Type:	Package
Title:	Analysis and Visualization of Multi-Omics Data
Version:	1.0.5
Maintainer:	Dongyue Yu <yudongyue@mail.nankai.edu.cn>
Description:	A tool for comprehensive transcriptomic data analysis, with a focus on transcript-level data preprocessing, expression profiling, differential expression analysis, and functional enrichment. It enables researchers to identify key biological processes, disease biomarkers, and gene regulatory mechanisms. 'TransProR' is aimed at researchers and bioinformaticians working with RNA-Seq data, providing an intuitive framework for in-depth analysis and visualization of transcriptomic datasets. The package includes comprehensive documentation and usage examples to guide users through the entire analysis pipeline. The differential expression analysis methods incorporated in the package include 'limma' (Ritchie et al., 2015, <doi:10.1093/nar/gkv007>; Smyth, 2005, <doi:10.1007/0-387-29362-0_23>), 'edgeR' (Robinson et al., 2010, <doi:10.1093/bioinformatics/btp616>), 'DESeq2' (Love et al., 2014, <doi:10.1186/s13059-014-0550-8>), and Wilcoxon tests (Li et al., 2022, <doi:10.1186/s13059-022-02648-4>), providing flexible and robust approaches to RNA-Seq data analysis. For more information, refer to the package vignettes and related publications.
Imports:	magrittr, stats, dplyr, rlang, tibble, sva, DESeq2, utils, edgeR, limma, ggplot2, ggVennDiagram, ggdensity, ggpubr, ggtree, hrbrthemes, grid, ggraph, tidygraph, tidyr, stringr, geomtextpath, ggnewscale, Hmisc, circlize, graphics, spiralize, ComplexHeatmap, grDevices
Suggests:	prettydoc, knitr, ggtreeExtra, rmarkdown, systemfonts
VignetteBuilder:	knitr
License:	MIT + file LICENSE
Encoding:	UTF-8
URL:	https://github.com/SSSYDYSSS/TransProRBook
BugReports:	https://github.com/SSSYDYSSS/TransProR/issues
LazyData:	true
RoxygenNote:	7.3.2
Depends:	R (≥ 4.3.0)
NeedsCompilation:	no
Packaged:	2025-06-16 07:45:14 UTC; Yu
Author:	Dongyue Yu [aut, cre, cph]
Repository:	CRAN
Date/Publication:	2025-06-16 08:20:02 UTC

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling 'rhs(lhs)'.

Process and Correct Batch Effects in TCGA's normal tissue and GTEX Data

Description

The function first extracts histological types from the provided TCGA's normal tissue data set. After displaying these types, the user is prompted to input specific types to retain. The data is then filtered based on this input. The GTEX and TCGA's normal tissue datasets are then combined and batch corrected.

Note: This function assumes that TCGA's normal samples and GTEX samples represent different batches.

Usage

Combat_Normal(
  TCGA_normal_data_path,
  gtex_data_path,
  CombatNormal_output_path,
  auto_mode = FALSE,
  default_input = "11,12"
)

Arguments

Details

This function takes a TCGA's normal tissue data set and a pre-saved GTEX data set, asks the user for specific TCGA's normal tissue types to retain, then merges the two datasets. The merged dataset is then corrected for batch effects using the ComBat_seq function from the 'sva' package.

The ComBat_seq function from the 'sva' package is used to correct batch effects. The function requires the 'sva' package to be installed and loaded externally.

The example code uses 'tempfile()' to generate temporary paths dynamically during execution. These paths are valid during the 'R CMD check' process, even if no actual files exist, because ‘tempfile()' generates a unique file path that does not depend on the user’s file system. Using 'tempfile()' ensures that the example code does not rely on specific external files and avoids errors during 'R CMD check'. CRAN review checks for documentation correctness and syntax parsing, not the existence of actual files, as long as the example code is syntactically valid.

Value

A data.frame with corrected values after the ComBat_seq adjustment. Note that this function also saves the combat_count_df data as an RDS file at the specified output path.

See Also

ComBat_seq

Examples

TCGA_normal_file <- system.file("extdata",
                                "SKCM_Skin_TCGA_exp_normal_test.rds",
                                package = "TransProR")
gtex_file <- system.file("extdata", "Skin_SKCM_Gtex_test.rds", package = "TransProR")
output_file <- file.path(tempdir(), "SKCM_Skin_Combat_Normal_TCGA_GTEX_count.rds")

SKCM_Skin_Combat_Normal_TCGA_GTEX_count <- Combat_Normal(
  TCGA_normal_data_path = TCGA_normal_file,
  gtex_data_path = gtex_file,
  CombatNormal_output_path = output_file,
  auto_mode = TRUE,
  default_input = "skip"
)
head(SKCM_Skin_Combat_Normal_TCGA_GTEX_count)[1:5, 1:5]

Function to Create a Venn Diagram of DEGs with Custom Colors

Description

This function creates a Venn Diagram using the 'ggVennDiagram' package. It allows customization of various aesthetic elements of the diagram, including colors.

Usage

Contrast_Venn(
  all_degs_venn,
  edge_colors,
  name_color,
  fill_colors,
  label_size = 4,
  edge_size = 3
)

Arguments

Value

A 'ggplot' object representing the Venn Diagram.

Examples

data("all_degs_venn", package = "TransProR")

edge_colors <- c("#1b62bb","#13822e","#332c3a","#9e2d39")
name_color <- c("#1b64bb","#13828e","#337c3a","#9e9d39")
fill_colors <- c("#e3f2fa", "#0288d1")

Contrast_degs_venn <- Contrast_Venn(all_degs_venn, edge_colors, name_color, fill_colors)

Differential Gene Expression Analysis using 'DESeq2'

Description

'DESeq2': Differential gene expression analysis based on the negative binomial distribution. This function utilizes the 'DESeq2' package to conduct differential gene expression analysis. It processes tumor and normal expression data, applies DESeq2 analysis, and outputs the results along with information on gene expression changes.

Usage

DESeq2_analyze(
  tumor_file,
  normal_file,
  output_file,
  logFC = 2.5,
  p_value = 0.01
)

Arguments

Details

The DESeq2 methodology is based on modeling count data using a negative binomial distribution, which allows for handling the variability observed in gene expression data, especially in small sample sizes. This approach is well-suited for RNA-Seq data analysis.

Value

A data frame of differential expression results.

References

DESeq2:Differential gene expression analysis based on the negative binomial distribution. For more information, visit the page: https://docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/Expression_mRNA_Pipeline/

Examples

# Define file paths for tumor and normal data from the data folder
tumor_file <- system.file("extdata",
                          "removebatch_SKCM_Skin_TCGA_exp_tumor_test.rds",
                          package = "TransProR")
normal_file <- system.file("extdata",
                           "removebatch_SKCM_Skin_Normal_TCGA_GTEX_count_test.rds",
                           package = "TransProR")
output_file <- file.path(tempdir(), "DEG_DESeq2.rds")

DEG_DESeq2 <- DESeq2_analyze(
  tumor_file = tumor_file,
  normal_file = normal_file,
  output_file = output_file,
  2.5,
  0.01
)

# View the top 5 rows of the result
head(DEG_DESeq2, 5)

Differential Gene Expression Analysis Using Wilcoxon Rank-Sum Test

Description

This function performs differential gene expression analysis using Wilcoxon rank-sum tests. It reads tumor and normal expression data, performs TMM normalization using 'edgeR', and uses Wilcoxon rank-sum tests to identify differentially expressed genes.

Usage

Wilcoxon_analyze(
  tumor_file,
  normal_file,
  output_file,
  logFC_threshold = 2.5,
  fdr_threshold = 0.05
)

Arguments

Value

A data frame of differential expression results.

References

Li, Y., Ge, X., Peng, F., Li, W., & Li, J. J. (2022). Exaggerated False Positives by Popular Differential Expression Methods When Analyzing Human Population Samples. Genome Biology, 23(1), 79. DOI: https://doi.org/10.1186/s13059-022-02648-4.

