Help for package shinyWGD

Type:

Package

Title:

'Shiny' Application for Whole Genome Duplication Analysis

Version:

1.0.0

Maintainer:

Jia Li <li081766@gmail.com>

Description:

Provides a comprehensive 'Shiny' application for analyzing Whole Genome Duplication ('WGD') events. This package provides a user-friendly 'Shiny' web application for non-experienced researchers to prepare input data and execute command lines for several well-known 'WGD' analysis tools, including 'wgd', 'ksrates', 'i-ADHoRe', 'OrthoFinder', and 'Whale'. This package also provides the source code for experienced researchers to adjust and install the package to their own server. Key Features 1) Input Data Preparation This package allows users to conveniently upload and format their data, making it compatible with various 'WGD' analysis tools. 2) Command Line Generation This package automatically generates the necessary command lines for selected 'WGD' analysis tools, reducing manual errors and saving time. 3) Visualization This package offers interactive visualizations to explore and interpret 'WGD' results, facilitating in-depth 'WGD' analysis. 4) Comparative Genomics Users can study and compare 'WGD' events across different species, aiding in evolutionary and comparative genomics studies. 5) User-Friendly Interface This 'Shiny' web application provides an intuitive and accessible interface, making 'WGD' analysis accessible to researchers and 'bioinformaticians' of all levels.

License:

GPL-3

Encoding:

UTF-8

RoxygenNote:

7.2.3

SystemRequirements:

pandoc (>= 1.12.3), pandoc-citeproc

Imports:

shiny, shinyalert, stringr, vroom, fs, tidyr, data.table, dplyr, ape, ks, mclust, htmltools, seqinr, httr, jsonlite

Suggests:

tidyverse, knitr, rmarkdown, DT, argparse, bslib, bsplus, english, fontawesome, igraph, shinyBS, shinyFiles, shinyWidgets, shinyjs, stringi, tools, testthat (≥ 3.0.0)

NeedsCompilation:

Packaged:

2024-11-13 01:08:49 UTC; jiali

Author:

Jia Li [aut, cre], Zhen Li [ctb], Arthur Zwaenepoel [ctb]

Repository:

CRAN

Date/Publication:

2024-11-13 15:10:01 UTC

Compute the -log10 of Poisson Distribution

Description

This function calculates the -log10 of the p-value of a Poisson distribution given the parameters.

Usage

CalHomoConcentration(m, n, q, k)

Arguments

m

The total number of trials.

n

The total number of possible outcomes.

q

The observed number of successful outcomes.

k

The expected number of successful outcomes.

Value

The -log10 of the p-value.

Compute the P-value of a Cluster using the Poisson Distribution

Description

This function computes the P-value of a cluster using the Poisson distribution.

Usage

CalPvalue(m, n, q, k)

Arguments

m

The total number of all anchored points.

n

The product of the remapped gene number of the query species and subject species.

q

The number of anchored points in the cluster.

k

The product of the remapped gene number of the segmented chromosomes of the query species and subject species.

Value

The computed P-value.

Count Ortholog Genes in a Species

Description

This function counts ortholog genes in a given species based on input data.

Usage

CountOrthologs(atomic.df, species)

Arguments

atomic.df

A data frame containing information about ortholog genes. It should have the following columns: - multiplicon: The multiplicon identifier. - geneX: The gene identifier in speciesX. - speciesX: The species name for geneX. - listX: The chromosome or list identifier for geneX. - coordX: The coordinate information for geneX. - geneY: The gene identifier in speciesY. - speciesY: The species name for geneY. - listY: The chromosome or list identifier for geneY. - coordY: The coordinate information for geneY. - level: The orthology level. - num_anchors: The number of anchors. - is_real: A flag indicating if the data is real. - Ks: The Ks value.

species

The species for which ortholog gene counts should be computed.

Value

A data frame summarizing the counts of ortholog genes for each chromosome.

Find Peaks in the Ks Distribution

Description

This function identifies peaks in a distribution of Ks (synonymous substitution rates) values.

