Add effective_connectivity() and grouping_matrix() - #12
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…ctivity Compute effective connectivity through multi-step pathways by input-normalising each step (colScaleM) and multiplying the successive adjacency matrices, following Schlegel et al. 2021 (eLife). grouping_matrix() builds the sparse 0/1 aggregation used to collapse the final output dimension to cell types. Offline unit tests included.
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Adds two sparse-matrix primitives to support multihop ("effective") connectivity, as used by the companion coconatfly PR for multihop cosine clustering.
What
effective_connectivity(matrices, group = NULL, normalise = TRUE)— estimates connectivity through multi-step pathways by input-normalising each step (colScaleM, so inputs to every postsynaptic cell sum to 1) and multiplying the successive adjacency matrices, per Schlegel et al. 2021 (10.7554/eLife.62576). Consecutive matrices are chained by name, so an output absent from the next matrix's rows contributes a zero (dead-end) path rather than erroring.grouping_matrix(ids, group)— sparse 0/1 aggregation matrix; right-multiplying collapses columns within a group (e.g. partner neurons to cell types).NAgroups drop out.Notes
groupcollapses the final output dimension. Because grouping is applied after normalisation and multiplication, and matrix multiplication is associative, grouping the final product is identical to grouping the last matrix's columns first — the per-neuron normalisation is always preserved.effective_connectivity(list(running, step), normalise = FALSE)), which avoids materialising every hop's matrix at once and allows pruning the frontier between hops. This is how coconatfly's multihop clustering drives it.Testing
tests/testthat/test-effective-connectivity.R— 15 offline assertions against a hand-computed pathway, covering normalisation, grouping, the associativity property, name-based alignment / dead ends, and the single-matrix case.Co-Authored-By: Claude Opus 4.8 noreply@anthropic.com
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