bandle-sim: Generate a dynamic spatial proteomics experiment

In ococrook/bandle: An R package for the Bayesian analysis of differential subcellular localisation experiments

Generate a dynamic spatial proteomics experiment

Description

A function to simulate dynamic spatial proteomics data using a bootstrap method

Usage

sim_dynamic(
  object,
  subsample = NULL,
  knn_par = 10L,
  fcol = "markers",
  numRep = 6L,
  method = "wild",
  batch = FALSE,
  frac_perm = FALSE,
  nu = 2,
  numDyn = 20L
)

Arguments

object	A instance of class MSnSet from which to generate a spatial proteomics dataset.
subsample	how many proteins to subsample to speed up analysis. Default is NULL.
knn_par	the number of nearest neighbours to use in KNN classification to simulate dataset. Default is 10
fcol	feature column to indicate markers. Default is "markers". Proteins with unknown localisations must be encoded as "unknown".
numRep	The total number of datasets to generate. Default is 6. An integer must be provided
method	The bootstrap method to use to simulate dataset. Default is "wild". refer to BANDLE paper for more details.
batch	Whether or not to include batch effects. Default is FALSE.
frac_perm	whether or not to permute the fractions. Default is FALSE
nu	parameter to generate residual inflated noise. Default is 2. See BANDLE paper for more details
numDyn	An integer number of protein to simulate dynamic transitions. Default is 20

Value

returns simulate dynamic lopit datasets and the name of the relocalated protein.

Examples

library(pRolocdata)
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, numRep = 6L, numDyn = 100L)

ococrook/bandle documentation built on Nov. 4, 2024, 12:27 a.m.