---
title: "Day 2"
canonical: "https://help.biobam.com/space/BTP/3313467394/Day%202"
format: markdown
---
DataSet: Check for more datasets with more patients and at least two conditions, and also check that it shows trajectories. (Priyansh) 


Practical Session 3 - 1h (Angeles)


Reproduce initial analysis with R (Seurat) [https://satijalab.org/seurat/articles/pbmc3k_tutorial.html](https://satijalab.org/seurat/articles/pbmc3k_tutorial.html)  


Steps:

- Object creation (Seurat/SingleCellExperiment), metadata
- Cell and feature QC
- Normalization, feature selection
- Dimension reduction: PCA
- Non-linear dim. reduction for visualization: tSNE
- Cell clustering
  - Seurat: [https://hbctraining.github.io/scRNA-seq_online/lessons/07_SC_clustering_cells_SCT.html](https://hbctraining.github.io/scRNA-seq_online/lessons/07_SC_clustering_cells_SCT.html)
  - OSCA: [https://bioconductor.org/books/3.17/OSCA.basic/clustering.html](https://bioconductor.org/books/3.17/OSCA.basic/clustering.html)
- Clustering QC
  - Seurat: [https://hbctraining.github.io/scRNA-seq_online/lessons/08_SC_clustering_quality_control.html](https://hbctraining.github.io/scRNA-seq_online/lessons/08_SC_clustering_quality_control.html)



Practical Session 4 - 1:15h (Angeles)


Cell type-level analysis in R (Seurat, SingleR, miloR)


Steps:

- *De novo cell type annotation: cluster biomarkers and gene sets (AUCell) *[http://bioconductor.org/books/3.17/OSCA.basic/cell-type-annotation.html#cell-type-annotation](http://bioconductor.org/books/3.17/OSCA.basic/cell-type-annotation.html#cell-type-annotation) * *
- Reference-based cell type annotation (SingleR) [http://bioconductor.org/books/release/SingleRBook/](http://bioconductor.org/books/release/SingleRBook/)
- Differential abundance testing (milo) [https://bioconductor.org/packages/release/bioc/vignettes/miloR/inst/doc/milo_demo.html](https://bioconductor.org/packages/release/bioc/vignettes/miloR/inst/doc/milo_demo.html)


Practical Session 5 - 1:30h (**Priyansh**)


Trajectory Inference 

**Theory and conceptual**

1. What is Trajectory Inference
  1. Experimental Considerations
  2. Sequencing Considerations
2. Concept of Cell-state ordering
  1. Importance of Cell states
  2. Methods of Ordering
3. Case Study/ Application/ Interpretation
  1. Tissue System (10x)
  2. Embryo System (SmartSeq)
4. Limitations


**Practical Session**

Pseudotime Ordering with Monocle3 [https://cole-trapnell-lab.github.io/monocle3/docs/trajectories/](https://cole-trapnell-lab.github.io/monocle3/docs/trajectories/) 

Principal Curves and differential topologies using Slingshot [https://bioconductor.org/packages/devel/bioc/vignettes/slingshot/inst/doc/vignette.html](https://bioconductor.org/packages/devel/bioc/vignettes/slingshot/inst/doc/vignette.html) and Condiments [https://hectorrdb.github.io/condiments/articles/condiments.html#differential-differentiation-1](https://hectorrdb.github.io/condiments/articles/condiments.html#differential-differentiation-1) 

Differential Genes over pseudotime with TradeSeq [https://statomics.github.io/tradeSeq/articles/tradeSeq.html](https://statomics.github.io/tradeSeq/articles/tradeSeq.html)