R: Train MISTy models

run_misty {mistyR}

R Documentation

Train MISTy models

Description

Trains multi-view models for all target markers, estimates the performance, the contributions of the view specific models and the importance of predictor markers for each target marker.

Usage

run_misty(
  views,
  results.folder = "results",
  seed = 42,
  target.subset = NULL,
  cv.folds = 10,
  cached = FALSE,
  append = FALSE,
  ...
)

Arguments

`views`	view composition.
`results.folder`	path to the top level folder to store raw results.
`seed`	seed used for random sampling to ensure reproducibility.
`target.subset`	subset of targets to train models for. If `NULL`, models will be trained for markers in the intraview.
`cv.folds`	number of cross-validation folds to consider for estimating the performance of the multi-view models.
`cached`	a `logical` indicating whether to cache the trained models and to reuse previously cached ones if they already exist for this sample.
`append`	a `logical` indicating whether to append the performance and coefficient files in the `results.folder`. Consider setting to `TRUE` when rerunning a workflow with different `target.subset` parameters.
`...`	all additional parameters are passed to `ranger()` for training the view-specific models (see Details for defaults).

Details

Default values passed to ranger() for training the view-specific models: num.trees = 100, importance = "impurity", num.threads = 1, seed = seed.

Value

Path to the results folder that can be passed to collect_results().

Examples

# Create a view composition of an intraview and a paraview with radius 10 then
# run MISTy for a single sample.

library(dplyr)

# get the expression data
data("synthetic")
expr <- synthetic[[1]] %>% select(-c(row, col, type))
# get the coordinates for each cell
pos <- synthetic[[1]] %>% select(row, col)

# compose
misty.views <- create_initial_view(expr) %>% add_paraview(pos, l = 10)

# run with default parameters
run_misty(misty.views)

[Package mistyR version 1.0.3 Index]