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Adler M, Tendler A, Hausser J, Korem Y, Szekely P, Bossel N, Hart Y, Karin O, Mayo A, Alon U. Controls for Phylogeny and Robust Analysis in Pareto Task Inference. Mol Biol Evol 2022; 39:msab297. [PMID: 34633456 PMCID: PMC8763096 DOI: 10.1093/molbev/msab297] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Understanding the tradeoffs faced by organisms is a major goal of evolutionary biology. One of the main approaches for identifying these tradeoffs is Pareto task inference (ParTI). Two recent papers claim that results obtained in ParTI studies are spurious due to phylogenetic dependence (Mikami T, Iwasaki W. 2021. The flipping t-ratio test: phylogenetically informed assessment of the Pareto theory for phenotypic evolution. Methods Ecol Evol. 12(4):696-706) or hypothetical p-hacking and population-structure concerns (Sun M, Zhang J. 2021. Rampant false detection of adaptive phenotypic optimization by ParTI-based Pareto front inference. Mol Biol Evol. 38(4):1653-1664). Here, we show that these claims are baseless. We present a new method to control for phylogenetic dependence, called SibSwap, and show that published ParTI inference is robust to phylogenetic dependence. We show how researchers avoided p-hacking by testing for the robustness of preprocessing choices. We also provide new methods to control for population structure and detail the experimental tests of ParTI in systems ranging from ammonites to cancer gene expression. The methods presented here may help to improve future ParTI studies.
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Affiliation(s)
- Miri Adler
- Klarman Cell Observatory, Eli and Edythe L. Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Avichai Tendler
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Jean Hausser
- Department of Cellular and Molecular Biology Stockholm, Karolinska Institute and SciLifeLab, Stockholm, Sweden
| | - Yael Korem
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Pablo Szekely
- Department of Biology, Duke University, Durham, NC, USA
| | - Noa Bossel
- Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel
| | - Yuval Hart
- Department of Psychology Jerusalem, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Omer Karin
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Avi Mayo
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Uri Alon
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
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Zimmer A, Korem Y, Rappaport N, Wilmanski T, Baloni P, Jade K, Robinson M, Magis AT, Lovejoy J, Gibbons SM, Hood L, Price ND. The geometry of clinical labs and wellness states from deeply phenotyped humans. Nat Commun 2021; 12:3578. [PMID: 34117230 PMCID: PMC8196202 DOI: 10.1038/s41467-021-23849-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 05/17/2021] [Indexed: 02/05/2023] Open
Abstract
Longitudinal multi-omics measurements are highly valuable in studying heterogeneity in health and disease phenotypes. For thousands of people, we have collected longitudinal multi-omics data. To analyze, interpret and visualize this extremely high-dimensional data, we use the Pareto Task Inference (ParTI) method. We find that the clinical labs data fall within a tetrahedron. We then use all other data types to characterize the four archetypes. We find that the tetrahedron comprises three wellness states, defining a wellness triangular plane, and one aberrant health state that captures aspects of commonality in movement away from wellness. We reveal the tradeoffs that shape the data and their hierarchy, and use longitudinal data to observe individual trajectories. We then demonstrate how the movement on the tetrahedron can be used for detecting unexpected trajectories, which might indicate transitions from health to disease and reveal abnormal conditions, even when all individual blood measurements are in the norm.
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Affiliation(s)
- Anat Zimmer
- grid.64212.330000 0004 0463 2320Institute for Systems Biology, Seattle, WA USA
| | - Yael Korem
- grid.13992.300000 0004 0604 7563Weizmann Institute, Rehovot, Israel
| | - Noa Rappaport
- grid.64212.330000 0004 0463 2320Institute for Systems Biology, Seattle, WA USA
| | - Tomasz Wilmanski
- grid.64212.330000 0004 0463 2320Institute for Systems Biology, Seattle, WA USA
| | - Priyanka Baloni
- grid.64212.330000 0004 0463 2320Institute for Systems Biology, Seattle, WA USA
| | - Kathleen Jade
- grid.64212.330000 0004 0463 2320Institute for Systems Biology, Seattle, WA USA
| | - Max Robinson
- grid.64212.330000 0004 0463 2320Institute for Systems Biology, Seattle, WA USA
| | - Andrew T. Magis
- grid.64212.330000 0004 0463 2320Institute for Systems Biology, Seattle, WA USA
| | - Jennifer Lovejoy
- grid.64212.330000 0004 0463 2320Institute for Systems Biology, Seattle, WA USA
| | - Sean M. Gibbons
- grid.64212.330000 0004 0463 2320Institute for Systems Biology, Seattle, WA USA
| | - Leroy Hood
- grid.64212.330000 0004 0463 2320Institute for Systems Biology, Seattle, WA USA ,Providence St Joseph Health, Seattle, WA USA
| | - Nathan D. Price
- grid.64212.330000 0004 0463 2320Institute for Systems Biology, Seattle, WA USA
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Houri-Ze’evi L, Korem Y, Sheftel H, Faigenbloom L, Toker IA, Dagan Y, Awad L, Degani L, Alon U, Rechavi O. A Tunable Mechanism Determines the Duration of the Transgenerational Small RNA Inheritance in C. elegans. Cell 2016; 165:88-99. [DOI: 10.1016/j.cell.2016.02.057] [Citation(s) in RCA: 110] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2015] [Revised: 01/19/2016] [Accepted: 02/24/2016] [Indexed: 01/13/2023]
