R: The CAPE data object

Cape-class {cape}

R Documentation

The CAPE data object

Description

The CAPE data object

Details

Class Cape defines a CAPE analysis object.

Slots

parameter_file: string, full path to YAML file with initialization parameters
yaml_parameters: string representing YAML CAPE parameters. See the vignette for more descriptions of individual parameter settings.
results_path: string, full path to directory for storing results (optional, a directory will be created if one is not specified)
save_results: Whether to save cape results. Defaults to FALSE.
use_saved_results: Whether to use existing results from a previous run. This can save time if re-running an analysis, but can lead to problems if the old run and new run have competing settings. If errors arise, and use_saved_results is set to TRUE, try setting it to FALSE, or deleting previous results.
pheno: A matrix containing the traits to be analyzed. Traits are in columns and individuals are in rows.
chromosome: A vector the same length as the number of markers indicating which chromosome each marker lives on.
marker_num: A vector the same length as the number of markers indicating the index of each marker
marker_location: A vector the same length as the number of markers indicating the genomic position of each marker. The positions are primarily used for plotting and can be in base pairs, centiMorgans, or dummy variables.
marker_selection_method: A string indicating how markers should be selected for the pairscan. Options are "top_effects" or "from_list." If "top_effects," markers are selected using main effect sizes. If "from_list" markers are specified using a vector of marker names. See select_markers_for_pairscan.
geno_names: The dimnames of the genotype array. The genotype array is a three-dimensional array in which rows are individuals, columns are alleles, and the third dimension houses the markers. Genotypes are pulled for analysis using get_geno based on geno_names. Only the individuals, alleles, and markers listed in geno_names are taken from the genotype matrix. Functions that remove markers and individuals from analysis always operate on geno_names in addition to other relevant slots. The names of geno_names must be "mouse", "allele", "locus."
geno: A three dimensional array holding genotypes for each animal for each allele at each marker. The genotypes are continuously valued probabilities ranging from 0 to 1. The dimnames of geno must be "mouse", "allele", and "locus," even if the individuals are not mice.
geno_for_pairscan: A two-dimensional matrix holding the genotypes that will be analyzed in the pairscan. Alleles are in columns and individuals are in rows. As in the geno array, values are continuous probabilities ranging from 0 to 1.
peak_density: The density parameter for select_markers_for_pairscan. Determines how densely markers under an individual effect size peak are selected for the pairscan if marker_selection_method is TRUE. Defaults to 0.5.
window_size: The window size used by select_markers_for_pairscan. It specifies how many markers are used to smooth effect size curves for automatic peak identification. If set to NULL, window_size is determined automatically. Used when marker_selection_method is TRUE.
tolerance: The wiggle room afforded to select_markers_for_pairscan in finding a target number of markers. If num_alleles_in_pairscan is 100 and the tolerance is 5, the algorithm will stop when it identifies anywhere between 95 and 105 markers for the pairscan.
ref_allele: A string of length 1 indicating which allele to use as the reference allele. In two-parent crosses, this is usually allele A. In DO/CC populations, we recommend using B as the reference allele. B is the allele from the C57Bl6/J mouse, which is often used as a reference strain.
alpha: The significance level for calculating effect size thresholds in the singlescan. If singlescan_perm is 0, this parameter is ignored.
covar_table: A matrix of covariates with covariates in columns and individuals in rows. Must be numeric.
num_alleles_in_pairscan: The number of alleles to test in the pairwise scan. Because Cape is computationally intensive, we usually need to test only a subset of available markers in the pairscan, particularly if the kinship correction is being used.
