% Generated by roxygen2: do not edit by hand

% Please edit documentation in R/EnrichCircoBar.R

\name{enrich_circo_bar}

\alias{enrich_circo_bar}

\title{Combine and Visualize Data with Circular Bar Chart}

\usage{

enrich_circo_bar(data_list)

}

\arguments{

\item{data_list}{A list of data frames to be combined.}

}

\value{

A `ggplot` object representing the Circular Bar Chart.

}

\description{

This function combines multiple data frames, arranges them, and visualizes the combined data

in a Circular Bar Chart using the 'ggplot2' and 'ggalluvial' packages.

}

\examples{

# Create sample data frames for each enrichment category

# 1. Biological Process (BP)

filtered_data_BP <- data.frame(

  Description = c(

    "immune response",

    "cell proliferation",

    "signal transduction",

    "apoptotic process",

    "metabolic process"

  ),

  Count = c(120, 85, 150, 60, 95),

  color = c(

    "#1f77b4",  # blue

    "#ff7f0e",  # orange

    "#2ca02c",  # green

    "#d62728",  # red

    "#9467bd"   # purple

  ),

  stringsAsFactors = FALSE

)

# 2. Cellular Component (CC)

filtered_data_CC <- data.frame(

  Description = c(

    "nucleus",

    "cytoplasm",

    "membrane",

    "mitochondrion",

    "extracellular space"

  ),

  Count = c(90, 110, 75, 65, 80),

  color = c(

    "#1f77b4",

    "#ff7f0e",

    "#2ca02c",

    "#d62728",

    "#9467bd"

  ),

  stringsAsFactors = FALSE

)

# 3. Molecular Function (MF)

filtered_data_MF <- data.frame(

  Description = c(

    "protein binding",

    "DNA binding",

    "enzyme activity",

    "transporter activity",

    "receptor activity"

  ),

  Count = c(140, 130, 100, 70, 90),

  color = c(

    "#1f77b4",

    "#ff7f0e",

    "#2ca02c",

    "#d62728",

    "#9467bd"

  ),

  stringsAsFactors = FALSE

)

# 4. Disease Ontology (DO)

filtered_data_DO <- data.frame(

  Description = c(

    "cancer",

    "cardiovascular disease",

    "neurological disorder",

    "metabolic disease",

    "infectious disease"

  ),

  Count = c(200, 150, 120, 90, 160),

  color = c(

    "#1f77b4",

    "#ff7f0e",

    "#2ca02c",

    "#d62728",

    "#9467bd"

  ),

  stringsAsFactors = FALSE

)

# 5. Reactome Pathways

filtered_data_Reactome <- data.frame(

  Description = c(

    "Cell Cycle",

    "Apoptosis",

    "DNA Repair",

    "Signal Transduction",

    "Metabolism of Proteins"

  ),

  Count = c(110, 95, 80, 130, 85),

  color = c(

    "#1f77b4",

    "#ff7f0e",

    "#2ca02c",

    "#d62728",

    "#9467bd"

  ),

  stringsAsFactors = FALSE

)

# 6. KEGG Pathways

filtered_data_kegg <- data.frame(

  Description = c(

    "PI3K-Akt signaling pathway",

    "MAPK signaling pathway",

    "NF-kappa B signaling pathway",

    "JAK-STAT signaling pathway",

    "Toll-like receptor signaling pathway"

  ),

  Count = c(175, 160, 145, 130, 155),

  color = c(

    "#1f77b4",

    "#ff7f0e",

    "#2ca02c",

    "#d62728",

    "#9467bd"

  ),

  stringsAsFactors = FALSE

)

# Combine all filtered data frames into a list

data_list <- list(

  BP = filtered_data_BP,

  CC = filtered_data_CC,

  MF = filtered_data_MF,

  DO = filtered_data_DO,

  Reactome = filtered_data_Reactome,

  KEGG = filtered_data_kegg

)

# Create the Circular Bar Chart

combined_and_visualized_data <- enrich_circo_bar(data_list)

}