Mega clúster pagando

But so far, Carlton has not started work yet. Guess the site looks huge next to the shophouses, and it should be about the same height as the surrounding HDB point block. The maximum height there is about m i think.

There have been a lot of improvement works in the Tanjong Pagar area, and the MRT xchange there is now quite a pleasant place. The White House will become the new International Arbitration Centre as well. The stage is set for the future Tanjong Pagar is quickly becoming another anchor of the CBD, pity it's not easy to photograph its skyline.

I guess the time is fast becoming ripe for another MRT line to pass through don't you think? the big Calton luggage had also disappeared I thought they had given up :lol: btw, have URA sold off the hotel site in front of Fujitsu tower previous IBM tower?

with the one beside Fuji Xerox with nice sea view actually this area should have a full blown shopping mall wow piang suddenly over people moved in can the current amenities in Tanjong Pagar cope? It's already too crowded after Icon TOP see the NTUC Fairmarket - always crowded morning to night including weekends.

I would had hoped I had the foresight to open a supermarket at it's present location :lol HDB always had a lousy foresight planning Wet market and foodmarket are attraction byitself. A good shopping mall as big as Suntec with Hotel or offices on top would be great near Xerox.

The hotels would be atthe upper levels to maximise space. Those living at duxton are able to work and shop there then. what a lousy planning I don't think living around there will be a good experience anymore Behind the temple. The plot of land beside White House is zone under commercial.

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AVS Forum. Deals Forum. Hence all eigenvalue-eigenvector pairs can be incorporated without causing a bottleneck in runtime. Consequently in VIA, the modified walk on a cluster-graph not only enables scalable pseudotime computation for large datasets in terms of runtime, but also preserves information about the global neighborhood relationships within the graph.

In the second stage of Step 2, VIA infers the directionality of the graph by biasing the edge-weights with the initial pseudotime computations, and refines the pseudotime through lazy-teleporting MCMC simulations on the forward biased graph.

Next Step 3 in Fig. The cell fate predictions obtained using this approach are more accurate and robust to changes in input data and parameters compared to other TI methods Fig. Trajectories towards identified terminal states are then resolved using lazy-teleporting MCMC simulations Step 4 in Fig.

The single-cell level KNN graph constructed in Step 1 is then used to project the lineage probabilities of trajectories pathways from root to cell fate , and temporal ordering derived from the cluster-graph topology onto a single-cell level.

Together, these four steps facilitate holistic topological visualization of TI on the single-cell level e. a Topologies of four representative synthetic datasets Multifurc1, Cyclic1, Disconn1, and Conn1 output by different TI methods.

The reference topologies are shown on the left. VIA is shown at the cluster graph level but can also be projected to the single-cell level as shown in later examples. b Composite accuracy score is shown for each method across all nine synthetic datasets detailed breakdown available in Supplementary Figs.

Note STREAM does not work on the Disconnected data producing highly distorted results and therefore excluded in Disconn1 and Disconn2. In VIA, the relaxation of edge constraints in computing lineage pathways and pseudotime enables accurate detection of cell fates and complex trajectories by avoiding prematurely imposing constraints on node-to-node mobility.

Other methods resort to constraints such as reducing the graph to a tree, imposing unidirectionality by thresholding edges based on pseudotime directionality, removing outgoing edges from terminal states 2 , 18 , and computing shortest paths for pseudotime 1 , 2.

The availability of a reference truth model for the synthetics datasets allows us to quantify TI accuracy using a composite metric which assesses multiple layers of the inferred trajectory including topology, pseudotime and lineage prediction. Terminal cell fate prediction is evaluated using the F1-score.

The breakdown of the composite score and further detail on each metric is available in Supplementary Note 3 and Supplementary Figs. The differences in accuracy between VIA and other methods is most significant for complex topologies, particularly those with disconnected components comprising various connected topologies, whilst the ability to accurately detect cell fates is highlighted by multilineage furcating topologies.

In the four-leaf multifurcation Fig. Monocle3 and STREAM typically only capture a single bifurcation and thus merge the pairs of leaves that otherwise arise from the second layer of bifurcation Fig. Even for the fairly simple cyclic topology Fig. This characteristic is important for robustly analyzing multiple levels of resolution in complex graph topologies, as also shown in our later investigation of the 1.

The performance comparison for the disconnected hybrid topologies Fig. Palantir overly fragments the two trajectories, whereas Monocle3 and Slingshot merge them, STREAM is not well suited to non-tree trajectories given the underlying structure is assumed to be a spanning tree.

We also show that VIA is flexible to using clustering methods other than PARC by substituting PARC with Kmeans clustering to show that the lazy-teleporting MCMCs still enable faithful recovery of various topologies as well as the associated cell fates Supplementary Note 6 and Figs.

The main drawback of using K-means is that under- or over-clustering can occur based on the user-choice of K, whereas methods like PARC enable a more data-driven resolution of the data where the recovery of less populous cell types is not dependent on an adequately large number of clusters.

To assess the performance of VIA on inferring real cellular trajectory, we first considered a range of scRNA-seq datasets, including hematopoiesis 2 , 20 , endocrine genesis, B-cell differentiation 21 , and embryonic stem ES cell differentiation in embryoid bodies We highlight human hematopoiesis as it has been extensively studied not only with scRNA-seq, but also other single-cell omics modalities, notably scATAC-seq.

Hence, it allows us to reliably assess lineage identification performance and downstream analyses using VIA. First, we show that VIA consistently reveals from the scRNA-seq dataset the typical hierarchical bifurcations during hematopoiesis that result in key committed lineages of hematopoietic stem cells HSCs to monocytic, lymphoid, erythroid, classical and plasmacytoid dendritic cell cDCs and pDCs lineages and megakaryocytes Fig.

