PomaLM: Linear Models
In pcastellanoescuder/POMA_package: Tools for Omics Data Analysis

PomaLM

R Documentation

Linear Models

Description

PomaLM performs a linear model on a SummarizedExperiment object.

Usage

PomaLM(data, x = NULL, y = NULL, adjust = "fdr")

Arguments

`data`	A `SummarizedExperiment` object.
`x`	Character vector. Indicates the names of independent variables. If it's NULL (default), all features will be used.
`y`	Character. Indicates the name of `colData` numeric columns to be used as dependent variable. If it's set to NULL, the first numeric variable in `colData` will be used as the dependent variable.
`adjust`	Character. Multiple comparisons correction method to adjust p-values. Available options are: "fdr" (false discovery rate), "holm", "hochberg", "hommel", "bonferroni", "BH" (Benjamini-Hochberg), and "BY" (Benjamini-Yekutieli).

Value

A list with results including plots and tables.

Author(s)

Pol Castellano-Escuder

Examples

data <- POMA::st000284 %>% # Example SummarizedExperiment object included in POMA
  PomaImpute() %>% 
  PomaNorm()

## Output is a list with objects `lm_table` (tibble) and `regression_plot` (ggplot2 object)
# Perform linear model with all features
data %>% 
  PomaLM()

# Perform linear model with two features
data %>% 
  PomaLM(x = c("x1_methyladenosine", "x2_deoxyuridine"))

pcastellanoescuder/POMA_package documentation built on Oct. 13, 2024, 8:47 p.m.