Examples

# Define file paths for tumor and normal data from the data folder
tumor_file <- system.file("extdata",
                          "removebatch_SKCM_Skin_TCGA_exp_tumor_test.rds",
                          package = "TransProR")
normal_file <- system.file("extdata",
                           "removebatch_SKCM_Skin_Normal_TCGA_GTEX_count_test.rds",
                           package = "TransProR")
output_file <- file.path(tempdir(), "Wilcoxon_rank_sum_testoutRst.rds")

# Run the Wilcoxon rank sum test
outRst <- Wilcoxon_analyze(
  tumor_file = tumor_file,
  normal_file = normal_file,
  output_file = output_file,
  logFC_threshold = 2.5,
  fdr_threshold = 0.01
)

# View the top 5 rows of the result
head(outRst, 5)

Add a boxplot layer to a 'ggtree' plot

Description

This function adds a boxplot layer to an existing ‘ggtree' plot object using ggtreeExtra’s geom_fruit for boxplots. It is primarily used to display statistical summaries of the data related to gene expressions or other metrics.

Usage

add_boxplot(
  p,
  data,
  fill_color = "#f28131",
  alpha = 0.6,
  offset = 0.22,
  pwidth = 0.5
)

Arguments

Value

A 'ggtree' plot object with the added boxplot layer.

Examples

# Check and load required packages
if (requireNamespace("ggtreeExtra", quietly = TRUE) &&
 requireNamespace("ggplot2", quietly = TRUE)) {
  library(ggtreeExtra)
  library(ggplot2)

  file_path <- system.file("extdata", "p_tree_test.rds", package = "TransProR")
  p <- readRDS(file_path)

  # Create boxplot data frame
  boxplot_data <- data.frame(
    Sample = rep(c("Species_A", "Species_B", "Species_C", "Species_D"), each = 30),
    value = c(
      rnorm(30, mean = 5, sd = 1),   # Data for Species_A
      rnorm(30, mean = 7, sd = 1.5), # Data for Species_B
      rnorm(30, mean = 6, sd = 1.2), # Data for Species_C
      rnorm(30, mean = 8, sd = 1.3)  # Data for Species_D
    )
  )

  # Call add_boxplot function to add boxplot layer
  p_with_boxplot <- add_boxplot(p, boxplot_data)
} else {
  message("Required packages 'ggtreeExtra' and 'ggplot2' are not installed.")
}

Add a new tile layer with dynamic scales to a 'ggtree' plot

Description

This function adds a new tile layer to an existing 'ggtree' plot object, allowing for separate scales for fill and alpha transparency. This is useful when you want to add additional data layers without interfering with the existing scales in the plot. It utilizes the 'ggnewscale' package to reset scales for new layers.

Usage

add_new_tile_layer(
  p,
  data,
  gene_colors,
  gene_label,
  alpha_value = c(0.3, 0.9),
  offset = 0.02,
  pwidth = 2
)

Arguments

Value

A 'ggtree' plot object with the added tile layer and new scales.

Examples

# Check and load required packages
if (requireNamespace("ggtreeExtra", quietly = TRUE) &&
 requireNamespace("ggplot2", quietly = TRUE) &&
  requireNamespace("ggnewscale", quietly = TRUE)) {
  library(ggtreeExtra)
  library(ggplot2)
  library(ggnewscale)

  file_path <- system.file("extdata", "p_tree_test.rds", package = "TransProR")
  p <- readRDS(file_path)

  # Create new expression data
  new_expression_data <- data.frame(
    Sample = rep(c("Species_A", "Species_B", "Species_C", "Species_D"), each = 3),
    Gene = rep(c("Gene6", "Gene7", "Gene8"), times = 4),
    Value = runif(12, min = 0, max = 1)  # Randomly generate expression values between 0 and 1
  )

  # Define new gene colors
  new_gene_colors <- c(
    Gene6 = "#0b5f63",
    Gene7 = "#074d41",
    Gene8 = "#1f5e27"
  )

  # Define gene label and alpha values
  gene_label <- "New Genes"
  alpha_value <- c(0.3, 0.9)

  # Add new tile layer
  p_with_new_layer <- add_new_tile_layer(
    p,
    new_expression_data,
    new_gene_colors,
    gene_label,
    alpha_value,
    offset = 0.02,
    pwidth = 2
  )
} else {
  message("Required packages 'ggtreeExtra', 'ggplot2', and 'ggnewscale' are not installed.")
}

Adjust Alpha Scale for Data Visualization

Description

This function dynamically adjusts the transparency scale for visualizations, especially useful when the range of data values varies significantly across different sources. It modifies the transparency scale based on the range of values present in the data, ensuring that the visualization accurately reflects variations within the data.

Usage

adjust_alpha_scale(data, name, range = c(0.2, 0.8))

Arguments

Value

A ggplot2 alpha scale adjustment layer.

Examples

# Assuming 'data' is a DataFrame with a 'value' column
plot_data <- data.frame(value = c(10, 20, 30, 40, 50))
ggplot2::ggplot(plot_data, ggplot2::aes(x = 1:nrow(plot_data), y = value)) +
  ggplot2::geom_point(ggplot2::aes(alpha = value)) +
  adjust_alpha_scale(plot_data, "Transparency Scale")

Adjust Color Tone by Modifying Saturation and Luminance

Description

This function adjusts the saturation and luminance of a given color. It works by converting the color from RGB to Luv color space, applying the scaling factors to the saturation and luminance, and then converting it back to RGB.

Usage

adjust_color_tone(color, saturation_scale, luminance_scale)

Arguments

Value

Returns a color in hexadecimal format adjusted according to the provided scales.

Examples

  adjusted_color <- adjust_color_tone("#FF0000", saturation_scale = 0.8, luminance_scale = 1.2)
  print(adjusted_color)

Adjust and Export Pathway Analysis Results

Description

This function processes a dataframe containing fgsea results. It adjusts pathway names by removing underscores, converting to lowercase, then capitalizing the first letter, and joining the components with spaces. It selects and merges the top upregulated and downregulated pathways based on enrichment score (ES) and p-value.

Usage

adjust_export_pathway(fgseaRes, nTop = 10)

Arguments

Value

A vector containing combined top upregulated and downregulated pathways.

Examples

# Create a synthetic fgseaRes dataframe
fgseaRes <- data.frame(
 pathway = c("KEGG_APOPTOSIS",
             "GO_CELL_CYCLE",
             "REACTOME_DNA_REPAIR",
             "KEGG_METABOLISM",
             "GO_TRANSPORT"),
 ES = c(0.45, -0.22, 0.56, -0.35, 0.33),
 pval = c(0.001, 0.02, 0.0003, 0.05, 0.01)
)

# Run the function to get top pathways
result <- adjust_export_pathway(fgseaRes = fgseaRes, nTop = 2)

All DEGs Venn Diagram Data

Description

A dataset containing the differentially expressed genes (DEGs) from four different statistical analysis methods: DESeq2, edgeR, limma, and Wilcoxon test. This dataset is used for generating Venn diagrams to compare the overlap of DEGs identified by different methods.

Usage

data(all_degs_venn)

Format

A list with the following components:

DESeq2

A vector of gene IDs or gene symbols identified as DEGs by the DESeq2 method.

edgeR

A vector of gene IDs or gene symbols identified as DEGs by the edgeR method.

limma

A vector of gene IDs or gene symbols identified as DEGs by the limma method.

Wilcoxon_test

A vector of gene IDs or gene symbols identified as DEGs by the Wilcoxon test method.

Source

The data was derived from differential expression analyses performed on a gene expression dataset using four commonly used statistical methods (DESeq2, edgeR, limma, and Wilcoxon test).

Examples

data(all_degs_venn)
# Example of plotting a Venn diagram using the dataset

edge_colors <- c("#1b62bb","#13822e","#332c3a","#9e2d39")
name_color <- c("#1b64bb","#13828e","#337c3a","#9e9d39")
fill_colors <- c("#e3f2fa", "#0288d1")

Contrast_degs_venn <- Contrast_Venn(all_degs_venn, edge_colors, name_color, fill_colors)

Add multiple layers to a 'ggtree' plot for visualizing gene expression and enrichment data

Description

This function sequentially adds multiple layers to a 'ggtree' plot, including gene expression data, boxplots for statistical summaries, and additional tile layers for pathway enrichment scores from SSGSEA and GSVA analyses. It utilizes separate functions for adding each type of layer and allows for the specification of gene colors as well as adjustments in aesthetics for each layer. The function is designed to work with specific data structures and assumes all functions for adding layers are defined and available.

Usage

circos_fruits(
  p,
  long_format_HeatdataDeseq,
  ssgsea_kegg_HeatdataDeseq,
  gsva_kegg_HeatdataDeseq,
  gene_colors
)

Arguments

Value

A 'ggtree' plot object with multiple layers added for comprehensive visualization.

