Title: A Toolkit for Omics Data Visualization
Version: 1.4.0
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: corpcor, data.table, dplyr, genpwr, ggbreak, ggplot2, ggrepel, ggsci, ggtext, grid, gridExtra, gtable, lulab.utils, magrittr, Matrix, patchwork, pheatmap, purrr, rlang, scales, showtext, stringr, sysfonts, systemfonts, tibble
NeedsCompilation: no
Packaged: 2025-10-11 11:26:51 UTC; luzh2
Author: Zhen Lu ORCID iD [aut, cre]
Maintainer: Zhen Lu <luzh29@mail2.sysu.edu.cn>
Repository: CRAN
Date/Publication: 2025-10-11 11:40:03 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:

MR_2SLS

Description

Perform Two-Stage Least Squares (2SLS) Mendelian Randomization analysis.

Usage

MR_2SLS(
  infile,
  outcome,
  outcome_name,
  prs_cols_match,
  regexpr_pattern,
  standardise = TRUE,
  digits = 3,
  .progress = TRUE
)

Arguments

infile

The input file containing the data.

outcome

The outcome variable.

outcome_name

The name of the outcome variable.

prs_cols_match

The columns to match for PRS.

regexpr_pattern

The regular expression pattern to use for matching.

standardise

Whether to standardise the prs data, Default: TRUE

digits

The number of decimal places to round to, Default: 3

.progress

Whether to show progress, Default: TRUE

Details

This function performs a Two-Stage Least Squares (2SLS) Mendelian Randomization analysis.

Value

A list containing the results of the 2SLS analysis.

Author(s)

Zhen Lu luzh29@mail2.sysu.edu.cn

Siyang Liu liusy99@mail.sysu.edu.cn

See Also

fread, as.data.table, setattr, setDT select, reexports map str_extract nearPD cov.shrink

plot_forest

Description

Creates a forest plot overlaid on a table.

Usage

plot_forest(
  p_left_data,
  point_estimate,
  ci_lower_bound,
  ci_upper_bound,
  ci_sep = ", ",
  p_right_data = NULL,
  precision_digits = 3,
  p_mid_width = 30,
  null_line_at = 1,
  output_path = NULL,
  dpi = 600,
  display = TRUE,
  font_family = c("MetroSans", "mono"),
  p_left_data_name = NULL,
  p_right_data_name = "Effect size (95% CI)",
  stripe_colour = "#eff3f2",
  background_colour = "white",
  x_scale_linear = TRUE,
  xlim = NULL,
  xbreaks = NULL,
  nudge_y = 0,
  nudge_x = 1,
  nudge_height = 0,
  nudge_width = 0,
  justify = 0,
  arrows = FALSE,
  arrow_labels = c("Lower", "Higher"),
  risk_colors = (ggsci::pal_npg("nrc"))(2),
  arrow_nudge_y = 0,
  add_plot = NULL,
  add_plot_width = 1,
  add_plot_gap = FALSE,
  point_sizes = 3,
  point_shapes = 16,
  p_mid_forest = NULL,
  lower_header_row = FALSE,
  render_as = "png",
  table_theme = NULL
)

Arguments

p_left_data

Data frame (required). The information to be displayed to the left of the forest plot.

point_estimate

Vector. The point estimates to be displayed in the forest plot.

ci_lower_bound

Vector. The lower confidence bounds.

ci_upper_bound

Vector. The upper confidence bounds.

ci_sep

String. What should separate the low and high confidence bounds? Default " to ".

p_right_data

Data frame (optional). Information to be displayed on the right side of the table. If not supplied, an Estimate column is generated automatically.

precision_digits

Integer. The number of decimal places on the point_estimate (default 3)

p_mid_width

Integer. The width of forest plot in characters (default 30)

null_line_at

Numeric. Default 1. Change to 0 if using absolute measures.

output_path

String. Where to save the image, default tempdir().

dpi

Numeric. The image resolution in dpi, default 600

display

Logical. Should the file be opened? Default TRUE.

font_family

String or character vector. The font to use for the ggplot and table. Default c("MetroSans", "mono"). The first available font is used.

