Collections of library structure and sequence of popular single cell genomic methods

SCENIC is an R package to infer Gene Regulatory Networks and cell types from single-cell RNA-seq data.

Spatial-eXpression-R: Cell type identification (including cell type mixtures) and cell type-specific differential expression for spatial transcriptomics

Cell types enrichment analysis

A Fully Bayesian Inference of Tumor Microenvironment composition and gene expression

This is the repository that contains the analysis of the lung adenocarcinoma single cell dataset

The bookdown version lives here:

A Systematic and Dynamic Pipeline for Single-Cell RNA Sequencing Analysis

Unified access to several second-generation deconvolution methods

Multiscale embedded gene co-expression network analysis

Mollecular Functional Portraits

BLADE: Bayesian Log-normAl DEconvolution for enhanced in silico microdissection of bulk gene expression data

Marker Selection by matching manifolds and elastic net

an integrative algorithm to distinguish spatially variable cell subclusters by reconstructing cells onto a pseudo space with spatial transcriptome references

Co-expression network management based on WGCNA + k-means

scAB detects multiresolution cell states with clinical significance

Trans-association between HLA and TCR-CDR3

Module 4 for HKU Single-cell Workshop

VAE methylation analysis using the Tybalt model developed by Way et al.

Infer and visualize cell-cell communication for single-cell RNA-seq data

基于Rasa构建QAbot