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Pagan Cluster Paga

Pagan Cluster Paga

CCluster novo prediction of Clusfer cell Clustef using single-cell transcriptome Pzga. A comparison Pagan Cluster Paga single-cell Blackjack Plus dinero real inference methods: towards more accurate and robust Pagan Cluster Paga. Also working on the fairly unique political systems I've created, including Cafotry and Ghogism. Satija R, Farrell JA, Gennert D, Schier AF, Regev A. Thank you so much for taking care to find two similar ones. Hamey 2Mireya Plass 3Jordi Solana 3Joakim S.

Pagan Cluster Paga -

Welcome to the first and perhaps only issue of the Reclaiming Cauldron, a compendium of creativity from around Reclaiming. Folks from around our network wrote, photo- graphed, painted, and even helped proofread.

Print edition or free download — pages of writings, artwork, photos, music and video links, book excerpts — and even some funny stuff! Pagan Cluster activist Lisa Fithian has released a new book of activist skills and stories, ranging from Seattle in to the present.

Check it out:. Online Ordering — various options. For anyone who wants to become more active in resistance or is just feeling overwhelmed or hopeless, Shut It Down offers strategies and actions you can take right now to promote justice and incite change in your own community.

In Shut It Down Fithian shares historic, behind-the-scenes stories from some of the most important people-powered movements of the past several decades.

She shows how movements that embrace direct action have always been, and continue to be, the most radical and rapid means for transforming the ills of our society.

Shut It Down is filled with instructions and inspiration for how movements can evolve as the struggle for social justice continues in the Trump era and beyond.

While recognizing that electoral politics, legislation, and policy are all important pathways to change, Shut It Down argues that civil disobedience is not just one of the only actions that remains when all else fails, but a spiritual pursuit that protects our deepest selves and allows us to reclaim our humanity.

Skip to content. Summary of Magic for Ukraine 2. Click here for slide show by Luke Hauser. The action, organized by Bay Area Extinction Rebellion as part of international protests during October , included people from Red Rebels, Decolonization, Playful Animal Parade, Mindful Direct Action, the Climate Anxiety Tent, DAWG, and WICCA Witches Invoking Creative Climate Action — our Bay Area Pagan Cluster!

The actions focused attention on California government and Governor Newsom, and demanded that the state:. Future Actions and Events: extinctionrebellionsfbay. Subscribe Subscribed. Sign me up. Already have a WordPress. com account?

Log in now. Reclaiming Customize Subscribe Subscribed Sign up Log in Report this content View site in Reader Manage subscriptions Collapse this bar. Loading Comments Partition-based graph abstraction PAGA resolves these fundamental problems by generating graph-like maps of cells that preserve both continuous and disconnected structure in data at multiple resolutions.

The data-driven formulation of PAGA allows to robustly reconstruct branching gene expression changes across different datasets and, for the first time, enabled reconstructing the lineage relations of a whole adult animal [ 13 ].

Furthermore, we show that PAGA-initialized manifold learning algorithms converge faster, produce embeddings that are more faithful to the global topology of high-dimensional data, and introduce an entropy-based measure for quantifying such faithfulness. Finally, we show how PAGA abstracts transition graphs, for instance, from RNA velocity and compare to previous trajectory-inference algorithms.

With this, PAGA provides a graph abstraction method [ 14 ] that is suitable for deriving interpretable abstractions of the noisy kNN-like graphs that are typically used to represent the manifolds arising in scRNA-seq data.

However, the complexity of G and noise-related spurious edges make it both hard to trace a putative biological process from progenitor cells to different fates and to decide whether groups of cells are in fact connected or disconnected.

Moreover, tracing isolated paths of single cells to make statements about a biological process comes with too little statistical power to achieve an acceptable confidence level.

Gaining power by averaging over distributions of single-cell paths is hampered by the difficulty of fitting realistic models for the distribution of these paths.

Partition-based graph abstraction generates a topology-preserving map of single cells. High-dimensional gene expression data is represented as a kNN graph by choosing a suitable low-dimensional representation and an associated distance metric for computing neighborhood relations—in most of the paper, we use PCA-based representations and Euclidean distance.

