-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathextra.qmd
68 lines (38 loc) · 2.91 KB
/
extra.qmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
---
title: "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.
::: callout-tip
### 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`](https://reprex.tidyverse.org/) package to enable you do this easily.
:::
- [Biostars](https://www.biostars.org/)
- [Bioinformatics StackExchange](https://bioinformatics.stackexchange.com/)
- [Bioconductor Forums](https://support.bioconductor.org/)
## 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](https://github.com/broadinstitute/infercnv) infers copy number variants based on gene expression -
- [scATOMIC](https://github.com/abelson-lab/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](https://cole-trapnell-lab.github.io/monocle3/docs/trajectories/) 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
- [ArchR paper](https://www.nature.com/articles/s41588-021-00790-6)
- [ArchR tutorial](https://www.archrproject.com/bookdown/index.html)
- [ArchR notebook](https://github.com/galib36/NorthernBUG13_multiome_workshop/blob/main/Northern_BUG_workshop.ipynb)
### CITE-seq
[Seurat vignette](https://satijalab.org/seurat/articles/multimodal_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](https://satijalab.org/seurat/articles/spatial_vignette)
### Ligand-receptor
It is useful to infer signalling that might be occurring between cell types in a sample
- [CellPhoneDB](https://cellphonedb.readthedocs.io/en/latest) Python based ligand-receptor integration tool -
- [CellChat](https://github.com/sqjin/CellChat) R based tool for cell-cell communication inference
- [NicheNet](https://github.com/saeyslab/nichenetr) R based tool for identification of ligands driving observed gene expression changes between experimental conditions
## Other courses
- [Orchestrating Single-Cell Analysis with Bioconductor](https://bioconductor.org/books/release/OSCA/) Book using SingleCellExperiment based approaches as alternative to `Seruat`.
- ["Best practices single cell"](https://www.sc-best-practices.org/) Python based guide to single cell sequencing best practices