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PopV: the variety of cell-type transfer tools for classify cell-types
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BioTuring

PopV uses popular vote of a variety of cell-type transfer tools to classify cell-types in a query dataset based on a test dataset. Using this variety of algorithms, they compute the agreement between those algorithms and use this agreement to predict which cell-types have a high likelihood of the same cell-types observed in the reference.
Required GPU
Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics
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BioTuring

Single-cell RNA sequencing (scRNA-seq) data have allowed us to investigate cellular heterogeneity and the kinetics of a biological process. Some studies need to understand how cells change state, and corresponding genes during the process, but it is challenging to track the cell development in scRNA-seq protocols. Therefore, a variety of statistical and computational methods have been proposed for lineage inference (or pseudotemporal ordering) to reconstruct the states of cells according to the developmental process from the measured snapshot data. Specifically, lineage refers to an ordered transition of cellular states, where individual cells represent points along. pseudotime is a one-dimensional variable representing each cell’s transcriptional progression toward the terminal state. Slingshot which is one of the methods suggested for lineage reconstruction and pseudotime inference from single-cell gene expression data. In this notebook, we will illustrate an example workflow for cell lineage and pseudotime inference using Slingshot. The notebook is inspired by Slingshot's vignette and modified to demonstrate how the tool works on BioTuring's platform.
Only CPU
slingshot
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
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

Trends

Evaluating Performance on Single-Cell Datasets using BioTuring Alpha, Scanpy and Seurat

BioTuring

Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of gene expression at the individual cell level, enabling researchers to uncover heterogeneity and dynamics within complex cellular populations. To analyze and interpret scRNA-seq da(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)
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
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)
Only CPU
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)
Only CPU
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)
Only CPU
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)
Only CPU
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)
Only CPU
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)
Only CPU
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)
Only CPU
SoupX
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