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

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

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

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.

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.

PIASO: Precise Integrative Analysis of Single-cell Omics

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

Compare several feature selection methods in scRNA-seq analysis

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

No description provided.

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 type, using its marker genes as indicators, we apply Logistic Regression on the complete set of genes with differential expression p-values to calculate a cell-type significance p-value. Finally, these p-values are compared to predict which one(s) are likely to be responsible for the differential gene expression pattern observed in the bulk RNA-seq experiments.

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

A library for differential expression and gene set enrichment analysis based on hydra and pydantic.

Software suite for marker gene identification and cell type integration from single cell RNA-sequencing data

A curated list of tools and methods for scRNA-seq cell type annotation

Permutation testing in single cell analysis

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.

A complete single-cell RNA-seq analysis pipeline using Scanpy on 10x Genomics PBMC data, including clustering, UMAP visualization, and marker gene detection.

MICTI- Marker gene Identification for Cell Type Identity

Unsupervised clustering of CD4+ T cells from scRNA-seq data using Louvain algorithm and marker gene analysis for subpopulation characterization.

Single-cell RNA-seq analysis of NSCLC tumor cells using Seurat to identify cellular heterogeneity and cluster-specific marker genes. Includes preprocessing, dimensionality reduction, clustering, and visualization.

neonMicrobe: Processing NEON soil microbe marker gene sequence data into ASV tables, duplicated from https://github.com/claraqin/neonMicrobe