Ding Lab
Bridge the biomedical data and discovery!

Software

Most of the softwares can be found inGithub

  • UNAGI: A deep generative model for deciphering cellular dynamics and in silico drug discovery in complex diseases

    UNAGI is a comprehensive unsupervised in-silico cellular dynamics and drug discovery framework. UNAGI adeptly deciphers cellular dynamics from human disease time-series single-cell data and facilitates in-silico drug perturbations to earmark therapeutic targets and drugs potentially active against complex human diseases. Please check here for details.

  • CellAgentChat: Harnessing agent-based frameworks to unravel cell-cell interactions from single-cell and spatial transcriptomics

    CellAgentChat constitutes a comprehensive framework integrating gene expression data and existing knowledge of signaling ligand-receptor interactions to compute the probabilities of cell-cell communication. Utilizing the principles of agent-based modeling (ABM), we characterize each cell agent through various attributes, including cell identities (e.g. cell type or clusters), gene expression profiles, ligand-receptor universe and spatial coordinates (optional). We quantify cellular interactions between sender and receiver cells based on the number of ligands secreted by the sender cells and subsequently received by the receiver cells. This process hinges upon three interrelated components: ligand diffusion rate (γl), receptor receiving rate (αr), and receptor conversion rate (βr). Please check here for details.

  • scCobra: allows contrastive cell embedding learning with domain adaptation for single cell data integration and harmonization

    scCobra effectively mitigates batch effects, minimizes over-correction, and ensures biologically meaningful data integration without assuming specific gene expression distributions. It enables online label transfer across datasets with batch effects, allowing continuous integration of new data without retraining. Additionally, scCobra supports batch effect simulation, advanced multi-omic integration, and scalable processing of large datasets. By integrating and harmonizing datasets from similar studies, scCobra expands the available data for investigating specific biological problems, improving cross-study comparability, and revealing insights that may be obscured in isolated datasets. Please check here for details.

  • MATES: a deep learning-based model for locus-specific quantification of transposable elements in single cell

    MATES is a deep-learning approach that accurately allocates multi-mapping reads to specific loci of TEs, utilizing context from adjacent read alignments flanking the TE locus. This development facilitates the exploration of single-cell heterogeneity and gene regulation through the lens of TEs, offering an effective transposon quantification tool for the single-cell genomics community. Please check here for details.

  • scCross: a deep generative model for unifying single-cell multi-omics with seamless integration, cross-modal generation, and in silico exploration

    scCross, harnessing variational autoencoder and generative adversarial network (VAE-GAN) principles, meticulously designed to integrate diverse single-cell multi-omics data. Incorporating biological priors, scCross adeptly aligns modalities with enhanced relevance. Its standout feature is generating cross-modality single-cell data and in-silico perturbations, enabling deeper cellular state examinations and drug explorations. More details are in our manuscript.

  • scSemiProfiler: Advancing Large-scale Single-cell Studies through Semi-profiling with Deep Generative Models and Active Learning

    scSemiProfiler is an innovative computational tool combining deep generative models and active learning to economically generate single-cell data for biological studies. It efficiently transforms bulk cohort data into detailed single-cell data using templates from selected representative samples. More details are in our manuscript.

  • SCDIFF: reconstruct cell differentiation trajectories and underlying regulatory networks from scRNA-seq RNA-seq data

    SCDIFF is a package written in python and javascript, designed to analyze the cell differentiation trajectories using time-series single cell RNA-seq data. It is able to predict the transcription factors and differential genes associated with the cell differentiation trajectories. It also visualizes the trajectories using an interactive tree-structure graph, in which nodes represent different sub-population cells (clusters). Please check here for details.

  • TBSP: Trajectory Inference Based on SNP information

    Several recent studies focus on the inference of developmental and response trajectories from single cell RNA-Seq (scRNA-Seq) data. A number of computational methods, often referred to as pseudo-time ordering, have been developed for this task. Recently, CRISPR has also been used to reconstruct lineage trees by inserting random mutations. However, both approaches suffer from drawbacks that limit their use. Here we develop a method (named TBSP) to detect significant, cell type specific, sequence mutations from scRNA-Seq data. We show that only a few mutations are enough for reconstructing good branching models. Integrating these mutations with expression data further improves the accuracy of the reconstructed models. Please check here for details.

  • iDREM: interactive Dynamic Regulatory Events Miner

    The Dynamic Regulatory Events Miner (DREM) software was initially developed to integrate static protein-DNA interaction data with time series gene expression data for reconstructing dynamic regulatory networks. In recent years, several additional types of high-throughput time series data have been used to study biological processes including time series miRNA expression, proteomics, epigenomics and single cell RNA-Seq. Integrating all available time series and static datasets in a unified model remains an important challenge and goal. To address this goal, and to enable interactive queries of the resulting learned models we have developed a new version of DREM termed interactive DREM (iDREM). iDREM provides support for all data types mentioned above and more. Importantly, it also allows users to interactively visualize a gene, TF, path or model-centric view of each of these data types, their interactions and their impact on the resulting model. We showcase the functionality of the new tool by applying it to integrate several data types from multiple labs for modeling brain development regulatory networks. Please read here for details.

  • TarPmiR: a new approach for microRNA target site prediction

    The identification of microRNA (miRNA) target sites is fundamentally important for studying gene regulation. There are dozens of computational methods available for miRNA target site prediction. Despite their existence, we still cannot reliably identify miRNA target sites, partially due to our limited understanding of the characteristics of miRNA target sites. The recently published CLASH (cross-linking ligation and sequencing of hybrids) data provide an unprecedented opportunity to study the characteristics of miRNA target sites and improve miRNA target site prediction methods. Applying four different machine learning approaches to the CLASH data, we identified seven new features of miRNA target sites. Combining these new features with those commonly used by existing miRNA target prediction algorithms, we developed an approach called TarPmiR for miRNA target site prediction. Testing on two human and one mouse non-CLASH datasets, we showed that TarPmiR predicted more than 74.2 % of true miRNA target sites in each dataset. Compared with three existing approaches, we demonstrated that TarPmiR is superior to these existing approaches in terms of better recall and better precision. Please read here for details.