Examples

# Check and load required packages
if (requireNamespace("ggtreeExtra", quietly = TRUE) &&
 requireNamespace("ggplot2", quietly = TRUE)) {
  library(ggtreeExtra)
  library(ggplot2)

  # Example data for gene expression, SSGSEA, and GSVA
  file_path <- system.file("extdata", "p_tree_test.rds", package = "TransProR")
  p <- readRDS(file_path)

  # Create gene expression data frame (long_format_HeatdataDeseq)
  long_format_HeatdataDeseq <- data.frame(
    Sample = rep(c("Species_A", "Species_B", "Species_C", "Species_D"), each = 5),
    Genes = rep(paste0("Gene", 1:5), times = 4),
    Value = runif(20, min = 0, max = 1)  # Randomly generate expression values between 0 and 1
  )

  # Create SSGSEA analysis results data frame (ssgsea_kegg_HeatdataDeseq)
  ssgsea_kegg_HeatdataDeseq <- data.frame(
    Sample = rep(c("Species_A", "Species_B", "Species_C", "Species_D"), each = 3),
    Genes = rep(c("Pathway1", "Pathway2", "Pathway3"), times = 4),
    Value = runif(12, min = 0, max = 1)  # Randomly generate enrichment scores between 0 and 1
  )

  # Create GSVA analysis results data frame (gsva_kegg_HeatdataDeseq)
  gsva_kegg_HeatdataDeseq <- data.frame(
    Sample = rep(c("Species_A", "Species_B", "Species_C", "Species_D"), each = 4),
    Genes = rep(c("PathwayA", "PathwayB", "PathwayC", "PathwayD"), times = 4),
    Value = runif(16, min = 0, max = 1)  # Randomly generate enrichment scores between 0 and 1
  )

  # Define gene and pathway colors (named vector), including all genes and pathways
  gene_colors <- c(
    # Genes for gene expression
    Gene1 = "#491588",
    Gene2 = "#301b8d",
    Gene3 = "#1a237a",
    Gene4 = "#11479c",
    Gene5 = "#0a5797",
    # Pathways for SSGSEA
    Pathway1 = "#0b5f63",
    Pathway2 = "#074d41",
    Pathway3 = "#1f5e27",
    # Pathways for GSVA
    PathwayA = "#366928",
    PathwayB = "#827729",
    PathwayC = "#a1d99b",
    PathwayD = "#c7e9c0"
  )

  # Call circos_fruits function to add multiple layers
  final_plot <- circos_fruits(
    p,
    long_format_HeatdataDeseq,
    ssgsea_kegg_HeatdataDeseq,
    gsva_kegg_HeatdataDeseq,
    gene_colors
  )
} else {
  message("Required packages 'ggtreeExtra' and 'ggplot2' are not installed.")
}

Process and Correct Batch Effects in Tumor Data

Description

The function first extracts histological types from the provided tumor data set. After displaying these types, the user is prompted to input specific types to retain. The data is then filtered based on this input.

Note: This example assumes that different tumor types represent different batches in a general sense. Users need to adjust the batch and group vectors based on real-life scenarios.

Usage

combat_tumor(
  tumor_data_path,
  CombatTumor_output_path,
  auto_mode = FALSE,
  default_input = "01,06"
)

Arguments

Details

This function takes a tumor data set, asks the user for specific tumor types to retain, and then corrects for batch effects using the ComBat_seq function from the 'sva' package.

The ComBat_seq function from the sva package is used to correct batch effects. The function requires the 'sva' package to be installed and loaded externally.

Value

A data.frame with corrected values after the ComBat_seq adjustment. Note that this function also saves the combat_count_df data as an RDS file at the specified output path.

See Also

ComBat_seq

Examples

tumor_file <- system.file("extdata",
                          "SKCM_Skin_TCGA_exp_tumor_test.rds",
                          package = "TransProR")
output_file <- file.path(tempdir(), "SKCM_combat_count.rds")

  SKCM_combat_count <- combat_tumor(
  tumor_data_path = tumor_file,
  CombatTumor_output_path = output_file,
  auto_mode = TRUE,
  default_input = "01,06"
)

head(SKCM_combat_count)[1:5, 1:5]

Compare and merge specific columns from two DEG data frames

Description

This function takes two DEG data frames, inner joins them by a specified gene column, checks if a specified column is identical across both data frames, and merges them if they are. The resulting data frame will have a merged column named after the compared column.

Usage

compare_merge(df1, df2, by_gene, compare_col, suffixes, df_name)

Arguments

Value

A data frame with processed columns.

Examples

# Create simulated DESeq2 data
DEG_deseq2 <- data.frame(
  Gene = c("Gene1", "Gene2", "Gene3", "Gene4", "Gene5"),
  change = c("up", "down", "no_change", "up", "down"),
  log2FoldChange = c(2.5, -3.2, 0.1, 1.8, -2.5),
  pvalue = c(0.01, 0.05, 0.9, 0.02, 0.03)
)

# Display the first 5 rows of the DESeq2 data
head(DEG_deseq2, 5)

# Create simulated edgeR data
DEG_edgeR <- data.frame(
  Gene = c("Gene1", "Gene2", "Gene3", "Gene4", "Gene5"),
  change = c("up", "down", "no_change", "no_change", "up"),
  log2FoldChange = c(2.3, -3.1, 0.2, 0.1, 2.7),
  pvalue = c(0.02, 0.04, 0.8, 0.6, 0.01)
)

# Display the first 5 rows of the edgeR data
head(DEG_edgeR, 5)

# Merge the DESeq2 and edgeR data
deseq2_edgeR <- compare_merge(
  df1 = DEG_deseq2,
  df2 = DEG_edgeR,
  by_gene = "Gene",
  compare_col = "change",
  suffixes = c("_1", "_2"),
  df_name = "deseq2_edgeR"
)

Create a base plot with gene expression data on a phylogenetic tree

Description

This function creates a base plot using 'ggtree' and 'ggtreeExtra' libraries, adding gene expression data as colored tiles to the plot. It allows for dynamic coloring of the genes and includes adjustments for alpha transparency based on the expression value.

Usage

create_base_plot(p, data, gene_colors, gene_label = "Gene")

Arguments

Value

A 'ggtree' plot object with the gene expression data added.

Examples

# Check and load required packages
if (requireNamespace("ggtreeExtra", quietly = TRUE) &&
 requireNamespace("ggplot2", quietly = TRUE)) {
  library(ggtreeExtra)
  library(ggplot2)

  file_path <- system.file("extdata", "p_tree_test.rds", package = "TransProR")
  p <- readRDS(file_path)

  # Create gene expression data frame
  expression_data <- data.frame(
    Sample = rep(c("Species_A", "Species_B", "Species_C", "Species_D"), each = 5),
    Gene = rep(paste0("Gene", 1:5), times = 4),
    Value = runif(20, min = 0, max = 1)  # Randomly generate expression values between 0 and 1
  )

  # Define gene colors (named vector)
  gene_colors <- c(
    Gene1 = "#491588",
    Gene2 = "#301b8d",
    Gene3 = "#1a237a",
    Gene4 = "#11479c",
    Gene5 = "#0a5797"
  )

  # Call create_base_plot function to add gene expression data
  p <- create_base_plot(p, expression_data, gene_colors)
} else {
  message("Required packages 'ggtreeExtra' and 'ggplot2' are not installed.")
}

Function to Filter Differentially Expressed Genes (DEGs)

Description

This function filters out genes based on their expression change status. It returns the names of genes which are not "stable".

Usage

deg_filter(df)

Arguments

Value

A vector of gene names that are differentially expressed.

Examples

DEG_deseq2_file <- system.file("extdata", "DEG_deseq2.rds", package = "TransProR")
DEG_deseq2 <- readRDS(DEG_deseq2_file)
DEG_deseq2_test <- deg_filter(DEG_deseq2)

Draw Dual-Sided Legends on a Plot

Description

This function creates two sets of legends, one on the left and one on the right side of a plot. It displays color-coded legends with labels corresponding to different data categories. Each legend entry consists of a colored rectangle and a text label. The left side legend has text aligned to the right of the color block, while the right side legend has text aligned to the left of the color block.

