Resources.md

TABLE OF CONTENT

• Dataset information
• Analysis container
• Manuals on scRNASeq data analysis
• Information on tools and workflows
  • Scanpy ecosystem
  • Normalization
  • Feature selection
  • Batch correction
  • Pseudobulk approaches
  • Metacell approaches
• Useful Literature

Dataset information

• Paper Reference: Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution
• DOI: https://doi.org/10.1016/j.cell.2021.07.039
• Original Code - Resources: Dataset Interactive Viewer
• You can download the starting files from GEO entry GSE162170 selecting from the supplementary files section GSE162170_rna_counts.tsv and GSE162170_rna_cell_metadata.txt
• To generate the h5ad used as input for the first notebook, you can follow the workflow illustrated here

Analysis container

• To retrieve the singularity image used for the analyses of Day 1, you can use this singularity command that will pull the image from dockerhub : singularity pull docker://cerebrassu/downstream:cerebra-24.06.1

Manuals on scRNASeq data analysis

1. Orchestrating Single-Cell Analysis with Bioconductor

2. Single-cell best practices

Information on tools and workflows

Scanpy ecosystem

• Scanpy documentation
• Scanpy tutorials
• Scanpy repo

Normalization

• Scran paper
• Scran R vignette
• Scran tutorial in Scanpy
• Benchmarking of normalization methods

Feature selection

• Triku paper
• Triku documentation

Batch correction

• Harmony Quick Start
• Harmony implementation in scanpy
• Benchmarking of batch correction methods
• Benchmarking atlas-level data integration

PseudoBulk calculation

• Decoupler pseudobulk workflow
• Decoupler general
• Pertpy
• Confronting false discoveries in single-cell differential expression
• Benchmarking study

Metacell calculation

• SEACells paper
• SEACells GitHub
• SEACells workflow
• Metacells2 paper
• Metacells2 website
• Metacells2 documentation
• Review on metacell approaches, pros and cons

Useful literature

Dimensionality reduction and UMAP debate

• Basic guidelines for single cell analysis here you can find the first indications and guidelines (2019 is already old in the single cell field but the principles still holds) for single cell data visualization
• UMAP documentation UMAP documentation is a very rich resource to properly check how it works and how it should be used
• UMAP for single cell visualization Original article introducing UMAP to visualise single cell data
• UMAP criticisms link1 link2
• Recent benchmarck The last is the link to the specific recent benchmarck on dimensionality reduction, but this is part of a great resource for the field, across many different tasks for single cell analysis Preprint Repository has context menu
• Differential abundance analysis with Milo