p_left_data_name

String or String Vector. The name vector for the left side data. Default NULL, which uses the column names of p_left_data.

p_right_data_name

String. The name vector for the right side data. Default "Effect size (95% CI)"

stripe_colour

Hex String. Colour to use for the table stripes, default "#eff3f2".

background_colour

Hex String or Colour Name. The colour of the background, default "white".

x_scale_linear

Logical. Default TRUE, change to FALSE for a log scale.

xlim

Vector. Manually specify limits for the x axis as a vector length 2, i.e. c(low, high)

xbreaks

Vector. X axis breaks to label. Specify limits in xlim if using this option.

nudge_y

Numeric. Allows small changes to the vertical alignment of the forest plot points. 1 unit is approximately the height of 1 row.

nudge_x

Numeric. Nudge the alignment horizontally. Default 1. Higher values make the entire plot wider and consequently space out the elements of the figure.

nudge_height

Numeric. Adjust the overall height of the plot output. Default is 0.

nudge_width

Numeric. Adjust the overall width of the plot output. Default is 0.

justify

Numeric Vector. This should be a numeric vector either of length 1 (in which case it will apply to all columns) or of length equal to the number of columns in p_left_data + 1 (for the point_estimate column). Each number in the vector dictates the column justification, with 0 being left, 0.5 being center, and 1 being right.

arrows

Logical. Should there be arrows displayed below the ggplot? Default FALSE. Specify xlim if using arrows.

arrow_labels

String Vector, length 2. Labels for the arrows. Set arrows to TRUE or this will have no effect.

risk_colors

Vector. Length 2. Colors for the effect measure. Default is ggsci::pal_npg("nrc")(2).

arrow_nudge_y

Numeric. Nudge the vertical position of the arrows. Default 0.

add_plot

A ggplot object to add to the right side of the table. To align correctly with rows, 1 unit is the height of a row and y = 0 for the center of the bottom row.

add_plot_width

Numeric. Width to display add_plot. Relative to the width of the forest plot, where 1 (the default) is the same width.

add_plot_gap

Logical. Should there be space added between the plot and the main figure? Default FALSE.

point_sizes

Vector. Length should be equal to 1 or nrow(p_left_data). The sizes of the points in the center plot, where 3.25 is the default.

point_shapes

Vector. Length should be equal to 1 or nrow(p_left_data). The shapes of the points in the center plot, where 16 (a filled circle) is the default.

p_mid_forest

A ggplot object to use instead of the central plot.

lower_header_row

Logical. If TRUE, drops the header down one row (In the table rather than above it, like the default value (FALSE))

render_as

String or Function. What output format should be used? Option is passed to ggplot2::ggsave() as the argument "device". Either pass a device function (e.g. png) or one of "eps", "ps", "tex" (pictex), "pdf", "jpeg", "tiff", "png", "bmp", "svg" or "wmf" (windows only).

table_theme

A gridExtra table theme. If specified, overwrites all table theme customization in other options. The default is a modified version of ttheme_minimal.

Details

This function creates a forest plot overlaid on a table. It is highly customizable, allowing for adjustments to fonts, colors, and layout.

Value

image

Author(s)

Zhen Lu luzh29@mail2.sysu.edu.cn

plot_gwas_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

trait_type

Character string specifying trait type: "bt" for binary (case-control) or "qt" for quantitative traits. Default: "bt".

n_cases

Number of cases in the study (required if trait_type = "bt").

n_controls

Number of controls in the study (required if trait_type = "bt").

sd_trait

Numeric, standard deviation of the quantitative trait (required if trait_type = "qt").

