| Title: | Graphical Visualizations Related to Genomic Prediction |
| Version: | 1.0.0 |
| Description: | Provides flexible tools for the visualization of genomic data. Supports interactive and static plots tailored for presentations and publications, with customizable features like colors, themes, and annotations to align with specific analytical and presentation goals. |
| URL: | https://github.com/DoviniJ/GPflexViz |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| Depends: | R (≥ 2.10) |
| Imports: | cowplot, dplyr, ggforce, ggplot2, gridExtra, nortest, plotly, reshape2, stats, utils |
| LazyData: | true |
| NeedsCompilation: | no |
| Packaged: | 2025-09-05 08:19:35 UTC; jaydy007 |
| Author: | Dovini Jayasinghe [aut, cre, cph], Hong Lee [aut, cph] |
| Maintainer: | Dovini Jayasinghe <dovini.jayasinghe@mymail.unisa.edu.au> |
| Repository: | CRAN |
| Date/Publication: | 2025-09-10 07:30:14 UTC |
gwas_data with all chromosome information This contains Single nucleotide polymorphism (SNP) information.
Description
gwas_data with all chromosome information This contains Single nucleotide polymorphism (SNP) information.
Usage
example_data1
Format
A dataframe with 11000 rows and 4 columns
- SNP
SNP ID
- CHR
Chromosome ID
- POS
Base Pair Position
- P_VALUE
p value
regression_data This contains estimation and p value data from a/the regression summary output/s.
Description
regression_data This contains estimation and p value data from a/the regression summary output/s.
Usage
example_data10
Format
A dataframe with 9 rows and 4 columns
- x_var
components displayed in the horizontal axis of the heatmap
- y_var
components displayed in the vertical axis of the heatmap
- estimate
regression coefficient
- p_value
p value
CI_data This contains individual level data for constructing confidence intervals
Description
CI_data This contains individual level data for constructing confidence intervals
Usage
example_data11
Format
A dataframe with 8 rows and 5 columns
- IID
Individual identification number
- PRS
Estimate (e.g. individual polygenic risk score (PRS))
- Variance
Variance of the estimate
- Lower_Limit
Lower limit of the confidence interval
- Upper_Limit
Upper limit of the confidence interval
gwas_data with partial chromosome information This contains Single nucleotide polymorphism (SNP) information.
Description
gwas_data with partial chromosome information This contains Single nucleotide polymorphism (SNP) information.
Usage
example_data2
Format
A dataframe with 1000 rows and 4 columns
- SNP
SNP ID
- CHR
Chromosome ID
- POS
Base Pair Position
- P_VALUE
p value
accuracy_data with full information This contains prediction accuracy related information.
Description
accuracy_data with full information This contains prediction accuracy related information.
Usage
example_data3
Format
A dataframe with 6 rows and 4 columns
- Model
Model name
- R_squared
Measuremnt of prediction accuracy
- p_value
p value of prediction accuracy
- se
standard error of prediction accuracy
accuracy_data with partial information This contains prediction accuracy related information.
Description
accuracy_data with partial information This contains prediction accuracy related information.
Usage
example_data4
Format
A dataframe with 6 rows and 2 columns
- Model
Model name
- R_squared
Measuremnt of prediction accuracy
comparison_data This contains prediction accuracy related information for model comparison.
Description
comparison_data This contains prediction accuracy related information for model comparison.
Usage
example_data5
Format
A dataframe with 15 rows and 3 columns
- Compare1
Model name 1
- Compare2
Model name 2
- p_value
p value of diffence between prediction accuracy
accuracy_diff_data This contains prediction accuracy related information for detailed model comparison (2 methods at a time).
Description
accuracy_diff_data This contains prediction accuracy related information for detailed model comparison (2 methods at a time).
Usage
example_data6
Format
A dataframe with 6 rows and 9 columns
- trait
Trait name
- method
Method name (there can be only 2 types)
- R2
Prediction accuracy
- lower_limit_R2
Lower limit of prediction accuracy
- upper_limit_R2
Upper limit of prediction accuracy
- lower_limit_difference_R2
Lower limit of prediction accuracy difference (common to the 2 methods)
- upper_limit_difference_R2
Upper limit of prediction accuracy difference (common to the 2 methods)
- p_value_difference_R2
p value of diffence between prediction accuracy (common to the 2 methods)
ld_data This contains Linkage Disequilibrium (LD) related data (e.g. pair-wise variant correlation squared).
Description
ld_data This contains Linkage Disequilibrium (LD) related data (e.g. pair-wise variant correlation squared).
Usage
example_data7
Format
A dataframe with 1000 rows and 7 columns
- CHR_A
Chromosome ID
- BP_A
Base pair position of the 1st variant
- SNP_A
First variant ID
- CHR_B
Chromosome ID
- BP_B
Base pair position of the 2nd variant
- SNP_B
Second variant ID
- R2
LD measurement (e.g. R squared)
distribution_data This contains data for consruction of histograms/density plots, with at least 1 column.
Description
distribution_data This contains data for consruction of histograms/density plots, with at least 1 column.
Usage
example_data8
Format
A dataframe with 100 rows and 3 columns
- Population_1
Data from the 1st population (e.g. phenotypes, polygenic risk scores (PRS))
- Population_2
Data from the 2nd population (e.g. phenotypes, polygenic risk scores (PRS))
- Population_3
Data from the 3rd population (e.g. phenotypes, polygenic risk scores (PRS))
correlation_matrix A 20x20 symmetric matrix containing correlation coefficients between various variables (e.g. varients, individuals).
Description
correlation_matrix A 20x20 symmetric matrix containing correlation coefficients between various variables (e.g. varients, individuals).
Usage
example_data9
Format
A matrix with 20 rows and 20 columns with identical names for rows and columns.
