scNPM-package: Single-Cell Network Propensity Matching

In scNPM: Single-Cell Network Propensity Matching

Single-Cell Network Propensity Matching

Description

Estimate and compare heterogeneous gene conditional-dependence networks across two conditions. A latent Gaussian mixture and probit observation mechanism represent cellular heterogeneity and zero observations. Protein interaction information supplies edge-specific prior weights, and network propensity matching guides stochastic updates of uncertain edges.

Details

Matching uses centered quadratic forms against all current treatment and control reference networks (2 * K features). Pairwise propensity regression uses the observed condition indicator, balances the two conditions' total weights and standardizes predictors within each training fold. Global scores remain available with pooled scaling. Default staged inference freezes a stable complete matching or selects a recent consensus map at the adaptation limit. It then disables feedback and discards a post-switch burn-in before retaining final-model draws. Staged fits use a four-state paired prior by default, with optional independent priors for compatibility. Paired precision updates and joint relabeling preserve frozen partners. No gene modules are introduced. Group networks use per-subtype BFDR selection from marginal slab probabilities. Directional edge selection controls an estimated posterior expected FDP, with a conservative all-counterparts option; it is not a uniform frequentist FDR guarantee. Coupled mode with global scores and greedy assignment retains the earlier continuous-feedback workflow.

The augmented criterion trace records likelihood, network-prior and SMD contributions. It is diagnostic and need not increase. HMC uses fixed step sizes, leapfrog counts and Metropolis acceptance correction.

Author(s)

Maintainer: Tao Liu [e-mail]

Authors:

• Tao Liu [e-mail]

See Also

run_scNPM, compute_qf_smd

scNPM documentation built on Oct. 19, 2026, 5:10 p.m.