simulate_data: simulate_data

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

simulate_data

Description

This function simulates counts from a linear model.

Usage

simulate_data(
  .data,
  .estimate_object,
  formula_composition,
  formula_variability = NULL,
  .sample = NULL,
  .cell_group = NULL,
  .coefficients = NULL,
  variability_multiplier = 5,
  number_of_draws = 1,
  mcmc_seed = sample(1e+05, 1)
)

Arguments

.data	A tibble including a cell_group name column | sample name column | read counts column | factor columns | Pvalue column | a significance column
.estimate_object	The result of sccomp_estimate execution. This is used for sampling from real-data properties.
formula_composition	A formula. The sample formula used to perform the differential cell_group abundance analysis
formula_variability	A formula. The formula describing the model for differential variability, for example ~treatment. In most cases, if differentially variability is of interest, the formula should only include the factor of interest as a large anount of data is needed to define variability depending to each factors.
.sample	A column name as symbol. The sample identifier
.cell_group	A column name as symbol. The cell_group identifier
.coefficients	The column names for coefficients, for example, c(b_0, b_1)
variability_multiplier	A real scalar. This can be used for artificially increasing the variability of the simulation for benchmarking purposes.
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")
library(dplyr)

estimate =
 sccomp_estimate(
 counts_obj ,
  ~ type, ~1,  sample, cell_group, count,
   cores = 1
 )

# Set coefficients for cell_groups. In this case all coefficients are 0 for simplicity.
counts_obj = counts_obj |> mutate(b_0 = 0, b_1 = 0)
# Simulate data
simulate_data(counts_obj, estimate, ~type, ~1, sample, cell_group, c(b_0, b_1))

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