CellChat

Build single-cell trajectories with the software that introduced **pseudotime**. Find out about cell fate decisions and the genes regulated as they're made. Group and classify your cells based on gene expression. Identify new cell types and states and the genes that distinguish them. Find genes that vary between cell types and states, over trajectories, or in response to perturbations using statistically robust, flexible differential analysis. In development, disease, and throughout life, cells transition from one state to another. Monocle introduced the concept of **pseudotime**, which is a measure of how far a cell has moved through biological progress. Many researchers are using single-cell RNA-Seq to discover new cell types. Monocle 3 can help you purify them or characterize them further by identifying key marker genes that you can use in follow-up experiments such as immunofluorescence or flow sorting. **Single-cell trajectory analysis** shows how cells choose between one of several possible end states. The new reconstruction algorithms introduced in Monocle 3 can robustly reveal branching trajectories, along with the genes that cells use to navigate these decisions.

The recent development of experimental methods for measuring chromatin state at single-cell resolution has created a need for computational tools capable of analyzing these datasets. Here we developed Signac, a framework for the analysis of single-cell chromatin data, as an extension of the Seurat R toolkit for single-cell multimodal analysis. **Signac** enables an end-to-end analysis of single-cell chromatin data, including peak calling, quantification, quality control, dimension reduction, clustering, integration with single-cell gene expression datasets, DNA motif analysis, and interactive visualization. Furthermore, Signac facilitates the analysis of multimodal single-cell chromatin data, including datasets that co-assay DNA accessibility with gene expression, protein abundance, and mitochondrial genotype. We demonstrate scaling of the Signac framework to datasets containing over 700,000 cells.

The recent development of single-cell RNA-sequencing (scRNA-seq) technology has enabled us to infer cell-type-specific co-expression networks, enhancing our understanding of cell-type-specific biological functions. However, existing methods proposed for this task still face challenges due to unique characteristics in scRNA-seq data, such as high sequencing depth variations across cells and measurement errors. CS-CORE (Su, C., Xu, Z., Shan, X. et al., 2023), an R package for cell-type-specific co-expression inference, explicitly models sequencing depth variations and measurement errors in scRNA-seq data. In this notebook, we will illustrate an example workflow of CS-CORE using a dataset of Peripheral Blood Mononuclear Cells (PBMC) from COVID patients and healthy controls (Wilk et al., 2020). The notebook content is inspired by CS-CORE's vignette and modified to demonstrate how the tool works on BioTuring's platform.

scVI-tools (single-cell variational inference tools) is a package for end-to-end analysis of single-cell omics data primarily developed and maintained by the Yosef Lab at UC Berkeley. scvi-tools has two components - Interface for easy use of a range of probabilistic models for single-cell omics (e.g., scVI, scANVI, totalVI). - Tools to build new probabilistic models, which are powered by PyTorch, PyTorch Lightning, and Pyro.