🔬 Benchmark Spatial Transcriptomics data analysis with optimized pipelines using Seurat and Giotto for reproducible results and biological insights.

scplotter is an R package that is built upon plotthis. It provides a set of functions to visualize single-cell sequencing data in an easy and efficient way.

JCAP CRISPR Mixscape Pipeline is a user-friendly R Shiny application for interactive single-cell CRISPR screen analysis. It enables rapid quality control, visualization, and differential expression discovery using Mixscape and Seurat, all in a point-and-click environment. Ideal for researchers working with Perturb-seq data.

R package with collection of functions created and/or curated to aid in the visualization and analysis of single-cell data using R.

Pipeline to process single cell transcriptomic data from raw count to final cell annotation.

Single-cell transcriptomic profiling of TNBC tumor microenvironment using R (Seurat) to map immune heterogeneity and resistance drivers.

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

Various utility functions for Seurat v5 single-cell analysis

An R/Seurat workflow for analyzing mouse-brain spatial transcriptomics data, including QC/normalization, spatially variable genes, and spatial visualization.

An R/Seurat single-cell RNA-seq analysis focused on GBM expression matrices, covering preprocessing, QC, dimensionality reduction, clustering, and differential expression.

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.

🏆 #1 Multi-LLM consensus framework | 550+ stars | 95% accuracy | 10+ LLM providers | Leading cell annotation tool

Generate high quality, publication ready visualizations for single cell transcriptomics data.

An R package providing tools for single-cell RNA-seq data analysis, enhancing existing methods with helper functions.

Transcriptomic profiling of E. coli biofilm formation using a pseudo-bulk RNA-seq pipeline. This project utilizes Salmon for quantification and Seurat (R) for PCA clustering and differential expression analysis to identify markers distinguishing Biofilm from Planktonic states.

Interactive CV: pawnchessmon.github.io/Introduction/

This project analyzes single-cell RNA-seq data from the Rh41 cancer cell line using Seurat, including QC, normalization, PCA, clustering, UMAP, and marker discovery.

Seurat 官网示例

R package developed for single-cell RNA-seq analysis. It was designed using the Seurat framework, and offers existing and novel single-cell analytic work flows.

An end-to-end Single-Cell Pipeline designed to facilitate comprehensive analysis and exploration of single-cell data.