The automated detection of these terminal states in VIA, as quantified by F1-scores on the annotated cells, remains robust to varying the number of neighbors in the KNN graph, and the number of PCs Fig. a VIA graph colored by inferred pseudotime. Identified terminal state nodes are outlined in red and labeled according to their representative annotated cell type b pseudo-temporal trends of marker genes for key minor populations see Supplementary Figs.

A side-by-side comparison of the inferred gene trends by each method provides a more holistic assessment of the quality of expression prediction and can be found in Supplementary Fig.

Pre-processed using k-mer Z Scores protocol yields a more challenging input as shown by the performance drop for other methods beyond 50PCs.

Slingshot can only recover the major cell populations monocytes, erythroid, and B cells and confuses the DC populations with the monocytes and the megakaryocytes with the erythroid cells. Palantir can only identify the DCs and megakaryocytes for a handful of parameter options, whereas VIA achieves this goal across a wider range of parameters Fig.

To verify that VIA reliably delineates the megakaryocyte, cDC and pDC lineages, we used VIA to automatically plot the lineage specific trends for selected marker genes. We showed that while both DC lineages exhibit elevated IRF8 , the CSF1R is specific to the cDC, and the CD remains elevated for pDCs whereas it is first up-regulated, then down-regulated in cDCs Fig.

Marker genes known to increase along a specific lineage are correlated against the pseudotime along each lineage as an indicator of correct cell ordering Fig. The gene trends inferred by each method are provided in Supplementary Fig. We use two common preprocessing pipelines 20 , 22 see Methods , intended to alleviate challenges posed by the sparsity of scATAC-seq data, to show that VIA consistently predicts the expected hierarchy of lineages furcating from hematopoietic progenitors to their descendants.

The graph topology of VIA colored by pseudotime captures the progression of multipotent progenitors MPPs toward the lymphoid-primed MPPs LMPP and the common myeloid progenitors CMPs which in turn give rise to the CLP and MEP lineages respectively.

The known joint contribution of LMPPs and CMPs towards the GMP lineage is also captured by the VIA graph. We verified the lineages identified by VIA by analyzing the changes in the accessibility of TF motifs associated with known regulators of the lineage commitments, e.

Again, we note that the detection of these lineages is less straightforward in other methods, which generally face a sharp drop in accuracy of detecting relevant cell fates as the input number of PCs exceeds ~50PCs e.

The quality of the lineage pathways and gene trends is indicated in Fig. Visual comparisons of the topologies and predicted gene trends of each method are shown in Supplementary Fig. We use a scRNA-seq dataset of E b TSNE colored by reference cell type annotations. c colored by inferred pseudotime with predicted cell fates in red-black circles.

e Gene-expression trends along pseudotime for each pancreatic islet. f Beta-2 subtype expresses Ins2 but not Ins1, suggestive of an immature Beta cell subtype. g Marker gene-pseudotime correlations along respective lineages.

Full comparison of gene trends can be referred to Supplementary Fig. In contrast, the well-delineated nodes of the VIA cluster-graph a result of the accurate terminal state prediction enabled by the lazy-teleporting MCMC property of VIA on the inferred topology lends itself to automatically detecting this small population of delta cells, together with all other key lineages alpha, beta and epsilon lineages Fig.

As evidenced by the corresponding gene-expression trend analysis, VIA detects all of the hormone-producing cells including delta cells which show exclusively elevated Hhex, Sst , and Cd24a Fig. To show that this is not a co-incidence of parameter choice, we verify that these populations can be identified for a wide range of chosen highly variable genes HVGs prior to PCA and number of PCs see Supplementary Fig.

Interestingly, consistent with an observation by Bastidas-Ponce et al. Interestingly, we find VIA often automatically detects two Beta-cell subpopulations Beta-1 and Beta-2 Fig. The pseudotime order within this Beta-cell heterogeneity 24 , 25 , undetectable by other TI methods as shown in the gene correlation comparisons Supplementary Fig.

We find that the immature Beta-2 population strongly expresses Ins2 , and weakly expresses Ins1 , followed by the mature Beta-1 cells which express both types of Ins 25 Fig. VIA graphs colored by Ins1 and Ins2 further show the difference in Ins expression by the two Beta populations.

Other methods that are also applicable to non-transcriptomic data, fail to uncover the two main lineages. a VIA graph for scRNA-seq data only and b scATAC-seq data only. c Gene-TF pair expression along VIA inferred pseudotime for each CM lineage see Supplementary Fig.

Colored by annotated cell-type, and experimental modality f Colored by VIA pseudotime with VIA-inferred trajectory towards Endothelial and Myocyte lineages projected on top g Accuracy of detecting the CM and Endo lineages in the individual and integrated data.

Other methods typically only detect the cardiomyocyte lineage the inability to detect a bifurcation is exacerbated when the number of input PCs increases , and instead falsely detect several intermediate and early stages as final cell fates.

For instance STREAM consistently merges the cardiomyocyte and endothelial lineages and instead presents the intermediate stage as a separate bifurcation. See Supplementary Figs.

PAGA does not offer automated cell fate prediction or lineage paths and is therefore not benchmarked for this dataset. The disparity in trajectory inference is evident in the scRNAseq and integrated data where Monocle3, Slingshot and Palantir do not resolve either of the two cell fates Fig.

We hypothesized that lowering the K number of nearest neighbors in Palantir and VIA would be more appropriate given the extremely low cell count ~ cells of the scRNA-seq dataset.

Whilst this approach did not alter the outcome for Palantir, we found that VIA is able to capture the transition from early to intermediate CPCs and finally lineage committed cells.