max_pair_cor: the maximum Pearson correlation between two markers. If their correlation exceeds this value, they will not be tested against each other in the pairscan. This threshold is set to prevent false positive arising from testing highly correlated markers. If this value is set to NULL, min_per_genotype must be specified.
min_per_genotype: minimum The minimum number of individuals allowable per genotype combination in the pair scan. If for a given marker pair, one of the genotype combinations is underrepresented, the marker pair is not tested. If this value is NULL, max_pair_cor must be specified.
pairscan_null_size: The total size of the null distribution. This is DIFFERENT than the number of permutations to run. Each permutation generates n choose 2 elements for the pairscan. So for example, a permutation that tests 100 pairs of markers will generate a null distribution of size 4950. This process is repeated until the total null size is reached. If the null size is set to 5000, two permutations of 100 markers would be done to get to a null distribution size of 5000.
p_covar: A vector of strings specifying the names of covariates derived from traits. See pheno2covar.
g_covar: A vector of strings specifying the names of covariates derived from genetic markers. See marker2covar.
p_covar_table: A matrix holding the individual values for each trait-derived covariate. See pheno2covar.
g_covar_table: A matrix holding the individual values for each marker-derived covariate. See marker2covar.
model_family: Indicates the model family of the phenotypes This can be either "gaussian" or "binomial". If this argument is length 1, all phenotypes will be assigned to the same family. Phenotypes can be assigned different model families by providing a vector of the same length as the number of phenotypes, indicating how each phenotype should be modeled. See singlescan.
scan_what: A string indicating whether "eigentraits", "normalized_traits", or "raw_traits" should be analyzed. See get_pheno.
ET: A matrix holding the eigentraits to be analyzed.
singular_values: Added by get_eigentraits. A vector holding the singular values from the singular value decomposition of the trait matrix. They are used in rotating the final direct influences back to trait space from eigentrait space. See get_eigentraits and direct_influence.
right_singular_vectors: Added by get_eigentraits. A matrix containing the right singular vectors from the singular value decomposition of the trait matrix. They are used in rotating the final direct influences back to trait space from eigentrait space. See get_eigentraits and direct_influence.
traits_scaled: Whether the traits should be mean-centered and standardized before analyzing.
traits_normalized: Whether the traits should be rank Z normalized before analyzing.
var_to_var_influences_perm: added in error_prop The list of results from the error propagation of permuted coefficients.
var_to_var_influences: added in error_prop The list of results from the error propagation of coefficients.
pval_correction: Options are "holm", "hochberg", "hommel", "bonferroni", "BH", "BY","fdr", "none"
linkage_blocks_collapsed: A list containing assignments of markers to linkage blocks calculated by linkage_blocks_network and plot_network. In this list there can be multiple markers assigned to a single linkage block.
linkage_blocks_full: A list containing assignments of markers to linkage blocks when no linkage blocks are calculated. In this list there can only be one marker per "linkage block". See linkage_blocks_network and plot_network.
var_to_var_p_val: The final table of cape interaction results calculated by error_prop.
max_var_to_pheno_influence: The final table of cape direct influences of markers to traits calculated by direct_influence.
collapsed_net: An adjacency matrix holding significant cape interactions between linkage blocks. See plot_network and get_network.
full_net: An adjacency matrix holding significant cape interactions between individual markers. See plot_network and get_network.
use_kinship: Whether to use a kinship correction in the analysis.
kinship_type: Which type of kinship matrix to use. Either "overall" for the overall kinship matrix or "ltco" for leave-two-chromosomes-out.
transform_to_phenospace: whether to transform to phenospace or not.

