{"database":"bioimages","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":[null],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-BIAD1507"],"repository":["bioimages"],"figure_sub":["Specimen","Annotations","Study Component","organisation","Biosample","Associations","Image acquisition"],"pubmed_authors":["Miguel Quintela-Fandino","Noah Greenwald","Silvana Mouron","Michael Angelo","Mako Goldston","Robert West","Jolene Ranek","Christine Camacho-Fullaway","Cameron Sowers","Alex Kong"],"additional_accession":[]},"is_claimable":false,"name":"QUICHE reveals structural definitions of anti-tumor responses in triple negative breast cancer","description":"While 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 groups","dates":{"release":"2024-12-17T00:00:00Z","modification":"2024-12-12T20:43:32.707Z","creation":"2024-12-09T19:50:38.614Z"},"accession":"S-BIAD1507","cross_references":{}}