%0 Journal Article %1 stanley2020vopo %A Stanley, Natalie %A Stelzer, Ina A. %A Tsai, Amy S. %A Fallahzadeh, Ramin %A Ganio, Edward %A Becker, Martin %A Phongpreecha, Thanaphong %A Nassar, Huda %A Ghaemi, Sajjad %A Maric, Ivana %A Culos, Anthony %A Chang, Alan L. %A Xenochristou, Maria %A Han, Xiaoyuan %A Espinosa, Camilo %A Rumer, Kristen %A Peterson, Laura %A Verdonk, Franck %A Gaudilliere, Dyani %A Tsai, Eileen %A Feyaerts, Dorien %A Einhaus, Jakob %A Ando, Kazuo %A Wong, Ronald J. %A Obermoser, Gerlinde %A Shaw, Gary M. %A Stevenson, David K. %A Angst, Martin S. %A Gaudilliere, Brice %A Aghaeepour, Nima %D 2020 %J Nature Communications %K auto cell clustering cytof cytometry gating heterogeneous mass meta myown p21 prediction predictive project:bmbf projectrelated single %N 1 %P 3738-- %R 10.1038/s41467-020-17569-8 %T VoPo leverages cellular heterogeneity for predictive modeling of single-cell data %U https://doi.org/10.1038/s41467-020-17569-8 %V 11 %X High-throughput single-cell analysis technologies produce an abundance of data that is critical for profiling the heterogeneity of cellular systems. We introduce VoPo (https://github.com/stanleyn/VoPo), a machine learning algorithm for predictive modeling and comprehensive visualization of the heterogeneity captured in large single-cell datasets. In three mass cytometry datasets, with the largest measuring hundreds of millions of cells over hundreds of samples, VoPo defines phenotypically and functionally homogeneous cell populations. VoPo further outperforms state-of-the-art machine learning algorithms in classification tasks, and identified immune-correlates of clinically-relevant parameters.

@article{stanley2020vopo, abstract = {High-throughput single-cell analysis technologies produce an abundance of data that is critical for profiling the heterogeneity of cellular systems. We introduce VoPo (https://github.com/stanleyn/VoPo), a machine learning algorithm for predictive modeling and comprehensive visualization of the heterogeneity captured in large single-cell datasets. In three mass cytometry datasets, with the largest measuring hundreds of millions of cells over hundreds of samples, VoPo defines phenotypically and functionally homogeneous cell populations. VoPo further outperforms state-of-the-art machine learning algorithms in classification tasks, and identified immune-correlates of clinically-relevant parameters.}, added-at = {2020-08-30T04:57:44.000+0200}, author = {Stanley, Natalie and Stelzer, Ina A. and Tsai, Amy S. and Fallahzadeh, Ramin and Ganio, Edward and Becker, Martin and Phongpreecha, Thanaphong and Nassar, Huda and Ghaemi, Sajjad and Maric, Ivana and Culos, Anthony and Chang, Alan L. and Xenochristou, Maria and Han, Xiaoyuan and Espinosa, Camilo and Rumer, Kristen and Peterson, Laura and Verdonk, Franck and Gaudilliere, Dyani and Tsai, Eileen and Feyaerts, Dorien and Einhaus, Jakob and Ando, Kazuo and Wong, Ronald J. and Obermoser, Gerlinde and Shaw, Gary M. and Stevenson, David K. and Angst, Martin S. and Gaudilliere, Brice and Aghaeepour, Nima}, biburl = {https://www.bibsonomy.org/bibtex/233c61f8a87bb70d3cd358416035b82b3/becker}, description = {impactfactor = {11.8}, impactfactor-year = {2020}, impcatfactor-source = {https://academic-accelerator.com/Impact-Factor-IF/Nature-Communications}}, doi = {10.1038/s41467-020-17569-8}, interhash = {4aedf83d8800dae69ba22f38f59bae13}, intrahash = {33c61f8a87bb70d3cd358416035b82b3}, issn = {20411723}, journal = {Nature Communications}, keywords = {auto cell clustering cytof cytometry gating heterogeneous mass meta myown p21 prediction predictive project:bmbf projectrelated single}, number = 1, pages = {3738--}, refid = {Stanley2020}, timestamp = {2022-02-21T23:54:07.000+0100}, title = {VoPo leverages cellular heterogeneity for predictive modeling of single-cell data}, url = {https://doi.org/10.1038/s41467-020-17569-8}, volume = 11, year = 2020 }