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Update 03 #8

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Update 03
michaelgmz Jul 5, 2021
2604e5b
Merge branch 'StatBiomed:master' into master
michaelgmz Jul 5, 2021
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34 changes: 10 additions & 24 deletions 03-RNAvelocity.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -13,22 +13,8 @@ pip install -U scvelo==0.2.2
pip install numba==0.51
```

To use Python packages within R environment, we will need the help of R `colorize("reticulate", "#316A9E")` library,
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)

colorize <- function(x, color) {
if (knitr::is_latex_output()) {
sprintf("\\textcolor{%s}{%s}",
color, x)
}
else if (knitr::is_html_output()) {
sprintf("<span style='color: %s;'>%s</span>",
color, x)
}
else x
}
```
To use Python packages within R environment, we will need the help of R reticulate library,

```{r}
# install.packages("reticulate")
library(reticulate)
Expand Down Expand Up @@ -62,9 +48,9 @@ adata <- scv$datasets$pancreas()
adata
```

To run velocity analysis on your own data, read your file with `colorize("adata = scv$read(file_path)", "#316A9E")`.
To run velocity analysis on your own data, read your file with 'adata = scv$read(file_path)'.

If you want to save model and parameters after processing, run the following command, `colorize("adata$write(file_path, compression = 'gzip')", "#316A9E")`.
If you want to save model and parameters after processing, run the following command, 'adata$write(file_path, compression = 'gzip').

Proportions of unspliced/spliced mRNA reads, UMAP embedding and cluster annotations can be printed and visualized using built-in functions.
```{r}
Expand Down Expand Up @@ -97,7 +83,7 @@ scv$pp$moments(adata, n_pcs = as.integer(30), n_neighbors = as.integer(30))

## Pseudotime based on Diffusion Map

Pseudotime, a part of standardized scRNA-seq analysis pipeline, is also implemented in this package, and can be compared with the `colorize("latent time", "#316A9E")` introduced in dynamical mode.
Pseudotime, a part of standardized scRNA-seq analysis pipeline, is also implemented in this package, and can be compared with the latent time introduced in dynamical mode.

```{r}
adata$uns$data$iroot <- which.min(adata$obsm['X_umap'][, 1])
Expand All @@ -120,21 +106,21 @@ scVelo has incorporated 3 modes for velocity estimation:

For deterministic and stochastic mode, the gene-specific velocities are obtained by fitting linear regression ratio (constant transcriptional state) between unspliced/spliced mRNA abundances.

Under linear assumptions, how the `colorize("observed abundances deviate from the steady state", "#b22e5b")` regression line is velocity.
Under linear assumptions, how the observed abundances deviate from the steady state regression line is velocity.

```{r}
scv$tl$velocity(adata, mode = "stochastic")
```

To calculate velocity graph, we need to run `colorize("velocity_graph()", "#316A9E")`. Velocity graph is the cosine correlation of potential cell transitions with velocity vector in high dimensional space. It summarizes the possible cell transition states and has dimension of ${n}_{obs} * {n}_{obs}$.
To calculate velocity graph, we need to run velocity_graph(). Velocity graph is the cosine correlation of potential cell transitions with velocity vector in high dimensional space. It summarizes the possible cell transition states and has dimension of ${n}_{obs} * {n}_{obs}$.

```{r}
scv$tl$velocity_graph(adata, sqrt_transform = TRUE)
```

## Diffusion-map Pseudotime with velocity

Pseudotime, a part of standardized scRNA-seq analysis pipeline, is also implemented in this package, and can be compared with the `colorize("latent time", "#316A9E")` introduced in dynamical mode.
Pseudotime, a part of standardized scRNA-seq analysis pipeline, is also implemented in this package, and can be compared with the latent time introduced in dynamical mode.

```{r}
scv$tl$velocity_pseudotime(adata)
Expand All @@ -155,7 +141,7 @@ plt$savefig('images/RNA-velo-fig5.png')

## Plot Results

Velocities are projected onto the specified embedding `colorize("basis", "#316A9E")` and can be visualized in one of the three ways:
Velocities are projected onto the specified embedding basis and can be visualized in one of the three ways:
- On single cell level
- On grid level
- Streamlines, which is most commonly used
Expand Down Expand Up @@ -206,7 +192,7 @@ plt$savefig('images/RNA-velo-fig9.png')

Dynamical mode does not necessarily rely on linear assumptions, instead it consider gene-specific rates of transcription, splicing and degradation rates as well as transient cell-states.

To use dynamical mode, we just need to call `colorize("revocer_dynamics()", "#316A9E")` before computing velocity.
To use dynamical mode, we just need to call revocer_dynamics() before computing velocity.

```{}
scv$tl$recover_dynamics(adata)
Expand Down