E-spatial

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E-spatial

Single-cell spatial explorer

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Harmony: fast, sensitive, and accurate integration of single cell data
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BioTuring

Single-cell RNA-seq datasets in diverse biological and clinical conditions provide great opportunities for the full transcriptional characterization of cell types. However, the integration of these datasets is challeging as they remain biological and techinical differences. **Harmony** is an algorithm allowing fast, sensitive and accurate single-cell data integration.
Only CPU
harmonpy
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.
Only CPU
CS-CORE
Geneformer: a deep learning model for exploring gene networks
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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 to work with ***Geneformer*** models. These notebooks include the instruction to: 1. Prepare input datasets 2. Finetune Geneformer model to perform specific task 3. Using finetuning models for cell classification and gene classification application
DoubletFinder: Doublet detection in single-cell RNA sequencing data using artificial nearest neighbors
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BioTuring

Single-cell RNA sequencing (scRNA-seq) data often encountered technical artifacts called "doublets" which are two cells that are sequenced under the same cellular barcode. Doublets formed from different cell types or states are called heterotypic and homotypic otherwise. These factors constrain cell throughput and may result in misleading biological interpretations. DoubletFinder (McGinnis, Murrow, and Gartner 2019) is one of the methods proposed for doublet detection. In this notebook, we will illustrate an example workflow of DoubletFinder. We use a 10x Genomics dataset which captures peripheral blood mononuclear cells (PBMCs) from a healthy donor stained with a panel of 31 TotalSeqâ„¢-B antibodies (BioLegend).

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scPRINT: Large Cell Model for scRNAseq data

BioTuring

scPRINT is a large transformer model built for the inference of gene networks (connections between genes explaining the cell's expression profile) from scRNAseq data. It uses novel encoding and decoding of the cell expression profile and new pre-training methodologies to learn a cell model. scPRINT can be used to perform the following analyses: - expression denoising: increase the resolution of your scRNAseq data - cell embedding: generate a low-dimensional representation of your dataset - label prediction: predict the cell type, disease, sequencer, sex, and ethnicity of your cells - gene network inference: generate a gene network from any cell or cell cluster in your scRNAseq dataset
Required GPU
scPrint