@article{cutlerArchetypalAnalysis1994,
  title = {Archetypal Analysis},
  author = {Cutler, Adele and Breiman, Leo},
  date = {1994},
  journaltitle = {Technometrics : a journal of statistics for the physical, chemical, and engineering sciences},
  shortjournal = {Technometrics},
  volume = {36},
  number = {4},
  pages = {338--347},
  issn = {0040-1706},
  doi = {10.1080/00401706.1994.10485840},
  abstract = {Archetypal analysis represents each individual in a data set as a mixture of individuals of pure type or archetypes. The archetypes themselves are restricted to being mixtures of the individuals in the data set. Archetypes are selected by minimizing the squared error in representing each individual as a mixture of archetypes. The usefulness of archetypal analysis is illustrated on several data sets. Computing the archetypes is a nonlinear least squares problem, which is solved using an alternating minimizing algorithm.},
  file = {/Users/pschafer/Zotero/storage/HI2K6PX3/1994_Cutler_Breiman_Archetypal Analysis_Technometrics.pdf}
}

@article{10.1126/science.1217405,
  title = {Evolutionary Trade-Offs, Pareto Optimality, and the Geometry of Phenotype Space},
  author = {Shoval, O. and Sheftel, H. and Shinar, G. and Hart, Y. and Ramote, O. and Mayo, A. and Dekel, E. and Kavanagh, K. and Alon, U.},
  date = {2012-06},
  journaltitle = {Science},
  shortjournal = {Science},
  volume = {336},
  number = {6085},
  eprint = {22539553},
  eprinttype = {pubmed},
  pages = {1157--1160},
  issn = {0036-8075, 1095-9203},
  doi = {10.1126/science.1217405},
  url = {https://www.science.org/doi/10.1126/science.1217405},
  urldate = {2024-12-07},
  abstract = {Managing Trade-Offs                            Most organisms experience selection on a host of traits to determine their likelihood to succeed evolutionarily. However, specific traits may experience trade-offs in determining an organism's optimal phenotype.                                Shoval                 et al.                              (p.               1157               ; see the Perspective by                                Noor and Milo                              ) relate physical traits to the task that they are optimizing using a Pareto curve, a power law probability distribution, to show that a single set of trait values optimizes performance at a given task and that performance decreases as an organism's phenotype moves away from this set of trait values. The results suggest how selection makes the best trade-offs for an arbitrary number of tasks and traits and may explain examples of evolutionary variation.                        ,              The fitness of an organism can be modeled graphically to determine how phenotypic trade-offs are maximized.           ,              Biological systems that perform multiple tasks face a fundamental trade-off: A given phenotype cannot be optimal at all tasks. Here we ask how trade-offs affect the range of phenotypes found in nature. Using the Pareto front concept from economics and engineering, we find that best–trade-off phenotypes are weighted averages of archetypes—phenotypes specialized for single tasks. For two tasks, phenotypes fall on the line connecting the two archetypes, which could explain linear trait correlations, allometric relationships, as well as bacterial gene-expression patterns. For three tasks, phenotypes fall within a triangle in phenotype space, whose vertices are the archetypes, as evident in morphological studies, including on Darwin’s finches. Tasks can be inferred from measured phenotypes based on the behavior of organisms nearest the archetypes.},
  langid = {english},
  local-url = {file://localhost/Users/pschafer/Documents/Papers\_PDFs/2012\_Shoval\_Alon\_Evolutionary},
  file = {/Users/pschafer/Zotero/storage/2YQ3Z3NF/papv2.pdf;/Users/pschafer/Zotero/storage/55HNZKBG/shoval.sm.v2.pdf;/Users/pschafer/Zotero/storage/QE2MRKCI/shoval.sm.pdf;/Users/pschafer/Zotero/storage/SRXMACSA/Shoval et al. - 2012 - Evolutionary Trade-Offs, Pareto Optimality, and the Geometry of Phenotype Space.pdf}
}

@article{Korem2015,
  title = {Geometry of the Gene Expression Space of Individual Cells},
  author = {Korem, Yael and Szekely, Pablo and Hart, Yuval and Sheftel, Hila and Hausser, Jean and Mayo, Avi and Rothenberg, Michael E. and Kalisky, Tomer and Alon, Uri},
  editor = {Iakoucheva, Lilia M.},
  date = {2015-07-10},
  journaltitle = {PLOS Computational Biology},
  shortjournal = {PLoS Comput Biol},
  volume = {11},
  number = {7},
  eprint = {26161936},
  eprinttype = {pubmed},
  pages = {e1004224},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1004224},
  url = {https://dx.plos.org/10.1371/journal.pcbi.1004224},
  urldate = {2024-12-07},
  abstract = {There is a revolution in the ability to analyze gene expression of single cells in a tissue. To understand this data we must comprehend how cells are distributed in a high-dimensional gene expression space. One open question is whether cell types form discrete clusters or whether gene expression forms a continuum of states. If such a continuum exists, what is its geometry? Recent theory on evolutionary trade-offs suggests that cells that need to perform multiple tasks are arranged in a polygon or polyhedron (line, triangle, tetrahedron and so on, generally called polytopes) in gene expression space, whose vertices are the expression profiles optimal for each task. Here, we analyze single-cell data from human and mouse tissues profiled using a variety of single-cell technologies. We fit the data to shapes with different numbers of vertices, compute their statistical significance, and infer their tasks. We find cases in which single cells fill out a continuum of expression states within a polyhedron. This occurs in intestinal progenitor cells, which fill out a tetrahedron in gene expression space. The four vertices of this tetrahedron are each enriched with genes for a specific task related to stemness and early differentiation. A polyhedral continuum of states is also found in spleen dendritic cells, known to perform multiple immune tasks: cells fill out a tetrahedron whose vertices correspond to key tasks related to maturation, pathogen sensing and communication with lymphocytes. A mixture of continuum-like distributions and discrete clusters is found in other cell types, including bone marrow and differentiated intestinal crypt cells. This approach can be used to understand the geometry and biological tasks of a wide range of single-cell datasets. The present results suggest that the concept of cell type may be expanded. In addition to discreet clusters in gene-expression space, we suggest a new possibility: a continuum of states within a polyhedron, in which the vertices represent specialists at key tasks.},
  langid = {english},
  local-url = {file://localhost/Users/pschafer/Documents/Papers\_PDFs/2015\_Korem\_Alon\_Geometry},
  pmcid = {PMC4498931},
  file = {/Users/pschafer/Zotero/storage/7DL9D4VP/pcbi.1004224.s032.pdf;/Users/pschafer/Zotero/storage/LZ4ZHVFF/Korem et al. - 2015 - Geometry of the Gene Expression Space of Individual Cells.pdf}
}

@article{Hart2015,
  title = {Inferring Biological Tasks Using Pareto Analysis of High-Dimensional Data},
  author = {Hart, Yuval and Sheftel, Hila and Hausser, Jean and Szekely, Pablo and Ben-Moshe, Noa Bossel and Korem, Yael and Tendler, Avichai and Mayo, Avraham E and Alon, Uri},
  date = {2015-03},
  journaltitle = {Nature Methods},
  shortjournal = {Nat Methods},
  volume = {12},
  number = {3},
  eprint = {25622107},
  eprinttype = {pubmed},
  pages = {233--235},
  issn = {1548-7091, 1548-7105},
  doi = {10.1038/nmeth.3254},
  url = {https://www.nature.com/articles/nmeth.3254},
  urldate = {2024-12-07},
  abstract = {Pareto task inference (ParTI) computes a polytype that encloses a data set and determines the enrichment of features around the vertices (archetypes) of the polytype, which allows the identification of the task the archetype represents. We present the Pareto task inference method (ParTI; http://www.weizmann.ac.il/mcb/UriAlon/download/ParTI) for inferring biological tasks from high-dimensional biological data. Data are described as a polytope, and features maximally enriched closest to the vertices (or archetypes) allow identification of the tasks the vertices represent. We demonstrate that human breast tumors and mouse tissues are well described by tetrahedrons in gene expression space, with specific tumor types and biological functions enriched at each of the vertices, suggesting four key tasks.},
  langid = {english},
  local-url = {file://localhost/Users/pschafer/Documents/Papers\_PDFs/2015\_Hart\_Alon\_Inferring},
  file = {/Users/pschafer/Zotero/storage/J9YMZEBQ/Hart et al. - 2015 - Inferring biological tasks using Pareto analysis o.pdf;/Users/pschafer/Zotero/storage/NTKXUJTP/Hart et al. - 2015 - Inferring biological tasks using Pareto analysis of high-dimensional data.pdf}
}

@article{10.1016/j.cels.2018.12.008,
  title = {Continuum of Gene-Expression Profiles Provides Spatial Division of Labor within a Differentiated Cell Type},
  author = {Adler, Miri and Korem Kohanim, Yael and Tendler, Avichai and Mayo, Avi and Alon, Uri},
  date = {2019-01},
  journaltitle = {Cell Systems},
  shortjournal = {Cell Systems},
  volume = {8},
  number = {1},
  eprint = {30638811},
  eprinttype = {pubmed},
  pages = {43-52.e5},
  issn = {24054712},
  doi = {10.1016/j.cels.2018.12.008},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S2405471218304824},
  urldate = {2024-12-07},
  abstract = {Single-cell gene expression reveals the diversity within a differentiated cell type. Often, cells of the same type show a continuum of gene-expression patterns. The origin of such continuum gene-expression patterns is unclear. To address this, we develop a theory to understand how a continuum provides division of labor in a tissue in which cells collectively contribute to several tasks. We find that a continuum is optimal when there are spatial gradients in the tissue that affect the performance in each task. The continuum is bounded inside a polyhedron whose vertices are expression profiles optimal at each task. We test this using single-cell gene expression for intestinal villi and liver hepatocytes, which form a curved 1D trajectory and a full 3D tetrahedron in gene-expression space, respectively. We infer the tasks for both cell types and characterize the spatial zonation of the task-specialist cells. This approach can be generally applied to other tissues.},
  langid = {english},
  local-url = {file://localhost/Users/pschafer/Documents/Papers\_PDFs/2019\_Adler\_Alon\_Continuum},
  file = {/Users/pschafer/Zotero/storage/PUHVRM3L/Adler et al. - 2019 - Continuum of Gene-Expression Profiles Provides Spatial Division of Labor within a Differentiated Cel.pdf}
}

@article{adlerDivisionOfLabor2023,
  title = {Emergence of Division of Labor in Tissues through Cell Interactions and Spatial Cues},
  author = {Adler, Miri and Moriel, Noa and Goeva, Aleksandrina and Avraham-Davidi, Inbal and Mages, Simon and Adams, Taylor S. and Kaminski, Naftali and Macosko, Evan Z. and Regev, Aviv and Medzhitov, Ruslan and Nitzan, Mor},
  date = {2023-05},
  journaltitle = {Cell Reports},
  shortjournal = {Cell Reports},
  volume = {42},
  number = {5},
  eprint = {37086403},
  eprinttype = {pubmed},
  pages = {112412},
  issn = {22111247},
  doi = {10.1016/j.celrep.2023.112412},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S2211124723004230},
  urldate = {2024-12-07},
  abstract = {Most cell types in multicellular organisms can perform multiple functions. However, not all functions can be optimally performed simultaneously by the same cells. Functions incompatible at the level of individual cells can be performed at the cell population level, where cells divide labor and specialize in different functions. Division of labor can arise due to instruction by tissue environment or through self-organization. Here, we develop a computational framework to investigate the contribution of these mechanisms to division of labor within a cell-type population. By optimizing collective cellular task performance under trade-offs, we find that distinguishable expression patterns can emerge from cell-cell interactions versus instructive signals. We propose a method to construct ligand-receptor networks between specialist cells and use it to infer division-oflabor mechanisms from single-cell RNA sequencing (RNA-seq) and spatial transcriptomics data of stromal, epithelial, and immune cells. Our framework can be used to characterize the complexity of cell interactions within tissues.},
  langid = {english},
  local-url = {file://localhost/Users/pschafer/Documents/Papers\_PDFs/2023\_Adler\_Nitzan\_Emergence},
  pmcid = {PMC10242439},
  keywords = {division of labor,enterocytes,fibroblasts,lateral inhibition,macrophages,morphogens,Pareto optimality,self-organization},
  file = {/Users/pschafer/Zotero/storage/MB525RYJ/Adler et al. - 2023 - Emergence of division of labor in tissues through .pdf;/Users/pschafer/Zotero/storage/MTUR8DBL/Adler et al. - 2023 - Emergence of division of labor in tissues through cell interactions and spatial cues.pdf}
}

@article{benMoshe2022,
  title = {The Spatiotemporal Program of Zonal Liver Regeneration Following Acute Injury},
  author = {Ben-Moshe, Shani and Veg, Tamar and Manco, Rita and Dan, Stav and Papinutti, Delfina and Lifshitz, Aviezer and Kolodziejczyk, Aleksandra A. and Bahar Halpern, Keren and Elinav, Eran and Itzkovitz, Shalev},
  date = {2022-06},
  journaltitle = {Cell Stem Cell},
  shortjournal = {Cell Stem Cell},
  volume = {29},
  number = {6},
  eprint = {35659879},
  eprinttype = {pubmed},
  pages = {973-989.e10},
  issn = {19345909},
  doi = {10.1016/j.stem.2022.04.008},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S1934590922001618},
  urldate = {2024-12-07},
  abstract = {The liver carries a remarkable ability to regenerate rapidly after acute zonal damage. Single-cell approaches are necessary to study this process, given the spatial heterogeneity of liver cell types. Here, we use spatially resolved single-cell RNA sequencing (scRNA-seq) to study the dynamics of mouse liver regeneration after acute acetaminophen (APAP) intoxication. We find that hepatocytes proliferate throughout the liver lobule, creating the mitotic pressure required to repopulate the necrotic pericentral zone rapidly. A subset of hepatocytes located at the regenerating front transiently upregulate fetal-specific genes, including Afp and Cdh17, as they reprogram to a pericentral state. Zonated endothelial, hepatic stellate cell (HSC), and macrophage populations are differentially involved in immune recruitment, proliferation, and matrix remodeling. We observe massive transient infiltration of myeloid cells, yet stability of lymphoid cell abundance, in accordance with a global decline in antigen presentation. Our study provides a resource for understanding the coordinated programs of zonal liver regeneration.},
  langid = {english},
  local-url = {file://localhost/Users/pschafer/Documents/Papers\_PDFs/2022\_Ben-Moshe\_Itzkovitz\_The},
  file = {/Users/pschafer/Zotero/storage/EMDXE7PX/Ben-Moshe et al. - 2022 - The spatiotemporal program of zonal liver regeneration following acute injury.pdf}
}

@article{10.1038/nature21065,
  title = {Single-Cell Spatial Reconstruction Reveals Global Division of Labour in the Mammalian Liver},
  author = {Halpern, Keren Bahar and Shenhav, Rom and Matcovitch-Natan, Orit and Tóth, Beáta and Lemze, Doron and Golan, Matan and Massasa, Efi E. and Baydatch, Shaked and Landen, Shanie and Moor, Andreas E. and Brandis, Alexander and Giladi, Amir and Stokar-Avihail, Avigail and David, Eyal and Amit, Ido and Itzkovitz, Shalev},
  date = {2017-02-16},
  journaltitle = {Nature},
  shortjournal = {Nature},
  volume = {542},
  number = {7641},
  eprint = {28166538},
  eprinttype = {pubmed},
  pages = {352--356},
  issn = {0028-0836, 1476-4687},
  doi = {10.1038/nature21065},
  url = {https://www.nature.com/articles/nature21065},
  urldate = {2024-12-07},
  abstract = {Single-molecule fluorescence in situ hybridization is performed to identify several landmark genes in the liver and their level of expression in single-cell RNA sequencing is used to spatially reconstruct the zonation of all liver genes. A few important liver genes are differentially expressed in a radial fashion from the central vein outward, but the extent of such spatial division, or zonation, has remained unknown. Shalev Itzkovitz, Ido Amit and colleagues performed single-molecule fluorescence in situ hybridization against several landmark genes, and used their level of expression to spatially reconstruct the zonation of all liver genes. They find that around half of the genes in the liver are zonated, and surprisingly also find that several genes peak in the middle of the liver lobules. The succession of gene expression profiles matches the position of proteins in the bile-acid biosynthetic cascade. The mammalian liver consists of hexagon-shaped lobules that are radially polarized by blood flow and morphogens1,2,3,4. Key liver genes have been shown to be differentially expressed along the lobule axis, a phenomenon termed zonation5,6, but a detailed genome-wide reconstruction of this spatial division of labour has not been achieved. Here we measure the entire transcriptome of thousands of mouse liver cells and infer their lobule coordinates on the basis of a panel of zonated landmark genes, characterized with single-molecule fluorescence in situ hybridization7. Using this approach, we obtain the zonation profiles of all liver genes with high spatial resolution. We find that around 50\% of liver genes are significantly zonated and uncover abundant non-monotonic profiles that peak at the mid-lobule layers. These include a spatial order of bile acid biosynthesis enzymes that matches their position in the enzymatic cascade. Our approach can facilitate the reconstruction of similar spatial genomic blueprints for other mammalian organs.},
  langid = {english},
  local-url = {file://localhost/Users/pschafer/Documents/Papers\_PDFs/2017\_Halpern\_Itzkovitz\_Single-cell},
  pmcid = {PMC5321580},
  file = {/Users/pschafer/Zotero/storage/ERMHINIP/Halpern et al. - 2017 - Single-cell spatial reconstruction reveals global division of labour in the mammalian liver.pdf}
}

