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BioTuring
In the realm of transcriptional dynamics, understanding the intricate interplay of regulatory proteins is crucial for deciphering processes ranging from normal development to disease progression. However, traditional RNA velocity methods often overlook the underlying regulatory drivers of gene expression changes over time. This gap in knowledge hinders our ability to unravel the mechanistic intricacies of these dynamic processes.
scKINETICs (Key regulatory Interaction NETwork for Inferring Cell Speed) (Burdziak et al, 2023) offers a dynamic model for gene expression changes that simultaneously learns per-cell transcriptional velocities and a governing gene regulatory network. By employing an expectation-maximization approach, scKINETICS quantifies the impact of each regulatory element on its target genes, incorporating insights from epigenetic data, gene-gene coexpression patterns and constraints dictated by the phenotypic manifold.
BioTuring
In this notebook, we present COMMOT (COMMunication analysis by Optimal Transport) to infer cell-cell communication (CCC) in spatial transcriptomic, a package that infers CCC by simultaneously considering numerous ligand–receptor pairs for either spatial transcriptomic data or spatially annotated scRNA-seq data equipped with spatial distances between cells estimated from paired spatial imaging data.
A collective optimal transport method is developed to handle complex molecular interactions and spatial constraints. Furthermore, we introduce downstream analysis tools to infer spatial signaling directionality and genes regulated by signaling using machine learning models.
BioTuring
PopV uses popular vote of a variety of cell-type transfer tools to classify cell-types in a query dataset based on a test dataset.
Using this variety of algorithms, they compute the agreement between those algorithms and use this agreement to predict which cell-types have a high likelihood of the same cell-types observed in the reference.
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
Recent spatial transcriptomics (ST) technologies have allowed us to capture cellular heterogeneity while retaining spatial information. However, ST datasets may lose single-cell resolution, limiting the discovery of cell-type-specific spatial patterns of localization and expression.
spacexr (Spatial-eXpression-R) is an R package providing two methods, i.e., Robust Cell Type Decomposition (RCTD) (Cable, Dylan M., et al., 2022) and Cell type-Specific Inference of Differential Expression (C-SIDE) (Cable, Dylan M., et al., 2022) for ST data. RCTD is proposed for cell type deconvolution, while leveraging references from another annotated single-cell RNA-seq data. C-SIDE identifies cell type-specific differential expression, accounting for localization of other cell types.
We will illustrate an example workflow in two notebooks, RCTD and C-SIDE, on a hippocampus Visium dataset provided by the authors. The notebooks are inspired from spacexr's vignettes and modified to demonstrate how the tool works on BioTuring's platform.