More importantly, VIA automatically generates a pseudotemporal ordering of relevant cells without requiring manual selection of relevant cells as done in Jia et al. Hence, VIA can be used to faithfully interpret relationships between transcription factor dynamics and gene expression in an unsupervised manner.

The highlighted gene and TF pairs in the cardiac lineage show a strong correlation between expression and accessibility of Gata and Homeobox Hox genes which are known to be related to the regulation of cardiomyocyte proliferation 29 , 30 , VIA is designed to be highly scalable and offers automated lineage prediction without extensive dimension reduction or subsampling even at large cell counts.

To showcase this, we use VIA to explore the 1. While this dataset is inaccessible to most TI methods from a runtime and memory perspective, VIA can efficiently resolve the underlying developmental heterogeneity, including nine major trajectories Fig.

Other methods like Slingshot and CellRank were deemed infeasible due to extremely long runtimes on much smaller datasets. Supplementary Table 3 for a summary of runtimes.

Going beyond the computational efficiency, VIA also preserves wider neighborhood information and reveals a globally connected topology of MOCA which is otherwise lost in the Monocle3 analysis which first reduces the input data dimensionality using UMAP. a MOCA graph trajectory nodes colored by pseudotime and shaded-colored regions corresponding to major cell groups.

Stem branch consists of epithelial cells derived from ectoderm and endoderm, leading to two main branches: 1 the mesenchymal and 2 the neural tube and neural crest.

Other major groups are placed in the biologically relevant neighborhoods, such as the adjacencies between hepatocyte and epithelial trajectories; the neural crest and the neural tube; as well as the links between early mesenchyme with both the hematopoietic cells and the endothelial cells see Supplementary Note 7.

b Colored by VIA pseudotime. c Lineage pathways and probabilities of neuronal, myocyte and WBC lineages. d VIA graph preserves key relationships across choice of number of PCs, whereas e UMAP embedding is first step in Monocle3 and highly susceptible to choice of number of PCs or K in KNN see Fig.

The overall cluster graph of VIA consists of three main branches that concur with the known developmental process at early organogenesis 32 Fig. It starts from the root stem which has a high concentration of E9. However, VIA is able to capture the overall pseudotime structure depicting early organogenesis Fig.

For instance, at the junction of the epithelial-to-mesenchymal branch, we find early mesenchymal cells from E9. Cells from later mesenchymal developmental stages e.

Similarly, at the junction of epithelial-to-neural tube, we find dorsal tube neural cells and notochord plate cells which are predominantly from E9.

VIA also consistently places the other smaller dispersed groups of trajectories e. As such, TI using VIA uniquely preserves both the global and local structures of the data. Whilst manifold-learning methods are often used to extensively reduce dimensionality to mitigate the computational burden of large single-cell datasets, they tend to incur loss of global information and be sensitive to input parameters.

VIA is sufficiently scalable to bypass such a step, and therefore retains a higher degree of neighborhood information when mapping large datasets. As shown in Fig. For instance, in UMAP, the neural tube group is sometimes shown as a single super group, and other times fragmented across the embedding without context of neighboring groups.

Similarly the hematopoietic supergroup is shown as a single, two or even three separate groups dispersed across the embedding landscape Fig. In contrast, VIA uncovers biologically consistent structures across the same range of parameters.

In VIA, the cells belonging to these fine-grained supergroups remain connected and neighborhood relationships are preserved, for instance the neural crest cells containing Peripheral Nervous System neurons and glial cells remain adjacent to the neural tube Figs. Broad applicability of TI beyond transcriptomic analysis is increasingly critical, but existing methods have limitations contending with the disparity in the data structure e.

We applied VIA on a time-series mass cytometry data 28 antibodies, 90K cells capturing murine ESCs differentiation toward mesoderm cells The mESCs are captured at 12 intervals within the first 11 days and hence provide sufficiently granular temporal annotation to allow a correlation assessment of the inferred pseudotimes.

Palantir and Monocle3 suffer from low connectivity of cells between the Day 0—1 and the subsequent early stages finding disconnected trajectories even when increasing K in KNN , and thus result in loss of pseudotime gradient and low correlation to the true annotations.

a UMAP plot colored by annotated days 0— Three regions of Day 10—11 marked in dotted black lines. b VIA cluster-graph colored by pseudotime. c Terminal states and VIA output projected onto UMAP. Terminal states are located in the areas containing Day 10—11 cells.

d Comparison of Pearson correlation of pseudotime and annotated Days across TI methods for varying number of K number of nearest neighbors. The effect is that Day 0 cells appear exaggeratedly far, while the remaining early and late cells are temporally squeezed. For Slingshot and STREAM there is no K NN setting thus only a single correlation value is presented.

e Gene expression of key mesodermal markers. f Example outputs of Palantir, PAGA and Slingshot with the terminal states black circles predicted by Slingshot and Palantir. STREAM places Day 10—11 cells in between Day 0 and Day 5—6 cells. More importantly, unlike previous analysis 36 of the same data which required chronological labels to visualize the chronological developmental hierarchy, we ran VIA without such supervised adjustments and accurately captured the sequential development.

In contrast, other methods struggle to identify the correct terminal states e. Such spatial information is typically obscured in sequencing data, but can effectively underpin cell states and functions without costly and time-consuming sequencing protocols.

However, trajectory predictions based on morphological profiles of single cells have only been scarcely studied until recently, but advancements in high-throughput imaging cytometry are now making large-scale image data generation and related studies feasible.

We thus sought to test if VIA can predict biologically relevant progress based on single-cell morphological snapshots captured by our recently developed high-throughput imaging flow cytometer, called FACED 13 —a technology that is at least times faster than state-of-the-art imaging flow cytometry IFC Fig.

a FACED high-throughput imaging flow cytometry of MDA-MB and MCF7 cells, followed by image reconstruction and biophysical feature extraction.

b Randomly sampled quantitative phase images QPI and fluorescence images FL of MCF7 cells and d MDA-MB cells. c Single-cell UMAP embedding colored by the known cell-cycle phase left , given by DNA-labeled fluorescence images.