N

Numeric, total sample size for quantitative traits (required if trait_type = "qt").

maf_levels

A numeric vector of Minor Allele Frequencies (MAFs) to test. Default: c(0.01, 0.02, 0.05, 0.10, 0.20, 0.50).

or_range

A numeric vector specifying the sequence of Odds Ratios (ORs) to test. Default: seq(1.01, 2.00, 0.001). Used when trait_type = "bt".

effect_size

A numeric vector specifying the sequence of effect sizes (beta) to test for quantitative traits. Default: seq(0.01, 0.30, 0.001). Used when trait_type = "qt".

alpha

The significance level (alpha) for the power calculation. Default: 5e-8.

plot_title

A string for the plot title. Default: Cases / Controls for binary traits or ⁠Sample Size⁠ for quantitative traits.

save_plot

Logical, whether to save the plot to a file. If FALSE, the plot object is only returned. Default: TRUE.

output_graphics

The file format for saving the plot. Currently supports "png" and "pdf". Default: "png".

width

The width of the saved plot in inches. Default: 17.

height

The height of the saved plot in inches. Default: 9.

dpi

The resolution of the saved plot in dots per inch. Default: 600.

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:

plot

The ggplot object for the power plot.

power_data

A data.table containing the full results from the power analysis.

Font Information

The MetroSans font included in this package 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_heatmap

Description

Generate a heatmap for a given correlation matrix.

Usage

plot_heatmap(
  cor_mat,
  scale = "none",
  show_rownames = FALSE,
  show_colnames = FALSE,
  labels_row = NULL,
  labels_col = NULL,
  breaks = seq(-1, 1, length.out = 101),
  legend_breaks = c(-1, -0.5, 0, 0.5, 1),
  legend_labels = c("-1.0", "-0.5", "0", "0.5", "1.0"),
  legend_name = "Correlation",
  output_path = NULL,
  display = TRUE,
  render_as = "png",
  width = 18,
  height = 16,
  dpi = 600
)

Arguments

cor_mat

A numeric matrix representing the correlation values.

scale

Character string indicating how to scale the data. Default is 'none'.

show_rownames

Logical indicating whether to show row names. Default is FALSE.

show_colnames

Logical indicating whether to show column names. Default is FALSE.

labels_row

Character vector of labels for the rows. Default is NULL.

labels_col

Character vector of labels for the columns. Default is NULL.

breaks

Numeric vector of breakpoints for the heatmap colors. Default is seq(-1, 1, length.out = 101).

legend_breaks

Numeric vector of breakpoints for the legend. Default is c(-1, -0.5, 0, 0.5, 1).

legend_labels

Character vector of labels for the legend. Default is c("-1.0", "-0.5", "0", "0.5", "1.0").

legend_name

Character string for the legend title. Default is 'Correlation'.

output_path

Character string for the output file path. Default is NULL.

display

Logical indicating whether to display the plot. Default is TRUE.

render_as

Character string indicating the file format to save the plot. Default is 'png'.

width

Numeric indicating the width of the plot. Default is 18.

height

Numeric indicating the height of the plot. Default is 16.

dpi

Numeric indicating the resolution of the plot. Default is 600.

Details

This function generates a heatmap for a given correlation matrix.

Value

A heatmap plot.

Author(s)

Zhen Lu luzh29@mail2.sysu.edu.cn

plot_qqman

Description

Create GWAS QQ & Manhattan Plots.

Usage

plot_qqman(
  plink_assoc_file,
  pheno_name,
  maf_filter = NULL,
  gwas_threshold = 5e-08,
  label_col = "SNP",
  output_graphics = "png",
  save_plot = TRUE,
  lambda1_qq_pos = c(2.1, -5.5),
  lambda2_qq_pos = c(1.565, -4)
)

Arguments

plink_assoc_file

Path to the PLINK association file.

pheno_name

Phenotype name.

maf_filter

Minor allele frequency filter, Default: NULL

gwas_threshold

The significance threshold for highlighting SNPs. Default: 5e-8.

label_col

The name of the column to use for labeling significant SNPs. Default: "SNP".

output_graphics

Output graphics format, Default: 'png'

save_plot

Logical, whether to save plots to files. If FALSE, plots are only displayed. Default: TRUE

lambda1_qq_pos

A numeric vector of length 2 specifying the c(hjust, vjust) for the lambda text in the QQ plot. Default: c(2.1, -5.5).

lambda2_qq_pos

A numeric vector of length 2 specifying the c(hjust, vjust) for the SNP count (N) text in the QQ plot. Default: c(1.565, -4.0).

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 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

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.")
  }