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BioTuring
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.
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).
BioTuring
Single-cell RNA sequencing (scRNA-seq) protocols often face challenges in measuring the expression of all genes within a cell due to various factors, such as technical noise, the sensitivity of scRNA-seq techniques, or sample quality. This limitation gives rise to a need for the prediction of unmeasured gene expression values (also known as dropout imputation) from scRNA-seq data.
ADImpute (Leote A, 2023) is an R package combining several dropout imputation methods, including two existing methods (DrImpute, SAVER), two novel implementations: Network, a gene regulatory network-based approach using gene-gene relationships learned from external data, and Baseline, a method corresponding to a sample-wide average..
This notebook is to illustrate an example workflow of ADImpute on sample datasets loaded from the package. The notebook content is inspired from ADImpute's vignette and modified to demonstrate how the tool works on BioTuring's platform.
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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.