- Row and Column Names
-
Both rows and columns are named VAR1 to VAR20, indicating the variables for which correlations are calculated.
flex_LD_decay function
Description
This function generates a linkage disequilibrium (LD) decay plot based on input LD data. It supports both static and interactive visualization with customizable aesthetics, themes, and optional smoothing curves.
Usage
flex_LD_decay(
ld_data,
interactive = TRUE,
user_colors = NULL,
user_title = "LD Decay Plot",
user_x_title = "Distance (Mb)",
user_y_title = "R-squared (LD)",
user_legend_title = "Chromosome",
user_plot_theme = theme_minimal(),
user_plot_theme_specs = theme(legend.title = element_text(size = 10), legend.text =
element_text(size = 10), title = element_text(size = 15), axis.text.x =
element_text(size = 10), axis.title.x = element_text(size = 10), axis.text.y =
element_text(size = 10), axis.title.y = element_text(size = 10)),
user_base = 1e+06,
user_smoothing = "loess",
add_smoothing = FALSE,
add_smoothing_per_chromosome = FALSE,
...
)
Arguments
ld_data |
Data frame containing LD information. |
interactive |
Logical. If |
user_colors |
Character vector of colors for chromosomes. If |
user_title |
Character. Title of the plot. Default is |
user_x_title |
Character. X-axis label. Default is |
user_y_title |
Character. Y-axis label. Default is |
user_legend_title |
Character. Legend title. Default is |
user_plot_theme |
A ggplot2 theme object. Default is |
user_plot_theme_specs |
Additional theme specifications applied to the plot. Default is a theme object with custom font sizes. |
user_base |
Numeric. Scaling factor for distance (e.g., 1e6 for Mb). Default is |
user_smoothing |
Character. Smoothing method to use in |
add_smoothing |
Logical. If |
add_smoothing_per_chromosome |
Logical. If |
... |
Additional arguments passed to |
Value
A ggplot object (static plot) or a plotly object (interactive plot),
depending on the value of interactive.
Examples
library(ggplot2)
library(plotly)
library(dplyr)
library(nortest)
library(ggforce)
library(reshape2)
library(gridExtra)
library(grid)
library(cowplot)
# Load example data
snp_ld_data <- example_data7
# Basic plot
flex_LD_decay(snp_ld_data)
# Add a general smoothing curve
flex_LD_decay(snp_ld_data, add_smoothing = TRUE)
# Add smoothing per chromosome
flex_LD_decay(snp_ld_data, add_smoothing = TRUE, add_smoothing_per_chromosome = TRUE)
# Customize smoothing parameters
flex_LD_decay(snp_ld_data, add_smoothing = TRUE, span = 0.5, se = FALSE)
# Use generalized additive model (GAM) for smoothing
flex_LD_decay(snp_ld_data, user_smoothing = "gam", add_smoothing = TRUE,
add_smoothing_per_chromosome = TRUE)
flex_accuracy function
Description
Creates a bar plot of Model prediction accuracies with the option to display p-values and make the plot interactive using Plotly.
Usage
flex_accuracy(
accuracy_data,
conf_level = 0.95,
display_CI = TRUE,
interactive = TRUE,
user_colors = NULL,
display_p_values = TRUE,
Models_to_display_p_values = NULL,
user_title = "Prediction Accuracy Plot",
user_x_title = "Model",
user_y_title = "Prediction Accuracy",
user_legend_title = NULL,
user_plot_theme = theme_minimal(),
user_plot_theme_specs = theme(legend.title = element_text(size = 10), legend.text =
element_text(size = 15), title = element_text(size = 15), axis.text.x =
element_text(size = 10), axis.title.x = element_text(size = 10), axis.text.y =
element_text(size = 10), axis.title.y = element_text(size = 10)),
user_p_value_prefix = NULL,
user_bar_width = 0.8,
geom_text_args = list(),
additional_ggplot_args = list()
)
Arguments
accuracy_data |
A data frame containing four columns: 'Model' (factor or character vector of Model names), 'R_squared' (numeric vector of prediction accuracies), 'p_value' (numeric vector of p-values) and 'se' (numeric vector of standard errors of prediction accuracies). |
conf_level |
A numeric value between 0 and 1 to indicate level of confidence; default is 0.95. |
display_CI |
Logical; if TRUE, returns confidence intervals (CIs). |
interactive |
Logical; if TRUE, returns an interactive Plotly plot. If FALSE, returns a static ggplot object. |
user_colors |
Optional; a vector of colors to use for the bars. If NULL, default ggplot2 colors are used. |
display_p_values |
Logical; if TRUE, p-values are displayed on the plot. |
Models_to_display_p_values |
Optional; a vector of Model names for which to display p-values. If NULL, p-values are displayed for all Models. |
user_title |
Character string setting the title of the plot. |
user_x_title |
Character string for the x-axis title. |
user_y_title |
Character string for the y-axis title. |
user_legend_title |
Optional; character string for the legend title. If NULL, the legend is hidden. |
user_plot_theme |
ggplot2 theme object to use for the base styling of the plot. |
user_plot_theme_specs |
ggplot2 theme object to apply additional styling. |
user_p_value_prefix |
Optional; character string to prefix before p-values in their annotations. |
user_bar_width |
Numeric value for the width of the bars in the plot. |
geom_text_args |
List of additional arguments to pass to geom_text for p-value annotation customization. |
additional_ggplot_args |
List of additional ggplot objects to add to the plot. |
Value
A ggplot object if 'interactive' is FALSE; otherwise, an interactive Plotly plot.
