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.
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.
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.
Single-cell RNA-seq datasets in diverse biological and clinical conditions provide great opportunities for the full transcriptional characterization of cell types.
However, the integration of these datasets is challeging as they remain biological and techinical differences. **Harmony** is an algorithm allowing fast, sensitive and accurate single-cell data integration.
SpatialData (Marconato, Luca, et al., 2023) is a framework for processing spatial omics data, including
- spatialdata-io: load data from common spatial omics technologies into spatialdata.
- spatialdata-plot: static plotting library for spatialdata.
- napari-spatialdata: napari plugin for interactive exploration and annotation of spatial data.
In this notebook, we will illustrate the visualization functions implemented in SpatialData for Visium data. For datasets from other spatial technologies, please check this document. Also, we will use spatial queries to retrieve all the spatial elements and instances that are within a given rectangular window or polygonal shape from an example Visium brain dataset.
The notebook content is inspired from SpatialData's vignette and modified to demonstrate how the tool works on BioTuring's platform.
SpatialData (Marconato, Luca, et al., 2023) is a framework for processing spatial omics data, including
spatialdata-io: load data from common spatial omics technologies into spatialdata.
spatialdata-plot: static plotting library for spatialdata.
napari-spatialdata: napari plugin for interactive exploration and annotation of spatial data.
In this notebook, we will illustrate an example to train a Dense Net which predicts cell types Xenium data from an associated H&E image. Particularly, we will access and combine images and annotations across different technologies, where the H&E image from Visium data, and the cell type information from overlapping Xenium data. Also, the two modalities are spatially aligned via an affine transformation.
The notebook content is inspired from SpatialData's vignette and modified to demonstrate how the tool works on BioTuring's platform.