resolve_complete_confounders_of_non_interest: Resolve Complete Confounders of Non-Interest

In stemangiola/tidyBulk: Brings transcriptomics to the tidyverse

Resolve Complete Confounders of Non-Interest

Description

This function identifies and resolves complete confounders among specified factors of non-interest within a 'SummarizedExperiment' object. Complete confounders occur when the levels of one factor are entirely predictable based on the levels of another factor. Such relationships can interfere with downstream analyses by introducing redundancy or collinearity.

Usage

resolve_complete_confounders_of_non_interest(se, ...)

Arguments

se	A 'SummarizedExperiment' object. This object contains assay data, row data (e.g., gene annotations), and column data (e.g., sample annotations).
...	Factors of non-interest (column names from 'colData(se)') to examine for complete confounders.

Details

The function systematically examines pairs of specified factors and determines whether they are completely confounded. If a pair of factors is found to be confounded, one of the factors is adjusted or removed to resolve the issue. The adjusted 'SummarizedExperiment' object is returned, preserving all assays and metadata except the resolved factors.

Complete confounders of non-interest can create dependencies between variables that may bias statistical models or violate their assumptions. This function systematically addresses this by: 1. Identifying pairs of factors in the specified columns that are fully confounded. 2. Resolving confounding by adjusting or removing one of the factors from the 'colData' slot.

The resolution strategy depends on the analysis context and can be modified in the helper function 'resolve_complete_confounders_of_non_interest_pair_SE()'. By default, the function removes one of the confounded factors.

Value

A 'SummarizedExperiment' object with resolved confounders. The object retains its structure, including assays and metadata, but the column data ('colData') is updated to reflect the resolved factors.

See Also

SummarizedExperiment for creating and handling 'SummarizedExperiment' objects.

Examples

# Load necessary libraries
library(SummarizedExperiment)
library(dplyr)

# Sample annotations
sample_annotations <- data.frame(
  sample_id = paste0("Sample", seq(1, 9)),
  factor_of_interest = c(rep("treated", 4), rep("untreated", 5)),
  A = c("a1", "a2", "a1", "a2", "a1", "a2", "a1", "a2", "a3"),
  B = c("b1", "b1", "b2", "b1", "b1", "b1", "b2", "b1", "b3"),
  C = c("c1", "c1", "c1", "c1", "c1", "c1", "c1", "c1", "c3"),
  stringsAsFactors = FALSE
)

# Simulated assay data
assay_data <- matrix(rnorm(100 * 9), nrow = 100, ncol = 9)

# Row data (e.g., gene annotations)
row_data <- data.frame(gene_id = paste0("Gene", seq_len(100)))

# Create SummarizedExperiment object
se <- SummarizedExperiment(
  assays = list(counts = assay_data),
  rowData = row_data,
  colData = DataFrame(sample_annotations)
)

# Apply the function to resolve confounders
se_resolved <- resolve_complete_confounders_of_non_interest(se, A, B, C)

# View the updated column data
colData(se_resolved)

stemangiola/tidyBulk documentation built on Nov. 21, 2024, 8:14 a.m.