Trace Cell Populations
======================


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   <div class="table-container">
     <table id="methods-table" class="display" style="width:100%">
       <thead>
         <tr>
           <th></th><th>Method</th><th>Year</th><th>Task</th>
           <th>Model</th><th>Published</th><th>Code</th>
         </tr>
       </thead>
       <tbody>
         <tr data-description="ARTEMIS combines variational autoencoder with an unbalanced Diffusion Schrödinger Bridge to reconstruct continuous trajectories from time-series data. The methods trains a VAE first, then jointly trains the VAE and uDSB by solving the forward-backward SDEs in the latent space using neural networks.">
           <td class="details-control"></td>
           <td><a href="https://academic.oup.com/bioinformatics/article/41/Supplement_1/i189/8199396">ARTEMIS</a></td>
           <td>2025</td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge task-badge">Trace Cell Populations</span></div></td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge model-badge">VAE</span><span class="badge model-badge">Schrödinger Bridge</span></div></td>


           <td class="published"><span style="color: #2E8B57;">✓</span></td>
            <td><a href="https://github.com/daifengwanglab/ARTEMIS" class="github-link">
                  <i class="fab fa-github" aria-hidden="true"></i>
                  <span class="sr-only">GitHub</span>
                </a></td>
         </tr>
         <tr data-description="CellFlow (Klein et al.) learns a neural vector field parameterised as a neural ODE, which predicts single-cell expression profiles under a wide range of experimental conditions. The model encodes perturbations (labels or embeddings), covariates (dosage, batch, time, etc.), and sample descriptors using specialised embeddings, aggregates these using permutation-invariant pooling (multi-head attention or deep sets), and uses the resulting joint embedding to condition the flow module. Flow matching and optimal transport are used to align control and perturbed cells, thereby modeling both cellular heterogeneity and perturbation-specific effects, and enabling accurate counterfactual predictions.">
           <td class="details-control"></td>
           <td><a href="https://www.biorxiv.org/content/10.1101/2025.04.11.648220v1.full.pdf">cellFlow</a></td>
           <td>2025</td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge task-badge">Trace Cell Populations</span><span class="badge task-badge">Context Transfer</span><span class="badge task-badge">Unseen Perturbation Prediction</span><span class="badge task-badge">Combinatorial Effect Prediction</span></div></td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge model-badge">Conditional Flow Matching</span><span class="badge model-badge">Optimal Transport</span></div></td>


           <td class="published"><span style="color: #DC143C;">✗</span></td>
            <td><a href="https://github.com/theislab/CellFlow" class="github-link">
                  <i class="fab fa-github" aria-hidden="true"></i>
                  <span class="sr-only">GitHub</span>
                </a></td>
         </tr>
         <tr data-description="CellOT learns mappings between control and perturbed cell state distributions by solving a dual formulation of the optimal transport problem. The approach learns optimal transport maps as the gradient of a convex potential function, which is approximated using input convex neural networks - (briefly) a specific type of neural network with convex-preserving constraints, such as non-negative weights and a predefined set of activation functions (e.g. ReLU). Instead of relying on regularisation-based OT (e.g. Entropy-regularised Sinkhorn), it jointly optimizes dual potentials (a pair of functions) via a max–min loss.">
           <td class="details-control"></td>
           <td><a href="https://www.nature.com/articles/s41592-023-01969-x">CellOT</a></td>
           <td>2023</td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge task-badge">Trace Cell Populations</span><span class="badge task-badge">Perturbation Responsiveness</span><span class="badge task-badge">Context Transfer</span></div></td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge model-badge">Dual (min-max) Formulation OT</span></div></td>


           <td class="published"><span style="color: #2E8B57;">✓</span></td>
            <td><a href="https://github.com/bunnech/cellot" class="github-link">
                  <i class="fab fa-github" aria-hidden="true"></i>
                  <span class="sr-only">GitHub</span>
                </a></td>
         </tr>
         <tr data-description="CFGen is a flow-based model for producing multi-modal scRNA-seq data. CFGen builds on cellFlow (Palma et al.) and explicitly models the discrete, over-dispersed nature of single-cell counts when generating synthetic data.">
           <td class="details-control"></td>
           <td><a href="https://openreview.net/forum?id=3MnMGLctKb">CFGen</a></td>
           <td>2024</td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge task-badge">Trace Cell Populations</span><span class="badge task-badge">Context Transfer</span></div></td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge model-badge">Optimal Transport</span><span class="badge model-badge">Multi-modal</span><span class="badge model-badge">Conditional Flow Matching</span></div></td>


