navis.nblast_smart¶

navis.nblast_smart(query, target=None, t=90, criterion='percentile', scores='forward', return_mask=False, normalized=True, use_alpha=False, smat='auto', limit_dist='auto', approx_nn=False, precision=64, n_cores=1, progress=True, smat_kwargs={})[source]¶

Smart(er) NBLAST query against target neurons.

In contrast to navis.nblast() this function will first run a “pre-NBLAST” in which only 10% of the query dotprops’ points are used. Using those initial scores, we select for each query the highest scoring targets and run the full NBLAST only on those query-target pairs (see t and criterion for fine-tuning).

Parameters:

query (Dotprops | NeuronList) – Query neuron(s) to NBLAST against the targets. Neurons should be in microns as NBLAST is optimized for that and have similar sampling resolutions.
target (Dotprops | NeuronList, optional) – Target neuron(s) to NBLAST against. Neurons should be in microns as NBLAST is optimized for that and have similar sampling resolutions. If not provided, will NBLAST queries against themselves.
t (int | float) – Determines for which pairs we will run a full NBLAST. See criterion parameter for details.
criterion ("percentile" | "score" | "N") –
Criterion for selecting query-target pairs for full NBLAST:
- ”percentile” runs full NBLAST on the t-th percentile
- ”score” runs full NBLAST on all scores above t
- ”N” runs full NBLAST on top t targets
return_mask (bool) – If True, will also return a boolean mask that shows which scores are based on a full NBLAST and which ones only on the pre-NBLAST.
scores ('forward' | 'mean' | 'min' | 'max') –
Determines the final scores:
- ’forward’ (default) returns query->target scores
- ’mean’ returns the mean of query->target and target->query scores
- ’min’ returns the minium between query->target and target->query scores
- ’max’ returns the maximum between query->target and target->query scores
use_alpha (bool, optional) – Emphasizes neurons’ straight parts (backbone) over parts that have lots of branches.
normalized (bool, optional) – Whether to return normalized NBLAST scores.
smat (str | pd.DataFrame | Callable) – Score matrix. If ‘auto’ (default), will use scoring matrices from FCWB. Same behaviour as in R’s nat.nblast implementation. If smat='v1' it uses the analytic formulation of the NBLAST scoring from Kohl et. al (2013). You can adjust parameter sigma_scaling (default to 10) using smat_kwargs. If smat=None the scores will be generated as the product of the distances and the dotproduct of the vectors of nearest-neighbor pairs. If Callable given, it passes distance and dot products as first and second argument respectively.
smat_kwargs (Dictionary with additional parameters passed to scoring) – functions.
limit_dist (float | "auto" | None) – Sets the max distance for the nearest neighbor search (distance_upper_bound). Typically this should be the highest distance considered by the scoring function. If “auto”, will extract that value from the scoring matrix. While this can give a ~2X speed up, it will introduce slight inaccuracies because we won’t have a vector component for points without a nearest neighbour within the distance limits. The impact depends on the scoring function but with the default FCWB smat, this is typically limited to the third decimal (0.0086 +/- 0.0027 for an all-by-all of 1k neurons).
approx_nn (bool) – If True, will use approximate nearest neighbors. This gives a >2X speed up but also produces only approximate scores. Impact depends on the use case - testing highly recommended!
precision (int [16, 32, 64] | str [e.g. "float64"] | np.dtype) – Precision for scores. Defaults to 64 bit (double) floats. This is useful to reduce the memory footprint for very large matrices. In real-world scenarios 32 bit (single)- and depending on the purpose even 16 bit (half) - are typically sufficient.
n_cores (int, optional) – Max number of cores to use for nblasting. Default is os.cpu_count() // 2. This should ideally be an even number as that allows optimally splitting queries onto individual processes.
progress (bool) – Whether to show progress bars.

Return type:

DataFrame

Returns:

scores (pandas.DataFrame) – Matrix with NBLAST scores. Rows are query neurons, columns are targets. The order is the same as in query/target and the labels are based on the neurons’ .id property.
mask (np.ndarray) – Only if return_mask=True: a boolean mask with same shape as scores that shows which scores are based on a full NBLAST and which ones only on the pre-NBLAST.

References

Costa M, Manton JD, Ostrovsky AD, Prohaska S, Jefferis GS. NBLAST: Rapid, Sensitive Comparison of Neuronal Structure and Construction of Neuron Family Databases. Neuron. 2016 Jul 20;91(2):293-311. doi: 10.1016/j.neuron.2016.06.012.

Examples

>>> import navis
>>> nl = navis.example_neurons(n=5)
>>> nl.units
<Quantity([8 8 8 8 8], 'nanometer')>
>>> # Convert to microns
>>> nl_um = nl * (8 / 1000)
>>> # Convert to dotprops
>>> dps = navis.make_dotprops(nl_um)
>>> # Run a NBLAST where only the top target from the pre-NBLAST is run
>>> # through a full NBLAST
>>> scores = navis.nblast_smart(dps[:3], dps[3:], t=1, criterion='N')