Usage

PeaksInKsDistributionValues(
  ks,
  binWidth = 0.1,
  maxK = 5,
  m = 3,
  peak.maxK = 2,
  spar = 0.25
)

Arguments

ks

A numeric vector containing Ks values for which peaks will be identified.

binWidth

A numeric value specifying the bin width for creating the histogram.

maxK

A numeric value indicating the maximum Ks value to consider.

m

An integer indicating the half-width of the neighborhood to consider when identifying peaks. A larger value of m makes peak detection less sensitive.

peak.maxK

A numeric value specifying the maximum Ks value to consider when identifying peaks.

spar

A numeric value controlling the smoothness of the spline fit. Higher values make the fit smoother.

Value

A numeric vector containing the identified peaks in the Ks distribution.

SiZer (Significant Zero Crossings)

Description

The SiZer (Significant Zero Crossings) method is a technique used for assessing the statistical significance of zero crossings in data density estimation.

Usage

SiZer(x, bw, gridsize, signifLevel = 0.05)

Arguments

x

A numeric vector containing the data for which you want to calculate SiZer.

bw

Bandwidth parameter for kernel density estimation. If not provided, default values are used.

gridsize

A vector specifying the grid size for SiZer. Default is c(401, 151).

signifLevel

The significance level for SiZer. Default is 0.05.

Value

A list containing SiZer results, including the SiZer curve, the SiZer map, and the bandwidth.

SignifFeatureRegion

Description

This function computes the significance of features based on gradient and curvature analysis.

Usage

SignifFeatureRegion(
  n,
  d,
  gcounts,
  gridsize,
  dest,
  bandwidth,
  signifLevel,
  range.x,
  grad = TRUE,
  curv = TRUE,
  neg.curv.only = TRUE
)

Arguments

n

The sample size.

d

The dimensionality of the data.

gcounts

A numeric vector representing data counts.

gridsize

A numeric vector specifying the grid size.

dest

A kernel density estimate.

bandwidth

The bandwidth parameter.

signifLevel

The significance level.

range.x

The range of x values.

grad

A logical value indicating whether to compute the gradient significance.

curv

A logical value indicating whether to compute the curvature significance.

neg.curv.only

A logical value indicating whether to consider negative curvature only.

Value

A list containing the significance results for gradient and curvature.

Extracts a timetree from TimeTree.org based on species names.

Description

This function takes a file with species names as input and a prefix to define the output.

Usage

TimeTreeFecher(input_file, prefix)

Arguments

input_file

A character string specifying the path to the file containing species names.

prefix

A character string providing the prefix for the output file.

Value

A timetree object representing the estimated divergence times between species.

Perform synteny analysis for identified clusters

Description

This function performs synteny analysis for clusters identified by hierarchical clustering.

Usage

analysisEachCluster(
  segmented_file,
  segmented_anchorpoints_file,
  genes_file,
  cluster_info_file,
  identified_cluster_file,
  hcheight = 0.3
)

Arguments

segmented_file

The path to the segmented chromosome file.

segmented_anchorpoints_file

The path to the segmented anchorpoints file.

genes_file

genes.txt created by i-ADHoRe.

cluster_info_file

The path to the clustering information file.

identified_cluster_file

The path to the output file for identified clusters.

hcheight

The cutoff height for cluster identification (default: 0.3).

Value

A list containing information about identified clusters and their p-values.

Bootstrap Peaks in the Ks Distribution

Description

This function performs bootstrapping on a given Ks (synonymous substitution rates) distribution to estimate peaks within the distribution.

Usage

bootStrapPeaks(
  ksRaw,
  binWidth = 0.1,
  maxK = 5,
  m = 3,
  peak.index = 1,
  peak.maxK = 2,
  spar = 0.25,
  rep = 1000,
  from = 0,
  to = maxK
)

Arguments

ksRaw

A numeric vector representing the raw Ks distribution to be bootstrapped.

binWidth

A numeric value indicating the bin width for histogram calculation.

maxK

A numeric value indicating the maximum Ks value to consider in the distribution.

m

An integer specifying the parameter for peak detection.

peak.index

An integer indicating the index of the peak to be estimated.

peak.maxK

A numeric value indicating the maximum Ks value for peak estimation.

spar

A numeric value controlling the smoothness of spline fitting.

rep

An integer specifying the number of bootstrap repetitions.

from

A numeric value indicating the lower bound for peak estimation.

to

A numeric value indicating the upper bound for peak estimation.

Value

A numeric vector containing bootstrapped peak estimates.

Calculate the Ks Distribution for Multiple Speices

Description

This function takes a list of data files, calculates the Ks distribution, and returns the results.