@online{heStarfyshRevealsHeterogeneous2022,
  title = {Starfysh Reveals Heterogeneous Spatial Dynamics in the Breast Tumor Microenvironment},
  date = {2022-11-24},
  eprinttype = {Bioinformatics},
  doi = {10.1101/2022.11.21.517420},
  url = {http://biorxiv.org/lookup/doi/10.1101/2022.11.21.517420},
  urldate = {2024-12-07},
  abstract = {Abstract                        Spatially-resolved gene expression profiling provides valuable insight into tissue organization and cell-cell crosstalk; however, spatial transcriptomics (ST) lacks single-cell resolution. Current ST analysis methods require single-cell RNA sequencing data as a reference for a rigorous interpretation of cell states and do not utilize associated histology images. Significant sample variation further complicates the integration of ST datasets, which is essential for identifying commonalities across tissues or altered cellular wiring in disease. Here, we present Starfysh, the first comprehensive computational toolbox for joint modeling of ST and histology data, dissection of refined cell states, and systematic integration of multiple ST datasets from complex tissues. Starfysh uses an auxiliary deep generative model that incorporates archetypal analysis and any known cell state markers to avoid the need for a single-cell-resolution reference in characterizing known or novel tissue-specific cell states. Additionally, Starfysh improves the characterization of spatial dynamics in complex tissues by leveraging histology images and enables the comparison of niches as spatial “hubs” across tissues. Integrative analysis of primary estrogen receptor-positive (ER             +             ) breast cancer, triple-negative breast cancer (TNBC), and metaplastic breast cancer (MBC) tumors using Starfysh led to the identification of heterogeneous patient- and disease-specific hubs as well as a shared stromal hub with varying spatial orientation. Our results show the ability to delineate the spatial co-evolution of tumor and immune cell states and their crosstalk underlying intratumoral heterogeneity in TNBC and revealed metabolic reprogramming shaping immunosuppressive hubs in aggressive MBC. Starfysh is publicly available (             https://github.com/azizilab/starfysh             ).},
  langid = {english},
  pubstate = {prepublished},
  file = {/Users/pschafer/Zotero/storage/SRUUXTVG/He et al. - 2022 - Starfysh reveals heterogeneous spatial dynamics in the breast tumor microenvironment.pdf},
  author = {He, Siyu and Jin, Yinuo and Nazaret, Achille and Shi, Lingting and Chen, Xueer and Rampersaud, Sham and Dhillon, Bahawar S. and Valdez, Izabella and others and Mehdi, Kaleem and Rohde, Madeline and McFaline-Figueroa, José L. and Blei, David and Leong, Kam W. and Rudensky, Alexander Y and Plitas, George and Azizi, Elham}
}

@article{morupArchetypalAnalysisMachine2012,
  title = {Archetypal Analysis for Machine Learning and Data Mining},
  author = {Mørup, Morten and Hansen, Lars Kai},
  date = {2012-03},
  journaltitle = {Neurocomputing},
  shortjournal = {Neurocomputing},
  volume = {80},
  pages = {54--63},
  issn = {09252312},
  doi = {10.1016/j.neucom.2011.06.033},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S0925231211006060},
  urldate = {2024-12-07},
  abstract = {Archetypal analysis (AA) proposed by Cutler and Breiman (1994) [7] estimates the principal convex hull (PCH) of a data set. As such AA favors features that constitute representative ‘corners’ of the data, i.e., distinct aspects or archetypes. We currently show that AA enjoys the interpretability of clustering –without being limited to hard assignment and the uniqueness of SVD – without being limited to orthogonal representations. In order to do large scale AA, we derive an efficient algorithm based on projected gradient as well as an initialization procedure we denote FURTHESTSUM that is inspired by the FURTHESTFIRST approach widely used for K-means (Hochbaum and Shmoys, 1985 [14]). We generalize the AA procedure to KERNEL-AA in order to extract the principal convex hull in potential infinite Hilbert spaces and derive a relaxation of AA when the archetypes cannot be represented as convex combinations of the observed data. We further demonstrate that the AA model is relevant for feature extraction and dimensionality reduction for a large variety of machine learning problems taken from computer vision, neuroimaging, chemistry, text mining and collaborative filtering leading to highly interpretable representations of the dynamics in the data. Matlab code for the derived algorithms is available for download from www.mortenmorup.dk.},
  langid = {english},
  file = {/Users/pschafer/Zotero/storage/HHR85VMR/Mørup and Hansen - 2012 - Archetypal analysis for machine learning and data mining.pdf}
}

@online{bauckhageNoteArchetypalAnalysis2014,
  title = {A Note on Archetypal Analysis and the Approximation of Convex Hulls},
  author = {Bauckhage, Christian},
  date = {2014-09-27},
  eprint = {1410.0642},
  eprinttype = {arXiv},
  eprintclass = {cs},
  doi = {10.48550/arXiv.1410.0642},
  url = {http://arxiv.org/abs/1410.0642},
  urldate = {2024-12-07},
  abstract = {We briefly review the basic ideas behind archetypal analysis for matrix factorization and discuss its behavior in approximating the convex hull of a data sample. We then ask how good such approximations can be and consider different cases. Understanding archetypal analysis as the problem of computing a convexity constrained low-rank approximation of the identity matrix provides estimates for archetypal analysis and the SiVM heuristic.},
  langid = {english},
  pubstate = {prepublished},
  keywords = {Computer Science - Computational Geometry,Computer Science - Numerical Analysis},
  file = {/Users/pschafer/Zotero/storage/RG43S5LF/Bauckhage - 2014 - A Note on Archetypal Analysis and the Approximation of Convex Hulls.pdf}
}

@article{10.1146/annurev-cellbio-120420-113830,
  title = {Tissue Biology: In Search of a New Paradigm},
  shorttitle = {Tissue Biology},
  author = {Adler, Miri and Chavan, Arun R. and Medzhitov, Ruslan},
  date = {2023-10-16},
  journaltitle = {Annual Review of Cell and Developmental Biology},
  shortjournal = {Annu. Rev. Cell Dev. Biol.},
  volume = {39},
  number = {1},
  eprint = {37607470},
  eprinttype = {pubmed},
  pages = {67--89},
  issn = {1081-0706, 1530-8995},
  doi = {10.1146/annurev-cellbio-120420-113830},
  url = {https://www.annualreviews.org/doi/10.1146/annurev-cellbio-120420-113830},
  urldate = {2024-12-07},
  abstract = {Animal tissues are made up of multiple cell types that are increasingly well-characterized, yet our understanding of the core principles that govern tissue organization is still incomplete. This is in part because many observable tissue characteristics, such as cellular composition and spatial patterns, are emergent properties, and as such, they cannot be explained through the knowledge of individual cells alone. Here we propose a complex systems theory perspective to address this fundamental gap in our understanding of tissue biology. We introduce the concept of cell categories, which is based on cell relations rather than cell identity. Based on these notions we then discuss common principles of tissue modularity, introducing compositional, structural, and functional tissue modules. Cell diversity and cell relations provide a basis for a new perspective on the underlying principles of tissue organization in health and disease.},
  langid = {english},
  local-url = {file://localhost/Users/pschafer/Documents/Papers\_PDFs/2023\_Adler\_Medzhitov\_Tissue},
  file = {/Users/pschafer/Zotero/storage/4SVH92GJ/Adler et al. - 2023 - Tissue Biology In Search of a New Paradigm.pdf}
}

@article{mohammadiMultiresolutionFrameworkCharacterize2020,
  title = {A Multiresolution Framework to Characterize Single-Cell State Landscapes},
  author = {Mohammadi, Shahin and Davila-Velderrain, Jose and Kellis, Manolis},
  date = {2020-10-26},
  journaltitle = {Nature Communications},
  shortjournal = {Nat Commun},
  volume = {11},
  number = {1},
  pages = {5399},
  issn = {2041-1723},
  doi = {10.1038/s41467-020-18416-6},
  url = {https://www.nature.com/articles/s41467-020-18416-6},
  urldate = {2024-12-07},
  abstract = {Abstract             Dissecting the cellular heterogeneity embedded in single-cell transcriptomic data is challenging. Although many methods and approaches exist, identifying cell states and their underlying topology is still a major challenge. Here, we introduce the concept of multiresolution cell-state decomposition as a practical approach to simultaneously capture both fine- and coarse-grain patterns of variability. We implement this concept in ACTIONet, a comprehensive framework that combines archetypal analysis and manifold learning to provide a ready-to-use analytical approach for multiresolution single-cell state characterization. ACTIONet provides a robust, reproducible, and highly interpretable single-cell analysis platform that couples dominant pattern discovery with a corresponding structural representation of the cell state landscape. Using multiple synthetic and real data sets, we demonstrate ACTIONet’s superior performance relative to existing alternatives. We use ACTIONet to integrate and annotate cells across three human cortex data sets. Through integrative comparative analysis, we define a consensus vocabulary and a consistent set of gene signatures discriminating against the transcriptomic cell types and subtypes of the human prefrontal cortex.},
  langid = {english},
  file = {/Users/pschafer/Zotero/storage/ISBL7AZD/Mohammadi et al. - 2020 - A multiresolution framework to characterize single.pdf;/Users/pschafer/Zotero/storage/TXHV366Z/Mohammadi et al. - 2020 - A multiresolution framework to characterize single-cell state landscapes.pdf}
}

@article{eugsterSpiderManHeroArchetypal2009,
  title = {From Spider-Man to Hero - Archetypal Analysis in {\mkbibemph{R}}},
  author = {Eugster, Manuel J. A. and Leisch, Friedrich},
  date = {2009},
  journaltitle = {Journal of Statistical Software},
  shortjournal = {J. Stat. Soft.},
  volume = {30},
  number = {8},
  issn = {1548-7660},
  doi = {10.18637/jss.v030.i08},
  url = {http://www.jstatsoft.org/v30/i08/},
  urldate = {2024-12-07},
  abstract = {Archetypal analysis has the aim to represent observations in a multivariate data set as convex combinations of extremal points. This approach was introduced by Cutler and Breiman (1994); they defined the concrete problem, laid out the theoretical foundations and presented an algorithm written in Fortran. In this paper we present the R package archetypes which is available on the Comprehensive R Archive Network. The package provides an implementation of the archetypal analysis algorithm within R and different exploratory tools to analyze the algorithm during its execution and its final result. The application of the package is demonstrated on two examples.},
  langid = {english},
  file = {/Users/pschafer/Zotero/storage/8SD68YKC/Eugster and Leisch - 2009 - From Spider-Man to Hero - Archetypal Analysis in R.pdf}
}

@article{turosChrysalisDecodingTissue2023,
  title = {Chrysalis: Decoding Tissue Compartments in Spatial Transcriptomics with Archetypal Analysis},
  shorttitle = {Chrysalis},
  author = {Túrós, Demeter and Vasiljevic, Jelica and Hahn, Kerstin and Rottenberg, Sven},
  date = {2023},
  journaltitle = {bioRxiv : the preprint server for biology},
  shortjournal = {bioRxiv},
  volume = {7},
  number = {1},
  pages = {2023.08.17.553606},
  issn = {2399-3642},
  doi = {10.1101/2023.08.17.553606},
  url = {https://www.nature.com/articles/s42003-024-07165-7},
  urldate = {2024-12-06},
  abstract = {Dissecting tissue compartments in spatial transcriptomics (ST) remains challenging due to limited spatial resolution and dependence on single-cell reference data. We present Chrysalis, a novel method to rapidly detect tissue compartments through spatially variable gene (SVG) detection and archetypal analysis without external references. We applied Chrysalis on ST datasets originating from various species, tissues and technologies and demonstrated state-of-the-art performance in identifying cellular niches.},
  langid = {english},
  file = {/Users/pschafer/Zotero/storage/995W3B4S/Túrós et al. - 2024 - Chrysalis decoding tissue compartments in spatial.pdf;/Users/pschafer/Zotero/storage/V4TER9TD/Túrós et al. - Chrysalis decoding tissue compartments in spatial transcriptomics with archetypal analysis.pdf}
}

@article{10.1038/s41467-023-42878-z,
  title = {NIPMAP: Niche-Phenotype Mapping of Multiplex Histology Data by Community Ecology},
  shorttitle = {NIPMAP},
  author = {El Marrahi, Anissa and Lipreri, Fabio and Kang, Ziqi and Gsell, Louise and Eroglu, Alper and Alber, David and Hausser, Jean},
  date = {2023-11-07},
  journaltitle = {Nature Communications},
  shortjournal = {Nat Commun},
  volume = {14},
  number = {1},
  eprint = {37935691},
  eprinttype = {pubmed},
  pages = {7182},
  issn = {2041-1723},
  doi = {10.1038/s41467-023-42878-z},
  url = {https://www.nature.com/articles/s41467-023-42878-z},
  urldate = {2024-12-07},
  abstract = {Abstract             Advances in multiplex histology allow surveying millions of cells, dozens of cell types, and up to thousands of phenotypes within the spatial context of tissue sections. This leads to a combinatorial challenge in (a) summarizing the cellular and phenotypic architecture of tissues and (b) identifying phenotypes with interesting spatial architecture. To address this, we combine ideas from community ecology and machine learning into niche-phenotype mapping (NIPMAP). NIPMAP takes advantage of geometric constraints on local cellular composition imposed by the niche structure of tissues in order to automatically segment tissue sections into niches and their interfaces. Projecting phenotypes on niches and their interfaces identifies previously-reported and previously-unreported spatially-driven phenotypes, concisely summarizes the phenotypic architecture of tissues, and reveals fundamental properties of tissue architecture. NIPMAP is applicable to both protein and RNA multiplex histology of healthy and diseased tissue. An open-source R/Python package implements NIPMAP.},
  langid = {english},
  local-url = {file://localhost/Users/pschafer/Documents/Papers\_PDFs/2023\_Marrahi\_Hausser\_NIPMAP-},
  pmcid = {PMC10630431},
  file = {/Users/pschafer/Zotero/storage/YNTXDQB3/El Marrahi et al. - 2023 - NIPMAP niche-phenotype mapping of multiplex histology data by community ecology.pdf}
}

@article{heStarfyshIntegratesSpatial2024,
  title = {Starfysh Integrates Spatial Transcriptomic and Histologic Data to Reveal Heterogeneous Tumor–Immune Hubs},
  date = {2024-03-21},
  journaltitle = {Nature Biotechnology},
  shortjournal = {Nat Biotechnol},
  pages = {1--13},
  issn = {1087-0156, 1546-1696},
  doi = {10.1038/s41587-024-02173-8},
  url = {https://www.nature.com/articles/s41587-024-02173-8},
  urldate = {2024-12-07},
  abstract = {Abstract             Spatially resolved gene expression profiling provides insight into tissue organization and cell–cell crosstalk; however, sequencing-based spatial transcriptomics (ST) lacks single-cell resolution. Current ST analysis methods require single-cell RNA sequencing data as a reference for rigorous interpretation of cell states, mostly do not use associated histology images and are not capable of inferring shared neighborhoods across multiple tissues. Here we present Starfysh, a computational toolbox using a deep generative model that incorporates archetypal analysis and any known cell type markers to characterize known or new tissue-specific cell states without a single-cell reference. Starfysh improves the characterization of spatial dynamics in complex tissues using histology images and enables the comparison of niches as spatial hubs across tissues. Integrative analysis of primary estrogen receptor (ER)-positive breast cancer, triple-negative breast cancer (TNBC) and metaplastic breast cancer (MBC) tissues led to the identification of spatial hubs with patient- and disease-specific cell type compositions and revealed metabolic reprogramming shaping immunosuppressive hubs in aggressive MBC.},
  langid = {english},
  file = {/Users/pschafer/Zotero/storage/3ESHD6R6/He et al. - 2024 - Starfysh integrates spatial transcriptomic and histologic data to reveal heterogeneous tumor–immune.pdf},
  author = {He, Siyu and Jin, Yinuo and Nazaret, Achille and Shi, Lingting and Chen, Xueer and Rampersaud, Sham and Dhillon, Bahawar S. and Valdez, Izabella and others and Mehdi, Kaleem and Rohde, Madeline and McFaline-Figueroa, José L. and Blei, David and Leong, Kam W. and Rudensky, Alexander Y. and Plitas, George and Azizi, Elham}
}