           <td class="published"><span style="color: #2E8B57;">✓</span></td>
            <td><a href="https://github.com/theislab/CFGen" class="github-link">
                  <i class="fab fa-github" aria-hidden="true"></i>
                  <span class="sr-only">GitHub</span>
                </a></td>
         </tr>
         <tr data-description="CINEMA-OT disentangles perturbation effects from confounding variation by decomposing the data with independent component analysis (ICA); ICA components correlated with the perturbation labels are identified using Chatterjee’s coefficient and excluded, yielding a background (confounder) latent space that predominantly reflects confounding factors. Optimal transport is then applied to this background space to align perturbed and control cells, thereby generating counterfactual cell pairs, and this OT map is used in downstream analyses. They also propose a reweighting variant (CINEMA-OT-W) to address differential cell type abundance by pre-aligning treated cells with k-nearest neighbor controls and balancing clusters prior to ICA and optimal transport.">
           <td class="details-control"></td>
           <td><a href="https://www.nature.com/articles/s41592-023-02040-5#Sec11">CINEMA-OT</a></td>
           <td>2023</td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge task-badge">Trace Cell Populations</span><span class="badge task-badge">Perturbation Responsiveness</span><span class="badge task-badge">Unsupervised Disentanglement</span></div></td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge model-badge">Unbalanced OT</span><span class="badge model-badge">Entropy-regularized Sinkhorn</span><span class="badge model-badge">ICA</span></div></td>


           <td class="published"><span style="color: #2E8B57;">✓</span></td>
            <td><a href="https://github.com/vandijklab/CINEMA-OT" class="github-link">
                  <i class="fab fa-github" aria-hidden="true"></i>
                  <span class="sr-only">GitHub</span>
                </a></td>
         </tr>
         <tr data-description="CondOT builds on CellOT to learn context-aware optimal transport maps by conditioning on an auxiliary variable. Instead of learning a fixed transport map, it learns a context-dependent transport map that adapts based on this auxiliary information. The OT map is modeled as the gradient of a convex potential using partially input convex neural networks, which ensures mathematical properties required for parametrised optimal transport. The auxiliary variables can be of different types: continuous (like dosage or spatial coordinates), categorical (like treatment groups, represented via one-hot encoding), or learned embeddings learned. Additionally, CondOT includes a separate neural module, a combinator network, for combinatorial predictions.">
           <td class="details-control"></td>
           <td><a href="https://proceedings.neurips.cc/paper_files/paper/2022/file/2d880acd7b31e25d45097455c8e8257f-Paper-Conference.pdf">CondOT</a></td>
           <td>2022</td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge task-badge">Trace Cell Populations</span><span class="badge task-badge">Unseen Perturbation Prediction</span><span class="badge task-badge">Combinatorial Effect Prediction</span><span class="badge task-badge">Context Transfer</span></div></td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge model-badge">Conditioned Dual (min-max) Formulation OT</span></div></td>


           <td class="published"><span style="color: #2E8B57;">✓</span></td>
            <td><a href="https://github.com/bunnech/condot/tree/main" class="github-link">
                  <i class="fab fa-github" aria-hidden="true"></i>
                  <span class="sr-only">GitHub</span>
                </a></td>
         </tr>
         <tr data-description="CoSpar is a computational method for inferring cell state transition maps from single-cell transcriptomics and lineage tracing data. It formalizes cell dynamics by estimating, for each initial cell state, the fractional transition probability to future states across time using optimal transport and incorporating clonal information through barcoding. CoSpar further constrains the inferred transition map to be both sparse—reflecting limited accessible states during differentiation or response—and locally coherent—so that similar initial states yield similar fate outcomes.">
           <td class="details-control"></td>
           <td><a href="https://www.nature.com/articles/s41587-022-01209-1">CoSpar</a></td>
           <td>2022</td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge task-badge">Trace Cell Populations</span></div></td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge model-badge">Optimal Transport</span><span class="badge model-badge">Sparsity</span></div></td>


           <td class="published"><span style="color: #2E8B57;">✓</span></td>
            <td><a href="https://github.com/ShouWenWang-Lab/cospar/tree/master" class="github-link">
                  <i class="fab fa-github" aria-hidden="true"></i>
                  <span class="sr-only">GitHub</span>
                </a></td>
         </tr>
         <tr data-description="CytoBridge approximates the Unbalanced Mean Field Schrödinger Bridge (UMFSB) to model unbalanced stochastic cell dynamics with cell-cell interactions from snapshot data. It uses neural networks and flow matching to infer transition velocity, growth/death rates, and interaction potentials. The framework offers a unified approach to model time-series transcriptomics, elucidating how intercellular communication influences cellular processes and behaviors. ">
           <td class="details-control"></td>
           <td><a href="https://openreview.net/pdf?id=Z6DJJIN8IJ">CytoBridge</a></td>
           <td>2025</td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge task-badge">Trace Cell Populations</span><span class="badge task-badge">Context Transfer</span><span class="badge task-badge">Seen Perturbation Prediction</span></div></td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge model-badge">Neural ODE</span><span class="badge model-badge">Flow Matching</span><span class="badge model-badge">Optimal Transport</span><span class="badge model-badge">Time-resolved</span><span class="badge model-badge">Mean Field Model</span></div></td>


