Commit a86f3d1d authored by Ruqian Lyu's avatar Ruqian Lyu
Browse files

fix typos

parent aaa63eae
Pipeline #961 passed with stage
in 4 seconds
......@@ -294,7 +294,6 @@ heatmap(heatdata, Colv = NA,
```
We will regress each gene on the pseudotime variable we have generated, using a general additive model (GAM). This allows us to detect non-linear patterns in gene expression.
## Monocle
......@@ -311,12 +310,12 @@ defined as a separate cell state.
### Monocle 2
`Monocle 2` [@Qiu2017-xq] uses a different approach, with dimensionality reduction and ordering performed by reverse
graph embedding (RGE), allowing it to detect branching events in an unsupervised manner. RGE, a machine-learning strategy, learns a
principal graph to describe the single-cell dataset. RGE also learns the mapping function of data points on the trajectory back to the original high dimentional space simutaneously. In doing so, it aims to position the latent points in the lower dimension space (along the trajectory) while also ensuring their corresponding
graph embedding (RGE), allowing it to detect branching events in an unsupervised manner. RGE, a machine-learning strategy, learns a principal graph to describe the single-cell dataset. RGE also learns the mapping function of data points on the trajectory back to the original high dimentional space simutaneously. In doing so, it aims to position the latent points in the lower dimension space (along the trajectory) while also ensuring their corresponding
positions in the input dimension are neighbors.
There are different ways of implementing the RGE framework, `Monocle 2` uses `DDRTree`(Discriminative dimensionality reduction via learning a tree) by default.
DDRTree learns latent points and the projection of latent points to the points in original input space, which is equivalent to "dimension reduction". In addition, it simutanously learns 'principal graph' for K-means soft clustered cetroids for the latent points. Principal graph is the spanning tree of those centroids.
`DDRTree` learns latent points and the projection of latent points to the points in original input space, which is equivalent to "dimension reduction". In addition, it simutanously learns 'principal graph' for K-means soft clustered centroids for the latent points. Principal graph is the spanning tree of those centroids.
DDRTree returns a principal tree of the centroids of cell clusters in low dimension, pseudotime is derived for individual cells by calculating geomdestic distance of their projections onto the tree from the root (user-defined or arbitrarily assigned).
......@@ -385,12 +384,11 @@ Monocle 2 performs pretty well on these cells.
### Monocle 3
[`Monocle3`](https://www.nature.com/articles/s41586-019-0969-x)[@Cao2019-cj] is the updated single-cell analysis toolkit for analysing large datasets. [Monocle 3](https://cole-trapnell-lab.github.io/monocle3/docs/starting/) is designed for use with absolute transcript counts (e.g. from UMI experiments). It first does dimension reduction with UMAP and then clusters the cells with Louvian/Leiden algorithms and merge adjacent groups into supergroup, and finaly resovles the trajectories individual cells can take during development, identifies
the locations of branches and convergences within each supergroup.
[`Monocle3`](https://www.nature.com/articles/s41586-019-0969-x)[@Cao2019-cj] is the updated single-cell analysis toolkit for analysing large datasets. [Monocle 3](https://cole-trapnell-lab.github.io/monocle3/docs/starting/) is designed for use with absolute transcript counts (e.g. from UMI experiments). It first does dimension reduction with UMAP, then it clusters the cells with Louvain/Leiden algorithms and merge adjacent groups into supergroup, and finaly resovles the trajectories individual cells can take during development within each supergroup.
In short, Monocle3 uses `UMAP` to construct a initial trajectory inference and refines it with learning principal graph.
It builds KNN graph in the UMAP dimensions and runs Louvain/Leiden algorithms om the KNN graph to derive communities; edges are drawn to connect communities that have more links (Partitioned Approximate Graph Abstraction (PAGA) graph). Each component of the PAGA grah is passed to the next step which is learning principal graph based on the SimplePPT algorithm. The pseudotime is calculated for individual cells by projecting the cells to their nearest point on the principal graph edge and measure geodesic distance along of principal points to the closest of their root nodes.
It builds KNN graph in the UMAP dimensions and runs Louvain/Leiden algorithms on the KNN graph to derive communities; edges are drawn to connect communities that have more links (Partitioned Approximate Graph Abstraction (PAGA) graph). Each component of the PAGA graph is passed to the next step which is learning principal graph based on the SimplePPT algorithm. The pseudotime is calculated for individual cells by projecting the cells to their nearest point on the principal graph edge and measure geodestic distance along of principal points to the closest of their root nodes.
```{r run_monocle3}
......@@ -423,7 +421,8 @@ cds <- order_cells(cds, root_cells = c("zy","zy.1","zy.2","zy.3") )
plot_cells(cds, color_cells_by="cell_type2", graph_label_size = 4, cell_size = 2,
group_label_size = 6)+ scale_color_manual(values = my_color)
plot_cells(cds, graph_label_size = 6, cell_size = 1, color_cells_by="pseudotime",
plot_cells(cds, graph_label_size = 6, cell_size = 1,
color_cells_by="pseudotime",
group_label_size = 6)
pdata_cds <- pData(cds)
pdata_cds$pseudotime_monocle3 <- monocle3::pseudotime(cds)
......
Supports Markdown
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment