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E-spatial

Single-cell spatial explorer

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Cell2location: Comprehensive mapping of tissue cell architecture via integrated single cell and spatial transcriptomic
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BioTuring

Cell2location is a principled Bayesian model that can resolve fine-grained cell types in spatial transcriptomic data and create comprehensive cellular maps of diverse tissues. Cell2location accounts for technical sources of variation and borrows statistical strength across locations, thereby enabling the integration of single cell and spatial transcriptomics with higher sensitivity and resolution than existing tools. This is achieved by estimating which combination of cell types in which cell abundance could have given the mRNA counts in the spatial data, while modelling technical effects (platform/technology effect, contaminating RNA, unexplained variance). This tutorial shows how to use cell2location method for spatially resolving fine-grained cell types by integrating 10X Visium data with scRNA-seq reference of cell types. Cell2location is a principled Bayesian model that estimates which combination of cell types in which cell abundance could have given the mRNA counts in the spatial data, while modelling technical effects (platform/technology effect, contaminating RNA, unexplained variance).
Required GPU
Cell2Location
expiMap: Biologically informed deep learning to query gene programs in single-cell atlases
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BioTuring

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).
Required GPU
expiMap
iBRIDGE: A Data Integration Method to Identify Inflamed Tumors from Single-Cell RNAseq Data and Differentiate Cell Type-Specific Markers of Immune-Cell Infiltration
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BioTuring

The development of immune checkpoint-based immunotherapies has been a major advancement in the treatment of cancer, with a subset of patients exhibiting durable clinical responses. A predictive biomarker for immunotherapy response is the pre-existing T-cell infiltration in the tumor immune microenvironment (TIME). Bulk transcriptomics-based approaches can quantify the degree of T-cell infiltration using deconvolution methods and identify additional markers of inflamed/cold cancers at the bulk level. However, bulk techniques are unable to identify biomarkers of individual cell types. Although single-cell RNA sequencing (scRNAseq) assays are now being used to profile the TIME, to our knowledge there is no method of identifying patients with a T-cell inflamed TIME from scRNAseq data. Here, we describe a method, iBRIDGE, which integrates reference bulk RNAseq data with the malignant subset of scRNAseq datasets to identify patients with a T-cell inflamed TIME. Utilizing two datasets with matched bulk data, we show iBRIDGE results correlated highly with bulk assessments (0.85 and 0.9 correlation coefficients). Using iBRIDGE, we identified markers of inflamed phenotypes in malignant cells, myeloid cells, and fibroblasts, establishing type I and type II interferon pathways as dominant signals, especially in malignant and myeloid cells, and finding the TGFβ-driven mesenchymal phenotype not only in fibroblasts but also in malignant cells. Besides relative classification, per-patient average iBRIDGE scores and independent RNAScope quantifications were utilized for threshold-based absolute classification. Moreover, iBRIDGE can be applied to in vitro grown cancer cell lines and can identify the cell lines that are adapted from inflamed/cold patient tumors.
Only CPU
iBRIDGE
ADImpute: Adaptive Dropout Imputer
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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.
Only CPU
ADImpute

Trends

Inference and analysis of cell-cell communication using CellChat

BioTuring

Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We construct a database of interactions among ligands, receptors and their cofactors that accurately represent known heteromeric molecular complexes. We then develop **CellChat**, a tool that is able to quantitatively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applying **CellChat** to mouse and human skin datasets shows its ability to extract complex signaling patterns.
Required GPU
CellChat
Bioturing Massive-scale Analysis Solution for CellChat: Running analysis for massive-scale data from Seurat dataset

BioTuring

This tool provides a user-friendly and automated way to analyze large-scale single-cell RNA-seq datasets stored in RDS (Seurat) format. It allows users to run various analysis tools on their data in one command, streamlining the analysis workflow and saving time. Note that this notebook is only for the demonstration of the tool. Users can run the tool directly through the command line. Currently, we support: - CellChat - Inference and analysis of cell-cell communication using CellChat
Only CPU
CellChat