Package 'GeneSCARAB'

Description

Function that takes a vector of gene phases (continuous measurements or parametric estimates) and returns statistical measures of their circular distribution and their deviations from a circular uniform distribution based on the Rayleigh, Kuiper, Hermans-Rasson and Rao tests

Usage

.create_circular_table(
  circa_vector,
  hr.on.large.sets.th = 400,
  iter.hr = 999,
  force.hr.th = 0.05,
  rao.on.large.sets.th = 400,
  iter.rao = 999,
  force.rao.th = 0.05
)

Arguments

Value

A table indicating number of phases used for computation (n); mean, rho, variance, first, second and third quantiles of the circular distribution in hours; and p-values for each of the four tests

Description

Function that takes a vector of gene phases (discrete measurements or non-parametric estimates) and returns statistical measures of their circular distribution and their deviations from a circular uniform distribution based on Rayleigh and adapted versions of Kuiper, Hermans-Rasson and Rao tests.

Usage

.create_circular_table_grouped(
  circa_vector,
  n_bins,
  hr.on.large.sets.th = 400,
  iter.hr = 999,
  force.hr.th = 0.05,
  rao.on.large.sets.th = 400,
  iter.rao = 999,
  force.rao.th = 0.05,
  kuiper.on.large.sets.th = 400,
  iter.kuiper = 999,
  force.kuiper.th = 0.05
)

Arguments

Value

A data.frame indicating number of phases used for computation (n); mean, rho, variance, first, second and third quartiles of the circular distribution in hours; and p-values for each of the four tests

Description

Function to extract all ancestors of given GO terms

Usage

.get_ancestor_for_gene_lists(go_vector)

Arguments

Value

A vector containing ancestors of GO terms

Description

Function to create axis and initialize plot

Usage

.helper_init_plot()

Value

Axis

Description

Helper function that calculates the p-value of an adaptation of the Hermans-Rasson test for grouped data (discrete phases) as implemented in the article "Grouped circular data in biology: advice for effectively implementing statistical procedures" https://doi.org/10.1007/s00265-020-02881-6.

Usage

.HermansRasson2PGroupedRad(sample, m, iter = 9999)

Arguments

Value

The p-value for the test

Description

A helper function that calculates the critical value for the HR test, as implemented in the article "Grouped circular data in biology: advice for effectively implementing statistical procedures" https://doi.org/10.1007/s00265-020-02881-6.

Usage

.HermansRasson2T(sample)

Arguments

Value

The critical value for the test

Description

Helper function checking correctness of input for complete_circular_table and complete_circular_table_grouped

Usage

.input_complete_circular_table(phase.list)

Arguments

Value

An error if input is not adequate

Description

Helper function checking correctness of input for .create_circular_table and .create_circular_table_grouped

Usage

.input_create_circular_table(circa_vector)

Arguments

Value

An error if input is not adequate

Description

Helper function checking correctness of input for create_gene_list_go and generating a data.frame with all the GO terms per gene

Usage

.input_create_gene_list_go(go_vector, org.package, go_column, id_column)

Arguments

Value

A data.frame showing GO terms per gene as appear in the annotation package

Description

Helper function checking correctness of input for create_gene_list_kegg and generating a data.frame with all the KOs per gene

Usage

.input_create_gene_list_kegg(
  ko_vector = c("all"),
  org.package,
  ko_column,
  id_column,
  ko_prefix = "map",
  species = "all"
)

Arguments

Value

A data.frame showing KO terms per gene as appear in the annotation package

Description

Helper function checking correctness of input for gene_contribution_to_set and returning a clean phase list

Usage

.input_gene_contribution(circa_result, phase_list)

Arguments

Value

A phase list containing only elements that appear in circa_result rows

Description

Helper function that calculates the p-value of an adaptation of the Kuiper test for grouped data (discrete phases) as implemented in the article "Grouped circular data in biology: advice for effectively implementing statistical procedures" https://doi.org/10.1007/s00265-020-02881-6.