Usage

calculateKsDistribution4wgd_multiple(
  files_list,
  binWidth = 0.1,
  maxK = 5,
  plot.mode = "weighted",
  include.outliers = FALSE,
  minK = 0,
  minAlnLen = 0,
  minIdn = 0,
  minCov = 0
)

Arguments

files_list

A list of file paths containing Ks data.

binWidth

The width of Ks bins for the distribution.

maxK

The maximum Ks value to consider.

plot.mode

The mode for plotting ("weighted", "average", "min", or "pairwise").

include.outliers

Whether to include outliers in the calculation.

minK

The minimum Ks value to include in the distribution.

minAlnLen

The minimum alignment length to include in the distribution.

minIdn

The minimum alignment identity to include in the distribution.

minCov

The minimum alignment coverage to include in the distribution.

Value

A list containing two data frames: "bar" for Ks distribution and "density" for density data.

Check File Existence in a Data Table

Description

This function checks the existence of files specified in a data table.

Usage

checkFileExistence(data_table, working_wd)

Arguments

data_table

A data table with file paths in columns V2 and V3.

working_wd

A path of the working directory

Value

This function has no return value. It prints messages to the console.

Check and Process GFF Input File from a Specific Path

Description

This function checks the type of GFF input file specified by its path and processes it accordingly.

Usage

check_gff_from_file(gff_input_name, gff_input_path, working_wd)

Arguments

gff_input_name

The informal name of the GFF input file.

gff_input_path

The path to the GFF input file.

working_wd

A character string specifying the working directory to be used.

Value

A string containing the processed GFF file's path.

Check and Prepare GFF/GTF Input File

Description

This function checks the file format of a GFF/GTF input file and prepares it for analysis. It can handle both uncompressed and compressed formats.

Usage

check_gff_input(gff_input_name, gff_input_path, working_wd)

Arguments

gff_input_name

A descriptive name for the GFF/GTF file.

gff_input_path

The file path to the GFF/GTF file.

working_wd

A character string specifying the working directory to be used.

Value

The path to the prepared GFF file for analysis.

Check and Process Proteome Input File From a Special Path

Description

This function checks the type of proteome input file and processes it accordingly.

Usage

check_proteome_from_file(proteome_name, proteome_input, working_wd)

Arguments

proteome_name

The informal name of the proteome input file.

proteome_input

The proteome input data.

working_wd

A character string specifying the working directory to be used.

Value

A string containing the processed proteome file's path.

Check and Process Proteome Input File

Description

This function checks the type of proteome input file and processes it accordingly.

Usage

check_proteome_input(proteome_name, proteome_input, working_wd)

Arguments

proteome_name

The informal name of the proteome input file.

proteome_input

The proteome input data.

working_wd

A character string specifying the working directory to be used.

Value

A string containing the processed proteome file's path.

Cluster Synteny Data and Generate Trees

Description

This function clusters synteny data based on calculated p-values and generates trees for both column-based and row-based clustering. It then saves the cluster information and trees to output files.

Usage

cluster_synteny(
  segmented_file,
  segmented_anchorpoints_file,
  genes_file,
  out_file
)

Arguments

segmented_file

A character string specifying the file path for segmented data.

segmented_anchorpoints_file

A character string specifying the file path for segmented anchorpoints.

genes_file

A character string specifying the file path for genes information created by i-ADHoRe.

out_file

A character string specifying the output file path for saving cluster information.

Value

NULL (output files are generated with the specified information).

Compute the Depth of Anchored Points

Description

This function calculates the depth of anchored points based on the provided parameters.

Usage

computing_depth(
  anchorpoint_ks_file,
  multiplicon_id,
  selected_query_chr,
  selected_subject_chr = NULL
)

Arguments

anchorpoint_ks_file

The file containing anchorpoint and Ks data.

multiplicon_id

The ID of the multiplicon to consider.

selected_query_chr

A list of selected query chromosomes.

selected_subject_chr

A list of selected subject chromosomes (optional).

Value

A list containing depth data frames, including "query_depth" and "subject_depth" if subject chromosomes are specified, or "depth" if not.

Compute the Depth of Anchored Points in a Paranome Comparison

Description

This function computes the depth of anchored points in a paranome comparison based on the provided parameters.