VIA inferred cluster-graph topology, nodes colored by pseudotime mid and UMAP colored by VIA pseudotime for MCF7. d-e VIA analysis repeated for MDA-MB cells. f Unsupervised image-feature-trends of global and local biophysical textures against VIA pseudotime for MCF7 and g MDA-MB cells see Supplementary Table 6 for feature definitions.

Cell cycle pseudotime boundaries are defined here as the intersection of the pseudotime probability density functions of each cell cycle stage annotated based on fluorescence intensity. Our FACED imaging platform captured multiple image contrasts of single cells, including FL, and quantitative phase images QPI , which measure high-resolution biophysical properties of cells, which are otherwise inaccessible in other methods Using the QPIs captured by FACED, we first generated spatially-resolved single-cell biophysical profiles of two live breast cancer cell types MDA-MB and MCF7 undergoing cell cycle progressions 38 features including cell shape, size, dry mass density, optical density and their subcellular textures see Supplementary Tables 6 , 7 for definitions of features.

The QPI together with the FL images of individual cells were also used to train a convolutional neural network-based regression model for predicting the DNA content.

In addition, the predicted percentages of cells in each cell cycle phases i. The variation in biophysical textures e. We find other methods on this dataset to be sensitive to the choice of early cells and detecting intermediate cells as terminal cell fates e.

The slowdown during the S-phase is missed by the gene trend prediction available in other methods. To probe subsets of the morphological features, we remove volume and volume related features e. We found that VIA is consistently able to reveal these trends in both cell lines, whereas other methods struggle to maintain the linear progression expected along the cell-cycle with spurious linkages emerging see Supplementary Figs.

These results further substantiate the growing body of work 41 , 42 , 43 , 44 on imaging biophysical cytometry for gaining a mechanistic understanding of biological systems, especially when combined with omics analysis With the growing scale and complexity of single-cell datasets, there is an unmet need for accurate cell fate prediction and lineage detection in complex topologies manifested in biology not limited to trees.

This challenge, broadly faced by the current TI methods, is compounded by susceptibility to algorithmic parameter changes, limited scalability to large data size; and insufficient generalizability to multi-omic data beyond transcriptomic data.

We introduced VIA, which alleviates these challenges by fast and scalable construction of cluster-graph of cells, followed by pseudotime, and reconstructing cell lineages based on lazy-teleporting random walks and MCMC simulations.

This strategy critically relaxes common constraints on graph traversal and causality that impede accurate prediction of elusive lineages and less populous cell fates. We validated the efficacy of these measures in terms of detecting various challenging topologies on simulated data, as well as robust prediction of cell fates and temporally changing feature trends on a variety biological processes spanning epigenomic, transcriptomic, integrated omic, as well as imaging and mass cytometric data to show that VIA detects pertinent biological lineages and their pathways that remain undetected by other methods.

Notably, VIA distinguished between dendritic subtypes in an scRNA-seq hematopoiesis dataset; identified the rare delta cell islet in pancreatic development, a population requiring manual assignment in other TI methods; and revealed the bifurcation towards cardiomyocyte and endothelial lineage commitment in a multi-omic scATAC-seq and scRNA-seq dataset which proved challenging for other methods.

In other methods, user parameter choice can incur fragmentation or spurious linkages in the modeled topology, and consequently only yield biologically sensible lineages for a narrow sweet spot of parameters see the summary in Supplementary Fig. We also demonstrated on the 1. Importantly, VIA not only recovers the fine-grained sub-trajectories, but also maintains global connectivity between related cell types and thus captures key relationships among lineages in early embryogenesis.

It also computes a more salient pseudotime measure supported by lazy-teleporting MCMCs, compared to other methods whose pseudotime scale was distorted at such high cell counts Supplementary Fig.

We showed that methods which require UMAP or t-SNE before parsing MOCA are highly susceptible to user defined input parameters that can significantly and unpredictably fragment the global topology.

We also assessed whether VIA can be generalized to non-transcriptomic single-cell datasets, especially those with significant dimensionality disparity compared to sequencing data.

We first applied VIA to the mESC CyTOF dataset and showed that the lazy-teleporting MCMCs strategy in VIA enables it to outperform other methods in correctly correlating the pseudotime of the mesoderm development to the annotated dates.

We finally explored the utility of VIA in analyzing emerging image-based single-cell biophysical profile data.

Overall, VIA offers an advancement to TI methods to robustly study a diverse range of single-cell data. Together with its scalable computation and efficient runtime, VIA could be useful for multifaceted exploratory analysis to uncover biological processes, potentially those deviated from the healthy trajectories.

VIA applies a scalable probabilistic method to infer cell state dynamics and differentiation hierarchies by organizing cells into trajectories along a pseudotime axis in a nearest-neighbor graph which is the basis for subsequent random walks.

Single cells are represented by graph nodes that are connected based on their feature similarity, e. A typical routine in VIA mainly consists of four steps:.

VIA first represents the single-cell data in a k-nearest-neighbor KNN graph where each node is a cluster of single cells. The clusters are computed by our recently developed clustering algorithm, PARC In brief, PARC is built on hierarchical navigable small world 46 accelerated KNN graph construction and a fast community-detection algorithm Leiden method 47 , which is further refined by data-driven pruning.

We employ the cluster-level topology, instead of a single-cell-level graph, for TI as it provides a coarser but clearer view of the key linkages and pathways of the underlying cell dynamics without imposing constraints on the graph edges.

