Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.00200 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914224329981952 |
|---|---|
| author | Hartung, Michael Maier, Andreas Burankova, Yuliya Delgado-Chaves, Fernando Isaeva, Olga I. Savchik, Alexey Patroni, Fábio Malta de Sá Lohmann, Jens J. G. He, Daniel Shannon, Casey Schulze, Jan-Ole Kaufmann, Katharina Chervontseva, Zoe Firoozbakht, Farzaneh Hartebrodt, Anne Probul, Niklas Tsoy, Olga Abisheva, Alexandra Zotova, Evgenia Singh, Kavya Van Steen, Kristel Kuehl, Malte Puelles, Victor G. Blumenthal, David B. Ester, Martin Laske, Tanja Baumbach, Jan Zolotareva, Olga |
| author_facet | Hartung, Michael Maier, Andreas Burankova, Yuliya Delgado-Chaves, Fernando Isaeva, Olga I. Savchik, Alexey Patroni, Fábio Malta de Sá Lohmann, Jens J. G. He, Daniel Shannon, Casey Schulze, Jan-Ole Kaufmann, Katharina Chervontseva, Zoe Firoozbakht, Farzaneh Hartebrodt, Anne Probul, Niklas Tsoy, Olga Abisheva, Alexandra Zotova, Evgenia Singh, Kavya Van Steen, Kristel Kuehl, Malte Puelles, Victor G. Blumenthal, David B. Ester, Martin Laske, Tanja Baumbach, Jan Zolotareva, Olga |
| contents | Unsupervised patient stratification is essential for disease subtype discovery, yet, despite growing evidence of molecular heterogeneity of non-oncological diseases, popular methods are benchmarked primarily using cancers with mutually exclusive molecular subtypes well-differentiated by numerous biomarkers. Evaluating 22 unsupervised methods, including clustering and biclustering, using simulated and real transcriptomics data revealed their inefficiency in scenarios with non-mutually exclusive subtypes or subtypes discriminated only by few biomarkers. To address these limitations and advance precision medicine, we developed UnPaSt, a novel biclustering algorithm for unsupervised patient stratification based on differentially expressed biclusters. UnPaSt outperformed widely used patient stratification approaches in the de novo identification of known subtypes of breast cancer and asthma. In addition, it detected many biologically insightful patterns across bulk transcriptomics, proteomics, single-cell, spatial transcriptomics, and multi-omics datasets, enabling a more nuanced and interpretable view of high-throughput data heterogeneity than traditionally used methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00200 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | UnPaSt: unsupervised patient stratification by biclustering of omics data Hartung, Michael Maier, Andreas Burankova, Yuliya Delgado-Chaves, Fernando Isaeva, Olga I. Savchik, Alexey Patroni, Fábio Malta de Sá Lohmann, Jens J. G. He, Daniel Shannon, Casey Schulze, Jan-Ole Kaufmann, Katharina Chervontseva, Zoe Firoozbakht, Farzaneh Hartebrodt, Anne Probul, Niklas Tsoy, Olga Abisheva, Alexandra Zotova, Evgenia Singh, Kavya Van Steen, Kristel Kuehl, Malte Puelles, Victor G. Blumenthal, David B. Ester, Martin Laske, Tanja Baumbach, Jan Zolotareva, Olga Machine Learning Genomics Unsupervised patient stratification is essential for disease subtype discovery, yet, despite growing evidence of molecular heterogeneity of non-oncological diseases, popular methods are benchmarked primarily using cancers with mutually exclusive molecular subtypes well-differentiated by numerous biomarkers. Evaluating 22 unsupervised methods, including clustering and biclustering, using simulated and real transcriptomics data revealed their inefficiency in scenarios with non-mutually exclusive subtypes or subtypes discriminated only by few biomarkers. To address these limitations and advance precision medicine, we developed UnPaSt, a novel biclustering algorithm for unsupervised patient stratification based on differentially expressed biclusters. UnPaSt outperformed widely used patient stratification approaches in the de novo identification of known subtypes of breast cancer and asthma. In addition, it detected many biologically insightful patterns across bulk transcriptomics, proteomics, single-cell, spatial transcriptomics, and multi-omics datasets, enabling a more nuanced and interpretable view of high-throughput data heterogeneity than traditionally used methods. |
| title | UnPaSt: unsupervised patient stratification by biclustering of omics data |
| topic | Machine Learning Genomics |
| url | https://arxiv.org/abs/2408.00200 |