Public fields

parameter_file: full path to YAML file with initialization parameters.
yaml_parameters: string representing YAML CAPE parameters. See the vignette for more descriptions of individual parameter settings.
results_path: string, full path to directory for storing results (optional, a directory will be created if one is not specified).
save_results: Whether to save cape results. Defaults to FALSE.
use_saved_results: Whether to use existing results from a previous run. This can save time if re-running an analysis, but can lead to problems if the old run and new run have competing settings. If errors arise, and use_saved_results is set to TRUE, try setting it to FALSE, or deleting previous results.
pheno: A matrix containing the traits to be analyzed. Traits are in columns and individuals are in rows.
chromosome: A vector the same length as the number of markers indicating which chromosome each marker lives on.
marker_num: A vector the same length as the number of markers indicating the index of each marker.
marker_location: A vector the same length as the number of markers indicating the genomic position of each marker. The positions are primarily used for plotting and can be in base pairs, centiMorgans, or dummy variables.
geno_names: The dimnames of the genotype array. The genotype array is a three-dimensional array in which rows are individuals, columns are alleles, and the third dimension houses the markers. Genotypes are pulled for analysis using get_geno based on geno_names. Only the individuals, alleles, and markers listed in geno_names are taken from the genotype matrix. Functions that remove markers and individuals from analysis always operate on geno_names in addition to other relevant slots. The names of geno_names must be "mouse", "allele", "locus."
geno: A three dimensional array holding genotypes for each animal for each allele at each marker. The genotypes are continuously valued probabilities ranging from 0 to 1. The dimnames of geno must be "mouse", "allele", and "locus," even if the individuals are not mice.
peak_density: The density parameter for select_markers_for_pairscan. Determines how densely markers under an individual effect size peak are selected for the pairscan if marker_selection_method is TRUE. Defaults to 0.5.
window_size: The window size used by select_markers_for_pairscan. It specifies how many markers are used to smooth effect size curves for automatic peak identification. If set to NULL, window_size is determined automatically. Used when marker_selection_method is TRUE.
tolerance: The wiggle room afforded to select_markers_for_pairscan in finding a target number of markers. If num_alleles_in_pairscan is 100 and the tolerance is 5, the algorithm will stop when it identifies anywhere between 95 and 105 markers for the pairscan.
ref_allele: A string of length 1 indicating which allele to use as the reference allele. In two-parent crosses, this is usually allele A. In DO/CC populations, we recommend using B as the reference allele. B is the allele from the C57Bl6/J mouse, which is often used as a reference strain.
alpha: The significance level for calculating effect size thresholds in the singlescan. If singlescan_perm is 0, this parameter is ignored.
covar_table: A matrix of covariates with covariates in columns and individuals in rows. Must be numeric.
num_alleles_in_pairscan: The number of alleles to test in the pairwise scan. Because Cape is computationally intensive, we usually need to test only a subset of available markers in the pairscan, particularly if the kinship correction is being used.
max_pair_cor: The maximum Pearson correlation between two markers. If their correlation exceeds this value, they will not be tested against each other in the pairscan. This threshold is set to prevent false positive arising from testing highly correlated markers. If this value is set to NULL, min_per_genotype must be specified.
min_per_genotype: minimum The minimum number of individuals allowable per genotype combination in the pair scan. If for a given marker pair, one of the genotype combinations is underrepresented, the marker pair is not tested. If this value is NULL, max_pair_cor must be specified.
pairscan_null_size: The total size of the null distribution. This is DIFFERENT than the number of permutations to run. Each permutation generates n choose 2 elements for the pairscan. So for example, a permutation that tests 100 pairs of markers will generate a null distribution of size 4950. This process is repeated until the total null size is reached. If the null size is set to 5000, two permutations of 100 markers would be done to get to a null distribution size of 5000.
p_covar: A vector of strings specifying the names of covariates derived from traits. See pheno2covar.
g_covar: A vector of strings specifying the names of covariates derived from genetic markers. See marker2covar.
p_covar_table: A matrix holding the individual values for each trait-derived covariate. See pheno2covar.
g_covar_table: A matrix holding the individual values for each marker-derived covariate. See marker2covar.
model_family: Indicates the model family of the phenotypes. This can be either "gaussian" or "binomial". If this argument is length 1, all phenotypes will be assigned to the same family. Phenotypes can be assigned different model families by providing a vector of the same length as the number of phenotypes, indicating how each phenotype should be modeled. See singlescan.
scan_what: A string indicating whether "eigentraits", "normalized_traits", or "raw_traits" should be analyzed. See get_pheno.
ET: A matrix holding the eigentraits to be analyzed.
singular_values: Added by get_eigentraits. A vector holding the singular values from the singular value decomposition of the trait matrix. They are used in rotating the final direct influences back to trait space from eigentrait space. See get_eigentraits and direct_influence.
right_singular_vectors: Added by get_eigentraits. A matrix containing the right singular vectors from the singular value decomposition of the trait matrix. They are used in rotating the final direct influences back to trait space from eigentrait space. See get_eigentraits and direct_influence.
traits_scaled: Whether the traits should be mean-centered and standardized before analyzing.
traits_normalized: Whether the traits should be rank Z normalized before analyzing.
var_to_var_influences_perm: added in error_prop. The list of results from the error propagation of permuted coefficients.
var_to_var_influences: added in error_prop. The list of results from the error propagation of coefficients.
pval_correction: Options are "holm", "hochberg", "hommel", "bonferroni", "BH", "BY","fdr", "none".
var_to_var_p_val: The final table of cape interaction results calculated by error_prop.
max_var_to_pheno_influence: The final table of cape direct influences of markers to traits calculated by direct_influence.
full_net: An adjacency matrix holding significant cape interactions between individual markers. See plot_network and get_network.
use_kinship: Whether to use a kinship correction in the analysis.
kinship_type: which type of kinship matrix to use
transform_to_phenospace: whether to transform to phenospace or not.