           <td class="published"><span style="color: #2E8B57;">✓</span></td>
            <td><a href="https://github.com/zhenyiizhang/CytoBridge.git" class="github-link">
                  <i class="fab fa-github" aria-hidden="true"></i>
                  <span class="sr-only">GitHub</span>
                </a></td>
         </tr>
         <tr data-description="DeepRUOT solves an Regularized Unbalanced Optimal Transport (RUOT) problem to reconstruct stochastic dynamics from sparse time-resolved snapshots. By introducing Fisher-regularization, the method simultaneously infers the transition velocity, growth/death rates without any prior knowledge. The framework transforms time-series transcriptomics data into a virtual dynamical system to identify single-cell growth and transition patterns, eliminating false transitions, and constructing the Waddington developmental landscape.">
           <td class="details-control"></td>
           <td><a href="https://openreview.net/forum?id=gQlxd3Mtru">DeepRUOT</a></td>
           <td>2025</td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge task-badge">Trace Cell Populations</span><span class="badge task-badge">Context Transfer</span><span class="badge task-badge">Seen Perturbation Prediction</span></div></td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge model-badge">Neural ODE</span><span class="badge model-badge">Flow Matching</span><span class="badge model-badge">Optimal Transport</span><span class="badge model-badge">Time-resolved</span><span class="badge model-badge">Fisher-regularized Unbalanced Dynamics</span></div></td>


           <td class="published"><span style="color: #2E8B57;">✓</span></td>
            <td><a href="https://github.com/zhenyiizhang/DeepRUOTv2.git" class="github-link">
                  <i class="fab fa-github" aria-hidden="true"></i>
                  <span class="sr-only">GitHub</span>
                </a></td>
         </tr>
         <tr data-description="The paper extends entropic Gromov-Wasserstein Optimal Transport and Co-Optimal Transport to incorporate perturbation labels for aligning data across different modalities from large-scale perturbation screens. The core innovation involves constraining the learned cross-modality coupling matrix to be &#34;label-compatible&#34;, meaning that the transport plan is informed by the perturbation labels and is only allowed to match between cells that have received the same perturbation label, which is achieved by modifying the Sinkhorn algorithm. This label-compatible alignment is used to train a model to estimate cellular responses to perturbations when measurements are available in only one modality.">
           <td class="details-control"></td>
           <td><a href="https://arxiv.org/pdf/2405.00838">GWOT</a></td>
           <td>2025</td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge task-badge">Trace Cell Populations</span><span class="badge task-badge">Context Transfer</span></div></td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge model-badge">Optimal Transport</span><span class="badge model-badge">Multi-modal</span></div></td>


           <td class="published"><span style="color: #2E8B57;">✓</span></td>
            <td><a href="https://genentech.github.io/Perturb-OT/" class="github-link">
                  <i class="fab fa-github" aria-hidden="true"></i>
                  <span class="sr-only">GitHub</span>
                </a></td>
         </tr>
         <tr data-description="Metric Flow Matching (MFM) constructs probability paths between source and target distributions by interpolating geodesics following a data-dependent Riemannian metric, ensuring that interpolations remain close to the data manifold rather than being straight lines in Euclidean space. MFM first learns these geodesics by minimizing a special cost function, and then regresses a vector field along a geodesic-based corrected path using a conditional flow matching objective.">
           <td class="details-control"></td>
           <td><a href="https://proceedings.neurips.cc/paper_files/paper/2024/file/f381114cf5aba4e45552869863deaaa7-Paper-Conference.pdf">MFM</a></td>
           <td>2024</td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge task-badge">Trace Cell Populations</span><span class="badge task-badge">Context Transfer</span><span class="badge task-badge">Seen Perturbation Prediction</span></div></td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge model-badge">Flow Matching</span><span class="badge model-badge">Optimal Transport</span><span class="badge model-badge">Time-resolved</span></div></td>