Usage

drawLegends(
  labels,
  colors,
  legend_width,
  x_positions,
  y_position,
  just_positions,
  text_alignments,
  font_size
)

Arguments

Value

Invisible. This function is called for its side effects of drawing legends on a plot.

Examples

labels <- c("Label1", "Label2", "Label3", "Label4", "Label5", "Label6")
colors <- c("#ff0000", "#00ff00", "#0000ff", "#ffff00", "#ff00ff", "#00ffff")

# Convert to 'unit' objects for grid
grid::grid.roundrect(
  x = grid::unit(0.5, "npc"),  # "npc" stands for normalized parent coordinates
  y = grid::unit(0.5, "npc"),
  width = grid::unit(0.1, "npc"),
  height = grid::unit(0.05, "npc"),
  gp = grid::gpar(fill = "red"),
  r = grid::unit(0.1, "npc")  # rounding radius
)

# Example of drawing legends with specific labels and colors
drawLegends(labels, colors, grid::unit(2, "cm"), c(0.225, 0.75), 0.5,
            list(c("left", "center"), c("right", "center")),
            list("right", "left"), 10)

Differential Gene Expression Analysis using 'edgeR'

Description

This function performs differential gene expression analysis using the 'edgeR' package. It reads tumor and normal expression data, merges them, filters low-expressed genes, normalizes the data, performs edgeR analysis, and outputs the results along with information on gene expression changes.

Usage

edgeR_analyze(
  tumor_file,
  normal_file,
  output_file,
  logFC_threshold = 2.5,
  p_value_threshold = 0.01
)

Arguments

Value

A data frame of differential expression results.

References

edgeR: Differential analysis of sequence read count data. For more information, visit the edgeR Bioconductor page: https://www.bioconductor.org/packages/release/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf

Examples

# Define file paths for tumor and normal data from the data folder
tumor_file <- system.file("extdata",
                         "removebatch_SKCM_Skin_TCGA_exp_tumor_test.rds",
                         package = "TransProR")
normal_file <- system.file("extdata",
                           "removebatch_SKCM_Skin_Normal_TCGA_GTEX_count_test.rds",
                           package = "TransProR")
output_file <- file.path(tempdir(), "DEG_edgeR.rds")

DEG_edgeR <- edgeR_analyze(
  tumor_file = tumor_file,
  normal_file = normal_file,
  output_file = output_file,
  2.5,
  0.01
)

# View the top 5 rows of the result
head(DEG_edgeR, 5)

Combine and Visualize Data with 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.

Usage

enrich_circo_bar(data_list)

Arguments

Value

A 'ggplot' object representing the Circular Bar Chart.

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)

Enrichment Polar Bubble Plot

Description

This function creates a polar bubble plot using 'ggplot2'. It is designed to visually represent data with methods and positional metrics integrated, highlighting specific IDs if necessary.

Usage

enrich_polar_bubble(final_combined_df_with_id_and_position, pal, highlight_ids)

Arguments

Value

A 'ggplot' object representing the enriched polar bubble plot.

Examples

  final_df <- data.frame(id = 1:10, Count = c(10, 20, 30, 40, 50, 60, 70, 80, 90, 100),
                         method = rep("Method1", 10),
                         Description = LETTERS[1:10],
                         point_position = seq(10, 100, 10),
                         test_color = sample(c("red", "blue"), 10, replace = TRUE))
  pal <- c("Method1" = "blue")
  highlight_ids <- c(1, 5, 9)
  enrich_polar_bubble(final_df, pal, highlight_ids)

Draw Chord Diagram with Legends

Description

This function creates a chord diagram from a specified dataframe and draws two sets of legends for it. It adjusts the track height of the chord diagram to optimize space and uses specified colors for the grid. Legends are drawn at specified positions with configurable text alignments and font sizes.

Usage

enrichment_circlize(
  all_combined_df,
  original_colors,
  labels,
  colors,
  labels2,
  colors2,
  font_size = 10
)

Arguments

Value

Invisible, primarily used for its side effects of drawing on a graphics device.

Examples

# Sample Chord Diagram Matrix
all_combined_df <- data.frame(
  A = c(10, 20, 30),
  B = c(15, 25, 35),
  C = c(5, 10, 15)
)
rownames(all_combined_df) <- c("A", "B", "C")

# Colors for the grid of the chord diagram (corresponding to columns of the matrix)
original_colors <- c("red", "green", "blue")

# Name the colors according to the sectors (A, B, C)
names(original_colors) <- colnames(all_combined_df)

# Labels and Colors for the First Legend
labels <- c("Label 1", "Label 2", "Label 3")
colors <- c("yellow", "purple", "cyan")

# Labels and Colors for the Second Legend
labels2 <- c("Label A", "Label B", "Label C")
colors2 <- c("orange", "pink", "brown")

# Font size for the legend texts (optional, default is 10)
font_size <- 10

# Call the enrichment_circlize function with the sample data
# This is just an example; the plot will be rendered in an appropriate graphics context
# such as RStudio's plot pane or an external plotting window.
plot1 <- enrichment_circlize(all_combined_df,
                             original_colors,
                             labels,
                             colors,
                             labels2,
                             colors2,
                             font_size
                             )

Create Spiral Plots with Legends Using 'spiralize' and 'ComplexHeatmap'

Description

This function initializes a spiral plot, adds tracks for pathways and samples, and generates legends based on the sample and pathway information in the provided data frame. It uses 'spiralize' for the spiral plot and 'ComplexHeatmap' for handling legends.

Usage

enrichment_spiral_plots(results)

Arguments

Value

No return value, called for side effects. This function generates spiral plots and adds legends based on sample and pathway information.

Examples

# Example: Creating enrichment spiral plots with legends

# Define the results data frame
results <- data.frame(
  Pathway = c("Pathway1", "Pathway1", "Pathway2", "Pathway2", "Pathway3"),
  Sample = c("Sample1", "Sample1", "Sample2", "Sample2", "Sample3"),
  Value = c(20, 30, 15, 35, 25),
  PathwayColor = c("red", "red", "blue", "blue", "orange"),
  SampleColor = c("green", "green", "purple", "purple", "cyan"),
  stringsAsFactors = FALSE
)

# Create the enrichment spiral plots with legends
enrichment_spiral_plots(results)

Extract and Count Descriptions with Specified Color

Description

This function filters a data frame for specified descriptions, selects the 'Description' and 'Count' columns, and adds a new column with a specified color.

Usage

extract_descriptions_counts(df, descriptions, color)

Arguments

Value

A data frame filtered by descriptions, containing 'Description', 'Count', and a new 'color' column.

Examples

# Generate Sample Input Data for extract_descriptions_counts Function

# Create a sample data frame with 'Description' and 'Count' columns
data <- data.frame(
  Description = c(
    "immunoglobulin production",
    "B cell mediated immunity",
    "T cell activation",
    "antigen processing and presentation",
    "cytokine signaling",
    "natural killer cell activity",
    "phagocytosis",
    "complement activation",
    "antibody-dependent cellular cytotoxicity",
    "regulatory T cell function"
  ),
  Count = c(
    150,  # immunoglobulin production
    200,  # B cell mediated immunity
    175,  # T cell activation
    125,  # antigen processing and presentation
    190,  # cytokine signaling
    160,  # natural killer cell activity
    140,  # phagocytosis
    180,  # complement activation
    130,  # antibody-dependent cellular cytotoxicity
    170   # regulatory T cell function
  ),
  stringsAsFactors = FALSE  # Ensure that strings are not converted to factors
)



descriptions_to_filter <- c("immunoglobulin production", "B cell mediated immunity")
specified_color <- "red"  # You can specify any color you desire
filtered_data_with_color <- extract_descriptions_counts(
  data, descriptions_to_filter,
  specified_color)
print(filtered_data_with_color)

Extract and Store Top Pathways for Each Sample

Description

This function processes a dataframe containing SSGSEA KEGG results. It allows specifying the number of top pathways to extract for each sample based on their scores, and stores these in a new dataframe with sample names and pathway scores.

Usage

extract_ntop_pathways(ssgsea_kegg, nTop = 5)

Arguments

Value

A dataframe with columns 'Pathway', 'Sample', and 'Value' representing the top pathways for each sample.