Active bindings

geno_for_pairscan: geno for pairscan
marker_selection_method: marker selection method
linkage_blocks_collapsed: linkage blocks collapsed
linkage_blocks_full: linkage blocks full
collapsed_net: collapsed net

Methods

Public methods

Cape$assign_parameters()
Cape$check_inputs()
Cape$check_geno_names()
Cape$new()
Cape$plotSVD()
Cape$plotSinglescan()
Cape$plotPairscan()
Cape$plotVariantInfluences()
Cape$plotNetwork()
Cape$plotFullNetwork()
Cape$writeVariantInfluences()
Cape$set_pheno()
Cape$set_geno()
Cape$create_covar_table()
Cape$save_rds()
Cape$read_rds()

Method `assign_parameters()`

Assigns variables from the parameter file to attributes in the Cape object.

Usage

Cape$assign_parameters()

Method `check_inputs()`

Checks the dimensionality of inputs and its consistency.

Usage

Cape$check_inputs()

Method `check_geno_names()`

Checks genotype names.

Usage

Cape$check_geno_names()

Method `new()`

Initialization method.

Usage

Cape$new(
  parameter_file = NULL,
  yaml_parameters = NULL,
  results_path = NULL,
  save_results = FALSE,
  use_saved_results = TRUE,
  pheno = NULL,
  chromosome = NULL,
  marker_num = NULL,
  marker_location = NULL,
  geno_names = NULL,
  geno = NULL,
  .geno_for_pairscan = NULL,
  peak_density = NULL,
  window_size = NULL,
  tolerance = NULL,
  ref_allele = NULL,
  alpha = NULL,
  covar_table = NULL,
  num_alleles_in_pairscan = NULL,
  max_pair_cor = NULL,
  min_per_genotype = NULL,
  pairscan_null_size = NULL,
  p_covar = NULL,
  g_covar = NULL,
  p_covar_table = NULL,
  g_covar_table = NULL,
  model_family = NULL,
  scan_what = NULL,
  ET = NULL,
  singular_values = NULL,
  right_singular_vectors = NULL,
  traits_scaled = NULL,
  traits_normalized = NULL,
  var_to_var_influences_perm = NULL,
  var_to_var_influences = NULL,
  pval_correction = NULL,
  var_to_var_p_val = NULL,
  max_var_to_pheno_influence = NULL,
  full_net = NULL,
  use_kinship = NULL,
  kinship_type = NULL,
  transform_to_phenospace = NULL,
  plot_pdf = NULL
)