The kNN graph is partitioned at a desired resolution where partitions represent groups of connected cells. For this, we usually use the Louvain algorithm, however, partitions can be obtained in any other way, too. A PAGA graph is obtained by associating a node with each partition and connecting each node by weighted edges that represent a statistical measure of connectivity between partitions, which we introduce in the present paper.

By discarding spurious edges with low weights, PAGA graphs reveal the denoised topology of the data at a chosen resolution and reveal its connected and disconnected regions.

Combining high-confidence paths in the PAGA graph with a random-walk-based distance measure on the single-cell graph, we order cells within each partition according to their distance from a root cell.

A PAGA path then averages all single-cell paths that pass through the corresponding groups of cells. This allows to trace gene expression changes along complex trajectories at single-cell resolution.

We address these problems by developing a statistical model for the connectivity of groups of cells, which we typically determine through graph-partitioning [ 17 — 19 ] or alternatively through clustering or experimental annotation.

Similar to modularity [ 20 ], the statistical model considers groups as connected if their number of inter-edges exceeds a fraction of the number of inter-edges expected under random assignment. The connection strength can be interpreted as confidence in the presence of an actual connection and allows discarding spurious, noise-related connections Additional file 1 : Note 1.

By averaging over such an ensemble of single-cell paths, it becomes possible to trace a putative biological process from a progenitor to fates in a way that is robust to spurious edges, provides statistical power, and is consistent with basic assumptions on a biological trajectory of cells Additional file 1 : Note 2.

Note that by varying the resolution of the partitioning, PAGA generates graphs at multiple resolutions, which enables a hierarchical exploration of data Fig. To trace gene dynamics at single-cell resolution, we extended existing random-walk-based distance measures Additional file 1 : Note 2, Reference [ 7 ] to the realistic case that accounts for disconnected graphs.

PAGA can thus be viewed as an easily interpretable and robust way of performing topological data analysis [ 9 , 21 ] Additional file 1 : Note 3.

The computationally almost cost-free coarse-resolution embeddings of PAGA can be used to initialize established manifold learning and graph drawing algorithms like UMAP [ 22 ] and ForceAtlas2 FA [ 23 ].

This strategy is used to generate the single-cell embeddings throughout this paper. In contrast to the results of previous algorithms, PAGA-initialized single-cell embeddings are faithful to the global topology, which greatly improves their interpretability.

To quantify this claim, we took a classification perspective on embedding algorithms and developed a cost function KL geo Box 1 and Additional file 1 : Note 4 , which captures faithfulness to global topology by incorporating geodesic distance along the representations of data manifolds in both the high-dimensional and the embedding space, respectively.

Independent of this, PAGA-initialized manifold learning converges about six times faster with respect to established cost functions in manifold learning Additional file 1: Figure S Hematopoiesis represents one of the most extensively characterized systems involving stem cell differentiation towards multiple cell fates and hence provides an ideal scenario for applying PAGA to complex manifolds.

We applied PAGA to simulated data Additional file 1 : Note 5 for this system and three experimental datasets: cells measured using MARS-seq [ 24 ], cells measured using Smart-seq2 [ 25 ], and 44, cells from a 10 × Genomics protocol [ 26 ].

These data cover the differentiation from stem cells towards cell fates including erythrocytes, megakaryocytes, neutrophils, monocytes, basophils, and lymphocytes. The PAGA graphs Fig.

Under debate is the origin of basophils. Studies have suggested both that basophils originate from a basophil-neutrophil-monocyte progenitor or, more recently, from a shared erythroid-megakaryocyte-basophil progenitor [ 27 , 28 ].

The PAGA graphs of the three experimental datasets highlight this ambiguity. While the dataset of Paul et al. falls in the former category, Nestorowa et al.

falls in the latter and Dahlin et al. Aside from this ambiguity that can be explained by insufficient sampling in Paul et al. and Nestorowa et al. Beyond consistent topology between cell subgroups, we find consistent continuous gene expression changes across all datasets—we observe changes of erythroid maturity marker genes Gata2 , Gata1 , Klf1 , Epor , and Hba-a2 along the erythroid trajectory through the PAGA graphs and observe sequential activation of these genes in agreement with known behavior.

