expr_cancor: Compute summary statistic based on gene expression features

In byzhang23/TreeCorTreat: Tree-based correlation screen for phenotype-associated transcriptomic features and cell types

Compute summary statistic based on gene expression features

Description

This function takes in a list of cluster-level PCA matrices as input and generates canonical correlation and p-value based on permutation test.

Usage

expr_cancor(
  pca.ls,
  sample_meta,
  response_variable,
  analysis_type,
  num_permutations,
  alternative,
  component_id = 1,
  ncores
)

Arguments

pca.ls	A list of cell cluster-specific PCA matrices
sample_meta	Sample-level metadata, where each row is a sample. Must contain 'sample' column and additional variables such as covariates or outcome of interest.
response_variable	A vector of response variables.
analysis_type	Either 'cancor' (canonical correlation) or 'regression' (F-statistic).
num_permutations	Number of permutations (by default: 1000).
alternative	A character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less".
ncores	Number of cores
componen_id	An index to extract canonical correlation component (by default: 1).

Value

A data frame for summary statistic (canonical correlation or F-stat), p-value and adjusted p-value for each node.

Author(s)

Boyang Zhang <[e-mail]>, Hongkai Ji

byzhang23/TreeCorTreat documentation built on May 7, 2024, 8:37 a.m.