Together with the strength of PARC in clustering scalability and sensitivity, this step critically allows VIA to faithfully reveal complex topologies namely cyclic, disconnected and multifurcating trajectories Fig. If the user prefers to use another clustering method or group-labels of cell types according to apriori information, VIA can easily accommodate such a substitution and the robustness of the lazy-teleporting random walks to different clustering approaches is shown in Supplementary Note 6 and Figs.

S30 — 32 for real and synthetic data. In the case of many clusters satisfying this criteria, it subsequently proceeds to select the cluster in the VIA graph that has connectivity metrics indicative of a root leaf node such as high out degree, low betweenness and low centrality.

The user can also choose to provide a specific single cell as the root node. In the case that the user wishes to select the root based on the VIA graph, one would save the VIA-cluster-graph labels and use them to guide selection of the root node as described in the first approach. The trajectories are then modeled in VIA as: i lazy-teleporting random walk paths along which the pseudotime is computed and further refined by ii MCMC simulations.

The root is a single cell chosen by the user. These two sub-steps are detailed as follows:. We first compute the pseudotime as the expected hitting time of a lazy-teleporting random walk on an undirected cluster-graph generated in Step 1.

The lazy-teleporting nature of this random walk ensures that as the sample size grows, the expected hitting time of each node does not converge to the stationary probability given by local node properties, but instead continues to incorporate the wider global neighborhood information Here we highlight the derivation of the closed form expression of the hitting time of this modified random walk with a detailed derivation in Supplementary Note 2.

The cluster graph constructed in VIA is defined as a weighted connected graph G V , E , W with a vertex set V of n vertices or nodes , i. where D is the n × n degree matrix, which is a diagonal matrix of the weighted sum of the degree of each node, i.

where k are the neighboring nodes connected to node i. where I is the identity matrix. Here we adapt the concept of personalized PageRank vector, originally used for recording or ranking personal preferences of a web-surfer toward particular website pages 48 , to rank the importance of other nodes clusters of cells to a given node, depending on the similarities among nodes related to P in the graph , and the lazy-teleporting random walk characteristics in the graph set by probabilities of teleporting and being lazy.

Based on this concept, one could model the likelihood to transit from one node cluster of cells to another, and thus construct the pseudotime based on the hitting time, which is a parameter describing the expected number of steps it takes for a random walk that starts at node i and visit node j for the first time.

Substituting Z Eq. The expected hitting time from node q to node r is given by 50 ,. We can substitute Eq. The hitting time metric computed in Step-1 is used to infer graph-directionality. This refinement step ensures that the pseudotime is robust to the spurious links or conversely, links that are too weakly weighted that can distort calculations based purely on the closed form solution of hitting times Supplementary Fig.

By using this 2-step pseudotime computation, VIA mitigates the issues of convergence issues and spurious edge-weights, both of which are common in random-walk pseudotime computation on large and complex datasets The algorithm uses the refined directed and weighted graph edges are re-weighted using the refined pseudotimes to predict which nodes represent the terminal states based on a consensus vote of pseudotime and multiple vertex connectivity properties, including out-degree i.

The consensus vote is performed on nodes that score above or below for out-degree the median in terms of connectivity properties. We show on multiple simulated and real biological datasets that VIA more accurately predicts the terminal states, across a range of input data dimensions and key algorithm parameters, than other methods attempting the same Supplementary Fig.

VIA then identifies the most likely path of each lineage by computing the likelihood of a node traversing towards a particular terminal state e.

These lineage likelihoods are computed as the visitation frequency under lazy-teleporting MCMC simulations from the root to a particular terminal state, i. Generalized additive models GAMs are used to draw edges found in the high-dimensional graph onto the lower dimensional visualization Fig.

An unsupervised downstream analysis of cell features e. Specifically, VIA plots the expression of features across pseudotime for each lineage by using the lineage likelihood properties to weight the GAMs.

A cluster-level lineage pathway is automatically produced by VIA to visualize feature heat maps at the cluster-level along a lineage-path to see the regulation of genes.

VIA provides the option of gene imputation before plotting the lineage specific gene trends. The imputation is fast as it relies on the single-cell KNN scKNN graph computed in Step 1. Using these datasets, we tested that VIA consistently and more accurately captures both tree and non-tree like structures compared to other methods Fig.

The types of topologies span multifurcating, cyclic, connected hybrid of cyclic and multifurcating and disconnected hybrid of the first three. All methods are subject to the same data preprocessing steps, PCA dimension reduction, and root-cell to initialize the path.

Absolute measurements of similarities are converted into a percentage scale before taking the arithmetic mean of the 5 metrics, see below which gives the composite accuracy.

Since PAGA does not predict lineages, the composite score is simply the average of the first 4 metrics for PAGA. A detailed explanation of the 5 metrics can be referred to Supplementary Note 3.

The 5 metrics are:. is used to measure the similarity of global graph topology. The IM ranges from 0 to 1 and equals the difference in spectral densities of two graphs. We compute the harmonic mean of recall and precision for the local branch accuracy relative to the reference model.

A False Negative edge in the inferred model is when there is an edge in the reference model between cell types that is absent in the inferred trajectory. A False Positive edge in the inferred model is an edge that is not actually present in the reference model. Pearson correlation coefficient is used as a measure of how closely the inferred pseudotime follows the true sampling times.

Similar to the F1-branch score, we use the harmonic mean of recall and precision to quantify the prediction accuracy of terminal states. The methods were mainly chosen based on their superior performance in a recent large-scale benchmarking study 5 , including a select few recent methods claiming to supersede those in the study.

Specifically, recent and popular methods exhibiting reasonable scalability, and automated cell fate prediction in multi-lineage trajectories, not limited to tree-topologies, were favored as candidates for benchmarking see Supplementary Table 1 for the key characteristics of methods.

