,12,20]]},"reference":[{"key":"2023020108574631800_btab847-B1","doi-asserted-by":"crossref","first-page":"1867","DOI":"10.1016\/j.cell.2016.11.048","article-title":"A multiplexed single-cell crispr screening platform enables systematic dissection of the unfolded protein response","volume":"167","author":"Adamson","year":"2016","journal-title":"Cell"},{"key":"2023020108574631800_btab847-B2","first-page":"232","article-title":"Spectral deconfounding via perturbed sparse linear models","volume":"21","author":"\u0106evid","year":"2020","journal-title":"J. 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