PomaDensity: Density Plots

In pcastellanoescuder/POMA_package: Tools for Omics Data Analysis

Density Plots

Description

PomaDensity generates a density plot for samples and features. This function can be used for data exploration (e.g., comparison between pre and post normalized datasets).

Usage

PomaDensity(
  data,
  x = "samples",
  outcome = NULL,
  feature_name = NULL,
  theme_params = list(legend_title = FALSE)
)

Arguments

data	A SummarizedExperiment object.
x	Character. Options are "samples" (to visualize sample density plots) and "features" (to visualize feature density plots). Default is "samples".
outcome	Character. Indicates the name of the colData column to be used as the outcome factor. Default is NULL (first factor variable in colData).
feature_name	Character vector. Indicates the feature/s to display. Default is NULL (all features will be displayed).
theme_params	List. Indicates theme_poma parameters.

Value

A ggplot object.

Author(s)

Pol Castellano-Escuder

Examples

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

# Sample density plots
data %>%
  PomaDensity(x = "samples",
              outcome = NULL)

# Sample density plots with covariate as outcome
data %>%
  PomaDensity(x = "samples",
              outcome = "gender") # change outcome

# All feature density plots
data %>%
  PomaDensity(x = "features",
              theme_params = list(legend_position = "none"))

# Specific feature density plots
data %>% 
  PomaDensity(x = "features", 
              feature_name = c("ornithine", "orotate"))

pcastellanoescuder/POMA_package documentation built on Nov. 28, 2024, 1:23 p.m.