Package 'ExpHunterSuite'

Title: Package for the Comprehensive Analysis of Transcriptomic Data
Description: The ExpHunterSuite implements a comprehensive protocol for the analysis of transcriptional data using established R packages and combines their results. It covers key steps including differential expression analysis, co-expression analysis, and functional enrichment for RNA-seq data. The package provides interactive R functions as well as command-line scripts that wrap these functions.
Authors: James Perkins
Maintainer: James Perkins <jimrperkins@gmail.com>
License: MIT + file LICENSE
Version: 1.21.0
Built: 2026-10-01 15:44:32 UTC
Source: https://github.com/bioc/ExpHunterSuite

Help Index

Calculates accuracy from a measures dataframe

Description

Calculates accuracy from a measures dataframe

Usage

acc(df)

Arguments

df

dataframe with measures

Value

calculated metric

Examples

df <- data.frame(TP = 4, FP = 3, TN = 2, FN = 1)
acc(df)

Output data from the main_degenes_Hunter function

Description

Output data from the main_degenes_Hunter function

Usage

data(degh_output)

Format

An output object from the main_degenes_Hunter function.

Examples

data(degh_output)

Main function to generate synthetic data using an specific exponential distribution for logFC

Description

Main function to generate synthetic data using an specific exponential distribution for logFC

Usage

degsynth(
  outfile = NULL,
  inputfile = NULL,
  replicates = 3,
  ngenes = 20000,
  DEGs_proportion = 0.2,
  FC_min = 1.3,
  FC_max = 3,
  P_up = 1,
  group = NULL
)

Arguments

outfile

output file
inputfile

input file
replicates

number of replicates
ngenes

number of genes
DEGs_proportion

numeric (0,1) proportion of DEG genes to be simulated
FC_min

minimum Fold-Change
FC_max

maximum Fold-Change
P_up

proportion og up-regulated genes
group

optional group identifiers seprated by commas

Value

synthetic data generated

Examples

synthetic_dataset <- degsynth()
# Returns simulated count dataset and indication of which are DE/not DE

download kegg db for a given organism

Description

download kegg db for a given organism

Usage

download_latest_kegg_db(current_organism_info, file)

Arguments

current_organism_info

organism info for which to download the file
file

where to save the file

Extract value from confusion matrix

Description

Extract value from confusion matrix

Usage

extract_from_cm(cm, real, pred, default = 0)

Arguments

cm

confusion matrix
real

slot name
pred

slot name
default

to return if value is not available

Value

value required or default if it is not available

Custom function to generate exponentially degradated foldchange range

Description

Custom function to generate exponentially degradated foldchange range

Usage

fcfunc(means, fcmin = 1.4, fcmax = 3, meanlog = 1, sdlog = 0.8)

Arguments

means

means vector
fcmin

minimum foldchange
fcmax

maximum foldchange
meanlog

param of rlnorm function
sdlog

param of rlnorm function

Value

a vector of foldchanges to be applied

Calculates F measure from a measures dataframe

Description

Calculates F measure from a measures dataframe

Usage

fmeasure(df)

Arguments

df

dataframe with measures

Value

calculated metric

Examples

df <- data.frame(TP = 4, FP = 3, TN = 2, FN = 1)
df$ppv <- ppv(df)
df$recall <- recall(df)
fmeasure(df)

get path for KEGG db for downloading/usage

Description

get path for KEGG db for downloading/usage

Usage

get_kegg_db_path(kegg_data_file, current_organism_info)

Arguments

kegg_data_file

file path. if null, inst/kegg_data_files will be used
current_organism_info

information for the organism, including KEGG code

Obtain an org_db object from a current_organism_info, obtained from subsetting the results of get_organism_table()

Description

Obtain an org_db object from a current_organism_info, obtained from subsetting the results of get_organism_table()

Usage

get_org_db(current_organism_info)

Arguments

current_organism_info

row from output of get_organism_table()

Value

org_db

Table with information abaut all organism available

Description

Table with information abaut all organism available

Usage

get_organism_table(file = NULL)

Arguments

file

to be loaded. If none given, internal organism table loaded

Value

organism table

Examples

ot <- get_organism_table()

Function to calculate full set of measures from a confusion data frame

Description

Function to calculate full set of measures from a confusion data frame

Usage

get_stats(df)

Arguments

df

confusion dataframe

Value

a data frame with several measures

Examples

df <- data.frame(TP = 4, FP = 3, TN = 2, FN = 1)
stats <- get_stats(df)

Obtain full set of measures from a confusion matrix

Description

Obtain full set of measures from a confusion matrix

Usage

get_stats_from_cm(cm)

Arguments

cm

confusion matrix

Value

a data frame with several measures

Examples

cm <- matrix(c(4,3,2,1), ncol = 2)
rownames(cm) <- c(TRUE,FALSE)
colnames(cm) <- c(TRUE,FALSE)
stats <- get_stats_from_cm(cm)

Method used to import hidden functions legally for CRAN check

Description

Method used to import hidden functions legally for CRAN check

Usage

get_unexported_function(pkg, fun)

Arguments

pkg

package
fun

function to be luaded

Value

function requried

Function to generate scatter plot for each gene in a hunter table and show logFC per package taking into account the variablity between them

Description

Function to generate scatter plot for each gene in a hunter table and show logFC per package taking into account the variablity between them

Usage

ht2logFCPlot(
  ht,
  var_filter = 0.001,
  title = "Filtered logFC",
  top = 50,
  alpha = 0.5
)

Arguments

ht

: hunter table dataframe
var_filter

: variability threshold to show gene into this graph (low variability will be removed)
title

: plot title
top

: plots only the top N items with more variance. NULL will not filter
alpha

: transparency of dots

Value

plot ready to be rendered

Author(s)

Fernando Moreno Jabato <jabato(at)uma(dot)com>

Hunter installation Function

Description

This function allows you to install scripts.