Examples
library(ggplot2)
library(plotly)
library(dplyr)
library(nortest)
library(ggforce)
library(reshape2)
library(gridExtra)
library(grid)
library(cowplot)
# Usage with default bar width
flex_accuracy(example_data3)
# Example to show only specific Models' p-values with thicker bars
flex_accuracy(example_data3, display_p_values = TRUE, Models_to_display_p_values =
c("Model A", "Model E"), user_bar_width = 0.8)
# Optionally add p-value annotations
flex_accuracy(example_data3, user_p_value_prefix = "p = ", geom_text_args =
list(data = example_data3, fontface = "bold", hjust = 0.5, color = "darkblue",
size = 3))
#adding additional plotting features
flex_accuracy(example_data3, user_bar_width = 0.5, display_p_values = TRUE,
Models_to_display_p_values = c("Model A", "Model E"),
geom_text_args = list(data = subset(example_data3,
Model %in% c("Model A", "Model E")), angle = 60,
fontface = "bold", hjust = 0.5, color = "darkblue", size = 5),
additional_ggplot_args = list(ggplot2::scale_y_continuous(limits = c(0, 1.3))))
# Adjust the geom_text_args to nudge p-value annotations above the bars
flex_accuracy(example_data3,
user_bar_width = 0.5,
display_p_values = TRUE,
Models_to_display_p_values = c("Model A", "Model E"),
geom_text_args = list(angle = 60, fontface = "bold", hjust = 1,
color = "darkblue", size = 5,
nudge_y = 0.2),
additional_ggplot_args = list(ggplot2::scale_y_continuous(limits = c(0, 1.5))))
flex_accuracy_diff function
Description
This function generates a customizable bar plot to compare prediction accuracy (e.g. R-squared values) between multiple models. It displays pairwise comparison lines and corresponding p-values. Interactive plots can be created using Plotly.
Usage
flex_accuracy_diff(
accuracy_data,
comparison_data,
interactive = TRUE,
user_colors = NULL,
user_segment_color = "darkblue",
display_p_values = TRUE,
models_to_display_p_values = NULL,
user_title = "Prediction Accuracy Comparison Plot",
user_x_title = "Model",
user_y_title = "Prediction Accuracy",
user_legend_title = NULL,
user_plot_theme = theme_minimal(),
user_plot_theme_specs = theme(legend.title = element_text(size = 10), legend.text =
element_text(size = 15), title = element_text(size = 15), axis.text.x =
element_text(size = 10), axis.title.x = element_text(size = 10), axis.text.y =
element_text(size = 10), axis.title.y = element_text(size = 10)),
user_p_value_prefix = NULL,
user_bar_width = 0.8,
geom_text_args = list(),
additional_ggplot_args = list()
)
Arguments
accuracy_data |
A data frame containing the models' R-squared values with two columns: 'Model' and 'R_squared' |
comparison_data |
A data frame containing pairwise comparisons with three columns: 'Compare1', 'Compare2' and 'p_value' |
interactive |
Logical. If 'TRUE', returns an interactive plotly object; otherwise, returns a static ggplot object. Default is 'TRUE'. |
user_colors |
Character vector of colors for the bars. If 'NULL', uses ggplot2 default colors. Default is 'NULL'. |
user_segment_color |
Character. Color of comparison lines and dashed segments. Default is "darkblue"'. |
display_p_values |
Logical. If 'TRUE', adds p-values to the plot for specified comparisons. Default is 'TRUE'. |
models_to_display_p_values |
Character vector of model pairs for which p-values should be displayed, formatted as "Model1 Model2"'. Default is 'NULL'. |
user_title |
Character. Title of the plot. Default is "Prediction Accuracy Comparison Plot"'. |
user_x_title |
Character. Title of the x-axis. Default is "Model"'. |
user_y_title |
Character. Title of the y-axis. Default is "Prediction Accuracy"'. |
user_legend_title |
Character. Title of the legend. Default is 'NULL'. |
user_plot_theme |
A ggplot2 theme to be applied to the plot. Default is 'theme_minimal()'. |
user_plot_theme_specs |
Additional theme specifications applied after 'user_plot_theme'. Default is a set of predefined theme customizations. |
user_p_value_prefix |
Character. Prefix to be added before displaying p-values. Default is 'NULL'. |
user_bar_width |
Numeric. Width of the bars. Default is '0.8'. |
geom_text_args |
List. Additional arguments for 'geom_text', such as color, font size, or angle. Default is an empty list. |
additional_ggplot_args |
List. Additional ggplot2 layers or arguments to be added to the plot. Default is an empty list. |
Value
A ggplot object if 'interactive' is FALSE; otherwise, an interactive Plotly plot.
Examples
library(ggplot2)
library(plotly)
library(dplyr)
library(nortest)
library(ggforce)
library(reshape2)
library(gridExtra)
library(grid)
library(cowplot)
accuracy_data <- example_data4
comparison_data <- example_data5
# Create a static plot
flex_accuracy_diff(accuracy_data, comparison_data, interactive = FALSE)
# Create an interactive plot
flex_accuracy_diff(accuracy_data, comparison_data)
# Display specific p-values
flex_accuracy_diff(accuracy_data, comparison_data,
models_to_display_p_values = c("Model A Model B"))
flex_accuracy_diff2 function
Description
This function generates detailed interactive or static visualizations comparing the accuracy of two methods across different traits. It uses ggplot2 and plotly for rendering the plots.
