plot_effects: Plot Interaction Effects
In cape: Combined Analysis of Pleiotropy and Epistasis for Diversity Outbred Mice

plot_effects

R Documentation

Plot Interaction Effects

Description

This function plots phenotypic effects of individual cape interactions. It serves as a wrapper for the functions plot_lines plot_bars plot_points, and plot_int_heat. Each of those functions plots individual cape interactions in different forms.

Usage

plot_effects(
  data_obj,
  geno_obj,
  marker1,
  marker2 = NULL,
  pheno_type = "normalized",
  plot_type = c("l", "p", "b", "h"),
  error_bars = "none",
  ymin = NULL,
  ymax = NULL,
  covar = NULL,
  marker1_label = NULL,
  marker2_label = NULL,
  bin_continuous_genotypes = TRUE,
  ref_centered = TRUE,
  gen_model1 = "Additive",
  gen_model2 = "Additive",
  bins_marker1 = 50,
  bins_marker2 = 50
)

Arguments

`data_obj`	A `Cape` object
`geno_obj`	A genotype object
`marker1`	A string indicating the name of the source marker in the interaction. This can also be the name of a covariate.
`marker2`	Another string indicating the name of the source marker in the interaction. This can also be the name of a covariate. Optional.
`pheno_type`	One of "eigentraits", "normalized_traits", or "raw_traits", indicating which traits to plot.
`plot_type`	A letter referring to the desired style of the plot. The choices are the following: "l" - line plots, "p" = points, "b" - bar plots, "h" - heat map.
`error_bars`	The type of error bars to plot. Choices are "none" (the default), "se" for standard error, or "sd" for standard deviation.
`ymin`	A minimum value for the y axes across all plots. If NULL, each y axis will be determined independently
`ymax`	A maximum value for the y axes across all plots. If NULL, each y axis will be dertermined independently
`covar`	A vector of strings indicating which covariates, if any, the traits should be adjusted for. If NULL, the covariates specified in the data_obj are used as default. To prevent adjusting for covariates, use "none".
`marker1_label`	A string to use as the label for marker1 If NULL, the string used for marker1 will be used.
`marker2_label`	A string to use as the label for marker2 If NULL, the string used for marker2 will be used.
`bin_continuous_genotypes`	If TRUE, genotypes (and covariate) values will be binned into 0, 0.5, and 1 values. This reduces the number of bins that traits need to be divided into, especially if there are only one or two individuals with a 0.49 genotype, for example. Binning may not be desirable when using the heatmap.
`ref_centered`	A logical value indicating whether to center the values on the reference allele. Defaults to TRUE.
`gen_model1`	One of "Additive", "Dominant", or "Recessive" indicating how the genotype should be coded for the first marker. If Additive, genotypes are coded as 0 for homozygous reference allele, 1 for homozygous alternate allele, and 0.5 for heterozygous. If Dominant, any allele probability greater than 0.5 is set to 1. If recessive, any allele probability less than or equal to 0.5 is set to 0. In other words, for the dominant coding, heterozygotes are grouped with the homozygous alternate genotypes: 0 vs. (0.5,1). This shows the effect of having any dose of the alternate allele. With a recessive coding, heterozygotes are grouped with the homozygous reference genotypes: (0, 0.5) vs. 1. This shows the effect of having two copies of the alternate allele vs. having fewer than two copies.
`gen_model2`	The same as gen_model1, but for the second marker.
`bins_marker1`	Only used for heatmap plotting. The number of bins for marker1 if it is a continuously valued marker or covariate. The bins are used to fit a linear model and predict outcomes for a 2D grid of marker1 and marker2 values. This argument can also be a vector of bin values for binning at specific values.
`bins_marker2`	The same as bins_marker1, but for marker2.

Details

The "h" option calls plot_int_heat, which fits linear models to each trait and both markers specified. It uses those models to predict phenotype values along continuously valued genotype bins and plots the predicted values as a heatmap.