           <td class="published"><span style="color: #2E8B57;">✓</span></td>
            <td><a href="https://github.com/kksniak/metric-flow-matching.git" class="github-link">
                  <i class="fab fa-github" aria-hidden="true"></i>
                  <span class="sr-only">GitHub</span>
                </a></td>
         </tr>
         <tr data-description="MIOFlow learns (stochastic) continuous dynamics from snapshot data by combining neural ODEs, manifold learning, and optimal transport. The method first trains a Geodesic Autoencoder to embed data into a latent space where geodesic-based distances are preserved, then models trajectories via a neural ODE and an ODE solver, and optimizes the Wasserstein loss between actual and predicted distributions.">
           <td class="details-control"></td>
           <td><a href="https://proceedings.neurips.cc/paper_files/paper/2022/file/bfc03f077688d8885c0a9389d77616d0-Paper-Conference.pdf">MioFlow</a></td>
           <td>2022</td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge task-badge">Trace Cell Populations</span></div></td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge model-badge">Neural ODE</span><span class="badge model-badge">Geodasic Autoencoder</span></div></td>


           <td class="published"><span style="color: #2E8B57;">✓</span></td>
            <td><a href="https://github.com/KrishnaswamyLab/MIOFlow" class="github-link">
                  <i class="fab fa-github" aria-hidden="true"></i>
                  <span class="sr-only">GitHub</span>
                </a></td>
         </tr>
         <tr data-description="MMFM (Multi-Marginal Flow Matching) builds on Flow Matching to model cell trajectories across time and conditions. MMFM generalises the Conditional Flow Matching framework to incorporate multiple time points using a spline-based conditional probability path. Moreover, it leverages ideas from classifier-free guidance to incorporate multiple conditions.">
           <td class="details-control"></td>
           <td><a href="https://openreview.net/pdf?id=hwnObmOTrV">MMFM</a></td>
           <td>2024</td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge task-badge">Trace Cell Populations</span><span class="badge task-badge">Context Transfer</span><span class="badge task-badge">Seen Perturbation Prediction</span></div></td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge model-badge">Flow Matching</span><span class="badge model-badge">Optimal Transport</span><span class="badge model-badge">Time-resolved</span></div></td>


           <td class="published"><span style="color: #2E8B57;">✓</span></td>
            <td><a href="github.com/Genentech/MMFM" class="github-link">
                  <i class="fab fa-github" aria-hidden="true"></i>
                  <span class="sr-only">GitHub</span>
                </a></td>
         </tr>
         <tr data-description="Moscot is a broad and scalable framework that recasts various single-cell mapping tasks as optimal transport problems, supporting formulations that compare distributions in shared (Wasserstein-type OT), distinct (Gromov-Wasserstein OT), and partially-overlapping feature spaces (fused-Gromov–Wasserstein OT). Beyond Entropy-regularized sinkhorn (Cuturi et al., 2013), moscot provides a user-friendly API to more recent OT strategies, such as low-rank and sparse Monge maps.">
           <td class="details-control"></td>
           <td><a href="https://www.nature.com/articles/s41586-024-08453-2">moscot</a></td>
           <td>2025</td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge task-badge">Trace Cell Populations</span></div></td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge model-badge">Unbalanced OT</span><span class="badge model-badge">Entropy-regularised Sinkhorn</span><span class="badge model-badge">Low-rank OT</span><span class="badge model-badge">Sparse Map OT</span></div></td>


           <td class="published"><span style="color: #2E8B57;">✓</span></td>
            <td><a href="https://github.com/theislab/moscot" class="github-link">
                  <i class="fab fa-github" aria-hidden="true"></i>
                  <span class="sr-only">GitHub</span>
                </a></td>
         </tr>
         <tr data-description="The paper introduces two key Flow Matching variants: (i) Optimal Transport CFM (OT-CFM), which uses optimal transport couplings (approximated with minibatch OT), to produce more robust flow inference and (ii) Schrödinger Bridge CFM solving the Schrödinger Bridge problem by using entropy-regularized OT couplings. ">
           <td class="details-control"></td>
           <td><a href="https://openreview.net/forum?id=CD9Snc73AW">OT-CFM</a></td>
           <td>2024</td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge task-badge">Trace Cell Populations</span></div></td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge model-badge">Flow Matching</span><span class="badge model-badge">Schrödinger Bridge</span></div></td>


