interpret_simlr_vector2: Interpret SiMLR Vector

In stnava/ANTsR: ANTs in R: Quantification Tools for Biomedical Images

Interpret SiMLR Vector

Description

This function interprets a vector from SiMLR (similarity-driven multivariate linear reconstruction) specifically focusing on a given variable (e.g., a specific principal component or cluster). It extracts and normalizes the vector associated with the specified SiMLR variable, sorts it to identify the top elements, and optionally filters out non-significant values. This function is useful for understanding the contribution of different features in the context of the SiMLR analysis. Assumes this input is generated by antspymm_simlr.

Usage

interpret_simlr_vector2(
  simlrResult,
  simlrVariable,
  n2show = 5,
  shortnames = TRUE,
  return_dataframe = FALSE
)

Arguments

simlrResult	a specific v matrix out of SiMLR
simlrVariable	A string specifying the variable within 'simlrResult' to interpret. The variable name should include both an identifier (e.g., "PC" for principal component) and a numeric index.
n2show	An integer specifying the number of top elements to show from the sorted, normalized vector. Defaults to 5. If 'NULL' or greater than the length of the vector, all elements are shown.
shortnames	boolean
return_dataframe	boolean

Value

A named vector of the top 'n2show' elements (or all if 'n2show' is 'NULL' or too large), sorted in decreasing order of their absolute values. Elements are named according to their identifiers in 'simlrMats' and filtered to exclude non-significant values (absolute value > 0).

Examples

# This example assumes you have SiMLR result `simlrResult`, matrices `simlrMats`, and you want to 
# interpret the first principal component "PC1".
# simlrResult <- list(v = list(PC = matrix(runif(20), ncol = 2)))
# simlrMats <- list(PC = matrix(runif(100), ncol = 10))
# simlrVariable <- "PC1"
# interpretedVector <- interpret_simlr_vector2(simlrResult$v[[1]] )
# print(interpretedVector)

stnava/ANTsR documentation built on April 13, 2025, 4:10 a.m.