sccomp_predict: sccomp_predict

In stemangiola/sccomp: Robust Outlier-aware Estimation of Composition and Heterogeneity for Single-cell Data

sccomp_predict

Description

This function replicates counts from a real-world dataset.

Usage

sccomp_predict(
  fit,
  formula_composition = NULL,
  new_data = NULL,
  number_of_draws = 500,
  mcmc_seed = sample(1e+05, 1)
)

Arguments

fit	The result of sccomp_estimate.
formula_composition	A formula. The formula describing the model for differential abundance, for example ~treatment. This formula can be a sub-formula of your estimated model; in this case all other factor will be factored out.
new_data	A sample-wise data frame including the column that represent the factors in your formula. If you want to predict proportions for 10 samples, there should be 10 rows. T
number_of_draws	An integer. How may copies of the data you want to draw from the model joint posterior distribution.
mcmc_seed	An integer. Used for Markov-chain Monte Carlo reproducibility. By default a random number is sampled from 1 to 999999. This itself can be controlled by set.seed()

Value

A nested tibble tbl with cell_group-wise statistics

Examples

data("counts_obj")

if(.Platform$OS.type == "unix")
  sccomp_estimate(
  counts_obj ,
   ~ type, ~1,  sample, cell_group, count,
    cores = 1
  ) |>

  sccomp_predict()

stemangiola/sccomp documentation built on May 17, 2024, 6:24 a.m.