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Cell-Cell communication

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SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies
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BioTuring

Spatial transcriptomic studies are becoming increasingly common and large, posing important statistical and computational challenges for many analytic tasks. Here, we present SPARK-X, a non-parametric method for rapid and effective detection of spatially expressed genes in large spatial transcriptomic studies. SPARK-X not only produces effective type I error control and high power but also brings orders of magnitude computational savings. We apply SPARK-X to analyze three large datasets, one of which is only analyzable by SPARK-X. In these data, SPARK-X identifies many spatially expressed genes including those that are spatially expressed within the same cell type, revealing new biological insights.
Only CPU
SPARK-X
Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram
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BioTuring

Charting an organs’ biological atlas requires us to spatially resolve the entire single-cell transcriptome, and to relate such cellular features to the anatomical scale. Single-cell and single-nucleus RNA-seq (sc/snRNA-seq) can profile cells comprehensively, but lose spatial information. Spatial transcriptomics allows for spatial measurements, but at lower resolution and with limited sensitivity. Targeted in situ technologies solve both issues, but are limited in gene throughput. To overcome these limitations we present Tangram, a method that aligns sc/snRNA-seq data to various forms of spatial data collected from the same region, including MERFISH, STARmap, smFISH, Spatial Transcriptomics (Visium) and histological images. **Tangram** can map any type of sc/snRNA-seq data, including multimodal data such as those from SHARE-seq, which we used to reveal spatial patterns of chromatin accessibility. We demonstrate Tangram on healthy mouse brain tissue, by reconstructing a genome-wide anatomically integrated spatial map at single-cell resolution of the visual and somatomotor areas.
Required GPU
Tangram
CellRank2: Unified fate mapping in multiview single-cell data
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BioTuring

CellRank2 (Weiler et al, 2023) is a powerful framework for studying cellular fate using single-cell RNA sequencing data. It can handle millions of cells and different data types efficiently. This tool can identify cell fate and probabilities across various data sets. It also allows for analyzing transitions over time and uncovering key genes in developmental processes. Additionally, CellRank2 estimates cell-specific transcription and degradation rates, aiding in understanding differentiation trajectories and regulatory mechanisms. In this notebook, we will use a primary tumor sample of patient T71 from the dataset GSE137804 (Dong R. et al, 2020) as an example. We have performed RNA-velocity analysis and pseudotime calculation on this dataset in scVelo (Bergen et al, 2020) notebook. The output will be then loaded into this CellRank2 notebook for further analysis. This notebook is based on the tutorial provided on CellRank2 documentation. We have modified the notebook and changed the input data to show how the tool works on BioTuring's platform.
Only CPU
CellRank
Inference and analysis of cell-cell communication using CellChat
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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

Trends

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(More)
Only CPU
CellChat
scGPT: Towards Building a Foundational Model for Single-Cell Multi-omics Using Generative AI

BioTuring

Generative pre-trained models have demonstrated exceptional success in various fields, including natural language processing and computer vision. In line with this progress, scGPT has been developed as a foundational model tailored specifically for t(More)
Required GPU
scgpt
Seurat
CopyKAT: Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes

BioTuring

Classification of tumor and normal cells in the tumor microenvironment from scRNA-seq data is an ongoing challenge in human cancer study. Copy number karyotyping of aneuploid tumors (***copyKAT***) (Gao, Ruli, et al., 2021) is a method proposed f(More)
Geneformer: a deep learning model for exploring gene networks

BioTuring

Geneformer is a foundation transformer model pretrained on a large-scale corpus of ~30 million single cell transcriptomes to enable context-aware predictions in settings with limited data in network biology. Here, we will demonstrate a basic workflow(More)
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 cofactor(More)
Required GPU
CellChat
BioTuring Data Converter: Seurat <=> Scanpy for single-cell data transcriptomic and spatial transcriptomics

BioTuring

This notebook illustrates how to convert data from a Seurat object into a Scanpy annotation data and a Scanpy annotation data into a Seurat object using the BioStudio data transformation library (currently under development). It facilitates continued(More)
Monorail-pipeline and Recount3

BioTuring

Monorail can be used to process local and/or private data, allowing results to be directly compared to any study in recount3. Taken together, Monorail-pipeline tools help biologists maximize the utility of publicly available RNA-seq data, especially (More)
Only CPU
recount3
Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram

BioTuring

Charting an organs’ biological atlas requires us to spatially resolve the entire single-cell transcriptome, and to relate such cellular features to the anatomical scale. Single-cell and single-nucleus RNA-seq (sc/snRNA-seq) can profile cells compre(More)
Required GPU
Tangram
Scanpy is a scalable toolkit for analyzing single-cell gene expression data built jointly with anndata.

BioTuring

SCANPY integrates the analysis possibilities of established R-based frameworks and provides them in a scalable and modular form. Specifically, SCANPY provides preprocessing comparable to SEURAT and CELL RANGER, visualization through TSNE, graph-d(More)
Only CPU
Scanpy
WGCNA: an R package for Weighted Gene Correlation Network Analysis

BioTuring

WGCNA: an R package for Weighted Gene Correlation Network Analysis Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing (More)
Only CPU
WGCNA
FunPat: Function-based Pattern analysis on RNA-seq time series data

BioTuring

Dynamic expression data, nowadays obtained using high-throughput RNA sequencing (RNA-seq), are essential to monitor transient gene expression changes and to study the dynamics of their transcriptional activity in the cell or response to stimuli. FunP(More)
Only CPU
FunPat
Monocle3 - An analysis toolkit for single-cell RNA-seq

BioTuring

Build single-cell trajectories with the software that introduced **pseudotime**. Find out about cell fate decisions and the genes regulated as they're made. Group and classify your cells based on gene expression. Identify new cell types and states a(More)
COMMOT: Screening cell-cell communication in spatial transcriptomics via collective optimal transport

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 sp(More)
Only CPU
COMMOT
MuSiC: Multi-subject Single-cell Deconvolution

BioTuring

Knowledge of cell type composition in disease relevant tissues is an important step towards the identification of cellular targets of disease. MuSiC is a method that utilizes cell-type specific gene expression from single-cell RNA sequencing (RNA-seq(More)
Only CPU
MuSiC
DWLS: Gene Expression Deconvolution Using Dampened Weighted Least Squares

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 ar(More)
Only CPU
DWLS
Notebooks
Only CPU
CellChat
Required GPU
scgpt
Seurat
Required GPU
CellChat
Only CPU
recount3
Required GPU
Tangram
Only CPU
Scanpy
Only CPU
WGCNA
Only CPU
FunPat
Only CPU
COMMOT
Only CPU
MuSiC
Only CPU
DWLS
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