Note

Click here to download the full example code

# Lineage tricks

This example shows some niche, but useful functionalities of `cellrank.tl.Lineage`

.

`cellrank.tl.Lineage`

is a lightweight wrapper around `numpy.ndarray`

containing names and colors which
stores the data in columns and allows for `pandas`

-like indexing. It also provides various methods, such as a
method for plotting some aggregate information for each column.

We use it primarily to store either the fate probabilities or the macrostates memberships, see Compute absorption probabilities or Compute macrostates to learn how to compute them.

```
import cellrank as cr
import numpy as np
np.random.seed(42)
```

The lineage class behaves like a `numpy`

array for the most part. The key differences are that only 2 dimensional
arrays are allowed as input and that it always tries to preserve it’s shape, even if scalar is requested.

The constructor requires the underlying array and the lineage names, which must be unique. The colors are optional and by default they are automatically generated.

```
lin = cr.tl.Lineage(
np.abs(np.random.normal(size=(10, 4))), names=["foo", "bar", "baz", "quux"]
)
lin /= lin.sum(1)
```

In some cases, this behavior is not desirable or can have unintended consequences. To access the underlying
`numpy`

array, use the `cellrank.tl.Lineage.X`

attribute.

```
lin.X
```

Out:

```
array([[0.17703771, 0.04927984, 0.23084765, 0.54283479],
[0.08318243, 0.0831766 , 0.5610116 , 0.27262937],
[0.24184977, 0.27949985, 0.23872966, 0.23992072],
[0.05446598, 0.43068153, 0.38828094, 0.12657155],
[0.27768531, 0.08615638, 0.24895063, 0.38720768],
[0.46035999, 0.07091629, 0.0212106 , 0.44751312],
[0.24948831, 0.05083536, 0.52749551, 0.17218082],
[0.17949245, 0.08716859, 0.17981159, 0.55352737],
[0.00433354, 0.33959804, 0.26409355, 0.39197487],
[0.05654772, 0.53056103, 0.35959305, 0.0532982 ]])
```

Lineages can also be transposed.

```
lin.T
```

Indexing into lineage can be done via the names as well.

```
lin[["foo", "bar"]]
```

Two or more lineage can be combined into by joining the names with “,”. This also automatically updates the color based on the combined lineages’ colors.

```
lin[["bar, baz, quux"]]
```

Most of the `numpy`

methods are supported by the `cellrank.tl.Lineage`

. One can also calculate the
entropy, which in [Setty *et al.*, 2019] is defined as the differentiation potential of cells.

```
lin.entropy(axis=1)
```

When subsetting the lineage and not selecting all of them, they will no longer sum to 1 and cannot be
interpreted as a probability distribution. We offer a method `cellrank.tl.Lineage.reduce`

which
can be used to solve this issue. Below we show only one out of many normalization techniques.

```
lin.reduce("foo, quux", "baz", normalize_weights="softmax")
```

Lastly, we can plot aggregate information about lineages, such as `numpy.mean()`

and others.

```
lin.plot_pie(np.mean, legend_loc="on data")
```

**Total running time of the script:** ( 0 minutes 2.959 seconds)

**Estimated memory usage:** 9 MB