@article{10.1038/s41587-023-01716-9,
  title = {SEACells Infers Transcriptional and Epigenomic Cellular States from Single-Cell Genomics Data},
  author = {Persad, Sitara and Choo, Zi-Ning and Dien, Christine and Sohail, Noor and Masilionis, Ignas and Chaligné, Ronan and Nawy, Tal and Brown, Chrysothemis C. and Sharma, Roshan and Pe’er, Itsik and Setty, Manu and Pe’er, Dana},
  date = {2023-12},
  journaltitle = {Nature Biotechnology},
  shortjournal = {Nat Biotechnol},
  volume = {41},
  number = {12},
  eprint = {36973557},
  eprinttype = {pubmed},
  pages = {1746--1757},
  issn = {1087-0156, 1546-1696},
  doi = {10.1038/s41587-023-01716-9},
  url = {https://www.nature.com/articles/s41587-023-01716-9},
  urldate = {2024-12-07},
  abstract = {Abstract             Metacells are cell groupings derived from single-cell sequencing data that represent highly granular, distinct cell states. Here we present single-cell aggregation of cell states (SEACells), an algorithm for identifying metacells that overcome the sparsity of single-cell data while retaining heterogeneity obscured by traditional cell clustering. SEACells outperforms existing algorithms in identifying comprehensive, compact and well-separated metacells in both RNA and assay for transposase-accessible chromatin (ATAC) modalities across datasets with discrete cell types and continuous trajectories. We demonstrate the use of SEACells to improve gene–peak associations, compute ATAC gene scores and infer the activities of critical regulators during differentiation. Metacell-level analysis scales to large datasets and is particularly well suited for patient cohorts, where per-patient aggregation provides more robust units for data integration. We use our metacells to reveal expression dynamics and gradual reconfiguration of the chromatin landscape during hematopoietic differentiation and to uniquely identify CD4 T cell differentiation and activation states associated with disease onset and severity in a Coronavirus Disease 2019 (COVID-19) patient cohort.},
  langid = {english},
  local-url = {file://localhost/Users/pschafer/Documents/Papers\_PDFs/2023\_Persad\_Pe’er\_SEACells},
  pmcid = {PMC10713451},
  file = {/Users/pschafer/Zotero/storage/HJQNU9LX/Persad et al. - 2023 - SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data.pdf}
}

@article{militeInterpretableMultiOmicsData2024,
  title = {Interpretable Multi-Omics Data Integration with Deep Archetypal Analysis},
  author = {Milite, Salvatore and Caravagna, Giulio and Sottoriva, Andrea},
  date = {2024},
  journaltitle = {bioRxiv : the preprint server for biology},
  shortjournal = {bioRxiv},
  pages = {2024.04.05.588238},
  doi = {10.1101/2024.04.05.588238},
  abstract = {High-throughput multi-omics technologies offer a lens through which the concurrent states of biological systems can be viewed. However, the integration and interpretation of high-dimensional, sparse, and noisy multi-omics datasets is challenging, often requiring leveraging algorithms for dimensionality reduction. Here we introduce Deep Archetypal Analysis for the Representation of Integrated Omics (DAARIO), a framework that combines archetypal analysis with deep learning. Using the concept of archetypes – extreme data points that define the geometry of the latent space – our model preserves the complexity of biological interactions while retaining an interpretable latent space, a crucial feature to decipher the underlying biology. We show on real and simulated multi-omics data how DAARIO outperforms other state-of-the-art methods in identifying parsimonious, interpretable, and biologically relevant patterns.},
  langid = {english},
  file = {/Users/pschafer/Zotero/storage/CZ7DUUGD/Milite et al. - 2024 - Interpretable Multi-Omics Data Integration with Deep Archetypal Analysis.pdf}
}

@article{hanProbabilisticMethodsApproximate2023,
  title = {Probabilistic Methods for Approximate Archetypal Analysis},
  author = {Han, Ruijian and Osting, Braxton and Wang, Dong and Xu, Yiming},
  date = {2023-02-02},
  journaltitle = {Information and Inference: A Journal of the IMA},
  volume = {12},
  number = {1},
  pages = {466--493},
  issn = {2049-8772},
  doi = {10.1093/imaiai/iaac008},
  url = {https://academic.oup.com/imaiai/article/12/1/466/6576183},
  urldate = {2024-12-07},
  abstract = {Abstract             Archetypal analysis (AA) is an unsupervised learning method for exploratory data analysis. One major challenge that limits the applicability of AA in practice is the inherent computational complexity of the existing algorithms. In this paper, we provide a novel approximation approach to partially address this issue. Utilizing probabilistic ideas from high-dimensional geometry, we introduce two preprocessing techniques to reduce the dimension and representation cardinality of the data, respectively. We prove that provided data are approximately embedded in a low-dimensional linear subspace and the convex hull of the corresponding representations is well approximated by a polytope with a few vertices, our method can effectively reduce the scaling of AA. Moreover, the solution of the reduced problem is near-optimal in terms of prediction errors. Our approach can be combined with other acceleration techniques to further mitigate the intrinsic complexity of AA. We demonstrate the usefulness of our results by applying our method to summarize several moderately large-scale datasets.},
  langid = {english},
  file = {/Users/pschafer/Zotero/storage/DMXCXCVB/Han et al. - 2023 - Probabilistic methods for approximate archetypal analysis.pdf}
}

@article{aloiseNPhardnessEuclideanSumofsquares2009,
  title = {NP-Hardness of Euclidean Sum-of-Squares Clustering},
  author = {Aloise, Daniel and Deshpande, Amit and Hansen, Pierre and Popat, Preyas},
  date = {2009-05},
  journaltitle = {Machine Learning},
  shortjournal = {Mach Learn},
  volume = {75},
  number = {2},
  pages = {245--248},
  issn = {0885-6125, 1573-0565},
  doi = {10.1007/s10994-009-5103-0},
  url = {http://link.springer.com/10.1007/s10994-009-5103-0},
  urldate = {2024-12-07},
  abstract = {A recent proof of NP-hardness of Euclidean sum-of-squares clustering, due to Drineas et al. (Mach. Learn. 56:9–33, 2004), is not valid. An alternate short proof is provided.},
  langid = {english},
  file = {/Users/pschafer/Zotero/storage/D8IEQVE2/Aloise et al. - 2009 - NP-hardness of Euclidean sum-of-squares clustering.pdf}
}

@article{wangNonlinearArchetypalAnalysis2022,
  title = {Non-Linear Archetypal Analysis of Single-Cell RNA-Seq Data by Deep Autoencoders},
  author = {Wang, Yuge and Zhao, Hongyu},
  editor = {Li, Mingyao},
  date = {2022-04-01},
  journaltitle = {PLOS Computational Biology},
  shortjournal = {PLoS Comput Biol},
  volume = {18},
  number = {4},
  pages = {e1010025},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1010025},
  url = {https://dx.plos.org/10.1371/journal.pcbi.1010025},
  urldate = {2024-12-07},
  abstract = {Advances in single-cell RNA sequencing (scRNA-seq) have led to successes in discovering novel cell types and understanding cellular heterogeneity among complex cell populations through cluster analysis. However, cluster analysis is not able to reveal continuous spectrum of states and underlying gene expression programs (GEPs) shared across cell types. We introduce scAAnet, an autoencoder for single-cell non-linear archetypal analysis, to identify GEPs and infer the relative activity of each GEP across cells. We use a count distribution-based loss term to account for the sparsity and overdispersion of the raw count data and add an archetypal constraint to the loss function of scAAnet. We first show that scAAnet outperforms existing methods for archetypal analysis across different metrics through simulations. We then demonstrate the ability of scAAnet to extract biologically meaningful GEPs using publicly available scRNA-seq datasets including a pancreatic islet dataset, a lung idiopathic pulmonary fibrosis dataset and a prefrontal cortex dataset.},
  langid = {english},
  file = {/Users/pschafer/Zotero/storage/G2NSAFRI/Wang and Zhao - 2022 - Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders.pdf}
}

@online{kellerDeepArchetypalAnalysis2020,
  title = {Deep Archetypal Analysis},
  author = {Keller, Sebastian Mathias and Samarin, Maxim and Wieser, Mario and Roth, Volker},
  date = {2020-01-24},
  eprint = {1901.10799},
  eprinttype = {arXiv},
  eprintclass = {cs},
  doi = {10.48550/arXiv.1901.10799},
  url = {http://arxiv.org/abs/1901.10799},
  urldate = {2024-12-07},
  abstract = {Deep Archetypal Analysis (DeepAA) generates latent representations of high-dimensional datasets in terms of intuitively understandable basic entities called archetypes. The proposed method extends linear Archetypal Analysis (AA), an unsupervised method to represent multivariate data points as convex combinations of extremal data points. Unlike the original formulation, Deep AA is generative and capable of handling side information. In addition, our model provides the ability for data-driven representation learning which reduces the dependence on expert knowledge. We empirically demonstrate the applicability of our approach by exploring the chemical space of small organic molecules. In doing so, we employ the archetype constraint to learn two different latent archetype representations for the same dataset, with respect to two chemical properties. This type of supervised exploration marks a distinct starting point and let us steer de novo molecular design.},
  langid = {english},
  pubstate = {prepublished},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning,Statistics - Machine Learning},
  file = {/Users/pschafer/Zotero/storage/LTS92WBL/Keller et al. - 2020 - Deep Archetypal Analysis.pdf}
}

@article{halloranArchetypalAnalysisInjury2022,
  title = {Archetypal Analysis of Injury in Kidney Transplant Biopsies Identifies Two Classes of Early AKI},
  author = {Halloran, Philip F. and Böhmig, Georg A. and Bromberg, Jonathan and Einecke, Gunilla and Eskandary, Farsad A. and Gupta, Gaurav and Myslak, Marek and Viklicky, Ondrej and Perkowska-Ptasinska, Agnieszka and Madill-Thomsen, Katelynn S. and {The INTERCOMEX Investigators}},
  date = {2022-04-07},
  journaltitle = {Frontiers in Medicine},
  shortjournal = {Front. Med.},
  volume = {9},
  pages = {817324},
  issn = {2296-858X},
  doi = {10.3389/fmed.2022.817324},
  url = {https://www.frontiersin.org/articles/10.3389/fmed.2022.817324/full},
  urldate = {2024-12-07},
  abstract = {All transplanted kidneys are subjected to some degree of injury as a result of the donation-implantation process and various post-transplant stresses such as rejection. Because transplants are frequently biopsied, they present an opportunity to explore the full spectrum of kidney response-to-wounding from all causes. Defining parenchymal damage in transplanted organs is important for clinical management because it determines function and survival. In this study, we classified the scenarios associated with parenchymal injury in genome-wide microarray results from 1,526 kidney transplant indication biopsies collected during the INTERCOMEX study. We defined injury groups by using archetypal analysis (AA) of scores for gene sets and classifiers previously identified in various injury states. Six groups and their characteristics were defined in this population: No injury, minor injury, two classes of acute kidney injury (“AKI,” AKI1, and AKI2), chronic kidney disease (CKD), and CKD combined with AKI. We compared the two classes of AKI, namely, AKI1 and AKI2. AKI1 had a poor function and increased parenchymal dedifferentiation but minimal response-to-injury and inflammation, instead having increased expression of PARD3, a gene previously characterized as being related to epithelial polarity and adherens junctions. In contrast, AKI2 had a poor function and increased response-to-injury, significant inflammation, and increased macrophage activity. In random forest analysis, the most important predictors of function (estimated glomerular filtration rate) and graft loss were injury-based molecular scores, not rejection scores. AKI1 and AKI2 differed in 3-year graft survival, with better survival in the AKI2 group. Thus, injury archetype analysis of injury-induced gene expression shows new heterogeneity in kidney response-to-wounding, revealing AKI1, a class of early transplants with a poor function but minimal inflammation or response to injury, a deviant response characterized as PC3, and an increased risk of failure. Given the relationship between parenchymal injury and kidney survival, further characterization of the injury phenotypes in kidney transplants will be important for an improved understanding that could have implications for understanding native kidney diseases (               ClinicalTrials.gov               \#NCT01299168).},
  langid = {english},
  file = {/Users/pschafer/Zotero/storage/RSYZ5KDT/Halloran et al. - 2022 - Archetypal Analysis of Injury in Kidney Transplant Biopsies Identifies Two Classes of Early AKI.pdf}
}

@article{kocillariSignatureParetoOptimization2018,
  title = {Signature of Pareto Optimization in the Escherichia Coli Proteome},
  author = {Koçillari, Loren and Fariselli, Piero and Trovato, Antonio and Seno, Flavio and Maritan, Amos},
  date = {2018-06-14},
  journaltitle = {Scientific Reports},
  shortjournal = {Sci Rep},
  volume = {8},
  number = {1},
  pages = {9141},
  issn = {2045-2322},
  doi = {10.1038/s41598-018-27287-3},
  url = {https://www.nature.com/articles/s41598-018-27287-3},
  urldate = {2024-12-07},
  abstract = {Abstract                            Proteins have coevolved with cellular environments to improve or preserve their functions, maintaining at the same time the degree of hydrophobicity necessary to fold correctly and enough solubility to perform their biological roles. Here, we study the               Escherichia coli               proteome using a Pareto front analysis in the solubility-hydrophobicity space. The results indicate the existence of a Pareto optimal front, a triangle whose vertices correspond to archetypal proteins specialized in distinct tasks, such as regulatory processes, membrane transport, outer-membrane pore formation, catalysis, and binding. The vertices are further enriched with proteins that occupy different subcellular compartments, namely, cytoplasmic, inner membrane, outer membrane, and outer membrane bounded periplasmic space. The combination of various enriching features offers an interpretation of how bacteria use the physico-chemical properties of proteins, both to drive them into their final destination in the cell and to have their tasks accomplished.},
  langid = {english},
  file = {/Users/pschafer/Zotero/storage/RBCE2FFS/Koçillari et al. - 2018 - Signature of Pareto optimization in the Escherichia coli proteome.pdf}
}

@article{turosChrysalisDecodingTissue2024,
  title = {Chrysalis: Decoding Tissue Compartments in Spatial Transcriptomics with Archetypal Analysis},
  shorttitle = {Chrysalis},
  author = {Túrós, Demeter and Vasiljevic, Jelica and Hahn, Kerstin and Rottenberg, Sven and Valdeolivas, Alberto},
  date = {2024-11-16},
  journaltitle = {Communications Biology},
  shortjournal = {Commun Biol},
  volume = {7},
  number = {1},
  pages = {1520},
  issn = {2399-3642},
  doi = {10.1038/s42003-024-07165-7},
  url = {https://www.nature.com/articles/s42003-024-07165-7},
  urldate = {2024-12-07},
  langid = {english},
  file = {/Users/pschafer/Zotero/storage/TU873WI3/Túrós et al. - 2024 - Chrysalis decoding tissue compartments in spatial transcriptomics with archetypal analysis.pdf}
}