Activation of neutrophil markers Elane , Cepbe , and Gfi1 and monocyte markers Irf8 , Csf1r , and Ctsg are seen towards the end of the neutrophil and monocyte trajectories, respectively.

While PAGA is able to capture the dynamic transcriptional processes underlying multi-lineage hematopoietic differentiation, previous algorithms often fail to robustly produce meaningful results Additional file 1 : Figures S8, S9, S PAGA consistently predicts developmental trajectories and gene expression changes across datasets for hematopoiesis.

The three columns correspond to PAGA-initialized single-cell embeddings, PAGA graphs, and gene changes along PAGA paths. The four rows of panels correspond to simulated data Additional file 1 : Note 5 and data from Paul et al.

The arrows in the last row mark the two trajectories to basophils. One observes both consistent topology of PAGA graphs and consistent gene expression changes along PAGA paths for 5 erythroid, 3 neutrophil, and 3 monocyte marker genes across all datasets.

The cell type abbreviations are as follows: Stem for stem cells, Ery for erythrocytes, Mk for megakaryocytes, Neu for neutrophils, Mo for monocytes, Baso for basophils, B for B cells, Lymph for lymphocytes.

Recently, Plass et al. While Plass et al. Each map preserves the topology of data, in contrast to state-of-the-art manifold learning where connected tissue types appear as either disconnected or overlapping Fig.

PAGA applied to a whole adult animal. a PAGA graphs for data for the flatworm Schmidtea mediterranea [ 13 ] at tissue, cell type, and single-cell resolution. We obtained a topologically meaningful embedding by initializing a single-cell embedding with the embedding of the cell-type PAGA graph.

Note that the PAGA graph is the same as in Reference [ 13 ], only that here, we neither highlight a tree subgraph nor used the corresponding tree layout for visualization.

b Established manifold learning for the same data violate the topological structure. c , d Predictions of RNA velocity evaluated with PAGA for two example lineages: epidermis and muscle. We show the RNA velocity arrows plotted on a single-cell embedding, the standard PAGA graph representing the topological information only epidermis , and the PAGA graph representing the RNA velocity information.

Even though the connections in PAGA graphs often correspond to actual biological trajectories, this is not always the case. This is a consequence of PAGA being applied to kNN graphs, which solely contain information about the topology of data. Recently, it has been suggested to also consider directed graphs that store information about cellular transition based on RNA velocity [ 29 ].

To include this additional information, which can add further evidence for actual biological transitions, we extend the undirected PAGA connectivity measure to such directed graphs Additional file 1 : Note 1.

Due the relatively sparsely sampled, high-dimensional feature space of scRNA-seq data, both fitting and interpreting an RNA velocity vector without including information about topology—connectivity of neighborhoods—is practically impossible.

PAGA provides a natural way of abstracting both topological information and information about RNA velocity. Next, we applied PAGA to 53, cells collected at different developmental time points embryo days from the zebrafish embryo [ 30 ].

The PAGA graph for partitions corresponding to embryo days accurately recovers the chain topology of temporal progression, whereas the PAGA graph for cell types provides easily interpretable overviews of the lineage relations Fig. Initializing a ForceAtlas2 layout with PAGA coordinates from fine cell types automatically produced a corresponding, interpretable single-cell embedding Fig.

Wagner et al. Comparing the PAGA graph for the fine cell types to the coarse-grained graph of Wagner et al. reproduced their result with high accuracy Fig.

PAGA applied to zebrafish embryo data of Wagner et al. a PAGA graphs obtained after running PAGA on partitions corresponding to embryo days, coarse cell types, more fine-grained cell types, and a PAGA-initialized single-cell embedding. Cell type assignments are from the original publication.

b Performance measurements of the PAGA prediction compared to the reference graph of Wagner et al. show high accuracy. False-positive edges and false-negative edges for the threshold indicated by a vertical line in the left panel are also shown. Comparing the runtimes of PAGA with the state-of-the-art UMAP [ 22 ] for 1.

