# Analysis Pipeline¶

The following tutorial gives an introduction to the basic analysis functions of the velocyto library.

## Velocyto Loom¶

Let’s start with loading the content of the .loom file into an interactive session of python.

```
import velocyto as vcy
vlm = vcy.VelocytoLoom("YourData.loom")
```

Different steps of analysis can be carried on by simply calling the methods of this VelocytoLoom object. New variables, normalized version of the data matrixes and other parameters will be stored as attributes of the “VelocytoLoom” object (method calls will not return any value). For example normalization and log transformation can be performed by calling the normalize method:

```
vlm.normalize("S", size=True, log=True)
vlm.S_norm # contains log normalized
```

The docstring of every function specifies which attributes will be generated or modified at each method call.

“VelocytoLoom” object supports some ready-made plotting functions. For example, one of the first checks is spliced/unspliced fractions of the dataset can be done by calling:

```
vlm.plot_fractions()
```

You can save the results of your analysis in a serialized object at any time by running:

```
vlm.dump_hdf5("my_velocyto_analysis")
```

In another session you can reload the vlm object by running:

```
load_velocyto_hdf5("my_velocyto_analysis.hdf5")
```

This is similar to what the `pickle`

module in python standard library is doing but here only the attributes of the `VelocytoLoom`

object are saved and stored as a hdf5 file.
Notice that the size on disk of the serialized file can change depending on the step of the analysis the object is saved (e.g. pre/post filtering or before/after calculating distance matrixes).

Note

VelocytoLoom object methods operate on the object attributes performing filtering, normalization adn other calcualtion. Therefore the order in which they are run is important to get a meaningful output from `velocyto`

.
We suggest calling these functions in the order shown in this tutorial or in the example notebooks.

## Start a new analysis - Preliminary Filtering¶

A good first stem is to clean up the data a bit. Let’s remove the cells with extremelly low unspliced detection

```
vlm.filter_cells(bool_array=vlm.initial_Ucell_size > np.percentile(vlm.initial_Ucell_size, 0.5))
```

Let’s try now to select relevant features for the downstream analysis. Let’s make velocyto aware of the clusters annotation, if we have some

```
vlm.set_clusters(vlm.ca["ClusterName"])
```

Now using the clustering annotation select the genes that are expressed above a threshold of total number of molecules in any of the clusters.

```
vlm.score_detection_levels(min_expr_counts=40, min_cells_express=30)
vlm.filter_genes(by_detection_levels=True)
```

We can perform feature selection.

```
vlm.score_cv_vs_mean(3000, plot=True, max_expr_avg=35)
vlm.filter_genes(by_cv_vs_mean=True)
```

Finally we can normalize our data by size (total molecule count)

```
vlm._normalize_S(relative_size=vlm.S.sum(0),
target_size=vlm.S.sum(0).mean())
vlm._normalize_U(relative_size=vlm.U.sum(0),
target_size=vlm.U.sum(0).mean())
```

For a better understend how to fine tune parameters please consult the API page or just inspect the docstring of each function.

## Preparation for gamma fit¶

For the preparation of the gamma fit we smooth the data using a kNN neighbors pooling approach. kNN neighbors can be calculated directly in gene expression space or reduced PCA space, using either correlation distance or euclidean distance. One example of set of parameters is provided below.

```
vlm.perform_PCA()
vlm.knn_imputation(n_pca_dims=20, k=500, balanced=True, b_sight=3000, b_maxl=1500, n_jobs=16)
```

## Gamma fit and extrapolation¶

To fit gamma to every gene that survived the filtering step run:

```
vlm.fit_gammas()
```

The fit can be visualized by calling plot_phase_portraits and listing the gene names:

```
vlm.plot_phase_portraits(["Igfbpl1", "Pdgfra"])
```

The calcualte velocity and extrapolate the future state of the cells:

```
vlm.predict_U()
vlm.calculate_velocity()
vlm.calculate_shift(assumption="constant_velocity")
vlm.extrapolate_cell_at_t(delta_t=1.)
```

In alternative extrapolation can be performed using the constant unspliced assumption (for more information consult our preprint)

```
vlm.calculate_shift(assumption="constant_unspliced", delta_t=10)
vlm.extrapolate_cell_at_t(delta_t=1.)
```

## Projection of velocity onto embeddings¶

The extrapolated cell state is a vector in expression space (available as the attribute vlm.Sx_sz_t). One of the most convenient way to visualize the extrapolated state is to project it on a low dimensional embedding that appropriately summarizes the variability of the data that is of interest. The embedding can be calculated with your favorite method or external package as soon as it is saved as an attribute of the VelocytoLoom object. For example, let’s use scikit-learn TSNE implementation and make it available as ts attribute as following:

```
from sklearn.manifold import TSNE
bh_tsne = TSNE()
vlm.ts = bh_tsne.fit_transform(vlm.pcs[:, :25])
```

Now we can project on vlm.ts by calling estimate_transition_prob.

Warning

For big datasets this code can take long time to run! We suggest to run it on multicore machines (since the implementation is fully multithreaded)

```
vlm.estimate_transition_prob(hidim="Sx_sz", embed="ts", transform="sqrt", psc=1,
n_neighbors=3500, knn_random=True, sampled_fraction=0.5)
vlm.calculate_embedding_shift(sigma_corr = 0.05, expression_scaling=True)
```

In case of very big dataset visualizations a good way to summarize the velocity is to visualize it as velocity field calculated on a grid.

```
vlm.calculate_grid_arrows(smooth=0.8, steps=(40, 40), n_neighbors=300)
plt.figure(None,(20,10))
vlm.plot_grid_arrows(quiver_scale=0.6,
scatter_kwargs_dict={"alpha":0.35, "lw":0.35, "edgecolor":"0.4", "s":38, "rasterized":True}, min_mass=24, angles='xy', scale_units='xy',
headaxislength=2.75, headlength=5, headwidth=4.8, minlength=1.5,
plot_random=True, scale_type="absolute")
```