Arguments

parameter_file: string, full path to YAML file with initialization parameters
yaml_parameters: string representing YAML CAPE parameters. See the vignette for more descriptions of individual parameter settings.
results_path: string, full path to directory for storing results (optional, a directory will be created if one is not specified)
save_results: Whether to save cape results. Defaults to TRUE.
use_saved_results: Whether to use existing results from a previous run. This can save time if re-running an analysis, but can lead to problems if the old run and new run have competing settings. If errors arise, and use_saved_results is set to TRUE, try setting it to FALSE, or deleting previous results.
pheno: A matrix containing the traits to be analyzed. Traits are in columns and individuals are in rows.
chromosome: A vector the same length as the number of markers indicating which chromosome each marker lives on.
marker_num: A vector the same length as the number of markers indicating the index of each marker
marker_location: A vector the same length as the number of markers indicating the genomic position of each marker. The positions are primarily used for plotting and can be in base pairs, centiMorgans, or dummy variables.
geno_names: The dimnames of the genotype array. The genotype array is a three-dimensional array in which rows are individuals, columns are alleles, and the third dimension houses the markers. Genotypes are pulled for analysis using get_geno based on geno_names. Only the individuals, alleles, and markers listed in geno_names are taken from the genotype matrix. Functions that remove markers and individuals from analysis always operate on geno_names in addition to other relevant slots. The names of geno_names must be "mouse", "allele", "locus."
geno: A three dimensional array holding genotypes for each animal for each allele at each marker. The genotypes are continuously valued probabilities ranging from 0 to 1. The dimnames of geno must be "mouse", "allele", and "locus," even if the individuals are not mice.
.geno_for_pairscan: A two-dimensional matrix holding the genotypes that will be analyzed in the pairscan. Alleles are in columns and individuals are in rows. As in the geno array, values are continuous probabilities ranging from 0 to 1.
peak_density: The density parameter for select_markers_for_pairscan. Determines how densely markers under an individual effect size peak are selected for the pairscan if marker_selection_method is TRUE. Defaults to 0.5.
window_size: The window size used by select_markers_for_pairscan. It specifies how many markers are used to smooth effect size curves for automatic peak identification. If set to NULL, window_size is determined automatically. Used when marker_selection_method is TRUE.
tolerance: The wiggle room afforded to select_markers_for_pairscan in finding a target number of markers. If num_alleles_in_pairscan is 100 and the tolerance is 5, the algorithm will stop when it identifies anywhere between 95 and 105 markers for the pairscan.
ref_allele: A string of length 1 indicating which allele to use as the reference allele. In two-parent crosses, this is usually allele A. In DO/CC populations, we recommend using B as the reference allele. B is the allele from the C57Bl6/J mouse, which is often used as a reference strain.
alpha: The significance level for calculating effect size thresholds in the singlescan. If singlescan_perm is 0, this parameter is ignored.
covar_table: A matrix of covariates with covariates in columns and individuals in rows. Must be numeric.
num_alleles_in_pairscan: The number of alleles to test in the pairwise scan. Because Cape is computationally intensive, we usually need to test only a subset of available markers in the pairscan, particularly if the kinship correction is being used.
max_pair_cor: the maximum Pearson correlation between two markers. If their correlation exceeds this value, they will not be tested against each other in the pairscan. This threshold is set to prevent false positive arising from testing highly correlated markers. If this value is set to NULL, min_per_genotype must be specified.
min_per_genotype: minimum The minimum number of individuals allowable per genotype combination in the pair scan. If for a given marker pair, one of the genotype combinations is underrepresented, the marker pair is not tested. If this value is NULL, max_pair_cor must be specified.
pairscan_null_size: The total size of the null distribution. This is DIFFERENT than the number of permutations to run. Each permutation generates n choose 2 elements for the pairscan. So for example, a permutation that tests 100 pairs of markers will generate a null distribution of size 4950. This process is repeated until the total null size is reached. If the null size is set to 5000, two permutations of 100 markers would be done to get to a null distribution size of 5000.
p_covar: A vector of strings specifying the names of covariates derived from traits. See pheno2covar.
g_covar: A vector of strings specifying the names of covariates derived from genetic markers. See marker2covar.
p_covar_table: A matrix holding the individual values for each trait-derived covariate. See pheno2covar.
g_covar_table: A matrix holding the individual values for each marker-derived covariate. See marker2covar.
model_family: Indicates the model family of the phenotypes This can be either "gaussian" or "binomial". If this argument is length 1, all phenotypes will be assigned to the same family. Phenotypes can be assigned different model families by providing a vector of the same length as the number of phenotypes, indicating how each phenotype should be modeled. See singlescan.
scan_what: A string indicating whether "eigentraits", "normalized_traits", or "raw_traits" should be analyzed. See get_pheno.
ET: A matrix holding the eigentraits to be analyzed.
singular_values: Added by get_eigentraits. A vector holding the singular values from the singular value decomposition of the trait matrix. They are used in rotating the final direct influences back to trait space from eigentrait space. See get_eigentraits and direct_influence.
right_singular_vectors: Added by get_eigentraits. A matrix containing the right singular vectors from the singular value decomposition of the trait matrix. They are used in rotating the final direct influences back to trait space from eigentrait space. See get_eigentraits and direct_influence.
traits_scaled: Whether the traits should be mean-centered and standardized before analyzing.
traits_normalized: Whether the traits should be rank Z normalized before analyzing.
var_to_var_influences_perm: added in error_prop The list of results from the error propagation of permuted coefficients.
var_to_var_influences: added in error_prop The list of results from the error propagation of coefficients.
pval_correction: Options are "holm", "hochberg", "hommel", "bonferroni", "BH", "BY","fdr", "none"
var_to_var_p_val: The final table of cape interaction results calculated by error_prop.
max_var_to_pheno_influence: The final table of cape direct influences of markers to traits calculated by direct_influence.
full_net: An adjacency matrix holding significant cape interactions between individual markers. See plot_network and get_network.
use_kinship: Whether to use a kinship correction in the analysis.
kinship_type: Which type of kinship matrix to use. Either "overall" or "ltco."
transform_to_phenospace: whether to transform to phenospace or not.
plot_pdf: logical. If TRUE, results are generated as pdf

