In pcastellanoescuder/POMA: Tools for Omics Data Analysis

Compiled date: r Sys.Date()

Last edited: 2023-12-14

License: r packageDescription("POMA")[["License"]]

knitr::opts_chunk$set(
  collapse = TRUE,
  fig.align = "center",
  comment = ">"
)

Installation

Run the following code to install the Bioconductor version of package.

# install.packages("BiocManager")
BiocManager::install("POMA")

Load Packages

library(POMA)
library(ggtext)
library(patchwork)

Load Data and Imputation

Let's create a cleaned SummarizedExperiment object from the sample data st000336 to explore the normalization effects.

example_data <- st000336 %>% 
  PomaImpute() # KNN imputation

example_data

Normalization

Here we will evaluate the normalization methods that POMA offers on the same SummarizedExperiment object to compare them [@normalization].

none <- PomaNorm(example_data, method = "none")
auto_scaling <- PomaNorm(example_data, method = "auto_scaling")
level_scaling <- PomaNorm(example_data, method = "level_scaling")
log_scaling <- PomaNorm(example_data, method = "log_scaling")
log_transformation <- PomaNorm(example_data, method = "log_transform")
vast_scaling <- PomaNorm(example_data, method = "vast_scaling")
log_pareto <- PomaNorm(example_data, method = "log_pareto")

Normalization effect on data dimensions

When we check for the dimension of the data after normalization we can see that all methods have the same effect on data dimension. PomaNorm only modifies the data dimension when the dataset contains only-zero features or zero-variance features.

dim(SummarizedExperiment::assay(none))
dim(SummarizedExperiment::assay(auto_scaling))
dim(SummarizedExperiment::assay(level_scaling))
dim(SummarizedExperiment::assay(log_scaling))
dim(SummarizedExperiment::assay(log_transformation))
dim(SummarizedExperiment::assay(vast_scaling))
dim(SummarizedExperiment::assay(log_pareto))

Normalization effect on samples

Here we can evaluate the normalization effects on samples [@normalization].

a <- PomaBoxplots(none, 
                  x = "samples") +
  ggplot2::ggtitle("Not Normalized")

b <- PomaBoxplots(auto_scaling, 
                  x = "samples", 
                  theme_params = list(legend_title = FALSE, legend_position = "none")) +
  ggplot2::ggtitle("Auto Scaling") +
  ggplot2::theme(axis.text.x = ggplot2::element_blank())

c <- PomaBoxplots(level_scaling, 
                  x = "samples", 
                  theme_params = list(legend_title = FALSE, legend_position = "none")) +
  ggplot2::ggtitle("Level Scaling") +
  ggplot2::theme(axis.text.x = ggplot2::element_blank())

d <- PomaBoxplots(log_scaling, 
                  x = "samples", 
                  theme_params = list(legend_title = FALSE, legend_position = "none")) +
  ggplot2::ggtitle("Log Scaling") +
  ggplot2::theme(axis.text.x = ggplot2::element_blank())

e <- PomaBoxplots(log_transformation, 
                  x = "samples", 
                  theme_params = list(legend_title = FALSE, legend_position = "none")) +
  ggplot2::ggtitle("Log Transformation") +
  ggplot2::theme(axis.text.x = ggplot2::element_blank())

f <- PomaBoxplots(vast_scaling, 
                  x = "samples", 
                  theme_params = list(legend_title = FALSE, legend_position = "none")) +
  ggplot2::ggtitle("Vast Scaling") +
  ggplot2::theme(axis.text.x = ggplot2::element_blank())

g <- PomaBoxplots(log_pareto, 
                  x = "samples", 
                  theme_params = list(legend_title = FALSE, legend_position = "none")) +
  ggplot2::ggtitle("Log Pareto") +
  ggplot2::theme(axis.text.x = ggplot2::element_blank())

a  
(b + c + d) / (e + f + g)

Normalization effect on features

Here we can evaluate the normalization effects on features.

h <- PomaDensity(none, 
                 x = "features", 
                 theme_params = list(legend_title = FALSE, legend_position = "none")) +
  ggplot2::ggtitle("Not Normalized")

i <- PomaDensity(auto_scaling, 
                 x = "features", 
                 theme_params = list(legend_title = FALSE, legend_position = "none")) +
  ggplot2::ggtitle("Auto Scaling") +
  ggplot2::theme(axis.title.x = ggplot2::element_blank(),
                 axis.title.y = ggplot2::element_blank())

j <- PomaDensity(level_scaling, 
                 x = "features", 
                 theme_params = list(legend_title = FALSE, legend_position = "none")) +
  ggplot2::ggtitle("Level Scaling") +
  ggplot2::theme(axis.title.x = ggplot2::element_blank(),
                 axis.title.y = ggplot2::element_blank())

k <- PomaDensity(log_scaling, 
                 x = "features", 
                 theme_params = list(legend_title = FALSE, legend_position = "none")) +
  ggplot2::ggtitle("Log Scaling") +
  ggplot2::theme(axis.title.x = ggplot2::element_blank(),
                 axis.title.y = ggplot2::element_blank())

l <- PomaDensity(log_transformation, 
                 x = "features", 
                 theme_params = list(legend_title = FALSE, legend_position = "none")) +
  ggplot2::ggtitle("Log Transformation") +
  ggplot2::theme(axis.title.x = ggplot2::element_blank(),
                 axis.title.y = ggplot2::element_blank())

m <- PomaDensity(vast_scaling, 
                 x = "features", 
                 theme_params = list(legend_title = FALSE, legend_position = "none")) +
  ggplot2::ggtitle("Vast Scaling") +
  ggplot2::theme(axis.title.x = ggplot2::element_blank(),
                 axis.title.y = ggplot2::element_blank())

n <- PomaDensity(log_pareto, 
                 x = "features", 
                 theme_params = list(legend_title = FALSE, legend_position = "none")) +
  ggplot2::ggtitle("Log Pareto") +
  ggplot2::theme(axis.title.x = ggplot2::element_blank(),
                 axis.title.y = ggplot2::element_blank())

h  
(i + j + k) / (l + m + n)