Usage

.KuiperPGroupedRad(sample, m, iter = 9999)

Arguments

Value

The p-value for the test

Description

Function to create tracks for dotplot

Usage

.legend_builder(phases.list, my_color)

Arguments

Value

Legend of the plots

Description

Function to create tracks for boxplot

Usage

.legend_builder_boxplot(quan.table, my_color)

Arguments

Value

Legend of the plots

Description

Function to determine the extract the Rayleigh p-value in the presence of f-fold symmetry (1, 2 or 3)

Usage

.multimodal_modes_test(phase_table)

Arguments

Value

A vector containing the p-values for 1,2 and 3 f-fold symmetry

Description

Function to extract KEGG pathways associated to a vector of KO terms and create a list containing this information

Usage

.pathways_from_kos(ko_vector, ko_prefix)

Arguments

Value

A list of pathways associated to the KO terms input

Description

Helper function that calculates the p-value of an adaptation of the Rao's spacing test for grouped data (discrete phases) as implemented in the article "Grouped circular data in biology: advice for effectively implementing statistical procedures" https://doi.org/10.1007/s00265-020-02881-6.

Usage

.RaoPGroupedRad(sample, m, iter = 9999)

Arguments

Value

The p-value for the test

Description

Helper function that calculates the p-value of Rao's spacing test for ungrouped data (continuous phases) as implemented in the article "Circular statistics meets practical limitations: a simulation-based Rao’s spacing test for non-continuous data" https://doi.org/10.1186/s40462-019-0160-x.

Usage

.RaoTestUngroupedRad(sample, iter = 9999)

Arguments

Value

The p-value for the test

Description

Helper function that calculates the critical value for Rao's spacing test as implemented in the article "Grouped circular data in biology: advice for effectively implementing statistical procedures" https://doi.org/10.1007/s00265-020-02881-6.

Usage

.RaoTestValue(sample)

Arguments

Value

The critical value for the test

Description

Function that takes the calculated circular summary, variance and p-values of Rayleigh, Kuiper, HR and Rao's tests and construct the result table.

Usage

.table_builder(
  circa_summary,
  circa_var,
  circa_kuiper,
  circa_ray,
  circa_hr,
  circa_rao
)

Arguments

Value

A table indicating number of phases used for computation (n); mean, rho, variance, first, second and third quantiles of the circular distribution in hours; and p-values for each of the four tests

Description

Function to create tracks for boxplots whose boxes intersect the origin. In this case, the segment must be cut from min to 24 and from 0 to max. Also, the box must be unfolded from first to 24 and from 0 to third quartiles.

Usage

.track_box_box_cut(tr.height, quan.table, i, my_color)

Arguments

Value

Plotted track

Description

Function to create tracks for boxplots that do not intersect the origin. If neither of the segments cut the sunrise,

Usage

.track_box_no_cut(tr.height, quan.table, i, my_color)

Arguments

Value

Plotted track

Description

Function to create tracks for boxplots whose segments intersect the origin.

Usage

.track_box_segments_cut(tr.height, quan.table, i, my_color)

Arguments

Value

Plotted track

Description

Function to create tracks for dotplot

Usage

.track_dot(tr.height, phases.list, i, my_color, my_transparent)

Arguments

Value

Axis

Description

Dataset example with phase information in two different conditions for Ostreococcus tauri from "Multiomics integration unveils photoperiodic plasticity in the molecular rhythms of marine phytoplankton" (https://doi.org/10.1093/plcell/koaf033)

Usage

data(circa_table_genescarab)

Format

Data frame

Description

Function to plot a circular boxplot showing the circular distribution of different processes or gene sets

Usage

circular_boxplot(res.table, color.palette = "Tam", tr.height = 0.1)

Arguments

Value

A PNG file containing the plot

Examples

org_Otaurireduced_eg_db <- load_example_annot()
data("circa_table_genescarab")
total_phases_table_sd <- data.frame(
    names = rownames(circa_table_genescarab),
    phase = as.numeric(
        circa_table_genescarab[["sd.peak.time.hours"]]
    )
)

functional_data <- AnnotationDbi::select(org_Otaurireduced_eg_db,
    keys = AnnotationDbi::keys(org_Otaurireduced_eg_db, keytype = "GID"),
    columns = c("GID", "GO")
)
complete_gos <- unique(functional_data$GO)
complete_gos <- complete_gos[!is.na(complete_gos)]

set.seed(2345)
subset_gos <- complete_gos[sample(
    seq_len(length(complete_gos)), 50,
    replace = FALSE
)]
go.list.test <- create_gene_list_go(
    go_vector = subset_gos,
    org.package = org_Otaurireduced_eg_db,
    go_column = "GO", id_column = "GID"
)
phases.list.sd <- gene_list_to_phases(
    go.list.test,
    total_phases_table_sd
)
phases.list.sd.clean <- phases.list.sd[which(
    sapply(phases.list.sd, nrow) != 0
)][seq_len(3)]
go.circa.table.sd <- complete_circular_table(
    phase.list = phases.list.sd.clean,
    hr.on.large.sets.th = 200,
    force.hr.th = 0.04,
    rao.on.large.sets.th = 200,
    force.rao.th = 0.04,
    iter.rao = 999, iter.hr = 999
)
circular_boxplot(go.circa.table.sd[seq_len(2), ],
    color.palette = "Tam"
)

