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
Quantifying experimental perturbations at single cell resolution
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.
Coarse-graining of large single-cell RNA-seq data into metacells
Harmony framework for connecting scRNA-seq data from discrete time points
Explore and share your scRNAseq clustering results
Data-driven Network-based Bayesian Inference of Drivers
A deep learning-based tool for alignment and integration of single cell genomic data across multiple datasets, species, conditions, batches
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.
Statistical quality evaluation of dimensionality reduction algorithms
An unofficial demultiplexing strategy for SPLiT-seq RNA-Seq data
Single cell type annotation guided by cell atlases, with freedom to be queer
R package - Analysis of Single Cell Expression, Normalisation and Differential expression (ascend)
Recurrent Variational Auto gene encoder
Cell type matching in single-cell RNA-sequencing data using FR-Match
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