E-spatial

Doublets are a characteristic error source in droplet-based single-cell sequencing data where two cells are encapsulated in the same oil emulsion and are tagged with the same cell barcode. Across type doublets manifest as fictitious phenotypes that can be incorrectly interpreted as novel cell types. DoubletDetection present a novel, fast, unsupervised classifier to detect across-type doublets in single-cell RNA-sequencing data that operates on a count matrix and imposes no experimental constraints. This classifier leverages the creation of in silico synthetic doublets to determine which cells in the input count matrix have gene expression that is best explained by the combination of distinct cell types in the matrix. In this notebook, we will illustrate an example workflow for detecting doublets in single-cell RNA-seq count matrices.

The recent development of single-cell RNA-sequencing (scRNA-seq) technology has enabled us to infer cell-type-specific co-expression networks, enhancing our understanding of cell-type-specific biological functions. However, existing methods proposed for this task still face challenges due to unique characteristics in scRNA-seq data, such as high sequencing depth variations across cells and measurement errors. CS-CORE (Su, C., Xu, Z., Shan, X. et al., 2023), an R package for cell-type-specific co-expression inference, explicitly models sequencing depth variations and measurement errors in scRNA-seq data. In this notebook, we will illustrate an example workflow of CS-CORE using a dataset of Peripheral Blood Mononuclear Cells (PBMC) from COVID patients and healthy controls (Wilk et al., 2020). The notebook content is inspired by CS-CORE's vignette and modified to demonstrate how the tool works on BioTuring's platform.

Power analyses are considered important factors in designing high-quality experiments. However, such analyses remain a challenge in single-cell RNA-seq studies due to the presence of hierarchical structure within the data (Zimmerman et al., 2021). As cells sampled from the same individual share genetic and environmental backgrounds, these cells are more correlated than cells sampled from different individuals. Currently, most power analyses and hypothesis tests (e.g., differential expression) in scRNA-seq data treat cells as if they were independent, thus ignoring the intra-sample correlation, which could lead to incorrect inferences. Hierarchicell (Zimmerman, K.D. and Langefeld, C.D., 2021) is an R package proposed to estimate power for testing hypotheses of differential expression in scRNA-seq data while considering the hierarchical correlation structure that exists in the data. The method offers four important categories of functions: data loading and cleaning, empirical estimation of distributions, simulating expression data, and computing type 1 error or power. In this notebook, we will illustrate an example workflow of Hierarchicell. The notebook is inspired by Hierarchicell's vignette and modified to demonstrate how the tool works on BioTuring's platform.

Single-cell RNA sequencing methods can profile the transcriptomes of single cells but cannot preserve spatial information. Conversely, spatial transcriptomics assays can profile spatial regions in tissue sections but do not have single-cell resolution. Here, Runmin Wei (Siyuan He, Shanshan Bai, Emi Sei, Min Hu, Alastair Thompson, Ken Chen, Savitri Krishnamurthy & Nicholas E. Navin) developed a computational method called CellTrek that combines these two datasets to achieve single-cell spatial mapping through coembedding and metric learning approaches. They benchmarked CellTrek using simulation and in situ hybridization datasets, which demonstrated its accuracy and robustness. They then applied CellTrek to existing mouse brain and kidney datasets and showed that CellTrek can detect topological patterns of different cell types and cell states. They performed single-cell RNA sequencing and spatial transcriptomics experiments on two ductal carcinoma in situ tissues and applied CellTrek to identify tumor subclones that were restricted to different ducts, and specific T-cell states adjacent to the tumor areas.