Title:	A Toolkit for Omics Data Visualization
Version:	1.1.3
Description:	Provides a suite of tools for the comprehensive visualization of multi-omics data, including genomics, transcriptomics, and proteomics. Offers user-friendly functions to generate publication-quality plots, thereby facilitating the exploration and interpretation of complex biological datasets. Supports seamless integration with popular R visualization frameworks and is well-suited for both exploratory data analysis and the presentation of final results. Key formats and methods are presented in Huang, S., et al. (2024) "The Born in Guangzhou Cohort Study enables generational genetic discoveries" <doi:10.1038/s41586-023-06988-4>.
License:	MIT + file LICENSE
URL:	https://github.com/Leslie-Lu/omixVizR
BugReports:	https://github.com/Leslie-Lu/omixVizR/issues
Encoding:	UTF-8
Language:	en-US
RoxygenNote:	7.3.2
Suggests:	testthat (≥ 3.0.0), spelling
Config/testthat/edition:	3
Imports:	data.table, dplyr, genpwr, ggbreak, ggplot2, ggrepel, ggsci, ggtext, grid, magrittr, purrr, scales, showtext, sysfonts
NeedsCompilation:	no
Packaged:	2025-07-20 14:15:45 UTC; luzh2
Author:	Zhen Lu [aut, cre]
Maintainer:	Zhen Lu <luzh29@mail2.sysu.edu.cn>
Repository:	CRAN
Date/Publication:	2025-07-20 17:50:05 UTC

omixVizR: A Toolkit for Omics Data Visualization

Description

Provides a suite of tools for the comprehensive visualization of multi-omics data, including genomics, transcriptomics, and proteomics. Offers user-friendly functions to generate publication-quality plots, thereby facilitating the exploration and interpretation of complex biological datasets. Supports seamless integration with popular R visualization frameworks and is well-suited for both exploratory data analysis and the presentation of final results. Key formats and methods are presented in Huang, S., et al. (2024) "The Born in Guangzhou Cohort Study enables generational genetic discoveries" doi:10.1038/s41586-023-06988-4.

Author(s)

Maintainer: Zhen Lu luzh29@mail2.sysu.edu.cn (ORCID)

See Also

Useful links:

• https://github.com/Leslie-Lu/omixVizR

• Report bugs at https://github.com/Leslie-Lu/omixVizR/issues

Plot GWAS Statistical Power

Description

Generates a statistical power analysis plot for GWAS studies. Supports binary (case-control) traits over a range of odds ratios and minor allele frequencies, and quantitative traits over a range of effect sizes and minor allele frequencies. This function uses the 'genpwr' package for calculations and creates a highly customized ggplot.

Usage

plot_gwas_power(
  trait_type = "bt",
  n_cases = NULL,
  n_controls = NULL,
  sd_trait = NULL,
  N = NULL,
  maf_levels = c(0.01, 0.02, 0.05, 0.1, 0.2, 0.5),
  or_range = seq(1.01, 2, 0.001),
  effect_size = seq(0.01, 0.3, 0.001),
  alpha = 5e-08,
  plot_title = NULL,
  save_plot = TRUE,
  output_graphics = "png",
  width = 17,
  height = 9,
  dpi = 600
)

Arguments

Details

This function automates the process of calculating and visualizing GWAS power for both binary (case-control) and quantitative traits. For binary traits, it analyzes power across odds ratios, while for quantitative traits, it analyzes power across effect sizes. It highlights the minimum OR/effect size required to achieve 80% power for the lowest and third-lowest MAF levels, adding dashed lines and color-coded labels for clarity.

Value

A list containing two elements:

Font Information

The MetroSans font included in this package is sourced from https://fontshub.pro/font/metro-sans-download#google_vignette. It is intended for academic research and non-commercial use only. For commercial use, please contact the font copyright holder.

The font files are included in the package's inst/extdata directory and are automatically loaded for plotting.

Author(s)

Zhen Lu luzh29@mail2.sysu.edu.cn

Examples

  # Binary trait example (case-control)
  power_results_bt <- plot_gwas_power(
    trait_type = "bt",
    n_cases = 4324,
    n_controls = 93945,
    save_plot = FALSE
  )

  # Quantitative trait example
  power_results_qt <- plot_gwas_power(
    trait_type = "qt",
    sd_trait = 0.09365788681305078,
    N = 10000,
    maf_levels = c(0.01, 0.02, 0.05, 0.10, 0.20, 0.50),
    effect_size = seq(0.01, 0.10, 0.001),
    save_plot = FALSE
  )

  # Access the ggplot object and data
  # print(power_results_bt$plot)
  # print(power_results_bt$power_data)

Plot GWAS QQ and Manhattan Plots

Description

Create GWAS QQ & Manhattan Plots.

Usage

plot_qqman(
  plink_assoc_file,
  pheno_name,
  maf_filter = NULL,
  output_graphics = "png",
  save_plot = TRUE,
  lambda1_qq_pos = c(2.1, -5.5),
  lambda2_qq_pos = c(1.565, -4)
)

Arguments

Details

This function reads a PLINK association file and generates Manhattan and QQ plots for the GWAS results.

Value

A list containing the ggplot objects for the Manhattan and QQ plots.

Font Information

The MetroSans font included in this package is sourced from https://fontshub.pro/font/metro-sans-download#google_vignette. It is intended for academic research and non-commercial use only. For commercial use, please contact the font copyright holder.

The font files are included in the package's inst/extdata directory and are automatically loaded for plotting.

Author(s)

Zhen Lu luzh29@mail2.sysu.edu.cn

Yanhong Liu liuyh275@mail2.sysu.edu.cn

Siyang Liu liusy99@mail.sysu.edu.cn

See Also

lulab.utils

Examples

  sample_file <- system.file("extdata", "sample_gwas.assoc.linear", package = "omixVizR")
  
  # Check if the file exists before running the example
  if (file.exists(sample_file)) {
    # Run the function with the sample data
    plots <- plot_qqman(
      plink_assoc_file = sample_file,
      pheno_name = "SamplePheno",
      save_plot = FALSE
    )
    # You can then access the plots like this:
    # print(plots$manhattan_plot)
    # print(plots$qq_plot)
  } else {
    message("Sample file not found, skipping example.")
  }