rli012/SingleCell

SingleCell

CITE-seq

Tapestri (mission bio)

Spatial transcriptomics

Courses/Tutorials

1. Broad Institute: Analysis of Single Cell RNAseq Data
2. Hemberg Lab: Analysis of Single Cell RNAseq Data
3. Bioconductor: Orchestrating Single-Cell Analysis with Bioconductor
4. ISMB: SingleCellTranscriptomeTutorial
5. ontoProc: Ontology interfaces for Bioconductor, with focus on cell type identification
6. Current best practices in single‐cell RNA‐seq analysis: a tutorial; https://github.com/theislab/single-cell-tutorial

Tools

1. Seurat
2. Scanpy

Trajectory inference

1. dynverse: https://benchmark.dynverse.org/

Cell Type Annotation

1. SingleR (R pacakge)
2. cellassign
3. celaref
4. Moana (Python package, for PBMC only)
5. CellO: Cell Ontology-based classification (Python package)
6. scMatch (Python package)
7. Cell Blast
8. Garnett

Databases

1. Human Cell Atlas Data Portal

2. Single Cell Portal

3. Human Tumor Atlas Network

4. Human Liver Cell Atlas

5. KIT Kidney Interactive Transcriptomics

6. Cell Markers

7. CellMarker

8. PanglaoDB

9. Human Cell Landscape

Literatures

Trajectory Inference

1. A comparison of single-cell trajectory inference methods. 2019. Nature Biotechnology

Cell Type Annotation

1. Hierarchical cell type classification using mass, heterogeneous RNA-seq data from human primary cells

2. Supervised classification enables rapid annotation of cell atlases

3. Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling

Reviews/Method Evaluation

1. Bias, robustness and scalability in single-cell differential expression analysis

2. Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data

3. Single-Cell RNA-Seq Technologies and Related Computational Data Analysis

4. Supervised clustering for single-cell analysis

5. A comparison of automatic cell identification methods for single-cell RNA sequencing data

6. Mapping human cell phenotypes to genotypes with single-cell genomics