E-spatial

Recent technological advancements have enabled spatially resolved transcriptomic profiling but at multi-cellular pixel resolution, thereby hindering the identification of cell-type-specific spatial patterns and gene expression variation. To address this challenge, we develop STdeconvolve as a reference-free approach to deconvolve underlying cell types comprising such multi-cellular pixel resolution spatial transcriptomics (ST) datasets. Using simulated as well as real ST datasets from diverse spatial transcriptomics technologies comprising a variety of spatial resolutions such as Spatial Transcriptomics, 10X Visium, DBiT-seq, and Slide-seq, we show that STdeconvolve can effectively recover cell-type transcriptional profiles and their proportional representation within pixels without reliance on external single-cell transcriptomics references. **STdeconvolve** provides comparable performance to existing reference-based methods when suitable single-cell references are available, as well as potentially superior performance when suitable single-cell references are not available. STdeconvolve is available as an open-source R software package with the source code available at https://github.com/JEFworks-Lab/STdeconvolve .

The development of large-scale single-cell atlases has allowed describing cell states in a more detailed manner. Meanwhile, current deep leanring methods enable rapid analysis of newly generated query datasets by mapping them into reference atlases. expiMap (‘explainable programmable mapper’) Lotfollahi, Mohammad, et al. is one of the methods proposed for single-cell reference mapping. Furthermore, it incorporates prior knowledge from gene sets databases or users to analyze query data in the context of known gene programs (GPs).

Single-cell RNA data allows cell-cell communications (***CCC***) methods to infer CCC at either the individual cell or cell cluster/cell type level, but physical distances between cells are not preserved Almet, Axel A., et al., (2021). On the other hand, spatial data provides spatial distances between cells, but single-cell or gene resolution is potentially lost. Therefore, integrating two types of data in a proper manner can complement their strengths and limitations, from that improve CCC analysis. In this pipeline, we analyze CCC on Visium data with single-cell data as a reference. The pipeline includes 4 sub-notebooks as following 01-deconvolution: This step involves deconvolution and cell type annotation for Visium data, with cell type information obtained from a relevant single-cell dataset. The deconvolution method is SpatialDWLS which is integrated in Giotto package. 02-giotto: performs spatial based CCC and expression based CCC on Visium data using Giotto method. 03-nichenet: performs spatial based CCC and expression based CCC on Visium data using NicheNet method. 04-visualization: visualizes CCC results obtained from Giotto and NicheNet.

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.