Usage
flex_accuracy_diff2(
accuracy_diff_data,
user_colors = c("lightblue", "lightpink"),
interactive = TRUE,
user_geom_bar = geom_bar(stat = "identity", position = position_dodge(0.5), width =
0.4),
user_geom_point = geom_point(aes(x = trait, y = R2diff), color = "black", size = 2),
user_accuracy_geom_errorbar = geom_errorbar(aes(ymin = lower_limit_R2, ymax =
upper_limit_R2), color = "black", width = 0.1, position = position_dodge(0.5)),
user_accuracy_diff_geom_errorbar = geom_errorbar(width = 0.05, color = "black"),
user_geom_text = geom_text(aes(x = trait, y = ymax + 0.1, label = p_value), hjust =
0.5, size = 3.5),
user_ylim_accuracy = ylim(0, 1),
user_ylim_accuracy_difference = ylim(0, 1),
user_accuracy_labs = labs(x = "", y = "R2", title = "R2 values"),
user_accuracy_diff_labs = labs(x = "", y = "Difference", title =
"Difference between R2"),
user_accuracy_theme = theme_minimal(),
user_accuracy_diff_theme = theme_minimal(),
user_accuracy_theme_specs = theme(legend.position = c(0.85, 0.85)),
user_accuracy_diff_theme_specs = theme(axis.title.x = element_blank(), axis.text.x =
element_blank(), axis.ticks.x = element_blank(), plot.margin = unit(c(1, 1, 1, 1),
"lines"))
)
Arguments
accuracy_diff_data |
A data frame containing nine columns and at least
two rows: |
user_colors |
A character vector of length 2 specifying colors for the two methods in the bar plot. |
interactive |
Logical, if |
user_geom_bar |
A ggplot2 geom_bar object for customizing the bar plot appearance. Accepts parameters like stat, position, and width to control the visual properties of the bars. |
user_geom_point |
A ggplot2 geom_point object for customizing the point plot appearance. This includes settings for color and size of points which represent the differences in accuracy. |
user_accuracy_geom_errorbar |
A ggplot2 geom_errorbar object for customizing the error bars in the accuracy plot. This involves setting parameters like color, width, and position to visually modify how error bars are displayed. |
user_accuracy_diff_geom_errorbar |
A ggplot2 geom_errorbar object for customizing the error bars in the accuracy difference plot. Similar to user_accuracy_geom_errorbar, but typically used to emphasize differences between methods. |
user_geom_text |
A ggplot2 geom_text object for adding text annotations to the plots. This can include parameters for positioning, size, and the label content, often used to display statistical significance or other annotations. |
user_ylim_accuracy |
A ggplot2 ylim function call for setting the y-axis limits in the accuracy plot. This helps in controlling the scale of the plot to better fit the data presentation. |
user_ylim_accuracy_difference |
A ggplot2 ylim function call for setting the y-axis limits in the accuracy difference plot. Useful for maintaining consistent visual scales across related plots. |
user_accuracy_labs |
A ggplot2 labs function call for setting labels and titles in the accuracy plot. This includes parameters to set the x-axis label, y-axis label, and the main title of the plot. |
user_accuracy_diff_labs |
A ggplot2 labs function call for setting labels and titles in the accuracy difference plot. Useful for distinguishing between different plots and providing clear, informative titles and labels. |
user_accuracy_theme |
A ggplot2 theme object for applying styling themes to the accuracy plot. This parameter can be used to apply a predefined theme or customize aspects like text, background, and grid lines. |
user_accuracy_diff_theme |
A ggplot2 theme object for applying styling themes to the accuracy difference plot. Allows for consistent or contrasting styles between different types of visualizations in the package. |
user_accuracy_theme_specs |
Additional ggplot2 theme modifications specifically for the accuracy plot. This can involve finer control over elements like legend position and plot margins. |
user_accuracy_diff_theme_specs |
Additional ggplot2 theme modifications specifically for the accuracy difference plot. Tailored to enhance or modify specific aspects of the plot's appearance beyond the base theme settings. |
Value
Either a ggplot or plotly object depending on the interactive
argument.
Examples
library(ggplot2)
library(plotly)
library(dplyr)
library(nortest)
library(ggforce)
library(reshape2)
library(gridExtra)
library(grid)
library(cowplot)
flex_accuracy_diff2(example_data6)
flex_boxplot function
Description
This function creates a flexible, optionally interactive boxplot which can be customized with different themes, colors, and additional annotations. It supports both traditional and interactive (Plotly) outputs.
Usage
flex_boxplot(
distribution_data,
interactive = TRUE,
user_colors = NULL,
user_title = "Boxplot",
user_x_title = NULL,
user_y_title = NULL,
user_legend_title = NA,
user_plot_theme = theme_minimal(),
user_plot_theme_specs = theme(legend.title = element_blank(), legend.text =
element_text(size = 10), title = element_text(size = 15), axis.text.x =
element_text(size = 10), axis.title.x = element_text(size = 10), axis.text.y =
element_text(size = 10), axis.title.y = element_text(size = 10)),
annotate_stats = FALSE,
annotate_outliers = FALSE,
annotate_stats_text_size = 3.5,
annotate_outliers_text_size = 3,
annotate_stats_text_color = "black",
annotate_outliers_text_color = "darkred",
annotate_outliers_text_vjust = -1,
annotate_stats_text_vjust = 1.5,
annotate_outliers_text_hjust = -0.5,
annotate_stats_text_hjust = 1
)
Arguments
distribution_data |
A dataframe where each column represents a data distribution (e.g. population) to be plotted with minimum of a single column. |
interactive |
Logical; if TRUE, the output is an interactive Plotly graph. Defaults to TRUE. |
user_colors |
A vector of colors to be used for the boxplots. If NULL, a default set of colors is used. |
user_title |
The main title of the plot. Defaults to "Boxplot". |
user_x_title |
Custom title for the x-axis. If NULL, defaults to the column names of distribution_data. |
user_y_title |
Custom title for the y-axis. If NULL, defaults to "Values". |
user_legend_title |
Title for the legend. Use NA to hide the legend. Defaults to NA. |
user_plot_theme |
A ggplot2 theme object to customize the appearance of the plot. Defaults to theme_minimal(). |
user_plot_theme_specs |
Additional ggplot2 theme specifications. |
annotate_stats |
Logical; if TRUE, adds text annotations for basic statistics (min, Q1, median, Q3, max) to the plot. |
annotate_outliers |
Logical; if TRUE, adds text annotations for outliers to the plot. |
annotate_stats_text_size |
Numeric; text size for statistics annotations. Defaults to 3.5. |
annotate_outliers_text_size |
Numeric; text size for outliers annotations. Defaults to 3. |
annotate_stats_text_color |
Character; text color for statistics annotations. Defaults to "black". |
annotate_outliers_text_color |
Character; text color for outliers annotations. Defaults to "darkred". |
annotate_outliers_text_vjust |
Numeric; vertical adjustment for outliers text annotations. |
annotate_stats_text_vjust |
Numeric; vertical adjustment for statistics text annotations. |
annotate_outliers_text_hjust |
Numeric; horizontal adjustment for outliers text annotations. |
annotate_stats_text_hjust |
Numeric; horizontal adjustment for statistics text annotations. |
Value
Depending on the value of interactive, returns either a Plotly
object and/or a ggplot object.