Description

Function to plot a circular dotplot showing the circular distribution of different processes or gene sets

Usage

circular_dotplot(phases.list, color.palette = "Tam", tr.height = 0.1)

Arguments

Value

A PNG file containing the plot

Examples

org_Otaurireduced_eg_db <- load_example_annot()
data("circa_table_genescarab")
total_phases_table_sd <- data.frame(
    names = rownames(circa_table_genescarab),
    phase = as.numeric(
        circa_table_genescarab[["sd.peak.time.hours"]]
    )
)

functional_data <- AnnotationDbi::select(org_Otaurireduced_eg_db,
    keys = AnnotationDbi::keys(org_Otaurireduced_eg_db, keytype = "GID"),
    columns = c("GID", "GO")
)
complete_gos <- unique(functional_data$GO)
complete_gos <- complete_gos[!is.na(complete_gos)]

set.seed(2345)
subset_gos <- complete_gos[sample(
    seq_len(length(complete_gos)), 50,
    replace = FALSE
)]
go.list.test <- create_gene_list_go(
    go_vector = subset_gos,
    org.package = org_Otaurireduced_eg_db,
    go_column = "GO", id_column = "GID"
)
phases.list.sd <- gene_list_to_phases(
    go.list.test, total_phases_table_sd
)
phases.list.sd.clean <- phases.list.sd[which(
    sapply(phases.list.sd, nrow) != 0
)][seq_len(3)]
circular_dotplot(phases.list.sd.clean, color.palette = "Tam")

Description

Function to plot a circular histogram showing the circular distribution of different processes or gene sets

Usage

circular_histogram(
  phases.list,
  color.palette = "Tam",
  tr.height = 0.08,
  nbins = 24
)

Arguments

Value

A PNG file containing the plot

Examples

org_Otaurireduced_eg_db <- load_example_annot()
data("circa_table_genescarab")
total_phases_table_sd <- data.frame(
    names = rownames(circa_table_genescarab),
    phase = as.numeric(
        circa_table_genescarab[["sd.peak.time.hours"]]
    )
)

functional_data <- AnnotationDbi::select(org_Otaurireduced_eg_db,
    keys = AnnotationDbi::keys(org_Otaurireduced_eg_db, keytype = "GID"),
    columns = c("GID", "GO")
)
complete_gos <- unique(functional_data$GO)
complete_gos <- complete_gos[!is.na(complete_gos)]

set.seed(2345)
subset_gos <- complete_gos[sample(
    seq_len(length(complete_gos)), 50,
    replace = FALSE
)]
go.list.test <- create_gene_list_go(
    go_vector = subset_gos,
    org.package = org_Otaurireduced_eg_db,
    go_column = "GO", id_column = "GID"
)
phases.list.sd <- gene_list_to_phases(
    go.list.test, total_phases_table_sd
)
phases.list.sd.clean <- phases.list.sd[which(
    sapply(phases.list.sd, nrow) != 0
)][seq_len(3)]
circular_histogram(phases.list.sd.clean,
    color.palette = "Tam", nbins = 48
)

Description

Function that takes a list of continuous phases associated with each process or gene set (output of the gene_list_to_phases function) and calculates the table of statistical measures and deviations from circular uniformity for each one using the Rayleigh, Kuiper, Hermans-Rasson, and Rao tests.

