CellChat

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.

The development of large-scale single-cell atlases has allowed describing cell states in a more detailed manner. Meanwhile, current deep leanring methods enable rapid analysis of newly generated query datasets by mapping them into reference atlases. expiMap (‘explainable programmable mapper’) Lotfollahi, Mohammad, et al. is one of the methods proposed for single-cell reference mapping. Furthermore, it incorporates prior knowledge from gene sets databases or users to analyze query data in the context of known gene programs (GPs).

CellRank2 (Weiler et al, 2023) is a powerful framework for studying cellular fate using single-cell RNA sequencing data. It can handle millions of cells and different data types efficiently. This tool can identify cell fate and probabilities across various data sets. It also allows for analyzing transitions over time and uncovering key genes in developmental processes. Additionally, CellRank2 estimates cell-specific transcription and degradation rates, aiding in understanding differentiation trajectories and regulatory mechanisms. In this notebook, we will use a primary tumor sample of patient T71 from the dataset GSE137804 (Dong R. et al, 2020) as an example. We have performed RNA-velocity analysis and pseudotime calculation on this dataset in scVelo (Bergen et al, 2020) notebook. The output will be then loaded into this CellRank2 notebook for further analysis. This notebook is based on the tutorial provided on CellRank2 documentation. We have modified the notebook and changed the input data to show how the tool works on BioTuring's platform.

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.