Examples
library(ggplot2)
library(plotly)
library(dplyr)
library(nortest)
library(ggforce)
library(reshape2)
library(gridExtra)
library(grid)
library(cowplot)
# Basic interactive boxplot
flex_boxplot(example_data8)
# Boxplot with custom colors, themes and titles
flex_boxplot(example_data8, user_plot_theme = ggplot2::theme_gray(),
user_colors = c("#6A3D9A", "#FF7F00", "gold1"),
user_title = "Customized Boxplot",
user_x_title = "Multiple Populations",
user_y_title = "Values",
user_legend_title = "Multiple Populations",
user_plot_theme_specs = ggplot2::theme(
legend.title = ggplot2::element_text(size = 10),
legend.text = ggplot2::element_text(size = 10),
title = ggplot2::element_text(size = 20),
axis.text.x = ggplot2::element_blank(),
axis.title.x = ggplot2::element_text(size = 15),
axis.text.y = ggplot2::element_text(size = 12),
axis.title.y = ggplot2::element_text(size = 15)
))
flex_correlation_plot function
Description
This function creates a correlation heatmap using ggplot2 and plotly, allowing for various customizations. It supports rendering both static and interactive correlation heatmaps of either the full, lower, or upper triangular matrix.
Usage
flex_correlation_plot(
correlation_matrix,
user_colors = c("darkred", "white", "steelblue4"),
display_names = TRUE,
interactive = TRUE,
user_lower_limit = -1,
user_upper_limit = 1,
user_mid_point = 0,
user_plotly_x_name = "VAR_A",
user_plotly_y_name = "VAR_B",
user_plotly_value_name = "r",
user_title = "Correlation Plot",
user_x_title = NULL,
user_y_title = NULL,
user_legend_title = "Correlation",
matrix_type = "full",
user_plot_theme = theme_minimal(),
user_plot_theme_specs = theme(legend.title = element_text(size = 10), legend.text =
element_text(size = 10), title = element_text(size = 15), axis.text.x =
element_text(size = 10), axis.title.x = element_text(size = 10), axis.text.y =
element_text(size = 10), axis.title.y = element_text(size = 10)),
user_zoom_range = NULL
)
Arguments
correlation_matrix |
A square matrix representing correlation coefficients with column names identical to row names. |
user_colors |
A vector of three colors representing the gradient of the heatmap for negative, neutral, and positive correlations respectively. Default is c("darkred", "white", "steelblue4"). |
display_names |
Logical, whether to display variable names on the axes. Default is TRUE. |
interactive |
Logical, indicating if the output should be an interactive plotly object. Default is TRUE. |
user_lower_limit |
The minimum value of the color gradient. Default is -1. |
user_upper_limit |
The maximum value of the color gradient. Default is 1. |
user_mid_point |
The midpoint value of the color gradient where the neutral color is centered. Default is 0. |
user_plotly_x_name |
The name to be used for the x-axis in the plotly plot. Default is "VAR_A". |
user_plotly_y_name |
The name to be used for the y-axis in the plotly plot. Default is "VAR_B". |
user_plotly_value_name |
The name to be used for the values in the plotly tooltip. Default is "r". |
user_title |
Title of the heatmap. Default is "Correlation Plot". |
user_x_title |
Custom x-axis title. If NULL, defaults to column names of the matrix. |
user_y_title |
Custom y-axis title. If NULL, defaults to row names of the matrix. |
user_legend_title |
Title for the legend. Default is "Correlation". |
matrix_type |
Specifies whether to plot the full matrix, the lower triangular part, or the upper triangular part. Default options are "full", "lower", "upper". |
user_plot_theme |
ggplot2 theme object for base theming of the plot. Default is theme_minimal(). |
user_plot_theme_specs |
Additional ggplot2 theme specifications to apply
on top of |
user_zoom_range |
Optional numeric vector specifying the indices of the matrix to zoom into; this disables interactivity. |
Value
A ggplot object if interactive = FALSE, otherwise a plotly interactive plot.
Examples
library(ggplot2)
library(plotly)
library(dplyr)
library(nortest)
library(ggforce)
library(reshape2)
library(gridExtra)
library(grid)
library(cowplot)
# Generate a symmetric correlation matrix
correlation_matrix <- example_data9
# Default full matrix heatmap
flex_correlation_plot(correlation_matrix)
# Lower triangular heatmap
flex_correlation_plot(correlation_matrix, matrix_type = "lower")
# Customized plot with different theme and colors
flex_correlation_plot(
correlation_matrix = correlation_matrix,
user_colors = c("gold2", "lightgrey", "darkblue"),
user_title = "Custom Correlation Plot",
user_x_title = "Variables",
user_y_title = "Variables",
user_legend_title = "Correlation Coefficient",
user_plot_theme = theme_classic(),
user_plot_theme_specs = theme(
legend.title = element_text(size = 12),
legend.text = element_text(size = 10),
title = element_text(size = 16),
axis.title.x = element_text(size = 12),
axis.text.x = element_text(size = 12, angle = 45, hjust = 1),
axis.text.y = element_text(size = 12),
axis.title.y = element_text(size = 12),
legend.position = "bottom")
)
flex_distribution function
Description
The flex_distribution function provides a flexible way to visualize data
distributions using histograms and density plots. It can be used to create
both basic and interactive plots with additional features such as density
curves, reference lines, and summary statistics annotations. The function
supports custom colors and various themes.