Usage

computing_depth_paranome(
  anchorpoint_ks_file,
  multiplicon_id,
  selected_query_chr
)

Arguments

anchorpoint_ks_file

The file containing anchor point and Ks value data.

multiplicon_id

The IDs of the multiplicons to consider.

selected_query_chr

The list of selected query chromosomes.

Value

A list containing the depth dataframe.

Create Ksrates Command Files from Shiny Input

Description

Create Ksrates Command Files from Shiny Input

Usage

create_ksrates_cmd(input, ksratesconf, cmd_file)

Arguments

input

The Input object of Shiny.

ksratesconf

The path to the Ksrates configuration file.

cmd_file

The path to the main Ksrates command file to be generated.

Create Ksrates Command Files from Data Table

Description

This function generates command files for running Ksrates and related analyses based on a data table and configuration file.

Usage

create_ksrates_cmd_from_table(data_table, ksratesconf, cmd_file, focal_species)

Arguments

data_table

The data table containing information about species.

ksratesconf

The path to the Ksrates configuration file.

cmd_file

The path to the main Ksrates command file to be generated.

focal_species

The name of the focal species.

Create Ksrates Configuration File Based on Data Table

Description

This function generates a Ksrates configuration file based on a data table and other parameters.

Usage

create_ksrates_configure_file_based_on_table(
  data_table,
  focal_species,
  newick_tree_file,
  ksrates_conf_file,
  species_info_file,
  working_wd
)

Arguments

data_table

The data table containing information about species, proteomes, and GFF files.

focal_species

The name of the focal species.

newick_tree_file

The path to the Newick tree file.

ksrates_conf_file

The path to the Ksrates configuration file to be generated.

species_info_file

The path to the species information file.

working_wd

A character string specifying the working directory to be used.

Create Ksrates Configuration File

Description

This function generates a configuration file for the Ksrates pipeline based on Shiny input.

Usage

create_ksrates_configure_file_v2(input, ksrates_conf_file, species_info_file)

Arguments

input

The Input object of Shiny.

ksrates_conf_file

The path to the Ksrates configuration file.

species_info_file

The path to the species information file.

Create ksrates Expert Parameter File

Description

Create ksrates Expert Parameter File

Usage

create_ksrates_expert_parameter_file(ksrates_expert_parameter_file)

Arguments

ksrates_expert_parameter_file

The file is used to store the ksrates expert parameter

dfltBWrange

Description

This function computes the default bandwidth range for kernel density estimation.

Usage

dfltBWrange(x, tau)

Arguments

x

The input data, which can be a numeric vector or matrix.

tau

A parameter used in bandwidth calculation.

Value

A list of bandwidth ranges for each dimension of the input data.

dfltCounts

Description

This function bins the input data into a regular grid.

Usage

dfltCounts(
  x,
  gridsize = rep(64, NCOL(x)),
  h = rep(0, NCOL(x)),
  supp = 3.7,
  range.x,
  w
)

Arguments

x

The input data, which should be a numeric matrix.

gridsize

A vector specifying the number of bins along each dimension.

h

A vector specifying the bandwidth (smoothing parameter) along each dimension.

supp

A parameter for determining the range of the bins.

range.x

A list specifying the range of values for each dimension.

w

A vector of weights for the data points.

Value

A list containing the binned counts and the range of values for each dimension.

Creating a Custom Download Button

Description

Use this function to create a custom download button or link. When clicked, it will initiate a browser download. The filename and contents are specified by the corresponding downloadHandler() defined in the server function.

Usage

downloadButton_custom(
  outputId,
  label = "Download",
  class = NULL,
  status = "primary",
  ...,
  icon = shiny::icon("download")
)

Arguments

outputId

The name of the output slot that the downloadHandler is assigned to.

label

The label that should appear on the button.

class

Additional CSS classes to apply to the tag, if any. Default NULL.

status

The status of the button; default is "primary."

...

Other arguments to pass to the container tag function.

icon

An icon() to appear on the button; default is icon("download").

Value

An HTML tag to allow users to download the object.

drvkde

Description

Compute the mth derivative of a binned d-variate kernel density estimate based on grid counts.

Usage

drvkde(x, drv, bandwidth, gridsize, range.x, binned = FALSE, se = TRUE, w)

Arguments

x

The input data.

drv

The order of the derivative to compute.

bandwidth

The bandwidth (smoothing parameter) along each dimension.

gridsize

The size of the grid.

range.x

A list specifying the range of values for each dimension.

binned

A logical indicating whether the input data is already binned.

se

A logical indicating whether to compute standard errors.

w

A vector of weights for the data points.