@article{nathEvolutionCoreArchetypal2021,
  title = {Evolution of Core Archetypal Phenotypes in Progressive High Grade Serous Ovarian Cancer},
  date = {2021-05-24},
  journaltitle = {Nature Communications},
  shortjournal = {Nat Commun},
  volume = {12},
  number = {1},
  pages = {3039},
  issn = {2041-1723},
  doi = {10.1038/s41467-021-23171-3},
  url = {https://www.nature.com/articles/s41467-021-23171-3},
  urldate = {2024-12-07},
  abstract = {Abstract             The evolution of resistance in high-grade serous ovarian cancer (HGSOC) cells following chemotherapy is only partially understood. To understand the selection of factors driving heterogeneity before and through adaptation to treatment, we profile single-cell RNA-sequencing (scRNA-seq) transcriptomes of HGSOC tumors collected longitudinally during therapy. We analyze scRNA-seq data from two independent patient cohorts to reveal that HGSOC is driven by three archetypal phenotypes, defined as oncogenic states that describe the majority of the transcriptome variation. Using a multi-task learning approach to identify the biological tasks of each archetype, we identify metabolism and proliferation, cellular defense response, and DNA repair signaling as consistent cell states found across patients. Our analysis demonstrates a shift in favor of the metabolism and proliferation archetype versus cellular defense response archetype in cancer cells that received multiple lines of treatment. While archetypes are not consistently associated with specific whole-genome driver mutations, they are closely associated with subclonal populations at the single-cell level, indicating that subclones within a tumor often specialize in unique biological tasks. Our study reveals the core archetypes found in progressive HGSOC and shows consistent enrichment of subclones with the metabolism and proliferation archetype as resistance is acquired to multiple lines of therapy.},
  langid = {english},
  file = {/Users/pschafer/Zotero/storage/RMCCJVTG/Nath et al. - 2021 - Evolution of core archetypal phenotypes in progressive high grade serous ovarian cancer.pdf},
  author = {Nath, Aritro and Cosgrove, Patrick A. and Mirsafian, Hoda and Christie, Elizabeth L. and Pflieger, Lance and Copeland, Benjamin and Majumdar, Sumana and Cristea, Mihaela C. and others and Traficante, Nadia and Salgia, Ravi and Werner, Theresa and Cohen, Adam L. and Moos, Philip and Chang, Jeffrey T. and Bowtell, David D. L. and Bild, Andrea H.}
}

@article{sethProbabilisticArchetypalAnalysis2016,
  title = {Probabilistic Archetypal Analysis},
  author = {Seth, Sohan and Eugster, Manuel J. A.},
  date = {2016-01},
  journaltitle = {Machine Learning},
  shortjournal = {Mach Learn},
  volume = {102},
  number = {1},
  pages = {85--113},
  issn = {0885-6125, 1573-0565},
  doi = {10.1007/s10994-015-5498-8},
  url = {http://link.springer.com/10.1007/s10994-015-5498-8},
  urldate = {2024-12-07},
  abstract = {Archetypal analysis represents a set of observations as convex combinations of pure patterns, or archetypes. The original geometric formulation of finding archetypes by approximating the convex hull of the observations assumes them to be real–valued. This, unfortunately, is not compatible with many practical situations. In this paper we revisit archetypal analysis from the basic principles, and propose a probabilistic framework that accommodates other observation types such as integers, binary, and probability vectors. We corroborate the proposed methodology with convincing real-world applications on finding archetypal soccer players based on performance data, archetypal winter tourists based on binary survey data, archetypal disaster-affected countries based on disaster count data, and document archetypes based on term-frequency data. We also present an appropriate visualization tool to summarize archetypal analysis solution better.},
  langid = {english},
  file = {/Users/pschafer/Zotero/storage/K293I69E/Seth and Eugster - 2016 - Probabilistic archetypal analysis.pdf}
}

@online{mairArchetypalAnalysisRethinking2024,
  title = {Archetypal Analysis++: Rethinking the Initialization Strategy},
  shorttitle = {Archetypal Analysis++},
  author = {Mair, Sebastian and Sjölund, Jens},
  date = {2024-05-13},
  eprint = {2301.13748},
  eprinttype = {arXiv},
  eprintclass = {cs},
  doi = {10.48550/arXiv.2301.13748},
  url = {http://arxiv.org/abs/2301.13748},
  urldate = {2024-12-07},
  abstract = {Archetypal analysis is a matrix factorization method with convexity constraints. Due to local minima, a good initialization is essential, but frequently used initialization methods yield either sub-optimal starting points or are prone to get stuck in poor local minima. In this paper, we propose archetypal analysis++ (AA++), a probabilistic initialization strategy for archetypal analysis that sequentially samples points based on their influence on the objective function, similar to k-means++. In fact, we argue that k-means++ already approximates the proposed initialization method. Furthermore, we suggest to adapt an efficient Monte Carlo approximation of k-means++ to AA++. In an extensive empirical evaluation of 15 real-world data sets of varying sizes and dimensionalities and considering two pre-processing strategies, we show that AA++ almost always outperforms all baselines, including the most frequently used ones.},
  langid = {english},
  pubstate = {prepublished},
  keywords = {Computer Science - Machine Learning},
  file = {/Users/pschafer/Zotero/storage/V2XZ53HJ/Mair and Sjölund - 2024 - Archetypal Analysis++ Rethinking the Initialization Strategy.pdf}
}

@article{gimbernat-mayolArchetypalAnalysisPopulation2022,
  title = {Archetypal Analysis for Population Genetics},
  author = {Gimbernat-Mayol, Julia and Dominguez Mantes, Albert and Bustamante, Carlos D. and Mas Montserrat, Daniel and Ioannidis, Alexander G.},
  editor = {Wheeler, Heather E.},
  date = {2022-08-25},
  journaltitle = {PLOS Computational Biology},
  shortjournal = {PLoS Comput Biol},
  volume = {18},
  number = {8},
  pages = {e1010301},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1010301},
  url = {https://dx.plos.org/10.1371/journal.pcbi.1010301},
  urldate = {2024-12-07},
  abstract = {The estimation of genetic clusters using genomic data has application from genome-wide association studies (GWAS) to demographic history to polygenic risk scores (PRS) and is expected to play an important role in the analyses of increasingly diverse, large-scale cohorts. However, existing methods are computationally-intensive, prohibitively so in the case of nationwide biobanks. Here we explore Archetypal Analysis as an efficient, unsupervised approach for identifying genetic clusters and for associating individuals with them. Such unsupervised approaches help avoid conflating socially constructed ethnic labels with genetic clusters by eliminating the need for exogenous training labels. We show that Archetypal Analysis yields similar cluster structure to existing unsupervised methods such as ADMIXTURE and provides interpretative advantages. More importantly, we show that since Archetypal Analysis can be used with lower-dimensional representations of genetic data, significant reductions in computational time and memory requirements are possible. When Archetypal Analysis is run in such a fashion, it takes several orders of magnitude less compute time than the current standard, ADMIXTURE. Finally, we demonstrate uses ranging across datasets from humans to canids.},
  langid = {english},
  file = {/Users/pschafer/Zotero/storage/T492NRSZ/Gimbernat-Mayol et al. - 2022 - Archetypal Analysis for population genetics.pdf}
}

@article{kellerLearningExtremalRepresentations2021,
  title = {Learning Extremal Representations with Deep Archetypal Analysis},
  author = {Keller, Sebastian Mathias and Samarin, Maxim and Arend Torres, Fabricio and Wieser, Mario and Roth, Volker},
  date = {2021-04},
  journaltitle = {International Journal of Computer Vision},
  shortjournal = {Int J Comput Vis},
  volume = {129},
  number = {4},
  pages = {805--820},
  issn = {0920-5691, 1573-1405},
  doi = {10.1007/s11263-020-01390-3},
  url = {http://link.springer.com/10.1007/s11263-020-01390-3},
  urldate = {2024-12-07},
  abstract = {Archetypes represent extreme manifestations of a population with respect to specific characteristic traits or features. In linear feature space, archetypes approximate the data convex hull allowing all data points to be expressed as convex mixtures of archetypes. As mixing of archetypes is performed directly on the input data, linear Archetypal Analysis requires additivity of the input, which is a strong assumption unlikely to hold e.g. in case of image data. To address this problem, we propose learning an appropriate latent feature space while simultaneously identifying suitable archetypes. We thus introduce a generative formulation of the linear archetype model, parameterized by neural networks. By introducing the distance-dependent archetype loss, the linear archetype model can be integrated into the latent space of a deep variational information bottleneck and an optimal representation, together with the archetypes, can be learned end-to-end. Moreover, the information bottleneck framework allows for a natural incorporation of arbitrarily complex side information during training. As a consequence, learned archetypes become easily interpretable as they derive their meaning directly from the included side information. Applicability of the proposed method is demonstrated by exploring archetypes of female facial expressions while using multi-rater based emotion scores of these expressions as side information. A second application illustrates the exploration of the chemical space of small organic molecules. By using different kinds of side information we demonstrate how identified archetypes, along with their interpretation, largely depend on the side information provided.},
  langid = {english},
  file = {/Users/pschafer/Zotero/storage/EU3UZ97H/Keller et al. - 2021 - Learning Extremal Representations with Deep Archetypal Analysis.pdf}
}

@article{10.1016/j.cell.2024.02.030,
  title = {Cellular Architecture of Evolving Neuroinflammatory Lesions and Multiple Sclerosis Pathology},
  author = {Kukanja, Petra and Langseth, Christoffer M. and Rubio Rodríguez-Kirby, Leslie A. and Agirre, Eneritz and Zheng, Chao and Raman, Amitha and Yokota, Chika and Avenel, Christophe and Tiklová, Katarina and Guerreiro-Cacais, André O. and Olsson, Tomas and Hilscher, Markus M. and Nilsson, Mats and Castelo-Branco, Gonçalo},
  date = {2024-04},
  journaltitle = {Cell},
  shortjournal = {Cell},
  volume = {187},
  number = {8},
  eprint = {38513664},
  eprinttype = {pubmed},
  pages = {1990-2009.e19},
  issn = {00928674},
  doi = {10.1016/j.cell.2024.02.030},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S0092867424002332},
  urldate = {2024-12-08},
  abstract = {Multiple sclerosis (MS) is a neurological disease characterized by multifocal lesions and smoldering pathology. Although single-cell analyses provided insights into cytopathology, evolving cellular processes underlying MS remain poorly understood. We investigated the cellular dynamics of MS by modeling temporal and regional rates of disease progression in mouse experimental autoimmune encephalomyelitis (EAE). By performing single-cell spatial expression profiling using in situ sequencing (ISS), we annotated disease neighborhoods and found centrifugal evolution of active lesions. We demonstrated that disease-associated (DA)-glia arise independently of lesions and are dynamically induced and resolved over the disease course. Single-cell spatial mapping of human archival MS spinal cords confirmed the differential distribution of homeostatic and DA-glia, enabled deconvolution of active and inactive lesions into sub-compartments, and identified new lesion areas. By establishing a spatial resource of mouse and human MS neuropathology at a single-cell resolution, our study unveils the intricate cellular dynamics underlying MS.},
  langid = {english},
  local-url = {file://localhost/Users/pschafer/Documents/Papers\_PDFs/2024\_Kukanja\_Castelo-Branco\_Cellular},
  file = {/Users/pschafer/Zotero/storage/9KF9JA4W/Kukanja et al. - 2024 - Cellular architecture of evolving neuroinflammatory lesions and multiple sclerosis pathology.pdf}
}

@article{perezSinglecellRNAseqReveals2022,
  title = {Single-Cell RNA-Seq Reveals Cell Type–Specific Molecular and Genetic Associations to Lupus},
  date = {2022-04-08},
  journaltitle = {Science},
  volume = {376},
  number = {6589},
  pages = {eabf1970},
  publisher = {American Association for the Advancement of Science},
  doi = {10.1126/science.abf1970},
  url = {https://www.science.org/doi/10.1126/science.abf1970},
  urldate = {2025-02-05},
  abstract = {Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease. Knowledge of circulating immune cell types and states associated with SLE remains incomplete. We profiled more than 1.2 million peripheral blood mononuclear cells (162 cases, 99 controls) with multiplexed single-cell RNA sequencing (mux-seq). Cases exhibited elevated expression of type 1 interferon–stimulated genes (ISGs) in monocytes, reduction of naïve CD4+ T cells that correlated with monocyte ISG expression, and expansion of repertoire-restricted cytotoxic GZMH+ CD8+ T cells. Cell type–specific expression features predicted case-control status and stratified patients into two molecular subtypes. We integrated dense genotyping data to map cell type–specific cis–expression quantitative trait loci and to link SLE-associated variants to cell type–specific expression. These results demonstrate mux-seq as a systematic approach to characterize cellular composition, identify transcriptional signatures, and annotate genetic variants associated with SLE.},
  keywords = {/unread},
  file = {/Users/pschafer/Zotero/storage/IEXFS9ZV/Perez et al. - 2022 - Single-cell RNA-seq reveals cell type–specific molecular and genetic associations to lupus.pdf},
  author = {Perez, Richard K. and Gordon, M. Grace and Subramaniam, Meena and Kim, Min Cheol and Hartoularos, George C. and Targ, Sasha and Sun, Yang and Ogorodnikov, Anton and others and Fong, Lawrence and Dall’Era, Maria and Balliu, Brunilda and Regev, Aviv and Yazdany, Jinoos and Criswell, Lindsey A. and Zaitlen, Noah and Ye, Chun Jimmie}
}

@article{eugsterWeightedRobustArchetypal2011,
  title = {Weighted and Robust Archetypal Analysis},
  author = {Eugster, Manuel J. A. and Leisch, Friedrich},
  date = {2011-03-01},
  journaltitle = {Computational Statistics \& Data Analysis},
  shortjournal = {Computational Statistics \& Data Analysis},
  volume = {55},
  number = {3},
  pages = {1215--1225},
  issn = {0167-9473},
  doi = {10.1016/j.csda.2010.10.017},
  url = {https://www.sciencedirect.com/science/article/pii/S0167947310004056},
  urldate = {2025-02-06},
  abstract = {Archetypal analysis represents observations in a multivariate data set as convex combinations of a few extremal points lying on the boundary of the convex hull. Data points which vary from the majority have great influence on the solution; in fact one outlier can break down the archetype solution. The original algorithm is adapted to be a robust M-estimator and an iteratively reweighted least squares fitting algorithm is presented. As a required first step, the weighted archetypal problem is formulated and solved. The algorithm is demonstrated using an artificial example, a real world example and a detailed simulation study.},
  langid = {english},
  keywords = {Breakdown point,Iteratively reweighted least squares,M-estimator,Robust archetypal analysis},
  file = {/Users/pschafer/Zotero/storage/522AY2XU/1-s2.0-S0167947310004056-main.pdf;/Users/pschafer/Zotero/storage/55GF4EJG/Eugster and Leisch - 2011 - Weighted and robust archetypal analysis.pdf;/Users/pschafer/Zotero/storage/GIHSVXH4/archetypes.pdf}
}

@article{coboArchetypalAnalysisApplications2022,
  title = {Archetypal Analysis and Applications},
  author = {Cobo, Guillermo García},
  date = {2022},
  url = {https://github.com/atmguille/archetypal-analysis/blob/main/Thesis/%5BEnglish%5D%20Archetypal%20analysis%20and%20applications.pdf},
  langid = {english},
  file = {/Users/pschafer/Zotero/storage/MBWVM34A/Cobo - Archetypal analysis and applications.pdf}
}

@inproceedings{jaggiRevisitingFrankWolfeProjectionFree2013,
  title = {Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization},
  shorttitle = {Revisiting Frank-Wolfe},
  booktitle = {Proceedings of the 30th International Conference on Machine Learning},
  author = {Jaggi, Martin},
  date = {2013-02-13},
  pages = {427--435},
  publisher = {PMLR},
  issn = {1938-7228},
  url = {https://proceedings.mlr.press/v28/jaggi13.html},
  urldate = {2025-02-08},
  abstract = {We provide stronger and more general primal-dual convergence results for Frank-Wolfe-type algorithms (a.k.a. conditional gradient) for constrained convex optimization, enabled by a simple framework of duality gap certificates. Our analysis also holds if the linear subproblems are only solved approximately (as well as if the gradients are inexact), and is proven to be worst-case optimal in the sparsity of the obtained solutions.    On the application side, this allows us to unify a large variety of existing sparse greedy methods, in particular for optimization over convex hulls of an atomic set, even if those sets can only be approximated, including sparse (or structured sparse) vectors or matrices, low-rank matrices, permutation matrices, or max-norm bounded matrices.    We present a new general framework for convex optimization over matrix factorizations, where every Frank-Wolfe iteration will consist of a low-rank update, and discuss the broad application areas of this approach.},
  eventtitle = {International Conference on Machine Learning},
  langid = {english},
  keywords = {/unread},
  file = {/Users/pschafer/Zotero/storage/HU8HQXUX/Jaggi - 2013 - Revisiting Frank-Wolfe Projection-Free Sparse Convex Optimization.pdf}
}