Usage
flex_distribution(
distribution_data,
interactive = TRUE,
user_colors = NULL,
plot_type = "histogram",
add_density = FALSE,
reference_line = "mean",
show_summary = FALSE,
user_title = "Distribution Plot",
user_x_title = NULL,
user_y_title = "Frequency",
user_legend_title = NA,
user_plot_theme = theme_minimal(),
user_plot_theme_specs = theme(legend.title = element_blank(), legend.text =
element_text(size = 10), title = element_text(size = 15), axis.text.x =
element_text(size = 10), axis.title.x = element_text(size = 10), axis.text.y =
element_text(size = 10), axis.title.y = element_text(size = 10)),
binwidth = NULL,
bins = NULL
)
Arguments
distribution_data |
A dataframe where each column represents a data distribution (e.g. population) to be plotted with minimum of a single column. |
interactive |
Logical, if TRUE (default), the plot will be interactive using Plotly, otherwise a ggplot object is returned. |
user_colors |
A vector of colors for the plots. If NULL, a set of default colors is used. |
plot_type |
The type of plot to create, with options "histogram" and/or "density". |
add_density |
Logical, if TRUE, adds a density curve to the plot. |
reference_line |
Specifies the type of reference line to add; options include "mean", "median", or FALSE for no line. |
show_summary |
Logical, if TRUE, displays summary statistics on the plot. |
user_title |
Title of the plot. |
user_x_title |
Custom X-axis title. If NULL, the name of the variable is used. |
user_y_title |
Custom Y-axis title, default is "Frequency". |
user_legend_title |
Title for the legend. Can be NA to exclude the legend title. |
user_plot_theme |
ggplot2 theme object for customizing the appearance of the plot. |
user_plot_theme_specs |
Further ggplot2 theme specifications. |
binwidth |
The width of the bins for the histogram (optional). |
bins |
The number of bins for the histogram (optional). |
Details
The function is designed to be flexible and allows extensive customization
of plot aesthetics and functionality. Density plots are added as an additional
layer when add_density is TRUE, and the reference line can be specified by the user.
Value
An object of class 'ggplot' or 'plotly' depending on the 'interactive' parameter.
Examples
library(ggplot2)
library(plotly)
library(dplyr)
library(nortest)
library(ggforce)
library(reshape2)
library(gridExtra)
library(grid)
library(cowplot)
# Histogram with custom colors
custom_colors <- c("red", "blue", "green")
flex_distribution(example_data8, user_colors = custom_colors)
# Density plot
flex_distribution(
example_data8,
plot_type = "density",
user_colors = custom_colors,
user_y_title = "Density"
)
#' # Basic histogram with density curves and summary statistics
flex_distribution(
example_data8,
add_density = TRUE,
show_summary = TRUE,
user_title = "Distribution with Densities and Summary",
user_y_title = "Density"
)
# Histogram with custom binwidth
flex_distribution(
example_data8,
binwidth = 0.5,
user_title = "Distribution with Custom Binwidth"
)
# Histogram with a specified number of bins
flex_distribution(
example_data8,
bins = 50,
user_title = "Distribution with Custom Bin Count"
)
flex_interval function
Description
This function visualizes individual polygenic risk scores (PRS) with corresponding confidence intervals. It supports customization of reference lines, colors, themes, and interactivity.
Usage
flex_interval(
CI_data,
user_ref_line = NULL,
user_ref_color = "red",
user_ref_linetype = "dashed",
user_ref_visible = TRUE,
user_point_color = "blue",
user_point_size = 3,
user_errorbar_color = "black",
user_errorbar_size = 0.2,
interactive = TRUE,
user_theme = theme_minimal(),
user_theme_specifications = theme(),
user_title = "PRS with Confidence Intervals",
user_x_label = "Individual",
user_y_label = "Polygenic Risk Score (PRS)"
)
Arguments
CI_data |
Data frame containing columns: |
user_ref_line |
Numeric or vector. Horizontal reference line(s) to be added.
If |
user_ref_color |
Character or vector. Color(s) of reference line(s).
Default is |
user_ref_linetype |
Character or vector. Linetype(s) for reference line(s).
Default is |
user_ref_visible |
Logical. Whether to display the reference line(s).
Default is |
user_point_color |
Character. Color of PRS points. Default is |
user_point_size |
Numeric. Size of PRS points. Default is |
user_errorbar_color |
Character. Color of confidence interval error bars.
Default is |
user_errorbar_size |
Numeric. Width of error bars. Default is |
interactive |
Logical. If |
user_theme |
A ggplot2 theme object. Default is |
user_theme_specifications |
Additional theme specifications. Default is an
empty |
user_title |
Character. Title of the plot. Default is |
user_x_label |
Character. X-axis label. Default is |
user_y_label |
Character. Y-axis label. Default is |
Value
A ggplot object (static plot) or a plotly object (interactive plot),
depending on the value of interactive.
Examples
library(ggplot2)
library(plotly)
library(dplyr)
library(nortest)
library(ggforce)
library(reshape2)
library(gridExtra)
library(grid)
library(cowplot)
set.seed(123)
example_data <- data.frame(
IID = paste0("ID_", 1:8),
PRS = rnorm(8, 0, 1),
Variance = runif(8, 0.01, 0.1)
)
example_data$Lower_Limit <- example_data$PRS - 1.96 * sqrt(example_data$Variance)
example_data$Upper_Limit <- example_data$PRS + 1.96 * sqrt(example_data$Variance)
CI_data <- example_data
# Basic
flex_interval(CI_data)
# Add custom reference lines
flex_interval(CI_data,
user_ref_line = c(1, -1),
user_ref_color = c("darkgreen", "darkred"),
user_ref_linetype = c("dotted", "twodash"))
# Static plot with theme customization
flex_interval(CI_data, interactive = FALSE,
user_theme_specifications = theme(axis.text.x = element_text(angle = 45, hjust = 1)),
user_point_color = "magenta",
user_errorbar_color = "darkred")
# Sorted PRS plot
ordered_data <- CI_data[order(-CI_data$PRS),]
ordered_data$IID <- factor(ordered_data$IID, levels = ordered_data$IID)
flex_interval(ordered_data, interactive = FALSE, user_ref_visible = FALSE)
flex_manhattan function
Description
This function creates a flexible Manhattan plot, which is useful for visualizing GWAS results. The plot can be generated as either a static or an interactive plot. The function allows customization of color schemes, titles, axis labels, and annotation features.
