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

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

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

Predict the celltype of a cluster from Seurat FindMarker data

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

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

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

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

CRAFT: Cellular Reprogramming Analysis with Integrated Framework and Mechanistic Insight

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

Performing scRNA-Seq analysis using OpenAI API calls

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

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

Compare several feature selection methods in scRNA-seq analysis

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

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

Accurate and fast cell marker gene identification with COSG

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…

Permutation testing in single cell analysis