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BioTuring
Advances in multi-omics have led to an explosion of multimodal datasets to address questions from basic biology to translation. While these data provide novel opportunities for discovery, they also pose management and analysis challenges, thus motivating the development of tailored computational solutions. `muon` is a Python framework for multimodal omics.
It introduces multimodal data containers as `MuData` object. The package also provides state of the art methods for multi-omics data integration. `muon` allows the analysis of both unimodal omics and multimodal omics.
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
Computational methods that model how the gene expression of a cell is influenced by interacting cells are lacking.
We present NicheNet, a method that predicts ligand–target links between interacting cells by combining their expression data with prior knowledge of signaling and gene regulatory networks.
We applied NicheNet to the tumor and immune cell microenvironment data and demonstrated that NicheNet can infer active ligands and their gene regulatory effects on interacting cells.
BioTuring
Single-cell RNA sequencing (scRNA-seq) data often encountered technical artifacts called "doublets" which are two cells that are sequenced under the same cellular barcode.
Doublets formed from different cell types or states are called heterotypic and homotypic otherwise. These factors constrain cell throughput and may result in misleading biological interpretations.
DoubletFinder (McGinnis, Murrow, and Gartner 2019) is one of the methods proposed for doublet detection. In this notebook, we will illustrate an example workflow of DoubletFinder. We use a 10x Genomics dataset which captures peripheral blood mononuclear cells (PBMCs) from a healthy donor stained with a panel of 31 TotalSeq™-B antibodies (BioLegend).
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BioTuring
Dampened weighted least squares (DWLS) is an estimation method for gene expression deconvolution, in which the cell-type composition of a bulk RNA-seq data set is computationally inferred. This method corrects common biases towards cell types that are characterized by highly expressed genes and/or are highly prevalent, to provide accurate detection across diverse cell types.
To begin, the user must input a bulk RNA-seq data set, along with a labeled representative single-cell RNA-seq data set that will serve to generate cell-type-specific gene expression profiles. Ideally, the single-cell data set will contain cells from all cell types that may be found in the bulk data. DWLS will return the cell-type composition of the bulk data.