Usage
flex_manhattan(
gwas_data,
interactive = TRUE,
user_colors = NULL,
user_y_cutoff = -log10(5e-08),
user_y_cutoff_color = "red",
user_title = "Manhattan Plot",
user_x_title = "Position",
user_y_title = "-log10(p-value)",
user_legend_title = "Chromosome",
user_plot_theme = theme_minimal(),
user_plot_theme_specs = theme(legend.title = element_text(size = 10), legend.text =
element_text(size = 10), title = element_text(size = 15), axis.text.x =
element_text(size = 10), axis.title.x = element_text(size = 10), axis.text.y =
element_text(size = 10), axis.title.y = element_text(size = 10)),
annotate_data = NULL,
annotate_column = "SNP",
annotate_labels = FALSE,
zoom_on_annotations = FALSE,
zoom_margin = 1,
...
)
Arguments
gwas_data |
A data frame containing columns for SNP ID ('SNP'), chromosome ('CHR'), position ('POS'), and p-values ('P_VALUE'). |
interactive |
Logical; if TRUE, returns an interactive plotly plot, otherwise a ggplot2 plot. |
user_colors |
A vector of colors for the chromosomes. If NULL, a default set of 22 colors is used. |
user_y_cutoff |
A numeric value for the y-axis cutoff, used to draw a horizontal line. |
user_y_cutoff_color |
Color of the y-axis cutoff line. |
user_title |
The main title of the plot. |
user_x_title |
The label for the x-axis. |
user_y_title |
The label for the y-axis. |
user_legend_title |
The title for the legend. If NULL, no legend is displayed. |
user_plot_theme |
ggplot2 theme object for styling the plot background and fonts. |
user_plot_theme_specs |
Additional ggplot2 theme specifications for custom styling. |
annotate_data |
A vector of SNP identifiers for which annotations are to be made. |
annotate_column |
The column name from |
annotate_labels |
Logical; if TRUE, annotations are added to the plot. |
zoom_on_annotations |
Logical; if TRUE, the plot will zoom in on annotated SNPs. |
zoom_margin |
Numeric; defines the margin around the zoomed area as a proportion of the range of the data. |
... |
Additional arguments to be passed to ggplot2 plotting functions. |
Value
An object of class 'ggplot' or 'plotly' depending on the 'interactive' parameter.
Examples
library(ggplot2)
library(plotly)
library(dplyr)
library(nortest)
library(ggforce)
library(reshape2)
library(gridExtra)
library(grid)
library(cowplot)
flex_manhattan(example_data1)
# Changing the y-axis cutoff to highlight significant p-values differently
flex_manhattan(example_data1,
user_y_cutoff = c(5, 4), # Multiple cutoffs
user_y_cutoff_color = c("darkred", "darkgreen")) # Corresponding colors
# Adding annotations for specific SNPs
annotated_snps <- c("SNP_2550", "SNP_4829", "SNP_8296")
flex_manhattan(example_data1, annotate_data = annotated_snps, annotate_labels = TRUE,
interactive = FALSE)
# Example of placing the legend at the bottom with a black outline
flex_manhattan(example_data1,
user_plot_theme_specs = theme(
legend.position = "bottom",
legend.box.background = element_rect(colour = "black")
))
flex_qqplot function
Description
This function generates a customizable quantile-quantile (QQ) plot for GWAS data, allowing for interactive exploration, zooming on specific SNPs, and inclusion of a Kolmogorov-Smirnov test result annotation.
Usage
flex_qqplot(
gwas_data,
interactive = TRUE,
display_ks = FALSE,
user_colors = c("dodgerblue", "darkorange"),
user_title = "QQ Plot",
user_x_title = "Theoretical Quantiles",
user_y_title = "Observed Quantiles",
user_plot_theme = theme_minimal(),
user_plot_theme_specs = theme(title = element_text(size = 15), axis.text =
element_text(size = 10), axis.title = element_text(size = 10)),
annotate_data = NULL,
annotate_column = "SNP",
annotate_labels = FALSE,
zoom_on_annotations = FALSE,
zoom_margin = 1,
...
)
Arguments
gwas_data |
A data frame containing columns for SNP ID ('SNP’), chromosome ('CHR'), position ('POS'), and p-values ('P_VALUE'). |
interactive |
Logical, whether to return an interactive plot (TRUE) or static ggplot (FALSE). |
display_ks |
Logical, whether to display the Kolmogorov-Smirnov (KS) test result. |
user_colors |
Character vector of length 2 specifying the colors for points and reference line. |
user_title |
The title of the plot. |
user_x_title |
The title for the x-axis. |
user_y_title |
The title for the y-axis. |
user_plot_theme |
A ggplot2 theme object to style the plot. |
user_plot_theme_specs |
Additional ggplot2 theme specifications to override in |
annotate_data |
A vector of SNP identifiers to annotate in the plot. |
annotate_column |
The name of the column in |
annotate_labels |
Logical, whether to label the annotated points. |
zoom_on_annotations |
Logical, whether to create a zoomed version of the plot focusing on annotated SNPs. |
zoom_margin |
Numeric, the margin size around zoomed points. |
... |
Additional arguments to be passed to ggplot2 plotting functions. |
Value
An object of class 'ggplot' or 'plotly' depending on the 'interactive' parameter.