Examples

# Example: Generating input data for the extract_ntop_pathways function

# Define example pathways
pathways <- c("Pathway_A", "Pathway_B", "Pathway_C", "Pathway_D", "Pathway_E",
              "Pathway_F", "Pathway_G", "Pathway_H", "Pathway_I", "Pathway_J")

# Define example samples
samples <- c("Sample_1", "Sample_2", "Sample_3")

# Generate random SSGSEA KEGG scores between 0 and 1
set.seed(123)  # For reproducibility
ssgsea_scores <- matrix(runif(length(pathways) * length(samples), min = 0, max = 1),
                        nrow = length(pathways), ncol = length(samples),
                        dimnames = list(pathways, samples))

# Convert to a data frame
ssgsea_kegg <- as.data.frame(ssgsea_scores)

# Extract the top 3 pathways for each sample
top_pathways <- extract_ntop_pathways(ssgsea_kegg, nTop = 3)

Extract Positive Pathways from SSGSEA Results and Select Random Samples

Description

This function processes the results of SSGSEA, specifically focusing on KEGG pathways. It extracts pathways with positive values from each sample and randomly selects a subset of them.

Usage

extract_positive_pathways(ssgsea_kegg, max_paths_per_sample = 5)

Arguments

Value

A data frame with selected pathways, samples, and their corresponding values.

Examples

# Example: Generating input data for the extract_positive_pathways function

# Define example pathways
pathways <- c("Pathway_1", "Pathway_2", "Pathway_3", "Pathway_4", "Pathway_5",
              "Pathway_6", "Pathway_7", "Pathway_8", "Pathway_9", "Pathway_10")

# Define example samples
samples <- c("Sample_A", "Sample_B", "Sample_C")

# Generate random SSGSEA KEGG scores including both positive and negative values
set.seed(456)  # For reproducibility
ssgsea_scores <- matrix(rnorm(length(pathways) * length(samples), mean = 0, sd = 1),
                        nrow = length(pathways), ncol = length(samples),
                        dimnames = list(pathways, samples))

# Convert to a data frame
ssgsea_kegg <- as.data.frame(ssgsea_scores)

# Use the extract_positive_pathways function to extract up to 3 positive pathways per sample
selected_positive_pathways <- extract_positive_pathways(ssgsea_kegg, max_paths_per_sample = 3)

Create faceted high-density region plots with optional points and density contours

Description

This function creates faceted high-density region plots using ggdensity for adding optional density rug and density contours, and scatter points. It also adds a regression line and Pearson correlation label. The plot is faceted by a grouping variable.

Usage

facet_density_foldchange(
  data,
  x_var,
  y_var,
  group_var,
  facet_var,
  palette,
  show_points = FALSE,
  show_density = TRUE,
  point_size = 2.5,
  point_alpha = 0.1,
  line_size = 1.6,
  cor_method = "pearson",
  cor_label_pos = c("left", 0.97),
  cor_vjust = NULL
)

Arguments

Value

A 'ggplot' object representing the high-density region plot.

Examples

combined_df_file <- system.file("extdata", "combined_df.rds", package = "TransProR")
combined_df <- readRDS(combined_df_file)
pal2 = c("#2787e0","#1a9ae0","#1dabbf","#00897b","#43a047","#7cb342")
all_facet_density_foldchange_name1 <- facet_density_foldchange(
  data = combined_df,
  x_var = "log2FoldChange_1",
  y_var = "log2FoldChange_2",
  group_var = "name",
  facet_var = "name",
  palette = pal2,
  show_points = TRUE,
  show_density = FALSE,
  point_size = 2,
  point_alpha = 0.1,
  line_size = 1.6,
  cor_method = "pearson",
  cor_label_pos = c("left", "top"),
  cor_vjust = 1
)

Filter Differentially Expressed Genes

Description

This function filters a data frame to identify genes with significant differential expression based on specified thresholds for p-values and log fold change. It allows for flexible input of column names for p-values and log fold change.

Usage

filter_diff_genes(
  data,
  p_val_col = "adj.P.Val",
  log_fc_col = "logFC",
  p_val_threshold = 0.05,
  log_fc_threshold = 1
)

Arguments

Value

A data frame with genes filtered by the specified criteria.

Examples

# Create a sample data frame with p-values and log fold changes
sample_data <- data.frame(
  adj.P.Val = c(0.03, 0.06, 0.02, 0.07),
  logFC = c(1.5, 0.8, -1.2, 1.1),
  gene = c("Gene1", "Gene2", "Gene3", "Gene4")
)

# Use the filter_diff_genes function to filter significant genes
filtered_genes <- filter_diff_genes(sample_data)
print(filtered_genes)

Function to Create a Venn Diagram of DEGs

Description

This function creates a Venn Diagram using the ggVennDiagram package. It allows customization of various aesthetic elements of the diagram.

Usage

four_degs_venn(degs_list)

Arguments

Value

A ggplot object representing the Venn Diagram.

Examples

data("all_degs_venn", package = "TransProR")
four_degs_venn <- four_degs_venn(all_degs_venn)

Gather graph edge from data frame Please note that this function is from the 'ggraph' package and has not been altered in functionality, but it has been optimized and iterated. It is not original content of 'TransProR'. However, since 'ggraph' caused frequent GitHub Action errors during the creation of 'TransProR', the author directly referenced the involved functions in 'TransProR'. This is not the author's original creation. All users please be aware!

Description

Gather graph edge from data frame Please note that this function is from the 'ggraph' package and has not been altered in functionality, but it has been optimized and iterated. It is not original content of 'TransProR'. However, since 'ggraph' caused frequent GitHub Action errors during the creation of 'TransProR', the author directly referenced the involved functions in 'TransProR'. This is not the author's original creation. All users please be aware!

Usage

gather_graph_edge(df, index = NULL, root = NULL)

Arguments

Value

A tibble of graph edges

Gather graph nodes from a data frame Please note that this function is from the 'ggraph' package and has not been altered in functionality, but it has been optimized and iterated. It is not original content of 'TransProR'. However, since 'ggraph' caused frequent GitHub Action errors during the creation of 'TransProR', the author directly referenced the involved functions in 'TransProR'. This is not the author's original creation. All users please be aware!

Description

Gather graph nodes from a data frame Please note that this function is from the 'ggraph' package and has not been altered in functionality, but it has been optimized and iterated. It is not original content of 'TransProR'. However, since 'ggraph' caused frequent GitHub Action errors during the creation of 'TransProR', the author directly referenced the involved functions in 'TransProR'. This is not the author's original creation. All users please be aware!

Usage

gather_graph_node(
  df,
  index = NULL,
  value = utils::tail(colnames(df), 1),
  root = NULL
)

Arguments

Value

a tibble of graph nodes

Merge Genes with Color Information Based on Up/Down Regulation

Description

This function merges selected genes with differential expression data and adds a color column based on up/down regulation.

Usage

gene_color(selected_genes, DEG_deseq2, up_color, down_color)

Arguments

Value

A data frame containing merged genes with an additional color column.

Examples

selected_genes_deseq2_file <- system.file("extdata",
                                          "selected_genes_deseq2.rds",
                                          package = "TransProR")
selected_genes_deseq2 <- readRDS(selected_genes_deseq2_file)
Diff_deseq2_file <- system.file("extdata", "Diff_deseq2.rds", package = "TransProR")
Diff_deseq2 <- readRDS(Diff_deseq2_file)

result_deseq2 <- gene_color(selected_genes_deseq2, Diff_deseq2, "#0000EE", "#fc4746")

Add gene highlights to a ggtree object

Description

This function enhances a 'ggtree' plot by adding highlights for specific genes. It adds both a semi-transparent fan-shaped highlight and a point at the node corresponding to each gene. Colors for each gene can be customized.

Usage

gene_highlights(ggtree_obj, genes_to_highlight, hilight_extend = 18)

Arguments

Value

A ggtree object with added gene highlights.

Examples

data("gtree", package = "TransProR")

# Define genes and their colors
genes_df <- data.frame(Symble = c("t5", "t9"),
                       color = c("#FF0000", "#0000FF"))

# Add highlights
gtree <- gene_highlights(gtree, genes_to_highlight = genes_df)

Create Pathway-Gene Mapping Data Frame

Description

This function takes multiple data frames and pathway IDs, merging them into a new data frame. Each data frame represents a type of analysis (e.g., BP, KEGG, MF, etc.).