Value

A list containing the estimated density or derivative, and optionally, standard errors.

Extract clusters based on specified scaffolds

Description

This function extracts clusters based on the specified scaffolds for both query and subject species. It filters the data frames containing segment information and atomic anchorpoints to retain only the relevant clusters.

Usage

extractCluster(segs.df, atomic.df, scaf.bycol, scaf.byrow)

Arguments

segs.df

A data frame containing segment information.

atomic.df

A data frame containing atomic anchorpoints.

scaf.bycol

A character vector specifying scaffolds for the query species.

scaf.byrow

A character vector specifying scaffolds for the subject species.

Value

A list containing two data frames: "segs" for segment information and "atomic" for atomic anchorpoints.

Extract the first part of a string by splitting it at tab characters.

Description

This function takes a string and splits it at tab characters. It then returns the first part of the resulting character vector.

Usage

extract_first_part(name)

Arguments

name

The input string to be split.

Value

Returns the first part of the input string.

Find Peaks in a Numeric Vector

Description

This function identifies peaks in a numeric vector by analyzing the shape of the curve.

Usage

find_peaks(x, m = 3)

Arguments

x

A numeric vector in which peaks will be identified.

m

An integer indicating the half-width of the neighborhood to consider when identifying peaks. A larger value of m makes peak detection less sensitive.

Value

A numeric vector containing the indices of the identified peaks in the input vector x.

Generate the Ks Distribution

Description

This function generates a Ks (synonymous substitution rates) distribution from raw Ks values.

Usage

generateKsDistribution(ksraw, speciesName = NULL, maxK = 5)

Arguments

ksraw

A numeric vector containing raw Ks values.

speciesName

(Optional) A character string specifying the species name associated with the Ks values.

maxK

A numeric value indicating the maximum Ks value to consider in the distribution.

Value

A numeric vector containing the binned Ks distribution.

Generate Kernel Density Estimates (KDE) for Ks Distribution

Description

This function generates Kernel Density Estimates (KDE) for the Ks (synonymous substitution rates) distribution.

Usage

generate_ksd(ks_df, bin_width = 0.01, maxK = 5)

Arguments

ks_df

A data frame containing Ks values.

bin_width

The width of each bin for KDE calculation.

maxK

The maximum Ks value for the distribution.

Value

A list containing the following components:

Ks: A numeric vector representing the KDE values.
bin_width: The width of each bin used for KDE calculation.
maxK: The maximum Ks value for the distribution.

Get Segmented Data from Anchorpoints and Ks Values

Description

This function extracts segmented data from anchorpoints and Ks (synonymous substitution rate) values, based on specified criteria, and writes the results to output files.

Usage

get_segments(
  genes_file,
  anchors_ks_file,
  multiplicons_file,
  segmented_file,
  segmented_anchorpoints_file,
  num_anchors = 10
)

Arguments

genes_file

A character string specifying the file path for genes information created by i-ADHoRe.

anchors_ks_file

A character string specifying the file path for anchorpoints Ks values data.

multiplicons_file

A character string specifying the file path for multiplicons information created by i-ADHoRe.

segmented_file

A character string specifying the output file path for segmented data.

segmented_anchorpoints_file

A character string specifying the output file path for segmented anchorpoints.

num_anchors

An integer specifying the minimum number of anchorpoints required.

Value

NULL (output files are generated with the specified information).

Check if an object is of class "ksv"

Description

This function checks if the provided object is of class "ksv."

Usage

is.ksv(x)

is.ksv(x)

Arguments

x

The object to be checked.

Value

Returns TRUE if the object is of class "ksv"; otherwise, returns FALSE.

Check if an Object is Not NULL

Description

This function checks if an object is not NULL.

Usage

is.not.null(x)

Arguments

x

An R object to check.

Value

A logical value indicating whether the object is not NULL.

Check if a file is in FASTA format with cds sequences.

Description

This function checks whether a given file is in FASTA format with cds sequences.

Usage

is_fasta_cds(file_path)

Arguments

file_path

The path to the input file.

Value

TRUE if the file is in FASTA format with cds sequences, FALSE otherwise.

ks_mclust_v2

Description

A wrapper to run emmix modeling using the mclust package.

