Cell type annotation for single-cell RNA-seq using multi-LLM consensus
A Multi-Modal AI Copilot for Single-Cell Analysis with Instruction Following
Unbiased single-cell transcriptomic data cell type identification
OTMODE: An Optimal Transport-Based Framework for Differential Feature Identification in Single-Cell Multi-Omics
graph-based cell type annotation toolkit for single-cell RNA-seq, ATAC-seq, and spatial omics
The official implementation for "SANGO".
cRegulon is an optimization model to identify combinatorial regulon from single cell expression and chromatin accessibility data.
Tutorial and learning materials for CyteTypeR, an R package for automated cell type annotation of scRNA-seq data using the CyteType API.
Data-centric marker distillation for zero-shot cell-type and spatial annotation with LLMs.
A curated list of tools and methods for scRNA-seq cell type annotation
cRegulon is an optimization model to identify combinatorial regulon from single cell expression and chromatin accessibility data.
Single-cell RNA-seq figure replication project demonstrating clustering, dimensionality reduction, and cell-type annotation using Scanpy in Python.
This repository contains an analysis pipeline for processing and visualizing single-cell RNA sequencing (scRNA-seq) data using the Seurat package in R. The dataset used is the Peripheral Blood Mononuclear Cells (PBMC) 3K dataset from 10X Genomics.
Python wrapper for Azimuth single-cell reference mapping: annotate cell types from AnnData using rpy2
Hierarchical and high-resolution cell-type identification for single-cell RNA-seq data based on ScType.
End-to-end scRNA-seq pipeline mapping 10 cardiac cell types across 11,046 developing heart cells using Scanpy with a cloud-native Nextflow + AWS Batch architecture for scalable deployment.
scRNA-seq-navigator is a post-alignment pipeline that utilizes Scanpy for data processing and scANVI and CellTypist for cell type annotations.
A comprehensive R/Seurat single-cell RNA-seq pipeline covering QC filtering, normalization, clustering, doublet detection, batch correction, cell-type annotation, differential expression, enrichment, and trajectory analysis.
Automated scRNA-seq cell annotation via local LLM
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