Usage

gene_map_pathway(
  BP_dataframe,
  BP_ids,
  KEGG_dataframe,
  KEGG_ids,
  MF_dataframe,
  MF_ids,
  REACTOME_dataframe,
  REACTOME_ids,
  CC_dataframe,
  CC_ids,
  DO_dataframe,
  DO_ids
)

Arguments

Value

A new data frame that includes pathways, gene, type, and value columns

Examples

# Simulating data for different analysis types

# Simulate Biological Process (BP) data frame
BP_df <- data.frame(
  ID = c("GO:0002376", "GO:0019724"),
  geneID = c("GENE1/GENE2", "GENE3/GENE4"),
  Description = c("Immune response", "Glycosylation process")
)

# Simulate KEGG data frame
KEGG_df <- data.frame(
  ID = c("12345", "67890"),
  geneID = c("GENE5/GENE6", "GENE7/GENE8"),
  Description = c("Pathway 1", "Pathway 2")
)

# Simulate Molecular Function (MF) data frame
MF_df <- data.frame(
  ID = c("ABC123", "DEF456"),
  geneID = c("GENE9/GENE10", "GENE11/GENE12"),
  Description = c("Molecular function A", "Molecular function B")
)

# Simulate REACTOME data frame
REACTOME_df <- data.frame(
  ID = c("R-HSA-12345", "R-HSA-67890"),
  geneID = c("GENE13/GENE14", "GENE15/GENE16"),
  Description = c("Pathway in Reactome 1", "Pathway in Reactome 2")
)

# Simulate Cellular Component (CC) data frame
CC_df <- data.frame(
  ID = c("GO:0005575", "GO:0005634"),
  geneID = c("GENE17/GENE18", "GENE19/GENE20"),
  Description = c("Cellular component A", "Cellular component B")
)

# Simulate Disease Ontology (DO) data frame
DO_df <- data.frame(
  ID = c("DOID:123", "DOID:456"),
  geneID = c("GENE21/GENE22", "GENE23/GENE24"),
  Description = c("Disease A", "Disease B")
)

# Example pathway IDs for each analysis
BP_ids <- c("GO:0002376", "GO:0019724")
KEGG_ids <- c("12345", "67890")
MF_ids <- c("ABC123", "DEF456")
REACTOME_ids <- c("R-HSA-12345", "R-HSA-67890")
CC_ids <- c("GO:0005575", "GO:0005634")
DO_ids <- c("DOID:123", "DOID:456")

# Generate the pathway-gene map using the gene_map_pathway function
pathway_gene_map <- gene_map_pathway(
  BP_dataframe = BP_df, BP_ids = BP_ids,
  KEGG_dataframe = KEGG_df, KEGG_ids = KEGG_ids,
  MF_dataframe = MF_df, MF_ids = MF_ids,
  REACTOME_dataframe = REACTOME_df, REACTOME_ids = REACTOME_ids,
  CC_dataframe = CC_df, CC_ids = CC_ids,
  DO_dataframe = DO_df, DO_ids = DO_ids
)

# Display the resulting pathway-gene mapping data frame
print(pathway_gene_map)

Get GTEx Expression Data for Specific Organ

Description

This function retrieves gene expression data from the GTEx project that is specific to a certain organ. It performs various checks and processing steps to ensure that the data is consistent and relevant to the specified organ. The filtered and cleaned data is saved as an RDS file for further analysis.

Usage

get_gtex_exp(
  organ_specific,
  file_path,
  probe_map_path,
  pheno_path,
  output_path
)

Arguments

Details

The function begins by checking if the gene expression and phenotype data files exist at the specified paths. It then loads these data files and processes them by setting appropriate row names, modifying column names for clarity, and filtering samples based on the specified organ. The function ensures that only samples present in both datasets are retained for consistency. It also removes any duplicate gene entries to prevent redundancy. Finally, the processed data is saved as an RDS file.

Value

A data frame containing gene expression data for the specified organ. Rows represent genes, and columns represent samples. Note that this function also saves the organ-specific GTEx data as an RDS file at the specified output path.

Note

The function will stop and throw an error if the input files do not exist, or if no samples are found for the specified organ.

CRITICAL: The 'output_path' parameter must end with '.rds' to be properly recognized by the function. It is also highly recommended that the path includes specific identifiers related to the target samples. Please structure the 'output_path' following this pattern: './your_directory/your_sample_type.gtex.rds'.

TCGA Expression Data Processing

Description

This function processes expression data and phenotype information, separates tumor and normal samples, and saves the results into different files. It's specifically designed for data obtained from TCGA.

Usage

get_tcga_exp(
  counts_file_path,
  gene_probes_file_path,
  phenotype_file_path,
  output_file_path
)

Arguments

Value

A list containing matrices for tumor and normal expression data.

Note

IMPORTANT: This function assumes that the input files follow a specific format and structure, typically found in TCGA data releases. Users should verify their data's compatibility. Additionally, the function does not perform error checking on the data's content, which users should handle through proper preprocessing.

CRITICAL: The 'output_file_path' parameter must end with '.rds' to be properly recognized by the function. It is also highly recommended that the path includes specific identifiers related to the target samples, as the function will create further subdivisions in the specified path for tumor or normal tissues. Please structure the 'output_file_path' following this pattern: './your_directory/your_sample_type.exp.rds'.

Author(s)

Dongyue Yu

Examples

counts_file <- system.file("extdata", "TCGA-SKCM.htseq_counts_test.tsv", package = "TransProR")
gene_probes_file <- system.file("extdata",
                                "TCGA_gencode.v22.annotation.gene.probeMap_test",
                                package = "TransProR")
phenotype_file <- system.file("extdata", "TCGA-SKCM.GDC_phenotype_test.tsv", package = "TransProR")
ouput_file <- file.path(tempdir(), "SKCM_Skin_TCGA_exp_test.rds")

SKCM_exp <- get_tcga_exp(
  counts_file_path = counts_file,
  gene_probes_file_path = gene_probes_file,
  phenotype_file_path = phenotype_file,
  output_file_path = ouput_file
)
head(SKCM_exp[["tumor_tcga_data"]])[1:5, 1:5]
head(SKCM_exp[["normal_tcga_data"]], n = 10) # Because there is only one column.

Phylogenetic Tree Object

Description

A dataset containing a phylogenetic tree object created using the 'ggtree' package. This tree represents the evolutionary relationships among a set of species or genes.

Usage

data(gtree)

Format

A 'ggtree' object.

Source

The phylogenetic tree was constructed based on sequence alignment data obtained from [Data Source, e.g., NCBI database, specific study, etc.].

Highlight Nodes in a Phylogenetic Tree with Custom Fill Colors and Transparency

Description

This function adds highlights to specific nodes in a phylogenetic tree represented by a 'ggtree' object. Users can specify the nodes to highlight along with custom fill colors, transparency, and extension options.

Usage

highlight_by_node(
  ggtree_object,
  nodes,
  fill_colors,
  alpha_values,
  extend_values
)

Arguments

Value

A modified 'ggtree' object with the specified nodes highlighted.

Examples

plot_file <- system.file("extdata", "tree_plot.rds", package = "TransProR")
p2_plot <- readRDS(plot_file)

# Please replace the following vectors with your specific values
nodes <- c(117, 129, 125, 127, 119,
           123, 139, 166, 124, 131, 217) # x-values of the nodes you want to highlight
fill_colors <- c("#CD6600", "#CD6600", "#CD6600",
                 "#CD6600", "#009933", "#009933",
                 "#009933", "#009933", "#9B30FF",
                 "#9B30FF", "#9B30FF") # Fill colors
alpha_values <- c(0.3, 0.3, 0.3, 0.3, 0.2, 0.3,
                  0.3, 0.3, 0.3, 0.3, 0.3) # Transparency values
extend_values <- c(25, 24, 24, 25, 25, 25,
                   24, 24, 25, 24, 24) # Values for the 'extend' parameter

p2 <- highlight_by_node(
  p2_plot,
  nodes,
  fill_colors,
  alpha_values,
  extend_values
)

Add Highlights for Genes on a Phylogenetic Tree

Description

This function adds highlights for specified genes on a phylogenetic tree object.

Usage

highlight_genes(ggtree_obj, genes_to_highlight, hilight_extend = 18)

Arguments

Value

A 'ggtree' object with added highlights for specified genes.