Usage

ks_mclust_v2(input_data)

Arguments

input_data

The input data for clustering and modeling.

Value

A data frame containing clustering and modeling results.

Map Informal Names to Latin Names

Description

This function reads information from an Excel file (XLS) containing columns "latin_name," "informal_name," and "gff." It extracts the "latin_name" and "informal_name" columns, performs some data manipulation, and returns a data frame with these two columns.

Usage

map_informal_name_to_latin_name(sp_gff_info_xls)

Arguments

sp_gff_info_xls

The path to the Excel file containing species information.

Value

A data frame with "latin_name" and "informal_name" columns.

Log-Normal mixturing analyses of a Ks distributions for the whole paranome

Description

Log-Normal mixturing analyses of a Ks distributions for the whole paranome

Usage

mix_logNormal_Ks(ksv, G = 1:5, k.nstart = 500, maxK = 5)

Arguments

ksv

A ksv object.

G

An integer vector specifying the range of the mixtured components. A BIC is calculated for each component. The default is G=1:5. For a formal analysis, it is recommended to use 1:10.

k.nstart

How many random sets should be chosen in the k-means clustering. For a formal analysis, it is recommended to use 500.

maxK

Maximum Ks values used in the mixture modeling analysis.

Value

A data frame with seven variables.

modeFinder

Description

Find the mode (peak) of a univariate distribution.

Usage

modeFinder(x, bw = 0.1, from = 0, to = 5)

Arguments

x

A numeric vector or a kernel density estimate (KDE).

bw

Bandwidth for the KDE. Default is 0.1.

from

Starting point for mode search. Default is 0.

to

Ending point for mode search. Default is 5.

Value

The mode (peak) of the distribution.

obtain_chromosome_length

Description

Process species information file and extract chromosome lengths and mRNA counts from GFF files.

Usage

obtain_chromosome_length(species_info_file)

Arguments

species_info_file

A character string specifying the path to the species information file.

Value

A list containing two data frames: len_df for chromosome lengths and num_df for mRNA counts.

obtain_chromosome_length_filter

Description

Process a data frame containing species information and extract chromosome lengths and mRNA counts from GFF files.

Usage

obtain_chromosome_length_filter(species_info_df)

Arguments

species_info_df

A data frame containing species information with columns "sp," "cds," and "gff."

Value

A list containing two data frames: len_df for chromosome lengths and num_df for mRNA counts.

Obtain coordinates for anchorpoints from GFF files

Description

This function takes a file containing anchorpoints, GFF files for two species, and species names, and retrieves the coordinates of anchorpoints and associated genes from the GFF files.

Usage

obtain_coordiantes_for_anchorpoints(
  anchorpoints,
  species1,
  gff_file1,
  out_file,
  species2 = NULL,
  gff_file2 = NULL
)

Arguments

anchorpoints

A file containing anchorpoints information with columns like gene_x, gene_y, and other relevant data.

species1

The name of the first species.

gff_file1

The path to the GFF file for the first species.

out_file

The output file where the results will be saved.

species2

(Optional) The name of the second species. Specify this parameter and gff_file2 if working with two species.

gff_file2

(Optional) The path to the GFF file for the second species.

Value

None. The function saves the results to the specified out_file.

Obtain Coordinates and Ks Values for Anchorpoints

Description

This function extracts coordinates and Ks (synonymous substitution rate) values for anchorpoints from input data and merges them into a single output file.

Usage

obtain_coordiantes_for_anchorpoints_ks(
  anchorpoints,
  anchorpoints_ks,
  genes_file,
  out_file,
  out_ks_file,
  species
)

Arguments

anchorpoints

A character string specifying the file path for anchorpoints data.

anchorpoints_ks

A character string specifying the file path for anchorpoints Ks values data.

genes_file

A character string specifying the file path for genes information.

out_file

A character string specifying the output file path for coordinates.

out_ks_file

A character string specifying the output file path for Ks values.

species

A character string specifying the species name.

Value

NULL (output files are generated with the specified information).

Obtain coordinates for segments in a comparison

Description

This function retrieves the coordinates for segments in a comparison based on the provided parameters.