@inproceedings{mairCoresetsArchetypalAnalysis2019,
  title = {Coresets for Archetypal Analysis},
  booktitle = {Advances in Neural Information Processing Systems},
  author = {Mair, Sebastian and Brefeld, Ulf},
  date = {2019},
  volume = {32},
  publisher = {Curran Associates, Inc.},
  url = {https://papers.nips.cc/paper_files/paper/2019/hash/7f278ad602c7f47aa76d1bfc90f20263-Abstract.html},
  urldate = {2025-02-09},
  abstract = {Archetypal analysis represents instances as linear mixtures of prototypes (the archetypes) that lie on the boundary of the convex hull of the data. Archetypes are thus  often better interpretable than factors computed by other matrix factorization techniques. However, the interpretability comes with high computational cost due to additional convexity-preserving constraints. In this paper, we propose efficient coresets for archetypal analysis. Theoretical guarantees are derived by showing that quantization errors of k-means upper bound archetypal analysis; the computation of a provable absolute-coreset can be performed in only two passes over the data. Empirically, we show that the coresets lead to improved performance on several data sets.},
  file = {/Users/pschafer/Zotero/storage/PQPRA9KL/Mair and Brefeld - 2019 - Coresets for Archetypal Analysis.pdf;/Users/pschafer/Zotero/storage/TZYBSD5U/supplementary_material.pdf}
}

@inproceedings{chenFastRobustArchetypal2014,
  title = {Fast and Robust Archetypal Analysis for Representation Learning},
  booktitle = {2014 IEEE Conference on Computer Vision and Pattern Recognition},
  author = {Chen, Yuansi and Mairal, Julien and Harchaoui, Zaid},
  date = {2014-06},
  pages = {1478--1485},
  publisher = {IEEE},
  location = {Columbus, OH, USA},
  doi = {10.1109/CVPR.2014.192},
  url = {https://ieeexplore.ieee.org/document/6909588},
  urldate = {2025-02-10},
  abstract = {We revisit a pioneer unsupervised learning technique called archetypal analysis [5], which is related to successful data analysis methods such as sparse coding [18] and non-negative matrix factorization [19]. Since it was proposed, archetypal analysis did not gain a lot of popularity even though it produces more interpretable models than other alternatives. Because no efficient implementation has ever been made publicly available, its application to important scientific problems may have been severely limited.},
  eventtitle = {2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  isbn = {978-1-4799-5118-5},
  langid = {english},
  file = {/Users/pschafer/Zotero/storage/7ZPUHQ2B/Chen et al. - 2014 - Fast and Robust Archetypal Analysis for Representation Learning.pdf}
}

@inproceedings{bauckhageArchetypalAnalysisAutoencoder2015,
  title = {Archetypal Analysis as an Autoencoder},
  author = {Bauckhage, Christian and Kersting, Kristian and Hoppe, F. and Thurau, Christian},
  date = {2015},
  url = {https://www.ml.informatik.tu-darmstadt.de/papers/autoencode2015nc2.pdf},
  urldate = {2025-02-10},
  abstract = {We present an efficient approach to archetypal analysis where we use sub-gradient algorithms for optimization over the simplex to determine archetypes and reconstruction coefficients. Runtime evaluations reveal our approach to be notably more efficient than previous techniques. As an practical application, we consider archetypal analysis for autoencoding.},
  eventtitle = {Workshop "New Challenges in Neural Computation" (NC2) 2015},
  langid = {english},
  file = {/Users/pschafer/Zotero/storage/EWH64XC9/Bauckhage et al. - 2015 - Archetypal analysis as an autoencoder.pdf}
}

@inproceedings{bauckhageMakingArchetypalAnalysis2009,
  title = {Making Archetypal Analysis Practical},
  booktitle = {Pattern Recognition},
  author = {Bauckhage, Christian and Thurau, Christian},
  editor = {Denzler, Joachim and Notni, Gunther and Süße, Herbert},
  date = {2009},
  pages = {272--281},
  publisher = {Springer},
  location = {Berlin, Heidelberg},
  doi = {10.1007/978-3-642-03798-6_28},
  abstract = {Archetypal analysis represents the members of a set of multivariate data as a convex combination of extremal points of the data. It allows for dimensionality reduction and clustering and is particularly useful whenever the data are superpositions of basic entities. However, since its computation costs grow quadratically with the number of data points, the original algorithm hardly applies to modern pattern recognition or data mining settings. In this paper, we introduce ways of notably accelerating archetypal analysis. Our experiments are the first successful application of the technique to large scale data analysis problems.},
  isbn = {978-3-642-03798-6},
  langid = {english},
  file = {/Users/pschafer/Zotero/storage/H2V7EZ59/Bauckhage and Thurau - 2009 - Making Archetypal Analysis Practical.pdf}
}

@article{clarksonCoresetsSparseGreedy2010,
  title = {Coresets, Sparse Greedy Approximation, and the Frank-Wolfe Algorithm},
  author = {Clarkson, Kenneth L.},
  date = {2010-09-03},
  journaltitle = {ACM Trans. Algorithms},
  shortjournal = {ACM Trans. Algorithms},
  volume = {6},
  number = {4},
  pages = {63:1--63:30},
  issn = {1549-6325},
  doi = {10.1145/1824777.1824783},
  url = {https://doi.org/10.1145/1824777.1824783},
  urldate = {2025-02-11},
  abstract = {The problem of maximizing a concave function f(x) in the unit simplex Δ can be solved approximately by a simple greedy algorithm. For given k, the algorithm can find a point x(k) on a k-dimensional face of Δ, such that f(x(k) ≥ f(x*) − O(1/k). Here f(x*) is the maximum value of f in Δ, and the constant factor depends on f. This algorithm and analysis were known before, and related to problems of statistics and machine learning, such as boosting, regression, and density mixture estimation. In other work, coming from computational geometry, the existence of ϵ-coresets was shown for the minimum enclosing ball problem by means of a simple greedy algorithm. Similar greedy algorithms, which are special cases of the Frank-Wolfe algorithm, were described for other enclosure problems. Here these results are tied together, stronger convergence results are reviewed, and several coreset bounds are generalized or strengthened.},
  langid = {english},
  keywords = {/unread},
  file = {/Users/pschafer/Zotero/storage/PA5WL7NE/Clarkson - 2010 - Coresets, sparse greedy approximation, and the Frank-Wolfe algorithm.pdf}
}

@article{muhlSinglecellAnalysisUncovers2020,
  title = {Single-Cell Analysis Uncovers Fibroblast Heterogeneity and Criteria for Fibroblast and Mural Cell Identification and Discrimination},
  date = {2020-08-07},
  journaltitle = {Nature Communications},
  shortjournal = {Nat Commun},
  volume = {11},
  number = {1},
  pages = {3953},
  publisher = {Nature Publishing Group},
  issn = {2041-1723},
  doi = {10.1038/s41467-020-17740-1},
  url = {https://www.nature.com/articles/s41467-020-17740-1},
  urldate = {2025-03-26},
  abstract = {Many important cell types in adult vertebrates have a mesenchymal origin, including fibroblasts and vascular mural cells. Although their biological importance is undisputed, the level of mesenchymal cell heterogeneity within and between organs, while appreciated, has not been analyzed in detail. Here, we compare single-cell transcriptional profiles of fibroblasts and vascular mural cells across four murine muscular organs: heart, skeletal muscle, intestine and bladder. We reveal gene expression signatures that demarcate fibroblasts from mural cells and provide molecular signatures for cell subtype identification. We observe striking inter- and intra-organ heterogeneity amongst the fibroblasts, primarily reflecting differences in the expression of extracellular matrix components. Fibroblast subtypes localize to discrete anatomical positions offering novel predictions about physiological function(s) and regulatory signaling circuits. Our data shed new light on the diversity of poorly defined classes of cells and provide a foundation for improved understanding of their roles in physiological and pathological processes.},
  langid = {english},
  keywords = {/unread,Cardiovascular biology,Transcriptomics},
  file = {/Users/pschafer/Zotero/storage/K9KAF5YC/Muhl et al. - 2020 - Single-cell analysis uncovers fibroblast heterogeneity and criteria for fibroblast and mural cell id.pdf},
  author = {Muhl, Lars and Genové, Guillem and Leptidis, Stefanos and Liu, Jianping and He, Liqun and Mocci, Giuseppe and Sun, Ying and Gustafsson, Sonja and others and Segerstolpe, Åsa and Raschperger, Elisabeth and Hansson, Emil M. and Björkegren, Johan L. M. and Peng, Xiao-Rong and Vanlandewijck, Michael and Lendahl, Urban and Betsholtz, Christer}
}

@article{linProjectedGradientMethods2007,
  title = {Projected Gradient Methods for Nonnegative Matrix Factorization},
  author = {Lin, Chih-Jen},
  date = {2007-10},
  journaltitle = {Neural Computation},
  volume = {19},
  number = {10},
  pages = {2756--2779},
  issn = {0899-7667},
  doi = {10.1162/neco.2007.19.10.2756},
  url = {https://ieeexplore.ieee.org/document/6795860},
  urldate = {2025-03-30},
  abstract = {Nonnegative matrix factorization (NMF) can be formulated as a minimization problem with bound constraints. Although bound-constrained optimization has been studied extensively in both theory and practice, so far no study has formally applied its techniques to NMF. In this letter, we propose two projected gradient methods for NMF, both of which exhibit strong optimization properties. We discuss efficient implementations and demonstrate that one of the proposed methods converges faster than the popular multiplicative update approach. A simple Matlab code is also provided.},
  eventtitle = {Neural Computation},
  file = {/Users/pschafer/Zotero/storage/AUREHXIV/Lin - 2007 - Projected Gradient Methods for Nonnegative Matrix Factorization.pdf;/Users/pschafer/Zotero/storage/PPBZYNW6/6795860.html}
}

@inproceedings{eggertSparseCodingNMF2004,
  title = {Sparse Coding and NMF},
  booktitle = {2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541)},
  author = {Eggert, J. and Korner, E.},
  date = {2004-07},
  volume = {4},
  pages = {2529-2533 vol.4},
  issn = {1098-7576},
  doi = {10.1109/IJCNN.2004.1381036},
  url = {https://ieeexplore.ieee.org/document/1381036},
  urldate = {2025-03-30},
  abstract = {Non-negative matrix factorization (NMF) is a very efficient parameter-free method for decomposing multivariate data into strictly positive activations and basis vectors. However, the method is not suited for overcomplete representations, where usually sparse coding paradigms apply. We show how to merge the concepts of non-negative factorization with sparsity conditions. The result is a multiplicative algorithm that is comparable in efficiency to standard NMF, but that can be used to gain sensible solutions in the overcomplete cases. This is of interest e.g. for the case of learning and modeling of arrays of receptive fields arranged in a visual processing map, where an overcomplete representation is unavoidable.},
  keywords = {Additives,Cost function,Encoding,Europe,Matrix decomposition,Principal component analysis,Sparse matrices,Time factors,Vector quantization,Visual system},
  file = {/Users/pschafer/Zotero/storage/RG3YUI7H/Eggert and Korner - 2004 - Sparse coding and NMF.pdf;/Users/pschafer/Zotero/storage/AIAA6KYG/1381036.html}
}

@article{militeMIDAADeepArchetypal2025,
  title = {MIDAA: Deep Archetypal Analysis for Interpretable Multi-Omic Data Integration Based on Biological Principles},
  shorttitle = {MIDAA},
  author = {Milite, Salvatore and Caravagna, Giulio and Sottoriva, Andrea},
  date = {2025-04-08},
  journaltitle = {Genome Biology},
  shortjournal = {Genome Biology},
  volume = {26},
  number = {1},
  pages = {90},
  issn = {1474-760X},
  doi = {10.1186/s13059-025-03530-9},
  url = {https://doi.org/10.1186/s13059-025-03530-9},
  urldate = {2025-04-15},
  abstract = {High-throughput multi-omic molecular profiling allows the probing of biological systems at unprecedented resolution. However, integrating and interpreting high-dimensional, sparse, and noisy multimodal datasets remains challenging. Deriving new biological insights with current methods is difficult because they are not rooted in biological principles but prioritise tasks like dimensionality reduction. Here, we introduce a framework that combines archetypal analysis, an approach grounded in biological principles, with deep learning. Using archetypes based on evolutionary trade-offs and Pareto optimality, MIDAA finds extreme data points that define the geometry of the latent space, preserving the complexity of biological interactions while retaining an interpretable output. We demonstrate that these extreme points represent cellular programmes reflecting the underlying biology. Moreover, we show that, compared to alternative methods, MIDAA can identify parsimonious, interpretable, and biologically relevant patterns from real and simulated multi-omics.},
  keywords = {/unread},
  file = {/Users/pschafer/Zotero/storage/AHTEUJ4X/Milite et al. - 2025 - MIDAA deep archetypal analysis for interpretable multi-omic data integration based on biological pr.pdf;/Users/pschafer/Zotero/storage/LFY87TWC/s13059-025-03530-9.html}
}

@misc{foxRobustRegrssion2013,
  title = {Robust Regrssion},
  author = {Fox, John and Weisberg},
  date = {2013-08-10},
  url = {http://users.stat.umn.edu/~sandy/courses/8053/handouts/robust.pdf},
  keywords = {/unread},
  file = {/Users/pschafer/Zotero/storage/RSNC2CSC/robust.pdf}
}

@book{petersenMatrixCookbook2012,
  title = {The Matrix Cookbook},
  author = {Petersen, Kaare Brandt and Pedersen, Michael Syskind},
  date = {2012},
  publisher = {Technical University of Denmark},
  url = {https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf},
  abstract = {Matrix identities, relations and approximations. A desktop reference for quick overview of mathematics of matrices.},
  langid = {english},
  keywords = {/unread,inverse,matrix derivative,Matrix identity,matrix relations}
}

@inproceedings{abrolGeometricApproachArchetypal2020,
  title = {A Geometric Approach to Archetypal Analysis via Sparse Projections},
  booktitle = {Proceedings of the 37th International Conference on Machine Learning},
  author = {Abrol, Vinayak and Sharma, Pulkit},
  date = {2020-11-21},
  pages = {42--51},
  publisher = {PMLR},
  issn = {2640-3498},
  url = {https://proceedings.mlr.press/v119/abrol20a.html},
  urldate = {2025-04-22},
  abstract = {Archetypal analysis (AA) aims to extract patterns using self-expressive decomposition of data as convex combinations of extremal points (on the convex hull) of the data. This work presents a computationally efficient greedy AA (GAA) algorithm. GAA leverages the underlying geometry of AA, is scalable to larger datasets, and has significantly faster convergence rate. To achieve this, archetypes are learned via sparse projection of data. In the transformed space, GAA employs an iterative subset selection approach to identify archetypes based on the sparsity of convex representations. The work further presents the use of GAA algorithm for extended AA models such as robust and kernel AA. Experimental results show that GAA is considerably faster while performing comparable to existing methods for tasks such as classification, data visualization/categorization.},
  eventtitle = {International Conference on Machine Learning},
  langid = {english},
  file = {/Users/pschafer/Zotero/storage/FRAJH6QS/Abrol and Sharma - 2020 - A Geometric Approach to Archetypal Analysis via Sparse Projections.pdf;/Users/pschafer/Zotero/storage/QGV5RWKW/Abrol and Sharma - 2020 - A Geometric Approach to Archetypal Analysis via Sparse Projections.pdf}
}

@online{damleGeometricApproachArchetypal2015,
  title = {A Geometric Approach to Archetypal Analysis and Non-Negative Matrix Factorization},
  author = {Damle, Anil and Sun, Yuekai},
  date = {2015-11-03},
  eprint = {1405.4275},
  eprinttype = {arXiv},
  eprintclass = {stat},
  doi = {10.48550/arXiv.1405.4275},
  url = {http://arxiv.org/abs/1405.4275},
  urldate = {2025-04-22},
  abstract = {Archetypal analysis and non-negative matrix factorization (NMF) are staples in a statisticians toolbox for dimension reduction and exploratory data analysis. We describe a geometric approach to both NMF and archetypal analysis by interpreting both problems as finding extreme points of the data cloud. We also develop and analyze an efficient approach to finding extreme points in high dimensions. For modern massive datasets that are too large to fit on a single machine and must be stored in a distributed setting, our approach makes only a small number of passes over the data. In fact, it is possible to obtain the NMF or perform archetypal analysis with just two passes over the data.},
  pubstate = {prepublished},
  keywords = {Statistics - Methodology},
  file = {/Users/pschafer/Zotero/storage/Q3PVYGYT/1405.4275}
}

