ntac 0.1.2

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This is a Python implementation of the Neuronal typing algorithm described in

Gregory Schwartzman, Ben Jourdan, David García-Soriano, Arie Matsliah. NTAC: Neuronal Type Assignment from Connectivity. Nature Communications 17, 1284 (2026)

Neuronal Type Assignment from Connectivity

NTAC (Neuronal Type Assignment from Connectivity) groups neurons into cell types based solely on synaptic connectivity. It comes in two variants:

• Seeded (semi-supervised): Requires a small fraction of neurons with known labels.
• Unseeded (unsupervised): Requires no labels.

The seeded implementation also supports an optional nonnegative node-feature block. When provided, the feature block is appended to the structural NTAC embedding and therefore influences every seeded iteration through the same weighted-Jaccard similarity used by baseline NTAC.

Installation:

Install NTAC with: pip install ntac

Install cudatoolkit with conda for optional speed for unseeded: conda install -c conda-forge cudatoolkit

Quickstart with NTAC:

import numpy as np
import scipy.sparse as sp
from ntac import Ntac, sbm, GraphData

# Generate an adjacency matrix and labels from an SBM graph 
A, labels = sbm(n=1000, k=4)



#NTAC requires a CSR array and labels as a string array
A_csr = sp.csr_array(A)
labels = np.array([str(l) for l in labels])
# NTAC can take in as input a CSR matrix and labels, 
# but it is easier to use the GraphData class for test_train split and metrics
data = GraphData(A_csr, labels=labels)

############################################
#Seeded NTAC
#use only 10% for training
train_indices, test_indices = data.test_train_split(train_size=0.1)
labels[test_indices] = "?" # "?" indicates a nodes is unlabeled
#Initialize NTAC with the data and labels
nt = Ntac(data=data, labels=labels)
# nt = Ntac(data=A_csr, labels=labels) # if you want to use CSR matrix directly
for i in range(5):
    print(f"Step {i}")
    nt.step()
    partition = nt.get_partition()
    #partition = nt.get_topk_partition(5) # if we want to get the top 5 labels for each node
    metrics = data.get_metrics(partition, test_indices, data.labels)
    print(f"Accuracy: {metrics['acc']:.3f}") #can also get ARI, weighted F1, and topk accuracy (if using get_topk_partition)

############################################
#Unseed NTAC example
print("Unseeded NTAC")
#This will ignore the labels, even when provided
nt.solve_unseeded(max_k = 4)
nt.map_partition_to_gt_labels(data.labels) #Use the Hungarian algorithm to map the partition to the ground truth labels
partition = nt.get_partition() #unseeded does not support topk partition
metrics = data.get_metrics(partition, range(data.n), data.labels)
print(f"Accuracy: {metrics['acc']:.3f} ARI: {metrics['ari']:.3f}", f"Weighted F1: {metrics['f1']:.3f}")

Seeded NTAC with external node features

import numpy as np
import scipy.sparse as sp
from ntac import Ntac

A = sp.csr_array(
    [
        [0.0, 1.0, 0.0],
        [1.0, 0.0, 0.0],
        [0.0, 0.0, 0.0],
    ]
)
labels = np.array(["A", "B", "?"], dtype=object)

# Features must be nonnegative because seeded NTAC still uses weighted Jaccard.
node_features = np.array(
    [
        [1.0, 0.0],
        [0.0, 1.0],
        [0.8, 0.2],
    ],
    dtype=float,
)

nt = Ntac(
    data=A,
    labels=labels,
    node_features=node_features,
    feature_weight=1.0,
)
nt.step()
print(nt.get_partition())

In practice, it is usually worth preprocessing external features before passing them to NTAC so they are nonnegative and on a scale comparable to the structural embedding.

Documentation & Examples using the Flywire dataset:

Link to Docs and further examples