Performance stress-tests in terms of lineage detection of each biological dataset, automated gene trend prediction along lineages, and pseudotime correlation were conducted over a range of key input parameters e. Methods that focus exclusively on a single data modality or on topology without predicting cell fates and their lineage pathways e.

All comparisons were run on a computer with an Intel R Xeon R W central processing unit 3. Details of parameter settings for each of the benchmarked methods can be found in Supplementary Tables 4 , 5 , with an emphasis on the rationale for changes deviating from default parameters.

Quantifying terminal state prediction accuracy for parameter tests was done using the F1-score, defined as the harmonic mean of recall and precision and calculated as:. Where tp is a true-positive: the identification of a terminal cluster that is in fact a final differentiated cell fate; fp is a false positive identification of a cluster as terminal when in fact it represents an intermediate state; and fn is a false negative where a known cell fate fails to be identified.

Downstream analysis enabled by the automated lineage prediction capabilities of each method is key to facilitating the exploration of biological data. The unsupervised gene-trend analysis inferred by VIA is compared to the lineage gene-trends predicted by other methods both quantitatively and qualitatively.

We follow an approach used by Chen et al. The gene-expression of such markers can be considered a surrogate for the correct sampling time and thus the resulting correlation is an indication of the accuracy of cell ordering by pseudotime.

We also provide a side-by-side comparison of the predicted topology and gene-trends generated by each method to visually assess how well separated the predicted lineages are e. Additionally, when methods cannot automatically detect all the relevant lineages, we either chose the most relevant lineage e.

Given that these nuances are not necessarily captured by the correlation coefficient,the outputs of the gene-trend plots inferred by each method are shown for three datasets which have multiple lineages of different abundances, and well known lineage markers scRNA-seq and scATAC-seq hematopoiesis, and endocrine genesis in Supplementary Figs.

It uses a cluster-graph representation to capture the underlying topology. PAGA computes a unified pseudotime by averaging the single-cell level diffusion pseudotime computed by DPT, but requires manual specification of terminal cell fates and clusters that contribute to lineages of interest in order to compare gene expression trends across lineages.

It uses diffusion-map components to represent the underlying trajectory. Pseudotimes are computed as the shortest path along a KNN-graph constructed in a low-dimensional diffusion component space, with edges weighted such that the distance between nodes corresponds to the diffusion pseudotime 57 DPT.

Terminal states are identified as extrema of the diffusion maps that are also outliers of the stationary distribution. The lineage-likelihood probabilities are computed using Absorbing Markov Chains constructed by removing outgoing edges of terminal states, and thresholding reverse edges.

It is designed to process low-dimensional embeddings of the single-cell data. By default Slingshot runs clustering based on Gaussian mixture modeling and recommends using the first few PCs as input.

Slingshot connects the clusters using a minimum spanning tree and then fits principle curves for each detected branch. It uses the orthogonal projection against each principal curve to fit a separate pseudotime for each lineage, and hence the gene expressions cannot be compared across lineages.

Also, the runtimes are prohibitively long for large datasets or high input dimensions. This method combines the information of RNA velocity computed using scVelo 58 and gene-expression to infer trajectories.

Given it is mainly suited for the scRNA-seq data, with the RNA-velocity computation limiting the overall runtime for larger dataset, we limit our comparison to the pancreatic dataset which the authors of CellRank used to highlight its performance.

The workflow consists of three steps: the first is to project the data to two or three dimensions using UMAP this is a strict requirement , followed by Louvain clustering on a K-Nearest Neighbor graph constructed in the low-dimensional UMAP space. A cluster-graph is then created and partitioned to deduce disconnected trajectories.

Subsequently, it learns a principal graph in the low-dimensional space along which it calculates pseudotimes as the geodesic distance from root to cell.

After selecting the desired number of PCs, STREAM projects the cells to a lower dimensional PCA space using a non-linear dimensionality reduction method such as Modified Locally Linear Embedding, Spectral Embedding or UMAP.

In the embedded space, STREAM constructs a tree-model trajectory using an Elastic Principal Graph implementation called ElPiGraph.

The results are visualized as a branching structure or re-organized as a subway plot relative to a user-designated starting branch.

It should also be noted that visualization e. VIA provides a subsampling option at the visualization stage to accelerate this process for large datasets without impacting the previous computational steps. However, to ensure fair comparisons between TI methods e.

The filtered cells are normalized by library size and log transformed. The top HVG are retained. Cells are renormalized by library count and scaled to unit variance and zero mean.

Supplementary Fig. We show the results generalize across a range of PCs for two values of K of the graph with higher accuracy in locating the later cell fates than Slingshot and Palantir Supplementary Fig.

This is a scRNA-seq dataset of cells representing human hematopoiesis 2. We used the filtered, normalized and log-transformed count matrix provided by Setty et al.

The cells were annotated using SingleR which automatically labeled cells based on the hematopoietic reference dataset Novershtern Hematopoietic Cell Data—GSE The annotations are in agreement with the labels inferred by Setty et al. for the seven clusters, including the root HSCs cluster that differentiates into six different lineages: monocytes, erythrocytes, and B cells, as well as the less populous megakaryocytes, cDCs, and pDCs.

VIA consistently identifies these lineages across a wider range of input parameters and data dimensions e. Notably, the upregulated gene expression trends of the small populations can be recovered in VIA, i. This is a midsized scRNA-seq dataset of 16, human cells in embryoid bodies EBs We followed the same pre-processing steps as Moon et al.

to filter out dead cells and those with too high or low library count. Cells are normalized by library count followed by square root transform.