           <td class="published"><span style="color: #2E8B57;">✓</span></td>
            <td><a href="https://github.com/atong01/conditional-flow-matching" class="github-link">
                  <i class="fab fa-github" aria-hidden="true"></i>
                  <span class="sr-only">GitHub</span>
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         </tr>
         <tr data-description="PRESCIENT models cellular differentiation as a stochastic diffusion process, where the drift term is parameterised as the negative gradient of a neural network-learned potential function. The model is trained using time-series single-cell RNA-seq data, and fits the potential function by minimizing the regularised Wasserstein distance between simulated and observed cell populations at each time point, explicitly incorporating cell proliferation by weighting cells according to their expected number of descendants. PRESCIENT can simulate differentiation trajectories for both observed and (in silico) perturbed cell states, enabling the prediction of cell fate outcomes under various genetic interventions.">
           <td class="details-control"></td>
           <td><a href="https://www.nature.com/articles/s41467-021-23518-w">Prescient</a></td>
           <td>2021</td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge task-badge">Trace Cell Populations</span><span class="badge task-badge">Seen Perturbation Prediction</span><span class="badge task-badge">Context Transfer</span></div></td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge model-badge">Diffusion</span></div></td>


           <td class="published"><span style="color: #2E8B57;">✓</span></td>
            <td><a href="https://github.com/gifford-lab/prescient" class="github-link">
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                  <span class="sr-only">GitHub</span>
                </a></td>
         </tr>
         <tr data-description="SBALIGN solves a Diffusion Schrödinger Bridge Problem with aligned data, i.e. where each sample from the source distribution is paired with a corresponding sample from the target distribution. It combines classical SB theory with Doob&#39;s h-transform to derive a novel loss -- parameterizing the drift and h-function with neural networks, enabling more stable training than iterative procedures.">
           <td class="details-control"></td>
           <td><a href="https://proceedings.mlr.press/v216/somnath23a/somnath23a.pdf">SBALIGN</a></td>
           <td>2023</td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge task-badge">Trace Cell Populations</span></div></td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge model-badge">Schrödinger Bridge</span></div></td>


           <td class="published"><span style="color: #2E8B57;">✓</span></td>
            <td><a href="https://github.com/vsomnath/aligned_diffusion_bridges " class="github-link">
                  <i class="fab fa-github" aria-hidden="true"></i>
                  <span class="sr-only">GitHub</span>
                </a></td>
         </tr>
         <tr data-description="scPRAM is a computational framework for predicting single-cell gene expression changes in response to perturbations. The method integrates three main components: a variational autoencoder (VAE), optimal transport, and an attention mechanism. The VAE encodes the gene expression data into a latent space. Optimal transport is applied in this latent space to match unpaired cells before and after perturbation by finding an optimal coupling between their distributions. For each test cell, the attention mechanism computes a perturbation vector by comparing its latent representation (query) against those of matched training cells (keys and values). The predicted post-perturbation response is generated by adding the perturbation vector to the query and decoding it back to gene expression space using the VAE decoder.">
           <td class="details-control"></td>
           <td><a href="https://academic.oup.com/bioinformatics/article/40/5/btae265/7646141">scPRAM</a></td>
           <td>2024</td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge task-badge">Context Transfer</span><span class="badge task-badge">Trace Cell Populations</span></div></td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge model-badge">VAE</span><span class="badge model-badge">OT</span><span class="badge model-badge">Attention</span></div></td>


           <td class="published"><span style="color: #2E8B57;">✓</span></td>
            <td><a href="https://github.com/jiang-q19/scPRAM" class="github-link">
                  <i class="fab fa-github" aria-hidden="true"></i>
                  <span class="sr-only">GitHub</span>
                </a></td>
         </tr>
         <tr data-description="Waddington-OT models developmental processes as time-varying  probability distributions in gene expression space and infers temporal couplings by solving an entropy-regularized, unbalanced optimal transport problem. Growth rate, estimated leveraging expression levels of genes associated with proliferation and apoptosis, is taken into consideration via unbalanced OT. Additionally, uses spectral clustering to obtain Gene Programmes, and subsequently associate those to predictive TFs.">
           <td class="details-control"></td>
           <td><a href="https://www.sciencedirect.com/science/article/pii/S009286741930039X?via%3Dihub">Waddington-OT</a></td>
           <td>2019</td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge task-badge">Trace Cell Populations</span><span class="badge task-badge">Linear Gene Programmes</span></div></td>

           <td><div style="display: flex; flex-wrap: wrap; gap: 0.3em;"><span class="badge model-badge">Unbalanced OT</span><span class="badge model-badge">Entropy-regularized Sinkhorn</span></div></td>


           <td class="published"><span style="color: #2E8B57;">✓</span></td>
            <td><a href="https://github.com/broadinstitute/wot" class="github-link">
                  <i class="fab fa-github" aria-hidden="true"></i>
                  <span class="sr-only">GitHub</span>
                </a></td>
         </tr>
       </tbody>
     </table>
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