@inproceedings{mairFramebasedDataFactorizations2017,
  title = {Frame-Based Data Factorizations},
  booktitle = {Proceedings of the 34th International Conference on Machine Learning},
  author = {Mair, Sebastian and Boubekki, Ahcène and Brefeld, Ulf},
  date = {2017-07-17},
  pages = {2305--2313},
  publisher = {PMLR},
  issn = {2640-3498},
  url = {https://proceedings.mlr.press/v70/mair17a.html},
  urldate = {2025-04-22},
  abstract = {Archetypal Analysis is the method of choice to compute interpretable matrix factorizations. Every data point is represented as a convex combination of factors, i.e., points on the boundary of the convex hull of the data. This renders computation inefficient. In this paper, we show that the set of vertices of a convex hull, the so-called frame, can be efficiently computed by a quadratic program. We provide theoretical and empirical results for our proposed approach and make use of the frame to accelerate Archetypal Analysis. The novel method yields similar reconstruction errors as baseline competitors but is much faster to compute.},
  eventtitle = {International Conference on Machine Learning},
  langid = {english},
  keywords = {/unread},
  file = {/Users/pschafer/Zotero/storage/UWGL3JLV/Mair et al. - 2017 - Frame-based Data Factorizations.pdf}
}

@inproceedings{dimentArchetypalAnalysisAudio2015,
  title = {Archetypal Analysis for Audio Dictionary Learning},
  booktitle = {2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)},
  author = {Diment, Aleksandr and Virtanen, Tuomas},
  date = {2015-10},
  pages = {1--5},
  publisher = {IEEE},
  location = {New Paltz, NY, USA},
  doi = {10.1109/WASPAA.2015.7336903},
  url = {http://ieeexplore.ieee.org/document/7336903/},
  urldate = {2025-04-22},
  eventtitle = {2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)},
  isbn = {978-1-4799-7450-4},
  file = {/Users/pschafer/Zotero/storage/N2HBF2YV/Diment and Virtanen - 2015 - Archetypal analysis for audio dictionary learning.pdf}
}

@inproceedings{meiOnlineDictionaryLearning2018,
  title = {Online Dictionary Learning for Approximate Archetypal Analysis},
  booktitle = {Computer Vision – ECCV 2018},
  author = {Mei, Jieru and Wang, Chunyu and Zeng, Wenjun},
  editor = {Ferrari, Vittorio and Hebert, Martial and Sminchisescu, Cristian and Weiss, Yair},
  date = {2018},
  pages = {501--516},
  publisher = {Springer International Publishing},
  location = {Cham},
  doi = {10.1007/978-3-030-01219-9_30},
  abstract = {Archetypal analysis is an unsupervised learning approach which represents data by convex combinations of a set of archetypes. The archetypes generally correspond to the extremal points in the dataset and are learned by requiring them to be convex combinations of the training data. In spite of its nice property of interpretability, the method is slow. We propose a variant of archetypal analysis which scales gracefully to large datasets. The core idea is to decouple the binding between data and archetypes and require them to be unit normalized. Geometrically, the method learns a convex hull inside the unit sphere and represents the data by their projections on the closest surfaces of the convex hull. By minimizing the representation error, the method pushes the convex hull surfaces close to the regions of the sphere where the data reside. The vertices of the convex hull are the learned archetypes. We apply the method to human faces and poses to validate its effectiveness in the context of reconstructions and classifications.},
  isbn = {978-3-030-01219-9},
  langid = {english},
  keywords = {Archetypal analysis,Convex hull,Sparsity},
  file = {/Users/pschafer/Zotero/storage/HDEI8G7X/Mei et al. - 2018 - Online Dictionary Learning for Approximate Archetypal Analysis.pdf}
}

@online{alcacerSurveyArchetypalAnalysis2025,
  title = {A Survey on Archetypal Analysis},
  author = {Alcacer, Aleix and Epifanio, Irene and Mair, Sebastian and Mørup, Morten},
  date = {2025-04-16},
  eprint = {2504.12392},
  eprinttype = {arXiv},
  eprintclass = {stat},
  doi = {10.48550/arXiv.2504.12392},
  url = {http://arxiv.org/abs/2504.12392},
  urldate = {2025-04-22},
  abstract = {Archetypal analysis (AA) was originally proposed in 1994 by Adele Cutler and Leo Breiman as a computational procedure to extract the distinct aspects called archetypes in observations with each observational record approximated as a mixture (i.e., convex combination) of these archetypes. AA thereby provides straightforward, interpretable, and explainable representations for feature extraction and dimensionality reduction, facilitating the understanding of the structure of high-dimensional data with wide applications throughout the sciences. However, AA also faces challenges, particularly as the associated optimization problem is non-convex. This survey provides researchers and data mining practitioners an overview of methodologies and opportunities that AA has to offer surveying the many applications of AA across disparate fields of science, as well as best practices for modeling data using AA and limitations. The survey concludes by explaining important future research directions concerning AA.},
  pubstate = {prepublished},
  keywords = {Computer Science - Machine Learning,Statistics - Machine Learning,Statistics - Methodology},
  file = {/Users/pschafer/Zotero/storage/4D9LINT7/Alcacer et al. - 2025 - A Survey on Archetypal Analysis.pdf}
}

@inproceedings{sulemanValidationArchetypalAnalysis2017,
  title = {Validation of Archetypal Analysis},
  booktitle = {2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
  author = {Suleman, Abdul},
  date = {2017-07},
  pages = {1--6},
  issn = {1558-4739},
  doi = {10.1109/FUZZ-IEEE.2017.8015385},
  url = {https://ieeexplore.ieee.org/document/8015385},
  urldate = {2025-04-23},
  abstract = {We use an information-theoretic criterion to assess the goodness-of-fit of the output of archetypal analysis (AA), also intended as a fuzzy clustering tool. It is an adaptation of an existing AIC-like measure to the specifics of AA. We test its effectiveness using artificial data and some data sets arising from real life problems. In most cases, the results achieved are similar to those provided by an external similarity index. The average reconstruction accuracy is about 93\%.},
  eventtitle = {2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
  keywords = {Data models,Estimation,Indexes,Linear programming,Matrix converters,Prototypes,Tools},
  file = {/Users/pschafer/Zotero/storage/I54C9DTC/Suleman - 2017 - Validation of archetypal analysis.pdf}
}

@online{wedenborgArchetypalAnalysisBinary2025,
  title = {Archetypal Analysis for Binary Data},
  author = {Wedenborg, A. Emilie J. and Mørup, Morten},
  date = {2025-02-06},
  eprint = {2502.04172},
  eprinttype = {arXiv},
  eprintclass = {cs},
  doi = {10.48550/arXiv.2502.04172},
  url = {http://arxiv.org/abs/2502.04172},
  urldate = {2025-04-26},
  abstract = {Archetypal analysis (AA) is a matrix decomposition method that identifies distinct patterns using convex combinations of the data points denoted archetypes with each data point in turn reconstructed as convex combinations of the archetypes. AA thereby forms a polytope representing trade-offs of the distinct aspects in the data. Most existing methods for AA are designed for continuous data and do not exploit the structure of the data distribution. In this paper, we propose two new optimization frameworks for archetypal analysis for binary data. i) A second order approximation of the AA likelihood based on the Bernoulli distribution with efficient closed-form updates using an active set procedure for learning the convex combinations defining the archetypes, and a sequential minimal optimization strategy for learning the observation specific reconstructions. ii) A Bernoulli likelihood based version of the principal convex hull analysis (PCHA) algorithm originally developed for least squares optimization. We compare these approaches with the only existing binary AA procedure relying on multiplicative updates and demonstrate their superiority on both synthetic and real binary data. Notably, the proposed optimization frameworks for AA can easily be extended to other data distributions providing generic efficient optimization frameworks for AA based on tailored likelihood functions reflecting the underlying data distribution.},
  pubstate = {prepublished},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Machine Learning,Statistics - Machine Learning},
  file = {/Users/pschafer/Zotero/storage/C3JHDTBU/Wedenborg and Mørup - 2025 - Archetypal Analysis for Binary Data.pdf;/Users/pschafer/Zotero/storage/AQBSNLAR/2502.html}
}

@article{richardsonReceiverOperatingCharacteristic2024,
  title = {The Receiver Operating Characteristic Curve Accurately Assesses Imbalanced Datasets},
  author = {Richardson, Eve and Trevizani, Raphael and Greenbaum, Jason A. and Carter, Hannah and Nielsen, Morten and Peters, Bjoern},
  date = {2024-06-14},
  journaltitle = {Patterns},
  shortjournal = {Patterns},
  volume = {5},
  number = {6},
  pages = {100994},
  issn = {2666-3899},
  doi = {10.1016/j.patter.2024.100994},
  url = {https://www.sciencedirect.com/science/article/pii/S2666389924001090},
  urldate = {2025-04-27},
  abstract = {Many problems in biology require looking for a “needle in a haystack,” corresponding to a binary classification where there are a few positives within a much larger set of negatives, which is referred to as a class imbalance. The receiver operating characteristic (ROC) curve and the associated area under the curve (AUC) have been reported as ill-suited to evaluate prediction performance on imbalanced problems where there is more interest in performance on the positive minority class, while the precision-recall (PR) curve is preferable. We show via simulation and a real case study that this is a misinterpretation of the difference between the ROC and PR spaces, showing that the ROC curve is robust to class imbalance, while the PR curve is highly sensitive to class imbalance. Furthermore, we show that class imbalance cannot be easily disentangled from classifier performance measured via PR-AUC.},
  keywords = {binary classification,imbalanced data,machine learning,performance metric,precision-recall,ROC curve},
  file = {/Users/pschafer/Zotero/storage/B5ZLXGQG/S2666389924001090.html}
}

@online{gillisSuccessiveProjectionAlgorithm2019,
  title = {Successive Projection Algorithm Robust to Outliers},
  author = {Gillis, Nicolas},
  date = {2019-08-12},
  eprint = {1908.04109},
  eprinttype = {arXiv},
  eprintclass = {eess},
  doi = {10.48550/arXiv.1908.04109},
  url = {http://arxiv.org/abs/1908.04109},
  urldate = {2025-04-27},
  abstract = {The successive projection algorithm (SPA) is a fast algorithm to tackle separable nonnegative matrix factorization (NMF). Given a nonnegative data matrix \$X\$, SPA identifies an index set \$\textbackslash mathcal\{K\}\$ such that there exists a nonnegative matrix \$H\$ with \$X \textbackslash approx X(:,\textbackslash mathcal\{K\})H\$. SPA has been successfully used as a pure-pixel search algorithm in hyperspectral unmixing and for anchor word selection in document classification. Moreover, SPA is provably robust in low-noise settings. The main drawbacks of SPA are that it is not robust to outliers and does not take the data fitting term into account when selecting the indices in \$\textbackslash mathcal\{K\}\$. In this paper, we propose a new SPA variant, dubbed Robust SPA (RSPA), that is robust to outliers while still being provably robust in low-noise settings, and that takes into account the reconstruction error for selecting the indices in \$\textbackslash mathcal\{K\}\$. We illustrate the effectiveness of RSPA on synthetic data sets and hyperspectral images.},
  langid = {english},
  pubstate = {prepublished},
  keywords = {Computer Science - Machine Learning,Computer Science - Numerical Analysis,Electrical Engineering and Systems Science - Image and Video Processing,Electrical Engineering and Systems Science - Signal Processing,Mathematics - Numerical Analysis,Statistics - Machine Learning},
  file = {/Users/pschafer/Zotero/storage/IMIZPXAC/Gillis - 2019 - Successive Projection Algorithm Robust to Outliers.pdf;/Users/pschafer/Zotero/storage/WXCR4KYV/1908.html}
}

@online{mirzasoleimanCoresetsDataefficientTraining2020,
  title = {Coresets for Data-Efficient Training of Machine Learning Models},
  author = {Mirzasoleiman, Baharan and Bilmes, Jeff and Leskovec, Jure},
  date = {2020-11-16},
  eprint = {1906.01827},
  eprinttype = {arXiv},
  eprintclass = {cs},
  doi = {10.48550/arXiv.1906.01827},
  url = {http://arxiv.org/abs/1906.01827},
  urldate = {2025-04-29},
  abstract = {Incremental gradient (IG) methods, such as stochastic gradient descent and its variants are commonly used for large scale optimization in machine learning. Despite the sustained effort to make IG methods more data-efficient, it remains an open question how to select a training data subset that can theoretically and practically perform on par with the full dataset. Here we develop CRAIG, a method to select a weighted subset (or coreset) of training data that closely estimates the full gradient by maximizing a submodular function. We prove that applying IG to this subset is guaranteed to converge to the (near)optimal solution with the same convergence rate as that of IG for convex optimization. As a result, CRAIG achieves a speedup that is inversely proportional to the size of the subset. To our knowledge, this is the first rigorous method for data-efficient training of general machine learning models. Our extensive set of experiments show that CRAIG, while achieving practically the same solution, speeds up various IG methods by up to 6x for logistic regression and 3x for training deep neural networks.},
  pubstate = {prepublished},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Machine Learning,Statistics - Machine Learning},
  file = {/Users/pschafer/Zotero/storage/FZESHVEE/Mirzasoleiman et al. - Coresets for Data-efficient Training of Machine Learning Models.pdf;/Users/pschafer/Zotero/storage/UXFII39G/Mirzasoleiman et al. - 2020 - Coresets for Data-efficient Training of Machine Learning Models.pdf;/Users/pschafer/Zotero/storage/WELSRZHU/1906.html}
}

@book{mincNonnegativeMatrices1988,
  title = {Nonnegative Matrices},
  author = {Minc, Henryk},
  date = {1988},
  series = {Wiley-Interscience Series in Discrete Mathematics and Optimization},
  edition = {2. print},
  publisher = {Wiley},
  location = {New York},
  isbn = {978-0-471-83966-8},
  langid = {english},
  pagetotal = {206},
  file = {/Users/pschafer/Zotero/storage/YQMXHVJ4/9778c76108afc419868602434b72bf7b.pdf}
}

@inproceedings{beckArchetypalAnalysisNeuronal2022,
  title = {Archetypal Analysis for Neuronal Clique Detection in Low-Rate Calcium Fluorescence Imaging},
  booktitle = {2022 44th Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC)},
  author = {Beck, Connor and Kunze, Anja and Zosso, Dominique},
  date = {2022-07},
  pages = {162--166},
  issn = {2694-0604},
  doi = {10.1109/EMBC48229.2022.9871404},
  url = {https://ieeexplore.ieee.org/document/9871404},
  urldate = {2025-04-30},
  abstract = {Archetypal analysis (AA) is a versatile data analysis method to cluster distinct features within a data set. Here, we demonstrate a framework showing the power of AA to spatio-temporally resolve events in calcium imaging, an imaging modality commonly used in neurobiology and neuroscience to capture neuronal communication patterns. After validation of our AA-based approach on synthetic data sets, we were able to characterize neuronal communication patterns in recorded calcium waves. Clinical relevance– Transient calcium events play an essential role in brain cell communication, growth, and network formation, as well as in neurodegeneration. To reliably interpret calcium events from personalized medicine data, where patterns may differ from patient to patient, appropriate image processing and signal analysis methods need to be developed for optimal network characterization.},
  eventtitle = {2022 44th Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC)},
  keywords = {Calcium,Feature extraction,Neurons,Optical imaging,Precision medicine,Reliability,Topology},
  file = {/Users/pschafer/Zotero/storage/SI8SQNFR/9871404.html}
}

@article{chapmanLargescaleViewMarine2022,
  title = {A Large-Scale View of Marine Heatwaves Revealed by Archetype Analysis},
  author = {Chapman, Christopher C. and Monselesan, Didier P. and Risbey, James S. and Feng, Ming and Sloyan, Bernadette M.},
  date = {2022-12-21},
  journaltitle = {Nature Communications},
  shortjournal = {Nat Commun},
  volume = {13},
  number = {1},
  pages = {7843},
  publisher = {Nature Publishing Group},
  issn = {2041-1723},
  doi = {10.1038/s41467-022-35493-x},
  url = {https://www.nature.com/articles/s41467-022-35493-x},
  urldate = {2025-04-30},
  abstract = {Marine heatwaves can have disastrous impacts on ecosystems and marine industries. Given their potential consequences, it is important to understand how broad-scale climate variability influences the probability of localised extreme events. Here, we employ an advanced data-mining methodology, archetype analysis, to identify large scale patterns and teleconnections that lead to marine extremes in certain regions. This methodology is applied to the Australasian region, where it identifies instances of anomalous sea-surface temperatures, frequently associated with marine heatwaves, as well as the broadscale oceanic and atmospheric conditions associated with those extreme events. Additionally, we use archetype analysis to assess the ability of a low-resolution climate model to accurately represent the teleconnection patterns associated with extreme climate variability, and discuss the implications for the predictability of these impactful events.},
  langid = {english},
  keywords = {Natural hazards,Physical oceanography},
  file = {/Users/pschafer/Zotero/storage/P4RDEKP4/Chapman et al. - 2022 - A large-scale view of marine heatwaves revealed by archetype analysis.pdf}
}