Finally the transformed counts are scaled to unit variance and zero mean. PCA is performed on the processed data before running each TI method. VIA identifies six cell fates, which, based on the upregulation of marker genes as cells proceed towards respective lineages, are in accord with the annotations given by Moon et al.

see the gene heatmap and changes in gene expression along respective lineage trajectories in Supplementary Fig. Note that Palantir and Slingshot do not capture the cardiac cell fate, and Slingshot also misses the neural crest see the F1-scores summary for terminal state detection Supplementary Fig.

This is a large and complex scRNA-seq dataset of mouse organogenesis cell atlas MOCA consisting of 1. The dataset contains cells from 61 embryos spanning 5 developmental stages from early organogenesis E9.

Of the 2 million cells profiled, 1. The authors of MOCA manually annotated 38 cell-types based on the differentially expressed genes of the clusters. In general, each cell type exclusively falls under one of 10 major and disjoint trajectories inferred by applying Monocle3 to the UMAP of MOCA.

The authors attributed the disconnected nature of the ten trajectories to the paucity of earlier stage common predecessor cells. We followed the same steps as Cao et al. PCA was applied to the top HVGs with the top 30 PCs selected for analysis. VIA analyzed the data in the high-dimensional PC space.

We bypass the step in Monocle3 4 which applies UMAP on the PCs prior to TI as this incurs an additional bias from choice of manifold-learning parameters and a further loss in neighborhood information.

As a result, VIA produces a more connected structure with linkages between some of the major cell types that become segregated in UMAP and hence Monocle3 , and favors a biologically relevant interpretation Fig. A detailed explanation of these connections graph-edges extending between certain major groups using references to literature on organogenesis is presented in Supplementary Note 3.

This is an scRNA-seq dataset of E Following steps by Lange et al. PCA was applied to the processed gene matrix. We assessed the performance of VIA and other TI methods CellRank, Palantir, Slingshot across a range of number of retained HVGs and input PCs Fig.

This scATAC-seq data profiles cells isolated from human bone marrow using FL activated cell sorting FACS , yielding 9 populations 20 : HSC, MPP, CMP, CLP, LMPP, GMP, MEP, mono, and plasmacytoid DCs Fig.

We examined TI results for two different preprocessing pipelines to gauge how robust VIA is on the scATAC-seq analysis which is known to be challenging for its extreme intrinsic sparsity. We used the pre-processed data consisting of PCA applied to the z-scores of the transcription factor TF motifs used by Buenrostro et a Their approach corrects for batch effects in select populations and weighting of PCs based on reference populations and hence involves manual curation.

We also employed a more general approach used by Chen et al. VIA infers the correct trajectories and the terminal cell fates for both of these inputs, again across a wide range of input parameters Fig. For the scRNA-seq data, the quality filtered genes and the size-factor normalized expression values are provided by Jia et al.

The accessibility of peaks was transformed to a binary representation as input for TF-IDF term frequency-inverse document frequency weighting prior to singular value decomposition SVD.

The highlighted TF motifs in the heatmap Fig. We tested the performance when varying the number of SVDs used. We also considered the outcome when merging the scATAC-seq and scRNA-seq data using Seurat3 Despite the relatively low cell count of both datasets, and the relatively under-represented scRNA-seq cell count, the two datasets overlapped reasonably well and allowed us to infer the expected lineages in an unsupervised manner Fig.

In contrast, Jia et al. performed a supervised TI by manually selecting cells relevant to the different lineages for the scATAC-seq cells and choosing the two diffusion components that best characterize the developmental trajectories in low dimension This is a mass cytometry or CyTOF dataset, consisting of 90, cells and 28 antibodies corresponding to ~ cells each from Day 0—11 measurements , that represents differentiation of mESC to mesoderm cells An arcsinh transform with a scaling factor of five was applied on all features—a standard procedure for CyTOF datasets, followed by normalization to unit variance and zero mean.

All 28 antibodies are used by the TI methods with the exception of Slingshot which requires PCA followed by subsetting of the first 5 PCs in order to computationally handle the high cell count Supplementary Fig. This is the in-house dataset of single-cell biophysical phenotypes of two different human breast cancer types MDA-MB and MCF7.

Following our recent image-based biophysical phenotyping strategy 63 , 64 we defined the spatially-resolved biophysical features of a cell in a hierarchical manner based on both bright-field and QPI captured by the FACED imaging flow cytometer i.

At the bulk level, we extracted the cell size, dry mass density, and cell shape. At the subcellular texture level, we parameterized the global and local textural characteristics of optical density and mass density at both the coarse and fine scales e.

This hierarchical phenotyping approach 63 , 64 allowed us to establish a single-cell biophysical profile of 38 features, which were normalized based on the z-score see Supplementary Tables 4 , 5. All these features, without any PCA, are used as input to VIA.

In order to weigh the features, we use a mutual information classifier to rank the features, based on the integrated FL intensity of the FL FACED images of the cells which serve as the ground truth of the cell-cycle stages.

Following normalization, the top three features which relate to cell size are weighted using a factor between 3 and The beam was then directed to the FACED module, which mainly consists of a pair of almost-parallel plane mirrors.

This module generated a linear array of 50 beamlets foci which were projected by an objective lens 40X, 0. Detailed configuration of the FACED module can be referred to Wu et al. The filtered orange FL signal was collected by the photomultiplier tube PMT rise time: 0.

On the other hand, the transmitted light through the cell was collected by another objective lens 40X, 0. The light was then split equally by the beamsplitter into two paths, each of which encodes different phase-gradient image contrasts of the same cell a concept similar to Scherlien photography MDA-MB ATCC and MCF7 ATCC , which are two different breast cancer cell lines, were used for the cell cycle study.

Further information on research design is available in the Nature Research Reporting Summary linked to this article. The Pancreatic data used in this study are available in the Gene Expression Omnibus GEO database under accession code GSE The B-cell data used in this study are available in the STATegraData GitHub repository.

