Analysis of single cell RNA-seq data course
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Updated
Apr 11, 2022 - TeX
Analysis of single cell RNA-seq data course
An end-to-end Single-Cell Pipeline designed to facilitate comprehensive analysis and exploration of single-cell data.
Papers with code for single cell related papers
Single cell trajectory detection
Clustering scRNAseq by genotypes
scGNN (single cell graph neural networks) for single cell clustering and imputation using graph neural networks
R package that automatically classifies the cells in the scRNA data by segregating non-malignant cells of tumor microenviroment from the malignant cells. It also infers the copy number profile of malignant cells, identifies subclonal structures and analyses the specific and shared alterations of each subpopulation.
BITFAM is a Bayesian approach and platform to infer transcription factor activities within individual cells using single cell RNA-sequencing data. Please see Gao S et al., Genome Research (2021) https://genome.cshlp.org/content/31/7/1296 for details.
A deep learning-based tool for alignment and integration of single cell genomic data across multiple datasets, species, conditions, batches
Harmony framework for connecting scRNA-seq data from discrete time points
Coarse-graining of large single-cell RNA-seq data into metacells
Data-driven Network-based Bayesian Inference of Drivers
Quantifying experimental perturbations at single cell resolution
Explore and share your scRNAseq clustering results
An unofficial demultiplexing strategy for SPLiT-seq RNA-Seq data
R package - Analysis of Single Cell Expression, Normalisation and Differential expression (ascend)
The following repository contains code for all scRNAseq analysis and visualization performed in the paper: Single cell resolution analysis of the human pancreatic ductal progenitor cell niche
Granular Functional Filtering (Gruffi) to isolate stressed cells
Single cell type annotation guided by cell atlases, with freedom to be queer
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