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Szekely P, Korem Y, Moran U, Mayo A, Alon U. The Mass-Longevity Triangle: Pareto Optimality and the Geometry of Life-History Trait Space. PLoS Comput Biol 2015; 11:e1004524. [PMID: 26465336 PMCID: PMC4605829 DOI: 10.1371/journal.pcbi.1004524] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2014] [Accepted: 08/26/2015] [Indexed: 12/14/2022] Open
Abstract
When organisms need to perform multiple tasks they face a fundamental tradeoff: no phenotype can be optimal at all tasks. This situation was recently analyzed using Pareto optimality, showing that tradeoffs between tasks lead to phenotypes distributed on low dimensional polygons in trait space. The vertices of these polygons are archetypes--phenotypes optimal at a single task. This theory was applied to examples from animal morphology and gene expression. Here we ask whether Pareto optimality theory can apply to life history traits, which include longevity, fecundity and mass. To comprehensively explore the geometry of life history trait space, we analyze a dataset of life history traits of 2105 endothermic species. We find that, to a first approximation, life history traits fall on a triangle in log-mass log-longevity space. The vertices of the triangle suggest three archetypal strategies, exemplified by bats, shrews and whales, with specialists near the vertices and generalists in the middle of the triangle. To a second approximation, the data lies in a tetrahedron, whose extra vertex above the mass-longevity triangle suggests a fourth strategy related to carnivory. Each animal species can thus be placed in a coordinate system according to its distance from the archetypes, which may be useful for genome-scale comparative studies of mammalian aging and other biological aspects. We further demonstrate that Pareto optimality can explain a range of previous studies which found animal and plant phenotypes which lie in triangles in trait space. This study demonstrates the applicability of multi-objective optimization principles to understand life history traits and to infer archetypal strategies that suggest why some mammalian species live much longer than others of similar mass.
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Affiliation(s)
- Pablo Szekely
- Department of Molecular Cell Biology, The Weizmann Institute of Science, Rehovot, Israel
| | - Yael Korem
- Department of Molecular Cell Biology, The Weizmann Institute of Science, Rehovot, Israel
| | - Uri Moran
- Department of Plant Science, The Weizmann Institute of Science, Rehovot, Israel
| | - Avi Mayo
- Department of Molecular Cell Biology, The Weizmann Institute of Science, Rehovot, Israel
| | - Uri Alon
- Department of Molecular Cell Biology, The Weizmann Institute of Science, Rehovot, Israel
- * E-mail:
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Korem Y, Szekely P, Hart Y, Sheftel H, Hausser J, Mayo A, Rothenberg ME, Kalisky T, Alon U. Geometry of the Gene Expression Space of Individual Cells. PLoS Comput Biol 2015; 11:e1004224. [PMID: 26161936 PMCID: PMC4498931 DOI: 10.1371/journal.pcbi.1004224] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2014] [Accepted: 03/04/2015] [Indexed: 12/14/2022] Open
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. In the past, biological experiments usually pooled together millions of cells, masking the differences between individual cells. Current technology takes a big step forward by measuring gene expression from individual cells. Interpreting this data is challenging because we need to understand how cells are arranged in a high dimensional gene expression space. Here we test recent theory that suggests that cells facing multiple tasks should be arranged in simple low dimensional polygons or polyhedra (generally called polytopes). The vertices of the polytopes are gene expression profiles optimal for each of the tasks. We find evidence for such simplicity in a variety of tissues—spleen, bone marrow, intestine—analyzed by different single-cell technologies. We find that cells are distributed inside polytopes, such as tetrahedrons or four-dimensional simplexes, with cells closest to each vertex responsible for a different key task. For example, intestinal progenitor cells that give rise to the other cell types show a continuous distribution in a tetrahedron whose vertices correspond to several key sub-tasks. Immune dendritic cells likewise are continuously distributed between key immune tasks. This approach of testing whether data falls in polytopes may be useful for interpreting a variety of single-cell datasets in terms of biological tasks.
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Affiliation(s)
- Yael Korem
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Pablo Szekely
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Yuval Hart
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Hila Sheftel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Jean Hausser
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Avi Mayo
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Michael E. Rothenberg
- Department of Medicine, Division of Gastroenterology and Hepatology, and Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California, United States of America
| | - Tomer Kalisky
- Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
| | - Uri Alon
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
- * E-mail:
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