Extra Resources

Getting help

Aside from this course there are a number of forums where you can ask search past questions and ask for help.

Reproducible examples

On forums you won’t be sharing your screen to share your problem! Good questions with minimal reproducible examples are most likely to get good answers. See the reprex package to enable you do this easily.

Cancer dataset analysis

Cancer single cell sequencing datasets may have a mixture of healthy and cancer cells. There are specialised tools for this sort of data:

  • Infercnv infers copy number variants based on gene expression -

  • scATOMIC uses a large dataset of cancer cell line scRNA-seq along with a hierarchal cell annotation model to identify normal cell types and cancer cell types

Trajectory analysis

Trajectory analysis helps map how individual cells change and develop over time e.g cell differentiation.

Monocle3 package is useful for constructing single-cell trajectories.

Multimodal data integration

In multimodal data, multiple omic measurements are taken within the same cell.

scATAC-seq

CITE-seq

Seurat vignette

Spatial integration

Seurat can also be used to analyse 10X Visium spatial transcriptomics data and scRNA-seq data can be projected onto this spatial data. There are a good set of tutorials here:

Seurat vignette

Ligand-receptor

It is useful to infer signalling that might be occurring between cell types in a sample

  • CellPhoneDB Python based ligand-receptor integration tool -

  • CellChat R based tool for cell-cell communication inference

  • NicheNet R based tool for identification of ligands driving observed gene expression changes between experimental conditions

Other courses