@online{javadiNonnegativeMatrixFactorization2017,
  title = {Non-Negative Matrix Factorization via Archetypal Analysis},
  author = {Javadi, Hamid and Montanari, Andrea},
  date = {2017-05-08},
  eprint = {1705.02994},
  eprinttype = {arXiv},
  eprintclass = {stat},
  doi = {10.48550/arXiv.1705.02994},
  url = {http://arxiv.org/abs/1705.02994},
  urldate = {2025-05-01},
  abstract = {Given a collection of data points, non-negative matrix factorization (NMF) suggests to express them as convex combinations of a small set of `archetypes' with non-negative entries. This decomposition is unique only if the true archetypes are non-negative and sufficiently sparse (or the weights are sufficiently sparse), a regime that is captured by the separability condition and its generalizations. In this paper, we study an approach to NMF that can be traced back to the work of Cutler and Breiman (1994) and does not require the data to be separable, while providing a generally unique decomposition. We optimize the trade-off between two objectives: we minimize the distance of the data points from the convex envelope of the archetypes (which can be interpreted as an empirical risk), while minimizing the distance of the archetypes from the convex envelope of the data (which can be interpreted as a data-dependent regularization). The archetypal analysis method of (Cutler, Breiman, 1994) is recovered as the limiting case in which the last term is given infinite weight. We introduce a `uniqueness condition' on the data which is necessary for exactly recovering the archetypes from noiseless data. We prove that, under uniqueness (plus additional regularity conditions on the geometry of the archetypes), our estimator is robust. While our approach requires solving a non-convex optimization problem, we find that standard optimization methods succeed in finding good solutions both for real and synthetic data.},
  pubstate = {prepublished},
  keywords = {Computer Science - Machine Learning,Statistics - Machine Learning},
  file = {/Users/pschafer/Zotero/storage/APU7UIQC/Javadi and Montanari - 2017 - Non-negative Matrix Factorization via Archetypal Analysis.pdf;/Users/pschafer/Zotero/storage/Q2BHVITX/1705.html}
}

@inproceedings{bachemOneshotCoresetsCase2018,
  title = {One-Shot Coresets: The Case of k-Clustering},
  shorttitle = {One-Shot Coresets},
  booktitle = {Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics},
  author = {Bachem, Olivier and Lucic, Mario and Lattanzi, Silvio},
  date = {2018-03-31},
  pages = {784--792},
  publisher = {PMLR},
  issn = {2640-3498},
  url = {https://proceedings.mlr.press/v84/bachem18a.html},
  urldate = {2025-05-04},
  abstract = {Scaling clustering algorithms to massive data sets is a challenging task. Recently, several successful approaches based on data summarization methods, such as coresets and sketches, were proposed. While these techniques provide provably good and small summaries, they are inherently problem dependent - the practitioner has to commit to a fixed clustering objective before even exploring the data. However, can one construct small data summaries for a wide range of clustering problems simultaneously? In this work, we affirmatively answer this question by proposing an efficient algorithm that constructs such one-shot summaries for k-clustering problems while retaining strong theoretical guarantees.},
  eventtitle = {International Conference on Artificial Intelligence and Statistics},
  langid = {english},
  file = {/Users/pschafer/Zotero/storage/MI3PMQMJ/Bachem et al. - 2018 - One-shot Coresets The Case of k-Clustering.pdf}
}

@inproceedings{bachemScalableMeansClustering2018,
  title = {Scalable k -Means Clustering via Lightweight Coresets},
  booktitle = {Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  author = {Bachem, Olivier and Lucic, Mario and Krause, Andreas},
  date = {2018-07-19},
  series = {KDD '18},
  pages = {1119--1127},
  publisher = {Association for Computing Machinery},
  location = {New York, NY, USA},
  doi = {10.1145/3219819.3219973},
  url = {https://dl.acm.org/doi/10.1145/3219819.3219973},
  urldate = {2025-05-04},
  abstract = {\textbackslash emphCoresets are compact representations of data sets such that models trained on a coreset are provably competitive with models trained on the full data set. As such, they have been successfully used to scale up clustering models to massive data sets. While existing approaches generally only allow for multiplicative approximation errors, we propose a novel notion of lightweight coresets that allows for both multiplicative and additive errors. We provide a single algorithm to construct lightweight coresets for k -means clustering as well as soft and hard Bregman clustering. The algorithm is substantially faster than existing constructions, embarrassingly parallel, and the resulting coresets are smaller. We further show that the proposed approach naturally generalizes to statistical k -means clustering and that, compared to existing results, it can be used to compute smaller summaries for empirical risk minimization. In extensive experiments, we demonstrate that the proposed algorithm outperforms existing data summarization strategies in practice.},
  isbn = {978-1-4503-5552-0},
  file = {/Users/pschafer/Zotero/storage/ASM77TAN/Bachem et al. - 2018 - Scalable k -Means Clustering via Lightweight Coresets.pdf}
}

@article{thogersenArchetypalAnalysisDiverse2013,
  title = {Archetypal Analysis of Diverse Pseudomonas Aeruginosatranscriptomes Reveals Adaptation in Cystic Fibrosis Airways},
  author = {Thøgersen, Juliane Charlotte and Mørup, Morten and Damkiær, Søren and Molin, Søren and Jelsbak, Lars},
  date = {2013-09-23},
  journaltitle = {BMC Bioinformatics},
  shortjournal = {BMC Bioinformatics},
  volume = {14},
  number = {1},
  pages = {279},
  issn = {1471-2105},
  doi = {10.1186/1471-2105-14-279},
  url = {https://doi.org/10.1186/1471-2105-14-279},
  urldate = {2025-05-05},
  abstract = {Analysis of global gene expression by DNA microarrays is widely used in experimental molecular biology. However, the complexity of such high-dimensional data sets makes it difficult to fully understand the underlying biological features present in the data.},
  keywords = {Archetypal analysis,Cystic fibrosis,Gene expression,Hypermutators,Pseudomonas aeruginosa},
  file = {/Users/pschafer/Zotero/storage/3USPJXYU/Thøgersen et al. - 2013 - Archetypal analysis of diverse Pseudomonas aeruginosatranscriptomes reveals adaptation in cystic fib.pdf;/Users/pschafer/Zotero/storage/DZ8V2SSH/1471-2105-14-279.html}
}

@inproceedings{vandijkFindingArchetypalSpaces2019,
  title = {Finding Archetypal Spaces Using Neural Networks},
  booktitle = {2019 IEEE International Conference on Big Data (Big Data)},
  author = {family=Dijk, given=David, prefix=van, useprefix=true and Burkhardt, Daniel B. and Amodio, Matthew and Tong, Alexander and Wolf, Guy and Krishnaswamy, Smita},
  date = {2019-12},
  pages = {2634--2643},
  doi = {10.1109/BigData47090.2019.9006484},
  url = {https://ieeexplore.ieee.org/document/9006484},
  urldate = {2025-05-05},
  abstract = {Archetypal analysis is a data decomposition method that describes each observation in a dataset as a convex combination of ”pure types” or archetypes. These archetypes represent extrema of a data space in which there is a trade-off between features, such as in biology where different combinations of traits provide optimal fitness for different environments. Existing methods for archetypal analysis work well when a linear relationship exists between the feature space and the archetypal space. However, such methods are not applicable to systems where the feature space is generated non-linearly from the combination of archetypes, such as in biological systems or image transformations. Here, we propose a reformulation of the problem such that the goal is to learn a non-linear transformation of the data into a latent archetypal space. To solve this problem, we introduce Archetypal Analysis network (AAnet), which is a deep neural network framework for learning and generating from a latent archetypal representation of data. We demonstrate state-of-the-art recovery of ground-truth archetypes in non-linear data domains, show AAnet can generate from data geometry rather than from data density, and use AAnet to identify biologically meaningful archetypes in single-cell gene expression data.},
  eventtitle = {2019 IEEE International Conference on Big Data (Big Data)},
  keywords = {archetypal analysis,computational biology,Computed tomography,Computer science,Data models,Decoding,Geometry,Neural networks,Optimization,representation learning},
  file = {/Users/pschafer/Zotero/storage/EI24Y58N/Dijk et al. - 2019 - Finding Archetypal Spaces Using Neural Networks.pdf;/Users/pschafer/Zotero/storage/8RP43DWK/9006484.html}
}

@article{behdinSparseNMFArchetypal,
  title = {Sparse NMF with Archetypal Regularization: Computational and Robustness Properties},
  author = {Behdin, Kayhan and Mazumder, Rahul},
  abstract = {We consider the problem of sparse nonnegative matrix factorization (NMF) using archetypal regularization. The goal is to represent a collection of data points as nonnegative linear combinations of a few nonnegative sparse factors with appealing geometric properties, arising from the use of archetypal regularization. We generalize the notion of robustness studied in Javadi and Montanari (2019) (without sparsity) to the notions of (a) strong robustness that implies each estimated archetype is close to the underlying archetypes and (b) weak robustness that implies there exists at least one recovered archetype that is close to the underlying archetypes. Our theoretical results on robustness guarantees hold under minimal assumptions on the underlying data, and applies to settings where the underlying archetypes need not be sparse. We present theoretical results and illustrative examples to strengthen the insights underlying the notions of robustness. We propose new algorithms for our optimization problem; and present numerical experiments on synthetic and real data sets that shed further insights into our proposed framework and theoretical developments.},
  langid = {english},
  file = {/Users/pschafer/Zotero/storage/N8ZY6KNI/Behdin and Mazumder - Sparse NMF with Archetypal Regularization Computational and Robustness Properties.pdf}
}

@book{alonIntroductionSystemsBiology2020,
  title = {An Introduction to Systems Biology: Design Principles of Biological Circuits},
  shorttitle = {An Introduction to Systems Biology},
  author = {Alon, Uri},
  date = {2020},
  edition = {Second edition},
  publisher = {CRC Press, Taylor \& Francis Group},
  location = {Boca Raton London New York},
  doi = {10.1201/9780429283321},
  isbn = {978-1-4398-3717-7 978-0-429-28332-1},
  langid = {english},
  pagetotal = {1},
  file = {/Users/pschafer/Zotero/storage/M46GVHKR/Alon - 2020 - An introduction to systems biology design principles of biological circuits.pdf;/Users/pschafer/Zotero/storage/VXVYY9L6/An Introduction to Systems Biology Design Principles of Biological Circuits .pdf}
}

@article{hochbaumBestPossibleHeuristic1985,
  title = {A Best Possible Heuristic for the K-Center Problem},
  author = {Hochbaum, Dorit S. and Shmoys, David B.},
  year = {1985},
  month = may,
  journal = {Mathematics of Operations Research},
  volume = {10},
  number = {2},
  pages = {180--184},
  publisher = {INFORMS},
  issn = {0364-765X},
  doi = {10.1287/moor.10.2.180},
  urldate = {2025-06-08},
  abstract = {In this paper we present a 2-approximation algorithm for the k-center problem with triangle inequality. This result is ``best possible'' since for any {$\delta$} {$<$} 2 the existence of {$\delta$}-approximation algorithm would imply that P = NP. It should be noted that no {$\delta$}-approximation algorithm, for any constant {$\delta$}, has been reported to date. Linear programming duality theory provides interesting insight to the problem and enables us to derive, in O({\textbar}E{\textbar} log {\textbar}E{\textbar}) time, a solution with value no more than twice the k-center optimal value. A by-product of the analysis is an O({\textbar}E{\textbar}) algorithm that identifies a dominating set in G2, the square of a graph G, the size of which is no larger than the size of the minimum dominating set in the graph G. The key combinatorial object used is called a strong stable set, and we prove the NP-completeness of the corresponding decision problem.},
  keywords = {dominating set,k-center problem,strong stable set},
  file = {/Users/pschafer/Zotero/storage/R5APTFGX/Hochbaum and Shmoys - 1985 - A Best Possible Heuristic for the k-Center Problem.pdf}
}

@article{sulemanIllconceivedInitializationArchetypal2017,
  title = {On Ill-Conceived Initialization in Archetypal Analysis},
  author = {Suleman, Abdul},
  year = {2017},
  month = dec,
  journal = {Advances in Data Analysis and Classification},
  volume = {11},
  number = {4},
  pages = {785--808},
  issn = {1862-5355},
  doi = {10.1007/s11634-017-0303-0},
  urldate = {2025-06-08},
  abstract = {We show that an improper initialization of the matrix of prototypes, \$\$\{{\textbackslash}mathbf \{V\}\}\$\$, can be misleading, and potentially gives rise to a degenerate fuzzy partition when performing fuzzy clustering by means of an archetypal analysis. Subsequently, we propose an algorithm to correct the initial guess for \$\$\{{\textbackslash}mathbf \{V\}\}\$\$, which is grounded in two theoretical results on convex hulls. A numerical experiment carried out to assess its accuracy, and involving more than 200,000 initializations, shows a failure rate of below 0.8\%.},
  langid = {english},
  keywords = {62H30,62H86,Archetypal analysis,Categorization,Concept Formation,Functional clustering,Fuzzy clustering,Initialization,Linear Algebra,Machine Learning,Matrix factorization,Polytopes,Structure Prediction},
}

@article{krohneClassificationSocialAnhedonia2019,
  title = {Classification of Social Anhedonia Using Temporal and Spatial Network Features from a Social Cognition fMRI Task},
  author = {Krohne, Laerke Gebser and Wang, Yi and Hinrich, Jesper L. and Moerup, Morten and Chan, Raymond C. K. and Madsen, Kristoffer H.},
  year = {2019},
  month = aug,
  journal = {Human Brain Mapping},
  volume = {40},
  number = {17},
  pages = {4965--4981},
  issn = {1065-9471},
  doi = {10.1002/hbm.24751},
  urldate = {2025-06-08},
  abstract = {Previous studies have suggested that the degree of social anhedonia reflects the vulnerability for developing schizophrenia. However, only few studies have investigated how functional network changes are related to social anhedonia. The aim of this fMRI study was to classify subjects according to their degree of social anhedonia using supervised machine learning. More specifically, we extracted both spatial and temporal network features during a social cognition task from 70 subjects, and used support vector machines for classification. Since impairment in social cognition is well established in schizophrenia-spectrum disorders, the subjects performed a comic strip task designed to specifically probe theory of mind (ToM) and empathy processing. Features representing both temporal (time series) and network dynamics were extracted using task activation maps, seed region analysis, independent component analysis (ICA), and a newly developed multi-subject archetypal analysis (MSAA), which here aimed to further bridge aspects of both seed region analysis and decomposition by incorporating a spotlight approach.We found significant classification of subjects with elevated levels of social anhedonia when using the times series extracted using MSAA, indicating that temporal dynamics carry important information for classification of social anhedonia. Interestingly, we found that the same time series yielded the highest classification performance in a task classification of the ToM condition. Finally, the spatial network corresponding to that time series included both prefrontal and temporal-parietal regions as well as insula activity, which previously have been related schizotypy and the development of schizophrenia.},
  pmcid = {PMC6865405},
  pmid = {31403748},
}

@article{olsenCombiningElectroMagnetoencephalography2022,
  title = {Combining Electro- and Magnetoencephalography Data Using Directional Archetypal Analysis},
  author = {Olsen, Anders S. and H{\o}egh, Rasmus M. T. and Hinrich, Jesper L. and Madsen, Kristoffer H. and M{\o}rup, Morten},
  year = {2022},
  month = jul,
  journal = {Frontiers in Neuroscience},
  volume = {16},
  publisher = {Frontiers},
  issn = {1662-453X},
  doi = {10.3389/fnins.2022.911034},
  urldate = {2025-06-08},
  abstract = {Metastable microstates in electro- and magnetoencephalographic (EEG {\textbackslash}\& MEG) measurements are usually determined using modified \$k\$-means accounting for polarity invariant states. However, hard state assignment approaches assume that the brain traverses microstates in a discrete rather than continuous fashion. We present multimodal, multisubject directional archetypal analysis as a scale and polarity invariant extension to archetypal analysis using a loss function based on the Watson distribution. With this method, EEG/MEG microstates are modeled using subject- and modality-specific {\textbackslash}textit\{archetypes\} that are representative, distinct topographic maps between which the brain continuously traverses. Archetypes are specified as convex combinations of unit norm input data based on a shared generator matrix, thus assuming that the timing of neural responses to stimuli is consistent across subjects and modalities. The input data is reconstructed as convex combinations of archetypes using a subject- and modality-specific continuous archetypal mixing matrix. We showcase the model on synthetic data and an openly available face perception event-related potential data set with concurrently recorded EEG and MEG. In synthetic and unimodal experiments, we compare our model to conventional Euclidean multisubject archetypal analysis. We also contrast our model to a directional clustering model with discrete state assignments to highlight the advantages of modeling state trajectories rather than hard assignments. We find that our approach successfully models scale and polarity invariant data, such as microstates, accounting for intersubject and intermodal variability. The model is readily extendable to other modalities ensuring component correspondence while elucidating spatiotemporal signal variability.},
  langid = {english},
}

