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Wrapper function for parameters for GPU NEBULA

params_nebula_gpu.Rd

GPU counterpart to bixverse::params_nebula(). Same knobs and defaults, minus reml: the device fit does not implement it. Stage two of NEBULA, the per-gene penalised fits, runs in f32 on the device and is finished on the host in f64, so the estimates sit close to the CPU ones rather than on them.

Usage

params_nebula_gpu(
  nebula_method = c("ln", "hl"),
  min_sigma = 1e-04,
  min_phi = 1e-04,
  max_sigma = 10,
  max_phi = 1000,
  cutoff_cell = 20,
  kappa = 800,
  cpc = 0.005,
  mincp = 5L,
  eps = 1e-06,
  gene_batch_size = 1000L,
  shrink_dispersion = TRUE
)

Arguments

nebula_method

String. Which variant to run. NEBULA downgrades "ln" to "hl" below 30 cells per subject, as the R package does. One of c("ln", "hl"). Defaults to "ln".

min_sigma

Numeric. Lower bound on the subject-level overdispersion. Defaults to 1e-04.

min_phi

Numeric. Lower bound on the cell-level overdispersion. Defaults to 1e-04.

max_sigma

Numeric. Upper bound on the subject-level overdispersion. Defaults to 10.0.

max_phi

Numeric. Upper bound on the cell-level overdispersion. Defaults to 1000.0.

cutoff_cell

Numeric. Refit both overdispersions when the product of the cells per subject and the estimated phi falls below this. Defaults to 20.0.

kappa

Numeric. Threshold on NEBULA's kappa_obs above which the subject-level overdispersion from stage one is trusted as is. Defaults to 800.0.

cpc

Numeric. Drop a gene whose mean count per cell is at most this. Defaults to 0.005.

mincp

Integer. Drop a gene expressed in fewer than this many cells. Defaults to 5L.

eps

Numeric. Absolute stopping tolerance for the optimiser. Defaults to 1e-06.

gene_batch_size

Integer. Genes read and fitted per batch. Bounds how much of the store is resident at once and changes nothing about the answer, since NEBULA is gene-independent. Defaults to 1000L.

shrink_dispersion

Boolean. Shrink the cell-level overdispersions towards an empirical Bayes prior once the sweep is done. Defaults to TRUE.

Value

A named list with the following elements:

  • nebula_method - String. Which variant to run. NEBULA downgrades "ln" to "hl" below 30 cells per subject, as the R package does. One of c("ln", "hl"). Defaults to "ln".

  • min_sigma - Numeric. Lower bound on the subject-level overdispersion. Defaults to 1e-04.

  • min_phi - Numeric. Lower bound on the cell-level overdispersion. Defaults to 1e-04.

  • max_sigma - Numeric. Upper bound on the subject-level overdispersion. Defaults to 10.0.

  • max_phi - Numeric. Upper bound on the cell-level overdispersion. Defaults to 1000.0.

  • cutoff_cell - Numeric. Refit both overdispersions when the product of the cells per subject and the estimated phi falls below this. Defaults to 20.0.

  • kappa - Numeric. Threshold on NEBULA's kappa_obs above which the subject-level overdispersion from stage one is trusted as is. Defaults to 800.0.

  • cpc - Numeric. Drop a gene whose mean count per cell is at most this. Defaults to 0.005.

  • mincp - Integer. Drop a gene expressed in fewer than this many cells. Defaults to 5L.

  • eps - Numeric. Absolute stopping tolerance for the optimiser. Defaults to 1e-06.

  • gene_batch_size - Integer. Genes read and fitted per batch. Bounds how much of the store is resident at once and changes nothing about the answer, since NEBULA is gene-independent. Defaults to 1000L.

  • shrink_dispersion - Boolean. Shrink the cell-level overdispersions towards an empirical Bayes prior once the sweep is done. Defaults to TRUE.

References

He, et al., Commun Biol, 2021