Usage

obtain_coordiantes_for_segments(
  seg_file,
  sp1,
  gff_file1,
  out_file,
  sp2 = NULL,
  gff_file2 = NULL
)

Arguments

seg_file

The file containing segment data.

sp1

The species name for the first genome.

gff_file1

The GFF file for the first genome.

out_file

The output file to store the merged position data.

sp2

The species name for the second genome (optional).

gff_file2

The GFF file for the second genome (optional).

Value

NULL (the results are saved in the output file).

Obtain Coordinates for Segments in Multiple Synteny Blocks

Description

This function extracts coordinates for segments within multiple synteny blocks based on input dataframes.

Usage

obtain_coordinates_for_segments_multiple(seg_df, gff_df, input, out_file)

Arguments

seg_df

A dataframe containing information about synteny segments.

gff_df

A dataframe containing GFF (General Feature Format) information.

input

A list containing input data, typically multiple synteny query chromosomes.

out_file

A character string specifying the output file path.

Value

A dataframe with coordinates for segments within multiple synteny blocks.

Compute the Mean of Ks values for Each Multiplicon

Description

This function takes as input a multiplicon file, an anchorpoint file, Ks values, and other relevant information. It calculates the mean of Ks values for each multiplicon and associates them with the corresponding data.

Usage

obtain_mean_ks_for_each_multiplicon(
  multiplicon_file,
  anchorpoint_file,
  species1,
  ks_file,
  outfile,
  anchorpointout_file,
  species2 = NULL
)

Arguments

multiplicon_file

A file containing multiplicon information.

anchorpoint_file

A file containing anchorpoints information with columns like geneX, geneY, and other relevant data.

species1

The name of the first species.

ks_file

A file containing Ks values.

outfile

The output file where the results will be saved.

anchorpointout_file

The output file for anchorpoint data with Ks values.

species2

(Optional) The name of the second species. Specify this parameter and ks_file if working with two species.

Value

None. The function saves the results to the specified outfile and anchorpointout_file.

Read the EMMIX output for a range of components

Description

Read the EMMIX output for a range of components

Usage

parse_EMMIX(emmix.out, G = 1:3)

Arguments

emmix.out

The output file from EMMIX software.

G

An integer vector specifying the range of the mixture components. The default is G=1:3.

Value

A data frame with seven variables.

Read the EMMIX output for a specify number of components

Description

Read the EMMIX output for a specify number of components

Usage

parse_one_EMMIX(emmix.out, ncomponent = 3)

Arguments

emmix.out

The output file from EMMIX software.

ncomponent

Number of components to read from the file.

Value

A data frame with seven variables.

Read the output file of wgd ksd

Description

Read the output file of wgd ksd

Usage

read.wgd_ksd(
  file,
  include_outliers = FALSE,
  min_ks = 0,
  min_aln_len = 0,
  min_idn = 0,
  min_cov = 0
)

Arguments

file

The output file of ⁠wgd ksd⁠

include_outliers

Include outliers or not, default FALSE.

min_ks

Minimum Ks value, default 0.

min_aln_len

Minimum alignment length, default 0.

min_idn

Minimum alignment identity, default 0.

min_cov

Minimum alignment coverage, default 0.

Value

A ksv object, which is a list including:

ks_df: the data frame that used for following analysis
ks_dist: a list including a vector of Ks values in the distribution
raw_df: raw data
filters: filters that applied to the raw data

Read Data from Uploaded File

Description

This function reads data from an uploaded file in a Shiny application and returns it as a data frame.

Usage

read_data_file(uploadfile)

Arguments

uploadfile

The object representing the uploaded file obtained through the Shiny upload function.

Value

A data frame containing the data from the uploaded file.

relativeRate

Description

Compute relative rates using input data files and statistical computations.

Usage

relativeRate(
  ksv2out_1_file,
  ksv2out_2_file,
  ksv_between_file,
  KsMax,
  low = 0.025,
  up = 0.975,
  bs = 1000
)

Arguments

ksv2out_1_file

A character string specifying the path to the first input data file.

ksv2out_2_file

A character string specifying the path to the second input data file.

ksv_between_file

A character string specifying the path to the third input data file.

KsMax

A numeric value representing a maximum threshold for Ks values.

low

A numeric value specifying the lower quantile for bootstrapping. Default is 0.025.

up

A numeric value specifying the upper quantile for bootstrapping. Default is 0.975.

bs

An integer specifying the number of bootstrap iterations. Default is 1000.