For complex and large data, the PAGA graph generally provides a more easily interpretable visualization of the clustering step in exploratory data analysis, where the limitations of two-dimensional representations become apparent Additional file 1 : Figure S PAGA graph visualizations can be colored by gene expression and covariates from annotation Additional file 1 : Figure S13 just as any conventional embedding method.

To assess how robustly graph and tree-inference algorithms recover a given topology, we developed a measure for comparing the topologies of two graphs by comparing the sets of possible paths on them Additional file 1 : Note 1.

Sampling widely varying parameters, which leads to widely varying clusterings, we find that the inferred abstraction of topology of data within the PAGA graph is much more robust than the underlying graph clustering algorithm Additional file 1 : Figure S5. While graph clustering alone is, as any clustering method, an ill-posed problem in the sense that many highly degenerate quasi-optimal clusterings exist and some knowledge about the scale of clusters is required, PAGA is not affected by this.

Several algorithms [ 5 , 10 — 12 ] have been proposed for reconstructing lineage trees Additional file 1 : Note 3, [ 4 ]. The main caveat of these algorithms is that they, unlike PAGA, try to explain any variation in the data with a tree-like topology. In particular, any disconnected distribution of clusters is interpreted as originating from a tree.

This produces qualitatively wrong results already for simple simulated data Supplementary Figure 6 and only works well for data that clearly conforms with a tree-like manifold Supplementary Figure 7. To establish a fair comparison on real data with the recent popular algorithm, Monocle 2, we reinvestigated the main example of Qiu et al.

This example is based on the data of Paul et al. While PAGA identifies the cluster as disconnected with a result that is unaffected by its presence, the prediction of Monocle 2 changes qualitatively if the cluster is taken into account Supplementary Figure 8.

The example illustrates the general point that real data almost always consists of dense and sparse—connected and disconnected—regions, some tree-like, some with more complex topology. In view of an increasing number of large datasets and analyses for even larger merged datasets, PAGA fundamentally addresses the need for scalable and interpretable maps of high-dimensional data.

In the context of the Human Cell Atlas [ 32 ] and comparable databases, methods for their hierarchical, multi-resolution exploration will be pivotal in order to provide interpretable accessibility to users.

PAGA allows to present information about clusters or cell types in an unbiased, data-driven coordinate system by representing these in PAGA graphs. In the context of the recent advances of the study of simple biological processes that involve a single branching [ 6 , 7 ], PAGA provides a similarly robust framework for arbitrarily complex topologies.

In view of the fundamental challenges of single-cell resolution studies due to technical noise, transcriptional stochasticity, and computational burden, PAGA provides a general framework for extending studies of the relations among single cells to relations among noise-reduced and computationally tractable groups of cells.

This could facilitate obtaining clearer pictures of underlying biology. In closing, we note that PAGA not only works for scRNA-seq based on distance metrics that arise from a sequence of chosen preprocessing steps, but can also be applied to any learned distance metric.

To illustrate this point, we used PAGA for single-cell imaging data when applied on the basis of a deep-learning-based distance metric. Eulenberg et al. Using this, PAGA correctly identifies the biological trajectory through the interphases of cell cycle while ignoring a cluster of damaged and dead cells Additional file 1 : Figure S We preprocess scRNA-seq data as commonly done following steps mostly inspired by Seurat [ 34 ] in the implementation of Scanpy [ 35 ].

These steps consist in basic filtering of the data, total count normalization, log1p logarithmization, extraction of highly variable genes, a potential regression of confounding factors, and a scaling to z -scores. On this corrected and homogenized representation of the count data, we perform a PCA and represent the data within the reduced space of principal components.

In the GitHub repository, each figure of the paper is reproduced in a dedicated notebook. Using the compressed and denoised representation of the data in the previous step, we construct a symmetrized kNN-like graph, typically using the approximate nearest neighbor search within UMAP [ 22 ].

While one might potentially choose different distance metrics, we always choose Euclidean distance. Depending on user choice, the graph is either weighed using adaptive Gaussian kernels [ 7 ] or the exponential kernel within UMAP [ 22 ].