Examples
library(ggplot2)
library(plotly)
library(dplyr)
library(nortest)
library(ggforce)
library(reshape2)
library(gridExtra)
library(grid)
library(cowplot)
flex_qqplot(example_data1, user_colors = c("black", "darkorange"))
flex_qqplot(example_data1, display_ks = TRUE, interactive = FALSE)
annotated_snps <- c("SNP_1", "SNP_2", "SNP_3")
flex_qqplot(example_data1, annotate_data = annotated_snps, annotate_labels = TRUE)
flex_qqplot(example_data1, annotate_data = annotated_snps, annotate_labels = TRUE,
display_ks = TRUE)
flex_qqplot(example_data1, annotate_data = annotated_snps, annotate_labels = TRUE,
zoom_on_annotations = TRUE, zoom_margin = 0.3, user_plot_theme = theme_bw())
flex_regression_summary function
Description
This function creates a flexible regression summary plot with customization options for interactivity, p-value intervals, legend sizes, color gradients, and more. It supports both ggplot2 and plotly outputs for static and interactive visualizations, respectively.
Usage
flex_regression_summary(
regression_data,
interactive = TRUE,
user_size_breaks = cut(regression_data$p_value, breaks = c(0, 0.01, 0.05, 0.1, 1),
labels = c("< 0.01", "0.05", "0.1", "1"), include.lowest = TRUE),
user_legend_break_sizes = c(12, 10, 8, 7),
user_geom_tile = geom_tile(color = "white", fill = "white", size = 5),
user_geom_point = geom_point(shape = 22, color = "white", stroke = 0),
user_gradient_bar = scale_fill_gradientn(colours = c("red4", "white", "steelblue4"),
limit = c(-max(abs(regression_data$estimate)) - 0.05,
max(abs(regression_data$estimate)) + 0.05), guide = guide_colorbar(title =
"Estimate", label.position = "right", barwidth = 1, barheight = 5, title.position =
"top", order = 1)),
user_geom_text = list(geom_text(data = subset(regression_data, (p_value <= 0.01)),
aes(label = "**"), size = 6, vjust = 0.5, hjust = 0.5, show.legend = FALSE, color =
"white", fontface = "bold"), geom_text(data = subset(regression_data, (p_value <=
0.05 & p_value > 0.01)), aes(label = "*"), size = 6, vjust = 0.5, hjust = 0.5,
show.legend = FALSE, color = "white", fontface = "bold")),
user_theme = theme_minimal(),
user_theme_specs = theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1,
size = 15, colour = "black"), axis.text.y = element_text(size = 15), axis.title.y =
element_text(size = 15), axis.title.x = element_text(size = 15), text =
element_text(family = "serif"), legend.text = element_text(size = 15), legend.title =
element_text(size = 15), legend.key.width = unit(0.01, "cm"), legend.key.height =
unit(0.01, "cm"), legend.box = "horizontal", legend.spacing.y = unit(0.5, "cm"),
legend.position = "right", legend.box.margin = margin(b = 15,
t = -20, l = 5, r
= 5), legend.box.just = ("bottom"), plot.margin = margin(b = 5, t = 20, l = 5, r =
5)),
user_labs = labs(x = "x variable", y = "y variable", fill = "Estimate", size =
"p value"),
user_tooltip = list("Estimate: ", "p-value: "),
user_size_legend_guides = guides(size = guide_legend(reverse = FALSE, label.position =
"right", title.position = "top", override.aes = list(colour = "grey", shape = 15,
fill = "white", size = user_legend_break_sizes - 3)))
)
Arguments
regression_data |
A dataframe containing the variables |
interactive |
Logical, if TRUE returns an interactive plotly object, otherwise returns a ggplot object. |
user_size_breaks |
Factor levels for breaking down the p-value into intervals. Default is predefined intervals at p < 0.01, 0.05, 0.1, and 1. |
user_legend_break_sizes |
A numeric vector indicating the sizes of points in the legend, corresponding to the p-value intervals. |
user_geom_tile |
Custom ggplot2 geom_tile layer, allows customization of tile appearance. |
user_geom_point |
Custom ggplot2 geom_point layer, allows customization of point markers. |
user_gradient_bar |
Custom ggplot2 scale_fill_gradientn for coloring the tiles based on the estimates. |
user_geom_text |
List of custom ggplot2 geom_text layers for adding text annotations based on significance. |
user_theme |
Custom ggplot2 theme, allows overriding the default minimal theme. |
user_theme_specs |
Custom modifications to the default or user-specified theme. |
user_labs |
Custom ggplot2 labs function for setting axis and legend titles. |
user_tooltip |
A list containing tooltip strings for interactive plots. |
user_size_legend_guides |
Custom ggplot2 guides for the size aesthetic in the legend. |
Value
A ggplot or plotly object depending on the interactive argument.
Examples
library(ggplot2)
library(plotly)
library(dplyr)
library(nortest)
library(ggforce)
library(reshape2)
library(gridExtra)
library(grid)
library(cowplot)
# Default usage
flex_regression_summary(example_data10)
# Custom p-value intervals and annotations
flex_regression_summary(example_data10, user_size_breaks = cut(example_data10$p_value,
breaks = c(0, 0.05, 1), labels = c("< 0.05", "1"), include.lowest = TRUE),
user_legend_break_sizes = c(12, 8),
user_geom_text = list(geom_text(data = subset(example_data10,
(p_value <= 0.05)),
aes(label = "sig"),
size = 4, vjust = 0.5, hjust = 0.5,
color = "black",
fontface = "italic")))
# Custom formatting and gradient colors
flex_regression_summary(example_data10, interactive = FALSE,
user_geom_point = geom_point(shape = 25,
color = "white", stroke = 0),
user_gradient_bar = scale_fill_gradientn(colours = c("purple4",
"white", "green4")))