Method `plotSVD()`

Plot Eigentraits

Usage

Cape$plotSVD(filename)

Arguments

filename: filename of result plot

Method `plotSinglescan()`

Plot results of single-locus scans

Usage

Cape$plotSinglescan(
  filename,
  singlescan_obj,
  width = 20,
  height = 6,
  units = "in",
  res = 300,
  standardized = TRUE,
  allele_labels = NULL,
  alpha = alpha,
  include_covars = TRUE,
  line_type = "l",
  pch = 16,
  cex = 0.5,
  lwd = 3,
  traits = NULL
)

Arguments

filename: filename of result plot.
singlescan_obj: a singlescan object from singlescan
width: width of result plot, default is 20.
height: height of result plot, default is 6.
units: units of result plot, default is "in".
res: resolution of result plot, default is 300.
standardized: If TRUE t statistics are plotted. If FALSE, effect sizes are plotted, default is TRUE
allele_labels: A vector of labels for the alleles if different that those stored in the data_object.
alpha: the alpha significance level. Lines for significance values will only be plotted if n_perm > 0 when singlescan was run. And only alpha values specified in singlescan can be plotted.
include_covars: Whether to include covariates in the plot.
line_type: as defined in plot
pch: see the "points()" R function. Default is 16 (a point).
cex: see the "points()" R function. Default is 0.5.
lwd: line width, default is 3.
traits: a vector of trait names to plot. Defaults to all traits.

Method `plotPairscan()`

Plot the result of the pairwise scan

Usage

Cape$plotPairscan(
  filename,
  pairscan_obj,
  phenotype = NULL,
  show_marker_labels = TRUE,
  show_alleles = FALSE
)

Arguments

filename: filename of result plot.
pairscan_obj: a pairscan object from pairscan
phenotype: The names of the phenotypes to be plotted. If NULL, all phenotypes are plotted.
show_marker_labels: If TRUE marker labels are plotted along the axes. If FALSE, they are omitted.
show_alleles: If TRUE, the allele of each marker is indicated by color.

Method `plotVariantInfluences()`

Plot cape coefficients

Usage

Cape$plotVariantInfluences(
  filename,
  width = 10,
  height = 7,
  p_or_q = p_or_q,
  standardize = FALSE,
  not_tested_col = "lightgray",
  covar_width = NULL,
  pheno_width = NULL
)

Arguments

filename: filename of result plot.
width: width of result plot, default is 10.
height: height of result plot, default is 7.
p_or_q: A threshold indicating the maximum p value (or q value if FDR was used) of significant interactions and main effects.
standardize: Whether to plot effect sizes (FALSE) or standardized effect sizes (TRUE), default is TRUE.
not_tested_col: The color to use for marker pairs not tested. Takes the same values as pos_col and neg_col, default is "lightgray".
covar_width: See pheno_width. This is the same effect for covariates.
pheno_width: Each marker and trait gets one column in the matrix. If there are many markers, this makes the effects on the traits difficult to see. pheno_width increases the number of columns given to the phenotypes. For example, if pheno_width = 11, the phenotypes will be shown 11 times wider than individual markers.

Method `plotNetwork()`

Plots cape results as a circular network

Usage

Cape$plotNetwork(
  filename,
  label_gap = 10,
  label_cex = 1.5,
  show_alleles = FALSE
)

Arguments

filename: filename of result plot.
label_gap: A numeric value indicating the size of the gap the chromosomes and their labels, default is 10.
label_cex: A numeric value indicating the size of the labels, default is 1.5.
show_alleles: TRUE show the alleles, FALSE does not show alleles. Default is FALSE.

Method `plotFullNetwork()`

Plot the final epistatic network in a traditional network view.