For all results shown in the manuscript, we used the exponential kernel. We consider all partitionings of interest of the kNN-like graph. To determine those, typically, we use the Louvain algorithm in the implementation of [ 37 ] at suitable resolutions, but PAGA works with any underlying clustering algorithm or experimentally generated groupings of observations.

In the present work, we exclusively used the Louvain algorithm. This measure is a test statistic quantifying the degree of connectivity of two partitions and has a close relation with modularity [ 20 ].

For each pair of clusters, PAGA connectivity is the ratio of the number of inter-edges between the clusters normalized with the number of inter-edges expected under random assignment of edges.

For estimating pseudotime, we use an extended version of diffusion pseudotime DPT Reference [ 7 ] that accounts for disconnected graphs.

The extension consists in a simple modification of the original algorithm that accounts for disconnected Eigen-subspaces of the graph adjacency matrix, which results in multiple subspaces of Eigen value 1 of the graph transition matrix.

Practically, we assign an infinite distance to cells that reside in disconnected clusters and compute distances among cells within connected regions in the graph as it would be done in DPT.

See Additional file 1 : Note 2, both for details and for a review of random-walk-based distances. For instance, we show the close relation of DPT to mean commute distance. PAGA achieves consistent i. For this initialization, the positions of nodes of the fine-grained graph that belong to a group corresponding to a node in the coarse-grained graph are randomly distributed in a non-overlapping rectangular region around the position of that node.

This procedure is repeated for all nodes of the coarse-grained graph. Non-overlapping regions are trivially ensured by choosing rectangles with half-edge lengths of half the distance to the nearest neighbor in the coarse-grained embedding.

Conversely, for a given fine-grained graph, we position nodes in the coarse-grained graph by placing them on the median coordinates of the positions of the corresponding nodes in the fine-grained graph. Wagner A, Regev A, Yosef N. Revealing the vectors of cellular identity with single-cell genomics.

Nat Biotechnol. Article CAS Google Scholar. Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, Lennon NJ, Livak KJ, Mikkelsen T. S, Rinn JL. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.

Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Saelens W, Cannoodt R, Todorov H, Saeys Y. A comparison of single-cell trajectory inference methods: towards more accurate and robust tools. Qiu X, Hill A, Packer J, Lin D, Ma YA, Trapnell C.

Single-cell mRNA quantification and differential analysis with census. Nat Methods. Wishbone identifies bifurcating developmental trajectories from single-cell data. Haghverdi L, Büttner M, Wolf FA, Buettner F, Theis FJ.

Diffusion pseudotime robustly reconstructs branching cellular lineages. Street K, Risso D, Fletcher RB, Das D, Ngai J, Yosef N, Purdom E, Dudoit S.

Slingshot: Cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics. Article Google Scholar. Rizvi AH, Camara PG, Kandror EK, Roberts TJ, Schieren I, Maniatis T, Rabadan R. Single-cell topological rna-seq analysis reveals insights into cellular differentiation and development.

Qiu P, Simonds EF, Bendall SC, Gibbs KD, Bruggner RV, Linderman M. D, Sachs K, Nolan GP, Plevritis SK. Extracting a cellular hierarchy from high-dimensional cytometry data with spade. Nat Biotechnology. Giecold G, Marco E, Garcia SP, Trippa L, Yuan GC.

Robust lineage reconstruction from high-dimensional single-cell data. Nucleic Acids Res. Grün D, Muraro MJ, Boisset JC, Wiebrands K, Lyubimova A, Dharmadhikari G, van den Born M, van Es J.

De novo prediction of stem cell identity using single-cell transcriptome data. Cell Stem Cell. Plass M, Solana J, Wolf FA, Ayoub S, Misios A, Glažar P, Obermayer B, Theis FJ, Kocks C, Rajewsky N.

Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics. Hu Y, Shi L. Visualizing large graphs. Wiley Interdiscip Rev Comput Stat.

van der Maaten L, Hinton G. Visualizing data using t-sne. J Mach Learn Res. Google Scholar. Islam S, Kjallquist U, Moliner A, Zajac P, Fan JB, Lonnerberg P, Linnarsson S.

