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Spatial charting of single-cell transcriptomes in tissues - celltrek
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BioTuring

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.
Only CPU
CellTrek
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.
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SPARK-X
CopyKAT: Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes
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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 for identifying copy number variations in single-cell transcriptomics data. It is used to predict aneuploid tumor cells and delineate the clonal substructure of different subpopulations that coexist within the tumor mass. In this notebook, we will illustrate a basic workflow of CopyKAT based on the tutorial provided on CopyKAT's repository. We will use a dataset of triple negative cancer tumors sequenced by 10X Chromium 3'-scRNAseq (GSM4476486) as an example. The dataset contains 20,990 features across 1,097 cells. We have modified the notebook to demonstrate how the tool works on BioTuring's platform.
CS-CORE: Cell-type-specific co-expression inference from single cell RNA-sequencing data
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BioTuring

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.
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CS-CORE

Trends

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)
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
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)
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COMMOT
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
Bioalpha-Biocolab: Enabling Large-Scale Data Uploads for BBrowserX single-cell analysis platform

BioTuring

Single-cell data analysis is revolutionizing biological research, but often these dataset sizes can be massive and pose challenges for submission process. Bioalpha-Biocolab addresses this issue by implementing advanced algorithms and leveraging effic(More)
Required GPU
AlphaSC
pySCENIC: Single-Cell rEgulatory Network Inference and Clustering

BioTuring

SCENIC Suite is a set of tools to study and decipher gene regulation. Its core is based on SCENIC (Single-Cell Regulatory Network Inference and Clustering) which enables you to infer transcription factors, gene regulatory networks and cell types from(More)
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pySCENIC
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)
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CellChat
SCEVAN: Single CEll Variational ANeuploidy analysis

BioTuring

In the realm of cancer research, grasping the intricacies of intratumor heterogeneity and its interplay with the immune system is paramount for deciphering treatment resistance and tumor progression. While single-cell RNA sequencing unveils diverse t(More)
Required GPU
scevan
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)
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recount3
NicheNet: modeling intercellular communication by linking ligands to target genes

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(More)
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nichenetr
Multimodal single-cell chromatin analysis with Signac

BioTuring

The recent development of experimental methods for measuring chromatin state at single-cell resolution has created a need for computational tools capable of analyzing these datasets. Here we developed Signac, a framework for the analysis of single-ce(More)
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Required PFP
signac
Spatially informed cell-type deconvolution for spatial transcriptomics - CARD

BioTuring

Many spatially resolved transcriptomic technologies do not have single-cell resolution but measure the average gene expression for each spot from a mixture of cells of potentially heterogeneous cell types. Here, we introduce a deconvolution metho(More)
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card
SpaCET: Cell type deconvolution and interaction analysis

BioTuring

Spatial transcriptomics (ST) technology has allowed to capture of topographical gene expression profiling of tumor tissues, but single-cell resolution is potentially lost. Identifying cell identities in ST datasets from tumors or other samples remain(More)
SoupX: removing ambient RNA contamination from droplet-based single-cell RNA sequencing data

BioTuring

Droplet-based single-cell RNA sequence analyses assume that all acquired RNAs are endogenous to cells. However, there is a certain amount of cell-free mRNAs floating in the input solution (referred to as 'the soup'), created from cells in the input s(More)
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SoupX
MUON: multimodal omics analysis framework

BioTuring

Advances in multi-omics have led to an explosion of multimodal datasets to address questions from basic biology to translation. While these data provide novel opportunities for discovery, they also pose management and analysis challenges, thus motiva(More)
Required GPU
muon
Notebooks
Required GPU
CellChat
Only CPU
COMMOT
Required GPU
scgpt
Seurat
Required GPU
AlphaSC
Only CPU
pySCENIC
Only CPU
CellChat
Required GPU
scevan
Only CPU
recount3
Only CPU
nichenetr
Required PFP
Only CPU
signac
Only CPU
card
Only CPU
SoupX
Required GPU
muon