The Mouse organogenesis data used in this study are available in the NCBI Gene Expression Omnibus database under accession code GSE The scATAC-seq Hematopoiesis data used in this study are available in the GEO database under accession code GSE Processed scATAC-seq data, which include PC values and TF scores per cell can be found in Data S1.

Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genom.

Article CAS Google Scholar. Setty, M. Characterization of cell fate probabilities in single-cell data with Palantir [published correction appears in Nat Biotechnol. Article CAS PubMed PubMed Central Google Scholar. Chen, H.

Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM. Article ADS PubMed PubMed Central CAS Google Scholar. Cao, J. The single-cell transcriptional landscape of mammalian organogenesis. Nature , — Article ADS CAS PubMed PubMed Central Google Scholar.

Saelens, W. A comparison of single-cell trajectory inference methods. Article CAS PubMed Google Scholar. Packer, J. A lineage- resolved molecular atlas of C.

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Demo: Workload Optimization Pack for Cluster Platforms

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Link invalid! Please ensure that you have the correct link. SEE ALSO. Sign Up. We have other newsletters you might enjoy. Take a look. KEYWORDS IN THIS ARTICLE Singapore property. BT is now on Telegram!

Nvidia supplier SK Hynix begins mass production of next generation memory chip. Longi layoffs speed shift in solar production away from China. Guess the site looks huge next to the shophouses, and it should be about the same height as the surrounding HDB point block.

The maximum height there is about m i think. There have been a lot of improvement works in the Tanjong Pagar area, and the MRT xchange there is now quite a pleasant place.

The White House will become the new International Arbitration Centre as well. The stage is set for the future Tanjong Pagar is quickly becoming another anchor of the CBD, pity it's not easy to photograph its skyline. I guess the time is fast becoming ripe for another MRT line to pass through don't you think?

the big Calton luggage had also disappeared I thought they had given up :lol: btw, have URA sold off the hotel site in front of Fujitsu tower previous IBM tower? with the one beside Fuji Xerox with nice sea view actually this area should have a full blown shopping mall wow piang suddenly over people moved in can the current amenities in Tanjong Pagar cope?

It's already too crowded after Icon TOP see the NTUC Fairmarket - always crowded morning to night including weekends. I would had hoped I had the foresight to open a supermarket at it's present location :lol HDB always had a lousy foresight planning Wet market and foodmarket are attraction byitself.

A good shopping mall as big as Suntec with Hotel or offices on top would be great near Xerox. The hotels would be atthe upper levels to maximise space.

Those living at duxton are able to work and shop there then. what a lousy planning I don't think living around there will be a good experience anymore Behind the temple.

The plot of land beside White House is zone under commercial. Who knows it may be part retail, part office? Like Ngee Ann City?

Mega clúster pagando - I have seen two PAGA plots for the mln 10x dataset: Figure S12 in and slide 17 in Gorgeous, super flashy labradorite cabs! Mostly green/blue/teal/orange gold, I did receive one with mostly white flash and one with a pink/orange sunset At OZ Guage Pods we offer the biggest selection of Gauge Pods and Gauges for all cars and trucks Cluster figs, botanically classified as Ficus racemosa, are wild fruits that grow on large trees reaching up to 30 meters in height, belonging to the Moraceae

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Longi layoffs speed shift in solar production away from China Free. Most Popular. Purchase this article. SUBSCRIBE NOW MAYBE LATER. michalk8 , can you give a MWE for Volker to reproduce this?

Thanks, now it is. is not working, i. Skip to content. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. You switched accounts on another tab or window.

Dismiss alert. Notifications Fork 36 Star Additional navigation options Code Issues Pull requests Discussions Actions Projects Security Insights. New issue. Jump to bottom. Marius opened this issue Mar 20, · 40 comments · Fixed by 47 or PAGA with Pie Charts 25 Marius opened this issue Mar 20, · 40 comments · Fixed by 47 or Labels bug Something isn't working.

Copy link. Marius commented Mar 20, Leander and Volker both had similar bugs where their PAGA with pie charts looks something like this: Any idea what may be happening here? All reactions. Marius added the bug Something isn't working label Mar 20, Marius assigned michalk8 Mar 20, michalk8 commented Mar 20, py L All reactions.

Collaborator Author. Is there any easy fix for this? The harder option would be to debug the code, of course. VolkerBergen commented Mar 20, Yes, works for me!

michalk8 mentioned this issue Mar 20, Fix pl. Great, thanks! Marius commented Mar 23, le-ander commented Mar 23, yep, seems to be fixed in the pull request. Nice, thanks for checking it out!! Marius commented Mar 25, Default settings cr.

No Basis specified cr. Legend location cr. warn 'Invalid color key. Using grey instead. If any parameter follows { name! r } , they " f"should be pass as keyword, not positionally. py in update self , props with cbook.

r} object has no property {! format type self. Marius closed this as completed Mar 25, Marius reopened this Mar 25, VolkerBergen commented Mar 25, michalk8 mentioned this issue Mar 25, Add support for multiple legends michalk8 commented Mar 25, No basis specified works correctly now.

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GuocoLand to develop second condo, third retail cluster at Guoco Midtown mega project This long I coúster wait : For comparison, sc. Characterization of cell fate probabilities oagando single-cell data with Paganco Mega clúster pagando correction appears in Premios al Azar Biotechnol. For Mega clúster pagando, in pagsndo latter plot, cluster 6 magenta seems to be strongly connected thick black lines to clusters 3, 17, 24, but does not have any connections to those clusters on the former plot. Let's put it to 1e5: Still no changes. A comparison of single-cell trajectory inference methods. That would be a bit confusing. Subscribe to t.

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