Examples

plot_file <- system.file("extdata", "tree_plot.rds", package = "TransProR")
p2_plot <- readRDS(plot_file)

selected_genes_deseq2_file <- system.file("extdata",
                                          "selected_genes_deseq2.rds",
                                          package = "TransProR")
selected_genes_deseq2 <- readRDS(selected_genes_deseq2_file)

Diff_deseq2_file <- system.file("extdata", "Diff_deseq2.rds", package = "TransProR")
Diff_deseq2 <- readRDS(Diff_deseq2_file)

result_deseq2 <- gene_color(selected_genes_deseq2, Diff_deseq2, "#0000EE", "#fc4746")

add_gene_highlights_p3 <- highlight_genes(p2_plot, result_deseq2, hilight_extend = 26)

Differential Gene Expression Analysis using limma and voom

Description

This function performs differential gene expression analysis using the 'limma' package with voom normalization. It reads tumor and normal expression data, merges them, filters low-expressed genes, normalizes the data, performs limma analysis, and outputs the results along with information on gene expression changes.

Usage

limma_analyze(
  tumor_file,
  normal_file,
  output_file,
  logFC_threshold = 2.5,
  p_value_threshold = 0.01
)

Arguments

Value

A data frame of differential expression results.

References

limma:Linear Models for Microarray and RNA-Seq Data User’s Guide. For more information, visit the page: https://www.bioconductor.org/packages/release/bioc/vignettes/limma/inst/doc/usersguide.pdf

Examples

# Define file paths for tumor and normal data from the data folder
tumor_file <- system.file("extdata",
                          "removebatch_SKCM_Skin_TCGA_exp_tumor_test.rds",
                          package = "TransProR")
normal_file <- system.file("extdata",
                           "removebatch_SKCM_Skin_Normal_TCGA_GTEX_count_test.rds",
                           package = "TransProR")
output_file <- file.path(tempdir(), "DEG_limma_voom.rds")

DEG_limma_voom <- limma_analyze(
  tumor_file = tumor_file,
  normal_file = normal_file,
  output_file = output_file,
  logFC_threshold = 2.5,
  p_value_threshold = 0.01
)

# View the top 5 rows of the result
head(DEG_limma_voom, 5)

Log transformation decision and application on data

Description

This function evaluates the need for a log transformation based on a set of criteria and applies a log2 transformation if necessary.

Usage

log_transform(data)

Arguments

Value

The original data or the data transformed with log2.

Author(s)

Dongyue Yu

Examples

file_path <- system.file("extdata",
                         "all_count_exp_test.csv",
                         package = "TransProR")
your_data <- read.csv(file_path,
                      row.names = 1)  # Assuming first column is row names (e.g., gene names)

TransformedData <- log_transform(data = your_data)

Create high-density region plot with optional points, density rugs, and contours

Description

This function creates a high-density region plot using hdr methods to add density rug and filled contours. It also adds a regression line and Pearson correlation label. Points can be added to the plot optionally.

Usage

merge_density_foldchange(
  data,
  x_var,
  y_var,
  group_var,
  palette = c("#3949ab", "#1e88e5", "#039be5", "#00897b", "#43a047", "#7cb342"),
  show_points = FALSE,
  point_size = 2.5,
  point_alpha = 0.2,
  x_lim = c(0, 20),
  y_lim = c(0, 20),
  cor_method = "pearson",
  line_size = 1.6,
  cor_label_pos = c("left", 0.97)
)

Arguments

Value

A ggplot object representing the high-density region plot.

Examples

combined_df_file <- system.file("extdata", "combined_df.rds", package = "TransProR")
combined_df <- readRDS(combined_df_file)
pal1 = c("#3949ab","#1e88e5","#039be5","#00897b","#43a047","#7cb342")

all_density_foldchange_name1 <- merge_density_foldchange(
  data = combined_df,
  x_var = "log2FoldChange_1",
  y_var = "log2FoldChange_2",
  group_var = "name",
  palette = pal1,
  show_points = FALSE,
  point_size = 2.5,
  point_alpha = 0.1,
  x_lim = c(0, 20),
  y_lim = c(0, 20),
  cor_method = "pearson",
  line_size = 1.6,
  cor_label_pos = c("left", "top")
)

Merge gene expression data from GTEx and TCGA datasets

Description

This function merges gene expression data obtained from the GTEx (Genotype-Tissue Expression) and TCGA (The Cancer Genome Atlas) datasets. It is assumed that both datasets are in '.rds' format and have genes as row names. The merged dataset is saved as an RDS file at the specified output path.

Usage

merge_gtex_tcga(
  gtex_data_path,
  tcga_exp_path,
  output_path = "./merged_gtex_tcga_data.rds"
)

Arguments

Details

It is assumed that both datasets are in '.rds' format and have genes as row names.

Value

A data frame where rows represent genes and columns represent samples. The data frame contains expression values from both GTEx and TCGA datasets. It saves the merged dataset to the path specified by 'output_path'.

Note

CRITICAL: The 'output_path' parameter must end with '.rds' to be properly recognized by the function. It is also highly recommended that the path includes specific identifiers related to the target samples. Please structure the 'output_path' following this pattern: './your_directory/merged.your_sample_type.gtex.tcga.data.rds'.

Examples

tumor_file <- system.file("extdata",
                          "removebatch_SKCM_Skin_TCGA_exp_tumor_test.rds",
                          package = "TransProR")
Normal_file <- system.file("extdata",
                           "removebatch_SKCM_Skin_Normal_TCGA_GTEX_count_test.rds",
                           package = "TransProR")
ouput_file <- file.path(tempdir(), "all_data.rds")

all_data <- merge_gtex_tcga(gtex_data_path = tumor_file,
                            tcga_exp_path = Normal_file,
                            output_path = ouput_file)

Merge Data Frames by Common Row Names with Additional Columns

Description

This function merges a list of data frames based on common row names. It adds an 'id' column to track the row order and a 'point_position' column calculated based on the maximum 'Count' value across all data frames. It filters data frames to include only common rows, sorts rows by the length of the 'Description' in descending order, and then merges them by rows.

Usage

merge_id_position(df_list)

Arguments

Value

A single data frame merged from the list, with additional 'id' and 'point_position' columns.

Examples

df1 <- data.frame(Description = c("DataA", "DataB"), Count = c(10, 20), row.names = c("R1", "R2"))
df2 <- data.frame(Description = c("DataC", "DataD"), Count = c(30, 40), row.names = c("R1", "R3"))
df_list <- list(df1, df2)
combined_df_test <- merge_id_position(df_list)

Merge Data Frames with Specific Method and Color Columns

Description

This function takes a list of data frames, a method name, and a list of colors. It adds a 'method' column and a 'test_color' column to each data frame, then merges all data frames by rows. It ensures that the color list length matches the list of data frames.

Usage

merge_method_color(df_list, method_name, color_list)

Arguments

Value

A single data frame merged from the list, with each originally provided data frame now having a 'method' and a 'test_color' column.

Examples

df1 <- data.frame(Description = c("A", "B"), Count = c(10, 20))
df2 <- data.frame(Description = c("C", "D"), Count = c(30, 40))
df_list <- list(df1, df2)
method_name <- "Method1"
color_list <- c("Red", "Blue")
combined_df_test <- merge_method_color(df_list, method_name, color_list)

Generate a graphical representation of pathway gene maps

Description

This function merges multiple gene-pathway related dataframes, processes them for graph creation, and visualizes the relationships in a dendrogram layout using the provided node and edge gathering functions from the 'ggraph' package.

Usage

new_ggraph(
  BP_dataframe,
  BP_ids,
  KEGG_dataframe,
  KEGG_ids,
  MF_dataframe,
  MF_ids,
  REACTOME_dataframe,
  REACTOME_ids,
  CC_dataframe,
  CC_ids,
  DO_dataframe,
  DO_ids
)

Arguments

Value

A 'ggraph' object representing the pathway gene map visualization.

Count Genes Present in Pathways Above a Threshold

Description

This function filters pathways that meet a count threshold and then counts the presence of specified genes in those pathways.

Usage

pathway_count(GO, count_threshold, enrich_data)

Arguments

Value

A data frame with columns "Symble" (gene symbol), "Description" (pathway description), and "Exists" (1 if gene is present, 0 otherwise).