Usage

complete_circular_table(
  phase.list,
  hr.on.large.sets.th = 400,
  iter.hr = 999,
  force.hr.th = 0.05,
  rao.on.large.sets.th = 400,
  iter.rao = 999,
  force.rao.th = 0.05
)

Arguments

Value

A data.frame indicating, for each process or gene set: number of phases used for computation (n); mean, rho, variance, first, second and third quantiles of the circular distribution in hours; p-values and adjusted p-values for each of the four tests

Examples

org_Otaurireduced_eg_db <- load_example_annot()
data("circa_table_genescarab")
total_phases_table_sd <- data.frame(
    names = rownames(circa_table_genescarab),
    phase = as.numeric(circa_table_genescarab[["sd.peak.time.hours"]])
)
functional_data <- AnnotationDbi::select(org_Otaurireduced_eg_db,
    keys = AnnotationDbi::keys(org_Otaurireduced_eg_db, keytype = "GID"),
    columns = c("GID", "GO")
)
complete_gos <- unique(functional_data$GO)
complete_gos <- complete_gos[!is.na(complete_gos)]

set.seed(2345)
subset_gos <- complete_gos[sample(
    seq_len(length(complete_gos)), 50,
    replace = FALSE
)]
go.list.test <- create_gene_list_go(
    go_vector = subset_gos,
    org.package = org_Otaurireduced_eg_db,
    go_column = "GO", id_column = "GID"
)
phases.list.sd <- gene_list_to_phases(
    go.list.test, total_phases_table_sd
)
phases.list.sd.clean <- phases.list.sd[which(
    sapply(phases.list.sd, nrow) != 0
)][seq_len(3)]
go.circa.table.sd <- complete_circular_table(
    phase.list = phases.list.sd.clean,
    hr.on.large.sets.th = 200,
    force.hr.th = 0.04,
    rao.on.large.sets.th = 200,
    force.rao.th = 0.04,
    iter.rao = 999, iter.hr = 999
)

Description

Function that takes a list of discrete phases associated with each process or gene set (output of the gene_list_to_phases function) and calculates the table of statistical measures and deviations from circular uniformity for each one using the adapted Rayleigh, Kuiper, Hermans-Rasson, and Rao tests.

Usage

complete_circular_table_grouped(
  phase.list,
  n_bins,
  hr.on.large.sets.th = 400,
  iter.hr = 999,
  force.hr.th = 0.05,
  rao.on.large.sets.th = 400,
  iter.rao = 999,
  force.rao.th = 0.05,
  kuiper.on.large.sets.th = 400,
  iter.kuiper = 999,
  force.kuiper.th = 0.05
)

Arguments

Value

A data.frame indicating, for each process or gene set: number of phases used for computation (n); mean, rho, variance, first, second and third quantiles of the circular distribution in hours; p-values and adjusted p-values for each of the four tests

Examples

org_Otaurireduced_eg_db <- load_example_annot()
data("circa_table_genescarab")
total_phases_table_sd <- data.frame(
    names = rownames(circa_table_genescarab),
    phase = as.numeric(
        circa_table_genescarab[["sd.peak.time.hours"]]
    )
)
total_phases_table_sd_grouped <- data.frame(
    names = total_phases_table_sd$names,
    phase = trunc(total_phases_table_sd$phase)
)

functional_data <- AnnotationDbi::select(org_Otaurireduced_eg_db,
    keys = AnnotationDbi::keys(org_Otaurireduced_eg_db, keytype = "GID"),
    columns = c("GID", "GO")
)
complete_gos <- unique(functional_data$GO)
complete_gos <- complete_gos[!is.na(complete_gos)]

set.seed(2345)
subset_gos <- complete_gos[sample(
    seq_len(length(complete_gos)), 50,
    replace = FALSE
)]
go.list.test <- create_gene_list_go(
    go_vector = subset_gos,
    org.package = org_Otaurireduced_eg_db,
    go_column = "GO", id_column = "GID"
)
phases.list.sd <- gene_list_to_phases(
    go.list.test, total_phases_table_sd_grouped
)
phases.list.sd.clean <- phases.list.sd[which(
    sapply(phases.list.sd, nrow) != 0
)][seq_len(3)]
go.circa.table.sd <- complete_circular_table_grouped(
    phase.list = phases.list.sd.clean, n_bins = 24,
    hr.on.large.sets.th = 200,
    force.hr.th = 0.04,
    rao.on.large.sets.th = 200,
    force.rao.th = 0.04,
    iter.rao = 999, iter.hr = 999
)

Description

Function to create a list of genes associated with each GO term based on a species annotation package. This includes all ancestor GO terms of the selected ones.

