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BioTuring
InferCNV is used to explore tumor single cell RNA-Seq data to identify evidence for somatic large-scale chromosomal copy number alterations, such as gains or deletions of entire chromosomes or large segments of chromosomes. This is done by exploring expression intensity of genes across positions of tumor genome in comparison to a set of reference 'normal' cells. A heatmap is generated illustrating the relative expression intensities across each chromosome, and it often becomes readily apparent as to which regions of the tumor genome are over-abundant or less-abundant as compared to that of normal cells.
**Infercnvpy** is a scalable python library to infer copy number variation (CNV) events from single cell transcriptomics data. It is heavliy inspired by InferCNV, but plays nicely with scanpy and is much more scalable.
BioTuring
Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links.
We construct a database of interactions among ligands, receptors and their cofactors that accurately represent known heteromeric molecular complexes. We then develop **CellChat**, a tool that is able to quantitatively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data.
CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets.
Applying **CellChat** to mouse and human skin datasets shows its ability to extract complex signaling patterns.
BioTuring
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.
BioTuring
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.
Trends
BioTuring
scGen is a generative model to predict single-cell perturbation response across cell types, studies and species (Nature Methods, 2019). scGen is implemented using the scvi-tools framework.
What you can do with scGen:
Train on a dataset with multiple cell types and conditions and predict the perturbation effect on the cell type which you only have in one condition. This scenario can be extended to multiple species where you want to predict the effect of a specific species using another or all the species.
Train on a dataset where you have two conditions (e.g. control and perturbed) and predict on second dataset with similar genes.
Remove batch effect on labeled data. In this scenario you need to provide cell_type and batch labels to the method. Note that batch_removal does not require all cell types to be present in all datasets (batches). If you have dataset specific cell type it will preserved as before.
We assume there exist two conditions in you dataset (e.g. control and perturbed). You can train the model and with your data and predict the perturbation for the cell type/species of interest.
We recommend to use normalized data for the training. A simple example for normalization can be performed using scanpy