Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data

marker-based purification of cell types from single-cell RNA-seq datasets

Multi-agent LLM driven cell type annotation for single-cell RNA-Seq data

Infer cell types in scRNA-seq data using bulk RNA-seq or gene sets

A machine learning method for the discovery of the minimum marker gene combinations for cell type identification from single-cell RNA sequencing

Accurate and fast cell marker gene identification with COSG

Accurate and fast cell marker gene identification with COSG

Multi-agent LLM driven cell type annotation for single-cell RNA-Seq data

Marker gene profile estimation method used in NeuroExpresso manuscript

A tool for identifying phylogenetic marker genes in Nucleocytoviricota (giant viruses) and generating concatenated alignments.

Processing NEON soil microbe marker gene sequence data into ASV tables.

PIASO: Precise Integrative Analysis of Single-cell Omics

Interactive visualization of marker genes and clustering in Slide-seq and single cell RNAseq data.

MiCV is a python dash-based web-application that enables researchers to upload raw scRNA-seq data and perform filtering, analysis, and manual annotation.

A hybrid approach to find spatially relevant marker genes in image based spatial transcriptomics data

The goal of LRcell is to identify specific sub-cell types that drives the changes observed in a bulk RNA-seq differential gene expression experiment. To achieve this, LRcell utilizes sets of cell marker genes acquired from single-cell RNA-sequencing (scRNA-seq) as indicators for various cell types in the tissue of interest. Next, for each cell t…

Compare several feature selection methods in scRNA-seq analysis

scCTS: identifying the cell type-specific marker genes from population-level single-cell RNA-seq

Data-centric marker distillation for zero-shot cell-type and spatial annotation with LLMs.