velociraptor 1.13.1

This package provides a lightweight interface between the Bioconductor `SingleCellExperiment`

data structure
and the scvelo Python package for RNA velocity calculations.
The interface is comparable to that of many other `SingleCellExperiment`

-compatible functions,
allowing users to plug in RNA velocity calculations into the existing Bioconductor analysis framework.
To demonstrate, we will use a data set from Hermann et al. (2018), provided via the *scRNAseq* package.
This data set contains gene-wise estimates of spliced and unspliced UMI counts for 2,325 mouse spermatogenic cells.

```
library(scRNAseq)
sce <- HermannSpermatogenesisData()
sce
```

```
## class: SingleCellExperiment
## dim: 54448 2325
## metadata(0):
## assays(2): spliced unspliced
## rownames(54448): ENSMUSG00000102693.1 ENSMUSG00000064842.1 ...
## ENSMUSG00000064369.1 ENSMUSG00000064372.1
## rowData names(0):
## colnames(2325): CCCATACTCCGAAGAG AATCCAGTCATCTGCC ... ATCCACCCACCACCAG
## ATTGGTGGTTACCGAT
## colData names(1): celltype
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
```

The full data set requires up to 12 GB of memory for the example usage presented in this vignette. For demonstration purposes, we downsample the data set to the first 500 cells. Feel free to skip this downsampling step if you have access to sufficient memory.

`sce <- sce[, 1:500]`

We assume that feature selection has already been performed by the user using any method
(see here for some suggestions).
In this case, we will use the variance of log-expressions from *scran* to select the top 2000 genes.

```
library(scuttle)
sce <- logNormCounts(sce, assay.type=1)
library(scran)
dec <- modelGeneVar(sce)
top.hvgs <- getTopHVGs(dec, n=2000)
```

We can plug these choices into the `scvelo()`

function with our `SingleCellExperiment`

object.
By default, `scvelo()`

uses the steady-state approach to estimate velocities,
though the stochastic and dynamical models implemented in scvelo can also be used by modifying the `mode`

argument.

```
library(velociraptor)
velo.out <- scvelo(sce, subset.row=top.hvgs, assay.X="spliced")
```

```
## computing neighbors
## finished (0:00:03) --> added
## 'distances' and 'connectivities', weighted adjacency matrices (adata.obsp)
## computing moments based on connectivities
## finished (0:00:00) --> added
## 'Ms' and 'Mu', moments of un/spliced abundances (adata.layers)
## computing velocities
## finished (0:00:00) --> added
## 'velocity', velocity vectors for each individual cell (adata.layers)
## computing velocity graph
##
... 100%
finished (0:00:00) --> added
## 'velocity_graph', sparse matrix with cosine correlations (adata.uns)
## computing terminal states
## identified 1 region of root cells and 1 region of end points .
## finished (0:00:00) --> added
## 'root_cells', root cells of Markov diffusion process (adata.obs)
## 'end_points', end points of Markov diffusion process (adata.obs)
## --> added 'velocity_length' (adata.obs)
## --> added 'velocity_confidence' (adata.obs)
## --> added 'velocity_confidence_transition' (adata.obs)
```

`velo.out`

```
## class: SingleCellExperiment
## dim: 2000 500
## metadata(4): neighbors velocity_params velocity_graph
## velocity_graph_neg
## assays(6): X spliced ... Mu velocity
## rownames(2000): ENSMUSG00000117819.1 ENSMUSG00000081984.3 ...
## ENSMUSG00000022965.8 ENSMUSG00000094660.2
## rowData names(3): velocity_gamma velocity_r2 velocity_genes
## colnames(500): CCCATACTCCGAAGAG AATCCAGTCATCTGCC ... CACCTTGTCGTAGGAG
## TTCCCAGAGACTAAGT
## colData names(7): velocity_self_transition root_cells ...
## velocity_confidence velocity_confidence_transition
## reducedDimNames(1): X_pca
## mainExpName: NULL
## altExpNames(0):
```

In the above call, we use the `"spliced"`

count matrix as a proxy for the typical exonic count matrix.
Technically, the latter is not required for the velocity estimation, but scvelo needs to perform a PCA and nearest neighbors search,
and we want to ensure that the neighbors detected inside the function are consistent with the rest of the analysis workflow (performed on the exonic counts).
There are some subtle differences between the spliced count matrix and the typical exonic count matrix - see `?scvelo`

for some commentary about this -
but the spliced counts are generally a satisfactory replacement if the latter is not available.

The `scvelo()`

function produces a `SingleCellExperiment`

containing all of the outputs of the calculation in Python.
Of particular interest is the `velocity_pseudotime`

vector that captures the relative progression of each cell
along the biological process driving the velocity vectors.
We can visualize this effect below in a \(t\)-SNE plot generated by *scater* on the top HVGs.

```
library(scater)
set.seed(100)
sce <- runPCA(sce, subset_row=top.hvgs)
sce <- runTSNE(sce, dimred="PCA")
sce$velocity_pseudotime <- velo.out$velocity_pseudotime
plotTSNE(sce, colour_by="velocity_pseudotime")
```

It is also straightforward to embed the velocity vectors into our desired low-dimensional space, as shown below for the \(t\)-SNE coordinates. This uses a grid-based approach to summarize the per-cell vectors into local representatives for effective visualization.

`embedded <- embedVelocity(reducedDim(sce, "TSNE"), velo.out)`

```
## computing velocity embedding
## finished (0:00:00) --> added
## 'velocity_target', embedded velocity vectors (adata.obsm)
```

```
grid.df <- gridVectors(sce, embedded, use.dimred = "TSNE")
library(ggplot2)
plotTSNE(sce, colour_by="velocity_pseudotime") +
geom_segment(data=grid.df, mapping=aes(x=start.1, y=start.2,
xend=end.1, yend=end.2, colour=NULL), arrow=arrow(length=unit(0.05, "inches")))
```