Examples

# Simulated gene list
GO <- c("Gene1", "Gene2", "Gene3", "Gene4", "Gene5")
# Simulated enrichment analysis data
enrich_data <- data.frame(
  ID = c("GO:0001", "GO:0002", "GO:0003"),
  Description = c("Pathway A", "Pathway B", "Pathway C"),
  Count = c(10, 4, 6),
  geneID = c("Gene1/Gene2/Gene3", "Gene4/Gene5", "Gene2/Gene6/Gene7")
)

# Example usage
count_threshold <- 5
result_df <- pathway_count(GO, count_threshold, enrich_data)

Describe Genes Present in Selected Pathways

Description

This function identifies genes present in selected pathways based on gene enrichment analysis results.

Usage

pathway_description(GO, selected_pathways_names, enrich_data)

Arguments

Value

A data frame with columns "Symble" (gene symbol), "Description" (pathway description), and "Exists" (1 if gene is present, 0 otherwise).

Examples

GO <- c("Gene1", "Gene2", "Gene3", "Gene4", "Gene5")
# Simulated enrichment analysis data
enrich_data <- data.frame(
  ID = c("Pathway1", "Pathway2", "Pathway3", "Pathway4"),
  Description = c("Apoptosis", "Cell Cycle", "Signal Transduction", "Metabolism"),
  geneID = c("Gene1/Gene3", "Gene2/Gene4", "Gene1/Gene2/Gene3", "Gene5"),
  Count = c(2, 2, 3, 1),
  stringsAsFactors = FALSE
)

# Example usage
result <- pathway_description(GO,
                              selected_pathways_names="Apoptosis",
                              enrich_data)

Prepare DESeq2 data for plotting

Description

This function reads a DESeq2 DEG data frame from an RDS file, filters it, adjusts the log2FoldChange to absolute values, adds a pseudo-count to pvalues, and transforms pvalues for plotting. The final data frame is returned and optionally saved to a new RDS file.

Usage

prep_deseq2(input_path, output_name = NULL)

Arguments

Value

A data frame with processed DESeq2 DEG data.

Examples

deseq2_file <- system.file("extdata",
                           "DEG_deseq2_test.rds",
                           package = "TransProR")
deseq2 <- prep_deseq2(deseq2_file)

Prepare edgeR DEG data for plotting

Description

This function reads an edgeR DEG data frame from an RDS file, filters it using deg_filter function, adjusts the logFC to absolute values, adds a pseudo-count to PValue, and transforms PValue for plotting. The final data frame is returned and optionally saved to a new RDS file.

Usage

prep_edgeR(input_path, output_name = NULL)

Arguments

Value

A data frame with processed edgeR DEG data.

Examples

edgeR_file <- system.file("extdata",
                          "DEG_edgeR_test.rds",
                          package = "TransProR")
edgeR <- prep_edgeR(edgeR_file)

Prepare limma-voom DEG data for plotting

Description

This function reads a limma-voom DEG data frame from an RDS file, filters it using deg_filter function, adjusts the logFC to absolute values, adds a pseudo-count to P.Value, and transforms P.Value for plotting. The final data frame is returned and optionally saved to a new RDS file.

Usage

prep_limma(input_path, output_name = NULL)

Arguments

Value

A data frame with processed limma-voom DEG data.

Examples

limma_file <- system.file("extdata",
                          "DEG_limma_voom_test.rds",
                          package = "TransProR")
limma <- prep_limma(limma_file)

Prepare Wilcoxon DEG data for plotting

Description

This function reads a Wilcoxon DEG data frame from an RDS file, filters it using deg_filter function, adjusts the log2foldChange to absolute values, adds a pseudo-count to pValues, and transforms pValues for plotting. The final data frame is returned and optionally saved to a new RDS file.

Usage

prep_wilcoxon(input_path, output_name = NULL)

Arguments

Value

A data frame with processed Wilcoxon DEG data.

Examples

wilcoxon_file <- system.file("extdata",
                             "Wilcoxon_rank_sum_testoutRst_test.rds",
                             package = "TransProR")
Wilcoxon <- prep_wilcoxon(wilcoxon_file)

Process Heatmap Data with Various Selection Options

Description

This function processes heatmap data ('heatdata') based on a given selection option. It allows customization of column names, selection of specific columns per group, or averaging columns based on a common prefix.

Usage

process_heatdata(
  heatdata,
  selection = 1,
  custom_names = NULL,
  num_names_per_group = NULL,
  prefix_length = 4
)

Arguments

Value

A processed data frame based on the specified selection option.

Examples

# Example heatmap data frame
heatdata <- data.frame(
  groupA_1 = c(1, 2, 3),
  groupA_2 = c(4, 5, 6),
  groupB_1 = c(7, 8, 9),
  groupB_2 = c(10, 11, 12)
)

# Selection 1: Use custom names for columns
custom_names <- c("Sample1", "Sample2", "Sample3", "Sample4")
processed_data1 <- process_heatdata(heatdata, selection = 1, custom_names = custom_names)

# Selection 2: Select a given number of columns per group based on a prefix
processed_data2 <- process_heatdata(heatdata, selection = 2, num_names_per_group = 1)

# Selection 3: Calculate the average of columns per group based on a prefix
processed_data3 <- process_heatdata(heatdata, selection = 3, prefix_length = 6)

Load and Process GTEX Phenotype Data to Retrieve Primary Site Counts

Description

This function reads the GTEX phenotype data from a specified path, renames its columns for better readability, and then returns a table of primary site counts.

Usage

seek_gtex_organ(path = "./download_data/GTEX_phenotype")

Arguments

Value

A table representing the count of samples per primary site.

Examples

# Get the file path to the example data in the package
path <- system.file("extdata", "GTEX_phenotype_test", package = "TransProR")
# Call the `seek_gtex_organ` function with the path and print the result
SeekGtexOrgan <- seek_gtex_organ(path = path)

Randomly Select Pathways with Limited Word Count

Description

This function randomly selects a specified number of pathways from a given list, ensuring that each selected pathway name does not exceed a specified number of words. It filters out pathways with names longer than the specified word limit before making the selection.

Usage

selectPathways(pathways, max_words = 10, num_select = 10)

Arguments

Value

A character vector of selected pathways.

Examples

pathway_list <- c("pathway_one response to stimulus",
                  "pathway_two cell growth and death",
                  "pathway_three regulation of cellular process",
                  "pathway_four metabolic process")
selected_pathways <- selectPathways(pathway_list, max_words = 5, num_select = 2)

Render a Spiral Plot Using Run-Length Encoding

Description

This function creates a spiral plot for visualizing sequential data in a compact and visually appealing way. It uses run-length encoding to represent the lengths and colors of sequences in the spiral.

Usage

spiral_newrle(x, samples, values, colors, labels = FALSE)

Arguments

Value

No return value, called for side effects. This function generates a spiral plot and optionally adds labels.

Examples

# Example: Creating a spiral plot using the spiral_newrle function

# Define example data
x <- c("A", "A", "B", "C")
samples <- c("Sample1", "Sample1", "Sample2", "Sample2")
values <- c(20, 30, 15, 35)
colors <- c("red", "blue", "green", "purple")
labels <- TRUE

# Initialize the spiral plot, setting the x-axis range and scaling
spiralize::spiral_initialize(xlim = c(0, sum(values)), scale_by = "curve_length",
                 vp_param = list(x = grid::unit(0, "npc"), just = "left"))

# Create a track for the spiral plot
spiralize::spiral_track(height = 0.5)

# Add segments to the spiral plot using run-length encoding
spiral_newrle(x, samples, values, colors, labels)

X-spline Statistic for ggplot2 (adapted from ggalt)

Description

This statistic adds an X-spline interpolation to your ggplot2 plots, allowing for smooth curves through a series of points. The implementation is adapted from the stat_xspline function in the ggalt package (GPL-3), originally authored by Bob Rudis.

Usage

stat_xspline(
  mapping = NULL,
  data = NULL,
  geom = "line",
  position = "identity",
  na.rm = TRUE,
  show.legend = NA,
  inherit.aes = TRUE,
  spline_shape = -0.25,
  open = TRUE,
  rep_ends = TRUE,
  ...
)

Arguments

Value

A ggplot2 layer that can be added to a plot.

License and Attribution

This code is adapted from the ggalt package (https://github.com/hrbrmstr/ggalt), which is licensed under GPL-3. Original author: Bob Rudis (bob@rud.is).

Computed variables

• x X coordinates of the spline.

• y Y coordinates of the spline.

Examples

library(ggplot2)
df <- data.frame(x = 1:10, y = cumsum(rnorm(10)))
ggplot(df, aes(x, y)) +
  geom_point() +
  stat_xspline(spline_shape = 0.5)