Usage

create_gene_list_go(go_vector = c("all"), org.package, go_column, id_column)

Arguments

Value

A list linking each GO term with the genes that share that annotation

Examples

org_Otaurireduced_eg_db <- load_example_annot()
go.list.test <- create_gene_list_go(
    go_vector = "GO:0005515",
    org.package = org_Otaurireduced_eg_db, go_column = "GO",
    id_column = "GID"
)

Description

Function to create a list of genes associated with each KEGG pathway based on a species annotation package with KO terms derived from KEGGREST, KAAS or similar tools. It also allows filtering by species group.

Usage

create_gene_list_kegg(
  ko_vector = c("all"),
  org.package,
  ko_column,
  id_column,
  ko_prefix = "map",
  species = "all"
)

Arguments

Value

A list linking each KEGG pathway with the genes that share that annotation

Examples

org_Otaurireduced_eg_db <- load_example_annot()
create_gene_list_kegg(
    ko_vector = "K10666", org.package = org_Otaurireduced_eg_db,
    ko_column = "KO", id_column = "GID", ko_prefix = "map",
    species = "plants"
)

Description

Function that measures the contribution to the mean and rho of the circular distribution of a process or gene set for each of its constituent genes by removing them one by one. For sets consisting on more than 100 genes, it takes a random sample of 100 gene phases to normalize and ensure noticiable changes.

Usage

gene_contribution_to_set(circa_result, phase_list)

Arguments

Value

A list whose elements are tables indicating the difference in circular mean and rho due to removing each gene for each process or gene set, and the rank of each difference

Examples

org_Otaurireduced_eg_db <- load_example_annot()
data("circa_table_genescarab")
total_phases_table_sd <- data.frame(
    names = rownames(
        circa_table_genescarab
    ),
    phase = as.numeric(
        circa_table_genescarab[["sd.peak.time.hours"]]
    )
)

functional_data <- AnnotationDbi::select(org_Otaurireduced_eg_db,
    keys = AnnotationDbi::keys(org_Otaurireduced_eg_db, keytype = "GID"),
    columns = c("GID", "GO")
)
complete_gos <- unique(functional_data$GO)
complete_gos <- complete_gos[!is.na(complete_gos)]

set.seed(2345)
subset_gos <- complete_gos[sample(
    seq_len(length(complete_gos)), 50,
    replace = FALSE
)]
go.list.test <- create_gene_list_go(
    go_vector = subset_gos,
    org.package = org_Otaurireduced_eg_db,
    go_column = "GO", id_column = "GID"
)
phases.list.sd <- gene_list_to_phases(
    go.list.test, total_phases_table_sd
)
phases.list.sd.clean <- phases.list.sd[which(
    sapply(phases.list.sd, nrow) != 0
)][seq_len(3)]
go.circa.table.sd <- complete_circular_table(
    phase.list = phases.list.sd.clean,
    hr.on.large.sets.th = 200,
    force.hr.th = 0.04,
    rao.on.large.sets.th = 200,
    force.rao.th = 0.04,
    iter.rao = 999, iter.hr = 999
)
gene_contributions <- gene_contribution_to_set(
    circa_result =
        go.circa.table.sd[1, ],
    phase_list = phases.list.sd.clean[rownames(
        go.circa.table.sd
    )[1]]
)

Description

Function that takes a list of genes associated with specific biological processes (GO, KEGG) or gene sets and a general phase table, and returns a list consisting of the phase tables specific to each set.

Usage

gene_list_to_phases(gene.list, phase.table)

Arguments

Value

A list containing phase tables for each process or gene set

Examples

org_Otaurireduced_eg_db <- load_example_annot()
data("circa_table_genescarab")

total_phases_table_sd <- data.frame(
    names = rownames(circa_table_genescarab),
    phase = as.numeric(
        circa_table_genescarab[["sd.peak.time.hours"]]
    )
)
go.list.test <- create_gene_list_go(
    go_vector = "GO:0005515",
    org.package = org_Otaurireduced_eg_db, go_column = "GO",
    id_column = "GID"
)
gene_list_to_phases(go.list.test, total_phases_table_sd)

Description

GeneSCARAB provides methods for analyzing the cohesion and clustering toward specific points in the cycle of biological sets (genes, metabolites, ...) for chronobiology studies following the proposed best practices in circular statistics.