Usage

Cape$plotFullNetwork(
  filename,
  zoom = 1.2,
  node_radius = 0.3,
  label_nodes = TRUE,
  label_offset = 0.4,
  label_cex = 0.5,
  bg_col = "lightgray",
  arrow_length = 0.1,
  layout_matrix = "layout_with_kk",
  legend_position = "topright",
  edge_lwd = 1,
  legend_radius = 2,
  legend_cex = 0.7,
  xshift = -1
)

Arguments

filename: filename of result plot.
zoom: Allows the user to zoom in and out on the image if the network is either running off the edges of the plot or too small in the middle of the plot, default is 1.2.
node_radius: The size of the pie chart for each node, default is 0.3.
label_nodes: A logical value indicating whether the nodes should be labeled. Users may want to remove labels for large networks, default is TRUE.
label_offset: The amount by which to offset the node labels from the center of the nodes, default is 0.4.
label_cex: The size of the node labels, default is 0.5.
bg_col: The color to be used in pie charts for non-significant main effects. Takes the same values as pos_col, default is "lightgray".
arrow_length: The length of the head of the arrow, default is 0.1.
layout_matrix: Users have the option of providing their own layout matrix for the network. This should be a two column matrix indicating the x and y coordinates of each node in the network, default is "layout_with_kk".
legend_position: The position of the legend on the plot, default is "topright".
edge_lwd: The thickness of the arrows showing the interactions, default is 1.
legend_radius: The size of the legend indicating which pie piece corresponds to which traits, default is 2.
legend_cex: The size of the labels in the legend, default is 0.7.
xshift: A constant by which to shift the x values of all nodes in the network, default is -1.

Method `writeVariantInfluences()`

Write significant cape interactions to a csv file.

Usage

Cape$writeVariantInfluences(
  filename,
  p_or_q = 0.05,
  include_main_effects = TRUE
)

Arguments

filename: filename of csv file
p_or_q: A threshold indicating the maximum adjusted p value considered significant. If an FDR method has been used to correct for multiple testing, this value specifies the maximum q value considered significant, default is 0.05.
include_main_effects: Whether to include main effects (TRUE) or only interaction effects (FALSE) in the output table, default is TRUE.

Method `set_pheno()`

Set phenotype

Usage

Cape$set_pheno(val)

Arguments

val: phenotype value.

Method `set_geno()`

Set genotype

Usage

Cape$set_geno(val)

Arguments

val: genotype value.

Method `create_covar_table()`

Create covariate table

Usage

Cape$create_covar_table(value)

Arguments

value: covariate values

Method `save_rds()`

Save to RDS file

Usage

Cape$save_rds(object, filename)

Arguments

object: data to be saved.
filename: filename of result RDS file.

Method `read_rds()`

Read RDS file

Usage

Cape$read_rds(filename)

Arguments

filename: RDS filename to be read.

Examples

## Not run: 
param_file <- "cape_parameters.yml"
results_path = "."
cape_obj <- read_population("cross.csv")
combined_obj <- cape2mpp(cape_obj)
pheno_obj <- combined_obj$data_obj
geno_obj <- combined_obj$geno_obj

data_obj <- Cape$new(parameter_file = param_file,
results_path = results_path, pheno = pheno_obj$pheno, chromosome = pheno_obj$chromosome, 
marker_num = pheno_obj$marker_num, marker_location = pheno_obj$marker_location, 
geno_names = pheno_obj$geno_names, geno = geno_obj)

## End(Not run)

[Package cape version 3.1.2 Index]

The CAPE data object

Description

Details

Slots

Public fields

Active bindings

Methods

Public methods

Method assign_parameters()

Usage

Method check_inputs()

Usage

Method check_geno_names()

Usage

Method new()

Usage

Arguments

Method plotSVD()

Usage

Arguments

Method plotSinglescan()

Usage

Arguments

Method plotPairscan()

Usage

Arguments

Method plotVariantInfluences()

Usage

Arguments

Method plotNetwork()

Usage

Arguments

Method plotFullNetwork()

Usage

Arguments

Method writeVariantInfluences()

Usage

Arguments

Method set_pheno()

Usage

Arguments

Method set_geno()

Usage

Arguments

Method create_covar_table()

Usage

Arguments

Method save_rds()

Usage

Arguments

Method read_rds()

Usage

Arguments

Examples

Method `assign_parameters()`

Method `check_inputs()`

Method `check_geno_names()`

Method `new()`

Method `plotSVD()`

Method `plotSinglescan()`

Method `plotPairscan()`

Method `plotVariantInfluences()`

Method `plotNetwork()`

Method `plotFullNetwork()`

Method `writeVariantInfluences()`

Method `set_pheno()`

Method `set_geno()`

Method `create_covar_table()`

Method `save_rds()`

Method `read_rds()`