bioimagesUnknownhttps://www.ebi.ac.uk/biostudies/studies/S-BIAD1507bioimagesSpecimenAnnotationsStudy ComponentorganisationBiosampleAssociationsImage acquisitionMiguel Quintela-FandinoNoah GreenwaldSilvana MouronMichael AngeloMako GoldstonRobert WestJolene RanekChristine Camacho-FullawayCameron SowersAlex KongfalseQUICHE reveals structural definitions of anti-tumor responses in triple negative breast cancerWhile recent innovations in spatial biology have driven new insights into how tissue organization is altered in disease, interpreting these datasets in a generalized and scalable fashion remains a challenge. Computational workflows for discovering condition-specific differences in tissue organization typically rely on pairwise comparisons or unsupervised clustering. In many cases, these approaches are computationally expensive, lack statistical rigor, and are insensitive to low-prevalence cellular niches that are nevertheless highly discriminative and predictive of patient outcomes. Here, we present QUICHE - an automated, scalable, and statistically robust method that can be used to discover cellular niches differentially enriched in spatial regions, longitudinal samples, or patient groups2024-12-17T00:00:00Z2024-12-12T20:43:32.707Z2024-12-09T19:50:38.614ZS-BIAD1507