Characterization of the single-cell transcriptional landscape by highly multiplex rna-seq. Genome Res. Data-driven phenotypic dissection of AML reveals progenitor—like cells that correlate with prognosis.

Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. J Stat Mech. Xu C, Su Z.

Pagan Cluster Paga fan since I was Pagan Cluster Paga, or about Pronósticos máquinas tragaperras futuras years now. Surprising as it may Clustr for some, before I read Calvin, Payan, or even Clusger, I was Pagan Cluster Paga Wright. This is especially the case with his atonement theology, which I have discussed and defended here. When I heard he was coming out with a big book on the atonement, The Day the Revolution BeganI was very excited. But he thinks in the Western church that message has been overshadowed by another, cut-rate gospel. Post by conlangconstructor Pagan Cluster Paga Tue Aug 24, am. Post by Clusfer » Tue Aug 24, am. Post by SLiV » Tue Aug 24, am. Post by loftyD » Tue Aug 24, pm. Post by Yačay » Tue Aug 24, pm.

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Doing anti-racist work can be tricky and may require strong facilitation skills. Facilitators might want to work in pairs or teams or rotate leadership. We are hoping that members of the Reclaiming community will find ways to support each other in this work.

Welcome to the first and perhaps only issue of the Reclaiming Cauldron, a compendium of creativity from around Reclaiming. Folks from around our network wrote, photo- graphed, painted, and even helped proofread.

Print edition or free download — pages of writings, artwork, photos, music and video links, book excerpts — and even some funny stuff!

Pagan Cluster activist Lisa Fithian has released a new book of activist skills and stories, ranging from Seattle in to the present.

Check it out:. Online Ordering — various options. For anyone who wants to become more active in resistance or is just feeling overwhelmed or hopeless, Shut It Down offers strategies and actions you can take right now to promote justice and incite change in your own community.

In Shut It Down Fithian shares historic, behind-the-scenes stories from some of the most important people-powered movements of the past several decades. She shows how movements that embrace direct action have always been, and continue to be, the most radical and rapid means for transforming the ills of our society.

Shut It Down is filled with instructions and inspiration for how movements can evolve as the struggle for social justice continues in the Trump era and beyond. While recognizing that electoral politics, legislation, and policy are all important pathways to change, Shut It Down argues that civil disobedience is not just one of the only actions that remains when all else fails, but a spiritual pursuit that protects our deepest selves and allows us to reclaim our humanity.

Skip to content. Summary of Magic for Ukraine 2. Click here for slide show by Luke Hauser. The action, organized by Bay Area Extinction Rebellion as part of international protests during Octoberincluded people from Red Rebels, Decolonization, Playful Animal Parade, Mindful Direct Action, the Climate Anxiety Tent, DAWG, and WICCA Witches Invoking Creative Climate Action — our Bay Area Pagan Cluster!

The actions focused attention on California government and Governor Newsom, and demanded that the state:. Future Actions and Events: extinctionrebellionsfbay.

Subscribe Subscribed. Sign me up. Already have a WordPress. com account? Log in now. Reclaiming Customize Subscribe Subscribed Sign up Log in Report this content View site in Reader Manage subscriptions Collapse this bar. Loading Comments Email Required Name Required Website.

: Pagan Cluster Paga

Sign in - Google Accounts Denny Weaver, Brian Zahnd , or Neo-Anabaptist D. Due the relatively sparsely sampled, high-dimensional feature space of scRNA-seq data, both fitting and interpreting an RNA velocity vector without including information about topology—connectivity of neighborhoods—is practically impossible. Note that by varying the resolution of the partitioning, PAGA generates graphs at multiple resolutions, which enables a hierarchical exploration of data Fig. To assess how robustly graph and tree-inference algorithms recover a given topology, we developed a measure for comparing the topologies of two graphs by comparing the sets of possible paths on them Additional file 1 : Note 1. Article Google Scholar Hu Y, Shi L. The extension consists in a simple modification of the original algorithm that accounts for disconnected Eigen-subspaces of the graph adjacency matrix, which results in multiple subspaces of Eigen value 1 of the graph transition matrix.
The World of Paga - The CBB

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kill something to blow off steam so he can love us in heaven. None of us should. Denny Weaver, Brian Zahnd , or Neo-Anabaptist D. Snyder Belousek paradigm. Much of his rhetoric flirts with them at points, but at points he still says very clearly that God has wrath, that he punishes sin, that Exile is the punishment of sin, and that punishment can even be a part of the righteousness of God insofar as it is part of his faithfulness to his covenant in which he threatened punishment as the result of idolatry.