@article{sulemanMeasuringCongruenceFuzzy2017,
  title = {Measuring the Congruence of Fuzzy Partitions in Fuzzy {\emph{C}}-Means Clustering},
  author = {Suleman, Abdul},
  year = {2017},
  month = mar,
  journal = {Applied Soft Computing},
  volume = {52},
  pages = {1285--1295},
  issn = {1568-4946},
  doi = {10.1016/j.asoc.2016.06.037},
  urldate = {2025-06-09},
  abstract = {We propose an internal cluster validity index for a fuzzy c-means algorithm which combines a mathematical model for the fuzzy c-partition and a heuristic search for the number of clusters in the data. Our index resorts to information theoretic principles, and aims to assess the congruence between such a model and the data that have been observed. The optimal cluster solution represents a trade-off between discrepancy and the complexity of the underlying fuzzy c-partition. We begin by testing the effectiveness of the proposed index using two sets of synthetic data, one comprising a well-defined cluster structure and the other containing only noise. Then we use datasets arising from real life problems. Our results are compared to those provided by several available indices and their goodness is judged by an external measure of similarity. We find substantial evidence supporting our index as a credible alternative to the cluster validation problem, especially when it concerns structureless data.},
}

@article{badiaDecoupler2022,
  title = {decoupleR: Ensemble of Computational Methods to Infer Biological Activities from Omics Data},
  shorttitle = {decoupleR},
  author = {{Badia-i-Mompel}, Pau and V{\'e}lez Santiago, Jes{\'u}s and Braunger, Jana and Geiss, Celina and Dimitrov, Daniel and {M{\"u}ller-Dott}, Sophia and Taus, Petr and Dugourd, Aurelien and Holland, Christian H and Ramirez Flores, Ricardo O and {Saez-Rodriguez}, Julio},
  editor = {Kuijjer, Marieke Lydia},
  year = {2022},
  month = jan,
  journal = {Bioinformatics Advances},
  volume = {2},
  number = {1},
  pages = {vbac016},
  issn = {2635-0041},
  doi = {10.1093/bioadv/vbac016},
  urldate = {2024-12-07},
  abstract = {Summary: Many methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor and Python package containing computational methods to extract these activities within a unified framework. decoupleR allows us to flexibly run any method with a given resource, including methods that leverage mode of regulation and weights of interactions, which are not present in other frameworks. Moreover, it leverages OmniPath, a meta-resource comprising over 100 databases of prior knowledge. Using decoupleR, we evaluated the performance of methods on transcriptomic and phospho-proteomic perturbation experiments. Our findings suggest that simple linear models and the consensus score across top methods perform better than other methods at predicting perturbed regulators.},
  langid = {english},
  pmcid = {PMC9710656},
  pmid = {36699385},
}

@article{subramanianGSEA2005,
  title = {Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-Wide Expression Profiles},
  shorttitle = {Gene Set Enrichment Analysis},
  author = {Subramanian, Aravind and Tamayo, Pablo and Mootha, Vamsi K. and Mukherjee, Sayan and Ebert, Benjamin L. and Gillette, Michael A. and Paulovich, Amanda and Pomeroy, Scott L. and Golub, Todd R. and Lander, Eric S. and Mesirov, Jill P.},
  year = {2005},
  month = oct,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {102},
  number = {43},
  pages = {15545--15550},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.0506580102},
  urldate = {2024-12-07},
  abstract = {Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.},
  langid = {english},
  pmcid = {PMC1239896},
  pmid = {16199517},
  keywords = {microarray},
}

@book{taoAnalysisFourthEdition2022,
  title = {Analysis I: Fourth Edition},
  shorttitle = {Analysis I},
  author = {Tao, Terence},
  year = {2022},
  series = {Texts and Readings in Mathematics},
  edition = {2nd ed. 2022},
  number = {37},
  publisher = {Springer Nature Singapore},
  address = {Singapore},
  doi = {10.1007/978-981-19-7261-4},
  isbn = {978-981-19-7261-4},
  langid = {english}
}

@book{taoAnalysisIIFourth2022,
  title = {Analysis II: Fourth Edition},
  shorttitle = {Analysis II},
  author = {Tao, Terence},
  year = {2022},
  series = {Texts and Readings in Mathematics},
  edition = {2nd ed. 2022},
  number = {38},
  publisher = {Springer Nature Singapore},
  address = {Singapore},
  doi = {10.1007/978-981-19-7284-3},
  isbn = {978-981-19-7284-3},
  langid = {english}
}

@article{frankAlgorithmQuadraticProgramming1956a,
  title = {An Algorithm for Quadratic Programming},
  author = {Frank, Marguerite and Wolfe, Philip},
  year = {1956},
  month = mar,
  journal = {Naval Research Logistics Quarterly},
  volume = {3},
  number = {1-2},
  pages = {95--110},
  issn = {0028-1441, 1931-9193},
  doi = {10.1002/nav.3800030109},
  urldate = {2025-06-29},
  copyright = {http://onlinelibrary.wiley.com/termsAndConditions\#vor},
  langid = {english}
}

@article{feldmanIntroductionCoresetsUpdated2020,
  title = {Introduction to Core-Sets: An Updated Survey},
  shorttitle = {Introduction to Core-Sets},
  author = {Feldman, Dan},
  year = {2020},
  month = nov,
  number = {arXiv:2011.09384},
  eprint = {2011.09384},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2011.09384},
  urldate = {2025-07-02},
  abstract = {In optimization or machine learning problems we are given a set of items, usually points in some metric space, and the goal is to minimize or maximize an objective function over some space of candidate solutions. For example, in clustering problems, the input is a set of points in some metric space, and a common goal is to compute a set of centers in some other space (points, lines) that will minimize the sum of distances to these points. In database queries, we may need to compute such a some for a specific query set of \$k\$ centers. However, traditional algorithms cannot handle modern systems that require parallel real-time computations of infinite distributed streams from sensors such as GPS, audio or video that arrive to a cloud, or networks of weaker devices such as smartphones or robots. Core-set is a "small data" summarization of the input "big data", where every possible query has approximately the same answer on both data sets. Generic techniques enable efficient coreset {\textbackslash}changed\{maintenance\} of streaming, distributed and dynamic data. Traditional algorithms can then be applied on these coresets to maintain the approximated optimal solutions. The challenge is to design coresets with provable tradeoff between their size and approximation error. This survey summarizes such constructions in a retrospective way, that aims to unified and simplify the state-of-the-art.},
  archiveprefix = {arXiv},
}

@article{damleGeometricApproachArchetypal2017,
  title = {A Geometric Approach to Archetypal Analysis and Nonnegative Matrix Factorization},
  author = {Damle, Anil and Sun, Yuekai},
  year = {2017},
  month = jul,
  journal = {Technometrics},
  volume = {59},
  number = {3},
  pages = {361--370},
  publisher = {ASA Website},
  issn = {0040-1706},
  doi = {10.1080/00401706.2016.1247017},
  urldate = {2025-07-04},
  abstract = {Archetypal analysis and nonnegative matrix factorization (NMF) are staples in a statistician's toolbox for dimension reduction and exploratory data analysis. We describe a geometric approach to both NMF and archetypal analysis by interpreting both problems as finding extreme points of the data cloud. We also develop and analyze an efficient approach to finding extreme points in high dimensions. For modern massive datasets that are too large to fit on a single machine and must be stored in a distributed setting, our approach makes only a small number of passes over the data. In fact, it is possible to obtain the NMF or perform archetypal analysis with just two passes over the data.},
  pmid = {30828109},
  keywords = {Convex hull,Group lasso,Random projections},
  file = {/Users/pschafer/Zotero/storage/BR2EV8WS/Damle and Sun - 2017 - A Geometric Approach to Archetypal Analysis and Nonnegative Matrix Factorization.pdf}
}

@article{liana2022,
  title = {Comparison of Methods and Resources for Cell-Cell Communication Inference from Single-Cell RNA-Seq Data},
  author = {Dimitrov, Daniel and T{\"u}rei, D{\'e}nes and {Garrido-Rodriguez}, Martin and Burmedi, Paul L. and Nagai, James S. and Boys, Charlotte and Ramirez Flores, Ricardo O. and Kim, Hyojin and Szalai, Bence and Costa, Ivan G. and Valdeolivas, Alberto and Dugourd, Aur{\'e}lien and {Saez-Rodriguez}, Julio},
  year = {2022},
  month = jun,
  journal = {Nature Communications},
  volume = {13},
  number = {1},
  pages = {3224},
  issn = {2041-1723},
  doi = {10.1038/s41467-022-30755-0},
  urldate = {2024-12-07},
  abstract = {Abstract             The growing availability of single-cell data, especially transcriptomics, has sparked an increased interest in the inference of cell-cell communication. Many computational tools were developed for this purpose. Each of them consists of a resource of intercellular interactions prior knowledge and a method to predict potential cell-cell communication events. Yet the impact of the choice of resource and method on the resulting predictions is largely unknown. To shed light on this, we systematically compare 16 cell-cell communication inference resources and 7 methods, plus the consensus between the methods' predictions. Among the resources, we find few unique interactions, a varying degree of overlap, and an uneven coverage of specific pathways and tissue-enriched proteins. We then examine all possible combinations of methods and resources and show that both strongly influence the predicted intercellular interactions. Finally, we assess the agreement of cell-cell communication methods with spatial colocalisation, cytokine activities, and receptor protein abundance and find that predictions are generally coherent with those data modalities. To facilitate the use of the methods and resources described in this work, we provide LIANA, a LIgand-receptor ANalysis frAmework as an open-source interface to all the resources and methods.},
  langid = {english},
  local-url = {file://localhost/Users/pschafer/Documents/Papers\_PDFs/2022\_Dimitrov\_Saez-Rodriguez\_Comparison},
  pmcid = {PMC9184522},
  pmid = {35680885},
  file = {/Users/pschafer/Zotero/storage/HT7E2LLS/Dimitrov et al. - 2022 - Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq d.pdf}
}

@article{lianaPlus2024,
  title = {LIANA+ Provides an All-in-One Framework for Cell--Cell Communication Inference},
  author = {Dimitrov, Daniel and Sch{\"a}fer, Philipp Sven Lars and Farr, Elias and {Rodriguez-Mier}, Pablo and Lobentanzer, Sebastian and {Badia-i-Mompel}, Pau and Dugourd, Aurelien and Tanevski, Jovan and Ramirez Flores, Ricardo Omar and {Saez-Rodriguez}, Julio},
  year = {2024},
  month = sep,
  journal = {Nature Cell Biology},
  volume = {26},
  number = {9},
  pages = {1613--1622},
  issn = {1465-7392, 1476-4679},
  doi = {10.1038/s41556-024-01469-w},
  urldate = {2024-12-07},
  abstract = {Abstract                            The growing availability of single-cell and spatially resolved transcriptomics has led to the development of many approaches to infer cell--cell communication, each capturing only a partial view of the complex landscape of intercellular signalling. Here we present LIANA+, a scalable framework built around a rich knowledge base to decode coordinated inter- and intracellular signalling events from single- and multi-condition datasets in both single-cell and spatially resolved data. By extending and unifying established methodologies, LIANA+ provides a comprehensive set of synergistic components to study cell--cell communication via diverse molecular mediators, including those measured in multi-omics data. LIANA+ is accessible at               https://github.com/saezlab/liana-py               with extensive vignettes (               https://liana-py.readthedocs.io/               ) and provides an all-in-one solution to intercellular communication inference.},
  langid = {english},
  local-url = {file://localhost/Users/pschafer/Documents/Papers\_PDFs/www.nature.com},
  file = {/Users/pschafer/Zotero/storage/CRE4TCLT/Dimitrov et al. - 2024 - LIANA+ provides an all-in-one framework for cell–cell communication inference.pdf}
}

@article{reactome2022,
  title = {The Reactome Pathway Knowledgebase 2022},
  year = {2022},
  month = jan,
  journal = {Nucleic Acids Research},
  volume = {50},
  number = {D1},
  pages = {D687-D692},
  issn = {0305-1048, 1362-4962},
  doi = {10.1093/nar/gkab1028},
  urldate = {2024-12-07},
  abstract = {The Reactome Knowledgebase (https://reactome.org), an Elixir core resource, provides manually curated molecular details across a broad range of physiological and pathological biological processes in humans, including both hereditary and acquired disease processes. The processes are annotated as an ordered network of molecular transformations in a single consistent data model. Reactome thus functions both as a digital archive of manually curated human biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. Recent curation work has expanded our annotations of normal and disease-associated signaling processes and of the drugs that target them, in particular infections caused by the SARS-CoV-1 and SARS-CoV-2 coronaviruses and the host response to infection. New tools support better simultaneous analysis of high-throughput data from multiple sources and the placement of understudied (`dark') proteins from analyzed datasets in the context of Reactome's manually curated pathways.},
  langid = {english},
  pmcid = {PMC8689983},
  pmid = {34788843},
  author = {Gillespie, Marc and Jassal, Bijay and Stephan, Ralf and Milacic, Marija and Rothfels, Karen and {Senff-Ribeiro}, Andrea and Griss, Johannes and Sevilla, Cristoffer and others and Bader, Gary and Demir, Emek and Sander, Chris and Haw, Robin and Wu, Guanming and Stein, Lincoln and Hermjakob, Henning and D'Eustachio, Peter}
}

@article{scanpy2018,
  title = {SCANPY: Large-Scale Single-Cell Gene Expression Data Analysis},
  shorttitle = {SCANPY},
  author = {Wolf, F. Alexander and Angerer, Philipp and Theis, Fabian J.},
  year = {2018},
  month = dec,
  journal = {Genome Biology},
  volume = {19},
  number = {1},
  pages = {15},
  issn = {1474-760X},
  doi = {10.1186/s13059-017-1382-0},
  urldate = {2024-12-07},
  abstract = {SCANPY is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells (https://github.com/theislab/Scanpy). Along with SCANPY, we present ANNDATA, a generic class for handling annotated data matrices (https://github.com/theislab/anndata).},
  langid = {english},
  pmcid = {PMC5802054},
  pmid = {29409532},
}

@article{10.1111/j.2517-6161.1995.tb02031.x,
  title = {Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing},
  author = {Benjamini, Yoav and Hochberg, Yosef},
  year = 1995,
  journal = {Journal of the Royal Statistical Society: Series B (Methodological)},
  volume = {57},
  number = {1},
  pages = {289--300},
  issn = {0035-9246},
  doi = {10.1111/j.2517-6161.1995.tb02031.x},
  langid = {english}
}

@article{mannTestWhetherOne1947,
  title = {On a Test of Whether One of Two Random Variables Is Stochastically Larger than the Other},
  author = {Mann, H. B. and Whitney, D. R.},
  year = 1947,
  month = mar,
  journal = {The Annals of Mathematical Statistics},
  volume = {18},
  number = {1},
  pages = {50--60},
  publisher = {Institute of Mathematical Statistics},
  issn = {0003-4851, 2168-8990},
  doi = {10.1214/aoms/1177730491},
  urldate = {2026-01-31},
}

@article{welchGeneralizationStudentsProblem1947,
  title = {The Generalization of `Student's' Problem When Several Different Population Variances Are Involved},
  author = {Welch, B. L.},
  year = 1947,
  journal = {Biometrika},
  volume = {34},
  number = {1/2},
  eprint = {2332510},
  eprinttype = {jstor},
  pages = {28--35},
  publisher = {[Oxford University Press, Biometrika Trust]},
  issn = {0006-3444},
  doi = {10.2307/2332510},
  urldate = {2026-01-31},
}