Details

Main workflow

The typical workflow is:

1. Load element phases as a table and species annotation package.

2. Construct the phases list for the desired sets or processes.

3. Compute circular statistics and departure from uniformity for each set.

4. For non-uniform sets, compare against global phase distribution.

5. In the case of multiple conditions, determine differentially timed sets between conditions.

6. For multimodal sets, analyze the number of modes and cluster its elements.

7. Calculate contribution of each element to its set's statistics.

8. Plot results as circular boxplot, dotplot or histogram.

Main functions

• load_example_annot() loads example annotation data.

• create_gene_list_go() creates a list of elements associated with each GO term based on a species annotation package.

• create_gene_list_kegg() create a list of elements associated with each KEGG pathway based on a species annotation package.

• gene_list_to_phases() takes a list of elements associated with specific sets and a complete phase table, and returns a list consisting of the individual phase tables of each set.

• complete_circular_table() calculates the table of statistical measures and deviations from circular uniformity of each set.

• complete_circular_table_grouped() calculates the table of statistical measures and deviations from circular uniformity of each set (for discrete phases).

• test_against_gen_dist() compares whether the distributions of specific sets significantly differ from the overall phase distribution of the entire dataset

• test_two_dist() compares whether the distributions of specific sets differ significantly between two different conditions or genotypes.

• gene_contribution_to_set() measures the contribution of each element of the set to the mean and rho of the entire set.

• multimodal_analysis() determines the most likely number of modes in the presence of f-fold symmetry (1, 2 or 3), cluster elements in f clusters and determine the circular statistic summary for each cluster

• circular_boxplot() creates circular boxplot.

• circular_dotplot() creates circular dotplot.

• circular_histogram() creates circular histogram.

See the package vignette for a complete tutorial.

Author(s)

Maintainer: Marcos Ramos-González [email protected] (ORCID)

Authors:

• Marcos Ramos-González [email protected] (ORCID)

Other contributors:

• Emma Serrano-Pérez [email protected] (ORCID) [contributor]

• Víctor Ramos-González [email protected] (ORCID) [contributor]

• Mercedes García-González [email protected] (ORCID) [contributor]

• Francisco J. Romero-Campero [email protected] (ORCID) [contributor]

See Also

Useful links:

• https://github.com/ramosgonzmarc/GeneSCARAB

• Report bugs at https://github.com/ramosgonzmarc/GeneSCARAB

Description

Loading SQlite containing GO and KO annnotation info for Ostreococcus tauri in AnnotationForge's output format. Original genome and annotation package were download from https://github.com/fran-romero-campero/AlgaeFUN and KO information was generated via KAAS (https://www.genome.jp/kaas-bin/kaas_main).

Usage

load_example_annot()

Value

OrgDb object

Examples

org_Otaurireduced_eg_db <- load_example_annot()

Description

Function to determine the most likely number of modes in the presence of f-fold symmetry (1, 2 or 3), cluster genes in f clusters and determine the statistic summary for each cluster

Usage

multimodal_analysis(phase_table)

Arguments

Value

A list containing the most likely mode number, the p-value for that number, the p-values corresponding to each mode number, the circular summary table for each cluster and the genes contained in each cluster

Examples

org_Otaurireduced_eg_db <- load_example_annot()
data("circa_table_genescarab")
total_phases_table_sd <- data.frame(
    names = rownames(circa_table_genescarab),
    phase = as.numeric(
        circa_table_genescarab[["sd.peak.time.hours"]]
    )
)
functional_data <- AnnotationDbi::select(org_Otaurireduced_eg_db,
    keys = AnnotationDbi::keys(org_Otaurireduced_eg_db, keytype = "GID"),
    columns = c("GID", "GO")
)
complete_gos <- unique(functional_data$GO)
complete_gos <- complete_gos[!is.na(complete_gos)]
set.seed(2345)
subset_gos <- complete_gos[sample(seq_len(
    length(complete_gos)
), 50, replace = FALSE)]
go.list.test <- create_gene_list_go(
    go_vector = subset_gos,
    org.package = org_Otaurireduced_eg_db,
    go_column = "GO", id_column = "GID"
)
phases.list.sd <- gene_list_to_phases(
    go.list.test,
    total_phases_table_sd
)
phases.list.sd.clean <- phases.list.sd[which(
    sapply(phases.list.sd, nrow) != 0
)]
go.circa.table.sd <- complete_circular_table(
    phase.list = phases.list.sd.clean,
    hr.on.large.sets.th = 200,
    force.hr.th = 0.04,
    rao.on.large.sets.th = 200,
    force.rao.th = 0.04,
    iter.rao = 999, iter.hr = 999
)
multi_table <- go.circa.table.sd[which(
    go.circa.table.sd$hr_p_value < 0.05 &
        go.circa.table.sd$n > 2
), ]
multi_go <- rownames(multi_table)[1]
phase_table_multi_go <- phases.list.sd.clean[[multi_go]]
multimodal_list <- multimodal_analysis(phase_table_multi_go)

Description

Function that compares whether the distributions of specific processes or gene sets differ significantly from the overall phase distribution of the entire study. Significance is calculated based on a MANOVA analysis as described in https://doi.org/10.1186/s40462-022-00323-8.