Value

A list containing computed relative rates and their confidence intervals.

Remove Genes Contain Stop Codons within the Sequence

Description

This function removes the gene contains stop codons (TAA, TAG, TGA, taa, tag, tga) within its sequence.

Usage

remove_inner_stop_codon_sequence(sequence)

Arguments

sequence

A nucleotide sequence as a character string.

Value

A character string or NULL.

Remove directories older than a specified day

Description

This function removes directories in the specified base directory that are older than a specified maximum age in days. It logs the removed directories and any errors encountered during removal.

Usage

remove_old_dirs(
  base_dir,
  max_age_in_days = 3,
  log_file = "remove_old_dirs.log",
  verbose = FALSE
)

Arguments

base_dir

The base directory to search for old directories.

max_age_in_days

The maximum age (in days) for directories to be considered old.

log_file

The name of the log file to store information about removed directories and errors.

verbose

A logical value indicating whether to print messages to the console.

Value

The function does not return anything. It logs information about removed directories and errors.

Replace Informal Names with Latin Names

Description

This function takes a data frame names_df containing "latin_name" and "informal_name" columns and an input string as input. It replaces informal species names in the input string with their corresponding Latin names based on the information in names_df. If the input string contains underscores ("_"), it assumes a comparison between two species and replaces both informal names. Otherwise, it replaces the informal name in the input string.

Usage

replace_informal_name_to_latin_name(names_df, input)

Arguments

names_df

A data frame with "latin_name" and "informal_name" columns.

input

The input string that may contain informal species names.

Value

A modified input string with informal names replaced by Latin names.

Resample a Ks Distribution

Description

This function resamples a given Ks (synonymous substitution rates) distribution.

Usage

resampleKsDistribution(ks, maxK = 5)

Arguments

ks

A numeric vector representing the Ks distribution to be resampled.

maxK

A numeric value indicating the maximum Ks value to consider in the distribution.

Value

A numeric vector containing a resampled Ks distribution.

A wrapper to run EM analysis of \(ln\) Ks values with k-means

Description

A wrapper to run EM analysis of \(ln\) Ks values with k-means

Usage

run_emmix_kmeas(v, k.centers = 2, k.nstart = 500)

Arguments

v

A list include a vector of Ks values namely ks_value, and a bolean variable namely log

k.centers

Number of k-means centers, default 2.

k.nstart

Number of random start of k-means clustering, default 10. For a formal analysis, it is recommended to use 500.

Value

A list, i.e., the original output of mclust::emV

The main code to run shinyWGD

Description

The main function to launch the Shiny application for whole genome duplication analysis. This function initializes the app and opens a Shiny interface that allows users to interactively analyze whole-genome duplication data.

Usage

runshinyWGD()

Value

No return value. This function is called for side effects, which include starting the Shiny application. The function launches a Shiny app in a web browser, where users can interact with the whole genome duplication analysis.

symconv.ks

Description

Perform symmetric convolution using FFT.

Usage

symconv.ks(rr, ss, skewflag)

Arguments

rr

The first input vector.

ss

The second input vector.

skewflag

A scalar value to apply skew correction.

Value

A vector representing the result of the symmetric convolution.

symconv2D.ks

Description

Perform symmetric 2D convolution using FFT.

Usage

symconv2D.ks(rr, ss, skewflag = rep(1, 2))

Arguments

rr

The first input matrix.

ss

The second input matrix.

skewflag

A vector of two scalar values for skew correction along each dimension.

Value

A matrix representing the result of the symmetric 2D convolution.

symconv3D.ks

Description

Perform symmetric 3D convolution using FFT.

Usage

symconv3D.ks(rr, ss, skewflag = rep(1, 3))

Arguments

rr

The first input 3D array.

ss

The second input 3D array.

skewflag

A vector of three scalar values for skew correction along each dimension.

Value

A 3D array representing the result of the symmetric 3D convolution.

symconv4D.ks

Description

Perform symmetric 4D convolution using FFT.

Usage

symconv4D.ks(rr, ss, skewflag = rep(1, 4), fftflag = rep(TRUE, 2))

Arguments

rr

The first input 4D array.

ss

The second input 4D array.

skewflag

A vector of four scalar values for skew correction along each dimension.

fftflag

A vector of two Boolean values for FFT flag.

Value

A 4D array representing the result of the symmetric 4D convolution.