scvelo 0.1.23

The concept of RNA velocity

RNA velocity enables you to infer directionality in your data by superimposing splicing information.

Every cell simultaneously contains newly transcribed (unspliced) pre-mRNA and mature (spliced) mRNA, the former detectable by the presence of introns. La Manno et al., (2018) have shown that the timescale of cellular development is comparable to the kinetics of mRNA lifecycle. Thus, estimating how much mRNA is produced from pre-mRNA, compared to how much of it degrades, enables us to predict its change in the near future. A balance of mRNA production to degradation indicates homeostasis while imbalance indicates a dynamic behaviour, i.e. induction or repression in gene expression. A systematic quantification of the relationship yields RNA velocity, the time derivative of mRNA abundance which tells us how gene expression evolves over time. Aggregating over all genes, we obtain a velocity vector which can be projected into a lower-dimensional embedding to show the movement of the individual cells.

Read your data

Read your data file (loom, h5ad, csv, …) using:

adata = scv.read(filename, cache=True)

which stores the data matrix (adata.X), annotation of cells / observations (adata.obs) and genes / variables (adata.var), unstructured annotation such as graphs (adata.uns) and additional data layers where spliced and unspliced counts are stored (adata.layers) .

If you already have an existing preprocessed adata object you can simply merge the spliced/unspliced counts via:

ldata = scv.read(filename.loom, cache=True)
adata = scv.utils.merge(adata, ldata)

If you do not have a datasets yet, you can still play around using one of the in-built datasets, e.g.:

adata = scv.datasets.dentategyrus()

Preprocessing

For velocity estimation basic preprocessing (i.e. gene selection and normalization) is sufficient, e.g. using:

scv.pp.filter_and_normalize(adata, **params)

For velocity estimation we need the first- and second-order moments (basically means and variances), computed with:

scv.pp.moments(adata, **params)

Velocity Tools

The core of the software is the efficient and robust estimation of velocities, obtained with:

scv.tl.velocity(adata, mode='stochastic', **params)

The velocities are vectors in gene expression space obtained by solving a stochastic model of transcriptional dynamics. The solution to the deterministic model is obtained by setting mode='deterministic'.

The velocities are stored in adata.layers just like the count matrices.

Now we would like to predict cell transitions that are in accordance with the velocity directions. These are computed using cosine correlation (i.e. find potential cell transitions that correlate with the velocity vector) and are stored in a matrix called velocity graph:

scv.tl.velocity_graph(adata, **params)

Using the graph you can then project the velocities into any embedding (such as UMAP, e.g. obtained with scanpy):

scv.tl.velocity_embedding(adata, basis='umap', **params)

Note, that translation of velocities into a graph is only needed for non-linear embeddings. In PCA space you can skip the velocity graph and directly project into the embedding using scv.tl.velocity_embedding(adata, basis='pca', direct_projection=True).

Visualization

Finally the velocities can be projected and visualized in any embedding (e.g. UMAP) on single cell level, grid level, or as streamplot:

scv.pl.velocity_embedding(adata, basis='umap', **params)
scv.pl.velocity_embedding_grid(adata, basis='umap', **params)
scv.pl.velocity_embedding_stream(adata, basis='umap', **params)

For every tool module there is a plotting counterpart, which allows you to examine your results in detail, e.g.:

scv.pl.velocity(adata, var_names=['gene_A', 'gene_B'], **params)
scv.pl.velocity_graph(adata, **params)