Choose the God of life, you get life. Choose the non-gods, the idols, obviously that will pay out in death. The problem begins when you see that his account of divine agency in punishment gets fuzzy.

On the one hand he admits God is at work in the process in order to rule out a simple, mechanic process of cause and effect , but then goes on to the very next page to almost reduce punishment language to merely a way of talking about the natural consequences of sin So he seems to take with his left what he gives with his right at times in a way that is confusing.

So, again, this is quite explicitly not an anti-penal-substitution work per se. Second, it is also clear he wants to make sure that nobody ever tells the story of Jesus and the atonement without setting it within and as the culmination of the story of Israel.

This is also an important thing to be avoided. He affirms most of the component pieces of penal substitution , then, even if he rearranges some of them and modifies others, especially in the reading of key texts, especially in Paul.

In that death, God condemns our sins, but we know what he really thinks about Jesus. And so he says:. But the punishment is on Sin itself, the combined, accumulated, and personified force that has wreaked such havoc in the world and in human lives…Paul does not say that God punished Jesus.

He declares that God punished Sin in the flesh of Jesus. Now, to be sure, the crucifixion was no less terrible an event because, with theological hindsight, the apostle could see that what was being punished was Sin itself rather than Jesus himself… Nor is it at all clear how this condemnation of the powers or Satan or the total situation deals with the guilt and liability of human punishment.

Another section I have questions about is Romans It seems easy to speak of a proleptic wrath executed now in the Messiah which counts for believers, which anticipates and corresponds to a judgment of wrath still to come in the future and would fall on them were they outside of Christ.

Or it is at least analogous to speaking of a proleptic resurrection in Christ now, which corresponds to a resurrection still to come in the future. Second, it is not necessarily the case that if is summarizing the effects of , we have a strict tautology.

These linguistic or exegetical points aside, I suppose my theological question is whether Wright believes Paul to be saying that wrath is something entirely future, which is staved off by an act which is definitely not the execution of wrath or propitiation in the present.

Off the top of my head, Ezekiel 7 clusters wrath and anger with the punishment of idolatry together as largely the same thing cf. Another way of saying it is that condemnation or punishment is wrath considered legally.

In which case, if Romans does speak of the condemnation of Sin because of which those who are in the Messiah no longer face condemnation in the future , it seems that Paul does think that at least Sin has suffered the wrath of God upon himself in the flesh of Jesus on the cross, though there will be wrath meted out in the future against sinners as well.

Or is it just that Wright thinks Paul is speaking of a different sacrificial logic in this passage? Now, I should say something very brief about his argument in chapter 13 about Romans Wright has changed his views from his commentaries and earlier books here.

He still thinks the term refers to the lid of the ark, or the mercy seat in the Tabernacle or Temple. But Wright no longer connects it to the logic of covering , nor to propitiation as he used to, where in the past God over-looked sin, but now he punishes it in Christ. On this view, the death of the sacrifice is ancillary to the all-important releasing of the blood which the priest manipulates in the Day of Atonement ceremony.

This blood symbolizes the power of life which cleanses. The idea is that through idolatry, humans become sinful, their sins leads to and bring the pollution of death. This defilement accumulates throughout the year around the people, the land, and the sanctuary. Jesus is the place where God and man, heaven and earth, meet, and this is enabled by his cleansing blood.

Now, Wright has many things going for him. Some of the more interesting bits of recent scholarship on sacrifice of late has been around pinning down just how that is supposed to work.

First, I suppose I simply disagree about the blood. Considering the wider use of the term blood in the Old and New Testaments, the dimension of life released by violent death within it cannot be entirely erased or reduced to life simpliciter.