Usage

test_against_gen_dist(phase.list, total.phase.table)

Arguments

Value

A data.frame with p-value and adjusted p-value (null hyphotesis: same distribution as the general one) for each process or gene set

Examples

org_Otaurireduced_eg_db <- load_example_annot()
data("circa_table_genescarab")
total_phases_table_sd <- data.frame(
    names = rownames(circa_table_genescarab),
    phase = as.numeric(
        circa_table_genescarab[["sd.peak.time.hours"]]
    )
)

functional_data <- AnnotationDbi::select(org_Otaurireduced_eg_db,
    keys = AnnotationDbi::keys(org_Otaurireduced_eg_db, keytype = "GID"),
    columns = c("GID", "GO")
)
complete_gos <- unique(functional_data$GO)
complete_gos <- complete_gos[!is.na(complete_gos)]

set.seed(2345)
subset_gos <- complete_gos[sample(
    seq_len(length(complete_gos)), 50,
    replace = FALSE
)]
go.list.test <- create_gene_list_go(
    go_vector = subset_gos,
    org.package = org_Otaurireduced_eg_db,
    go_column = "GO", id_column = "GID"
)
phases.list.sd <- gene_list_to_phases(
    go.list.test, total_phases_table_sd
)
phases.list.sd.clean <- phases.list.sd[which(
    sapply(phases.list.sd, nrow) != 0
)][seq_len(3)]
not_experimentally_distributed_gos <- test_against_gen_dist(
    phases.list.sd.clean,
    total_phases_table_sd
)

Description

Function that compares whether the distributions of specific processes or gene sets differ significantly between two different conditions or genotypes. The two lists must have the same number of elements, although the number of genes associated with each process or gene set may differ. Significance is calculated based on a MANOVA analysis as described in https://doi.org/10.1186/s40462-022-00323-8.

Usage

test_two_dist(phase.list.1, phase.list.2)

Arguments

Value

A data.frame with p-value and adjusted p-value (null hyphotesis: same distribution in both conditions) for each process or gene set

Examples

org_Otaurireduced_eg_db <- load_example_annot()
data("circa_table_genescarab")
total_phases_table_sd <- data.frame(
    names = rownames(circa_table_genescarab),
    phase = as.numeric(
        circa_table_genescarab[["sd.peak.time.hours"]]
    )
)
total_phases_table_ld <- data.frame(
    names = rownames(circa_table_genescarab),
    phase = as.numeric(
        circa_table_genescarab[["ld.peak.time.hours"]]
    )
)

functional_data <- AnnotationDbi::select(org_Otaurireduced_eg_db,
    keys = AnnotationDbi::keys(org_Otaurireduced_eg_db, keytype = "GID"),
    columns = c("GID", "GO")
)
complete_gos <- unique(functional_data$GO)
complete_gos <- complete_gos[!is.na(complete_gos)]

set.seed(2345)
subset_gos <- complete_gos[sample(
    seq_len(length(complete_gos)), 50,
    replace = FALSE
)]
go.list.test <- create_gene_list_go(
    go_vector = subset_gos,
    org.package = org_Otaurireduced_eg_db,
    go_column = "GO", id_column = "GID"
)
phases.list.sd <- gene_list_to_phases(
    go.list.test, total_phases_table_sd
)
phases.list.sd.clean <- phases.list.sd[which(
    sapply(phases.list.sd, nrow) != 0
)][seq_len(3)]

phases.list.ld <- gene_list_to_phases(
    go.list.test, total_phases_table_ld
)
phases.list.ld.clean <- phases.list.ld[which(
    sapply(phases.list.ld, nrow) != 0
)][names(phases.list.sd.clean)]


diff_distributed_gos <- test_two_dist(
    phases.list.ld.clean, phases.list.sd.clean
)