Welcome to CDR-g’s documentation!

CDR-g (CDR-genomics) is a tool for extracting condition-dependent sources of variation from single cell data.

CDR-g extends the CDR algorithm 1 initially proposed by Dr Leonardo Portes dos Santos and Professor Michael Small to large single cell datasets.

The source code for this package is available on github.

Motivation

In complex single-cell RNA-sequencing experiments, a crucial analysis is to identify conditional sources of transcriptional variation. Differential expression and differential co-expression are well established strategies for analysing RNA seq data. However, the challenge in these types of analyses in single cell datasets is that:

• each condition may be composed of multiple “subconditions”, which escape detection

• variation may be shared between conditions.

CDR-g overcomes these limitations, allowing sensitive, condition-dependent variation to be performed in an unsupervised manner within a single analysis.

Contents:

• Installation
  • PyPI
  • Docker
• Algorithm description
• Usage
  • Overview
  • Example (snakemake) workflows
  • Walkthrough analysis
• API
  • Cell filtering
  • CDR algorithm
  • Results and output

Examples:

• Monocyte dataset
  • Introduction
  • Data pre-processing
  • Running a CDR-g analysis
  • Running gene set enrichment
  • Comparing gene set activation between conditions
  • Viewing and saving results

Reference

1

Portes LL, Small M. Navigating differential structures in complex networks. Phys Rev E. 2020 Dec;102(6-1):062301. doi: 10.1103/PhysRevE.102.062301. PMID: 33466036.

Indices and tables

• Index

• Module Index

• Search Page