Single Cell Rna Seq
Showing 13 of 13 projects
A Python library for deep probabilistic modeling and analysis of single-cell and spatial omics data.
A comprehensive collection of notes, tutorials, and resources for RNA-seq data analysis, covering alignment, quantification, differential expression, and more.
Fast, sensitive, and accurate integration of single-cell RNA-seq data across multiple datasets, batches, or experimental conditions.
A scalable Python toolkit for analyzing and visualizing spatial molecular data from tissue sections.
An R package that predicts doublets (multiple cells mistaken as one) in single-cell RNA sequencing data using artificial nearest neighbor analysis.
A scalable Python toolkit for RNA velocity analysis in single cells using dynamical modeling.
An automated cell type annotation tool for single-cell RNA-seq data using logistic regression classifiers.
An R package to infer gene regulatory networks and identify cell types from single-cell RNA-seq data.
A geometric deep learning model that predicts transcriptional outcomes of single and multi-gene perturbations from single-cell RNA-seq data.
A BERT-based foundation model pretrained on large-scale scRNA-seq data for automated cell type annotation in single-cell analysis.
A zero-shot foundation model for generating universal embeddings from single-cell gene expression data.
A large transformer foundation model for single-cell RNA sequencing data analysis, including gene network inference, denoising, and cell annotation.
A pre-trained language model for single-cell RNA sequencing data that encodes cell-cell relations and accelerates inference for downstream tasks.
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