Second, and this may be too broadly formulaic, but ever since Genesis , death just is the punishment for sin, the curse of the Law, the outworking of wrath, etc.

in Scripture. While not all deaths are suffered as the direct judgment of God, theologically there is no death which is not the result of the curse and wrath of God.

It seems very hard, then, to eliminate this meaning entirely from the Day of Atonement sacrifices. Third, I am not sure Wright lets the Passover and the Day of Atonement interplay do enough work. In the Passover, there is protection from the wrath of God against guilty sinners, yes through the covering of blood, but also through the death for death equation.

He takes into account all of the most recent developments in the very dense literature of sacrifice and comes up with a nuanced account of substitution and sacrifice, which includes all that Wright says and more. Perhaps his attunement to the ubiquity of that problem accounts for much of their differences.

As I noted earlier, Wright is very helpful in not separating what God has joined together: kingdom and cross, the forgiveness of sins and the defeat of the powers. Robust lineage reconstruction from high-dimensional single-cell data. Nucleic Acids Res.

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Traag V. GitHub repository. Wolf FA, Hamey F, Plass M, Solana J, Dahlin JS, Göttgens B, Rajewsky N, Simon L, Theis FJ. PAGA: Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells.

Download references. thanks N. Yosef and D. Wagner for stimulating discussions, S. Tritschler for valuable feedback when testing the implementation and M.

Luecken and V. We thank reviewer 1 for pointing us to the review of [ 14 ]. acknowledges support by the Helmholtz Postdoc Programme, Initiative and Networking Fund of the Helmholtz Association. is supported by a grant from the Swedish Research Council.

Work in B. is the recipient of a Medical Research Council PhD Studentship. The work from M. was funded by the German Center for Cardiovascular Research DZHK BER 1.

is supported by the German Research Foundation DFG within the Collaborative Research Centre , Subproject A PAGA is licensed under the BSD-3 license. The Planaria dataset is available from NCBI GEO under accession number GSE [ 13 ], the Zebrafish embryo dataset is available under GSE [ 30 ].

Helmholtz Center Munich — German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Munich, Germany. Department of Haematology and Wellcome and Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.

Fiona K. Hamey, Joakim S. Berlin Institute for Medical Systems Biology, Max-Delbrück Center for Molecular Medicine, Berlin, Germany. Department of Medicine, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden. Department of Mathematics, Technische Universität München, Munich, Germany.

You can also search for this author in PubMed Google Scholar. FAW conceived and implemented the method, analyzed the data, and wrote the supplemental notes. FKH analyzed the data of Dahlin et al. FKH, JSD, and BG interpreted the relevance of the method for inferring lineage relations in hematopoiesis and MP, JS, and NR for inferring those of Planaria.

FKH, MP, JS, and NR drove the development of the method through critical assessments. LS and FJT contributed to the conception of the project. FJT supervised the project and wrote parts of Supplemental Note 1. FAW and FJT wrote the paper with contributions from all coauthors. All authors read and approved the final manuscript.

Correspondence to Fabian J. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.

Reprints and permissions. Wolf, F. et al. PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells.

Genome Biol 20 , 59 Download citation. Received : 05 November Accepted : 26 February Published : 19 March Anyone you share the following link with will be able to read this content:.

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Skip to main content. Search all BMC articles Search. Download PDF. Method Open access Published: 19 March PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells F.

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Collection list Email Required Name Pagn Website. Of course, no volume is without its Pagan Cluster Paga, so go ahead Apuestas Deportivas Electrizantes take Consejos de apuestas efectivos up, just bear some Pagan Cluster Paga these Paa in mind. Cluwter to the Clusterr and Clustsr only issue of the Reclaiming Cauldron, a compendium of creativity from around Reclaiming. I have been working on Paga since I was about ten years old. I took a special reading class in my M. For each pair of clusters, PAGA connectivity is the ratio of the number of inter-edges between the clusters normalized with the number of inter-edges expected under random assignment of edges. Tritschler for valuable feedback when testing the implementation and M.

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Pagan Cluster Paga

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