Usage

install_DEgenes_hunter(to)

Arguments

to

Path to folder in which copy Hunter scripts

Value

void

Examples

install_DEgenes_hunter("./")

Load a GMT format file and return a dataframe in correct format

Description

Load a GMT format file and return a dataframe in correct format

Usage

load_and_parse_gmt(gmt_file)

Arguments

gmt_file

file to be loaded

Value

GMT loaded info

Examples

gmt_file <- system.file("extData", 
"toy_categories_1.gmt", package = "ExpHunterSuite")
load_and_parse_gmt(gmt_file)

Loads Hunter DEG analysis table and simulated TRUE DEG vector. Concat in training/testing ML data frame format and returns it.

Description

Loads Hunter DEG analysis table and simulated TRUE DEG vector. Concat in training/testing ML data frame format and returns it.

Usage

load_synth_dataset(
  folder = NULL,
  hunter_table_file = "hunter_results_table.txt",
  prediction_file_regex = "_predv"
)

Arguments

folder

where files are placed
hunter_table_file

DEG analys result table file name
prediction_file_regex

regex to find TRUE DEG file generated

Value

a data frame with DEG result and real predictions column

Examples

# Used to load a synth + DE analysis result folder
ht <- load_synth_dataset() 
# Will return an empty object, you need a correct folder

Loads stored WGCNA results in DEgenes Hunter expression analysis package folder and returns in compact object

Description

Loads stored WGCNA results in DEgenes Hunter expression analysis package folder and returns in compact object

Usage

load_WGCNA_results(path, main_deg_table)

Arguments

path

of expression analysis results (Hunter folder)
main_deg_table

dataframe with main DEG analysis results

Value

WGCNA results loaded

Perform enrichment analysis on multiple lists of genes

Description

Perform enrichment analysis on multiple lists of genes

Usage

main_clusters_to_enrichment(
  input_file = NULL,
  gene_attribute_file = NULL,
  org_db = NULL,
  gene_keytype = "ENTREZID",
  temp_file = "enr_tmp.RData",
  force = FALSE,
  all_funsys = c("BP", "MF", "CC"),
  task_size = 1,
  current_organism_info = NULL,
  workers = 1,
  pvalcutoff = 0.1,
  qvalcutoff = 0.2,
  all_custom_gmt = NULL,
  kegg_data_file = NULL,
  universe = NULL
)

Arguments

input_file

input file path. File must contain 1 cluster per line clusterA ..tab.. gene1,gene2
gene_attribute_file

file to map genes to attributes e.g. fold change
org_db

org_db object
gene_keytype

ID used in the input file
temp_file

enrichments will be read from this file if it exists, written to it if it doesnt
force

if true, to run enrichments even if the temp_file exists
all_funsys

list of GOs/dbs to use for enrichment analysis
task_size

number of elements per packages used
current_organism_info

organism info from text table,
workers

cores to be used if parallel features are going to be used
pvalcutoff

adjusted p-value threshold to use for clusterProfiler,
qvalcutoff

q-value threshold to use for clusterProfiler,
all_custom_gmt

list of custom annotation sets
kegg_data_file

if KEGG included in enrich_dbs path needs
universe

background of genes to use as universe

Main DEgenes Hunter Function

Description

This function allows you to perform the DEG analysis with different algorithms.

Usage

main_degenes_Hunter(
  raw = NULL,
  pseudocounts = FALSE,
  target = NULL,
  count_var_quantile = 0,
  external_DEA_data = NULL,
  output_files = getwd(),
  reads = 2,
  minlibraries = 2,
  filter_type = "separate",
  p_val_cutoff = 0.05,
  lfc = 1,
  modules = "DELNW",
  minpack_common = 4,
  model_variables = "",
  numerics_as_factors = TRUE,
  string_factors = NULL,
  numeric_factors = NULL,
  WGCNA_memory = 5000,
  WGCNA_norm_method = "DESeq2",
  WGCNA_deepsplit = 2,
  WGCNA_min_genes_cluster = 20,
  WGCNA_detectcutHeight = 0.995,
  WGCNA_mergecutHeight = 0.25,
  WGCNA_all = FALSE,
  WGCNA_blockwiseNetworkType = "signed",
  WGCNA_blockwiseTOMType = "signed",
  WGCNA_minCoreKME = 0.5,
  WGCNA_minCoreKMESize = NULL,
  WGCNA_minKMEtoStay = 0.2,
  WGCNA_corType = "pearson",
  multifactorial = "",
  library_sizes = NULL
)

Arguments

raw

Dataframe with raw counts per sample and feature
pseudocounts

boolean, activate if the input contains pseudocounts
target

set with control and treatment information
count_var_quantile

quantile variance threshold
external_DEA_data

external DEA set
output_files

otput files path
reads

set of reads
minlibraries

minimum of libraries to use a set of data
filter_type

filter type to ba applied. Allowed: "separate" or "global"
p_val_cutoff

p-value threshold
lfc

minimum logFold Change
modules

modules to be executed. Allowed: DESeq2 (D), NOISeq (N), Limma (L), EdgeR (E), WGCNA (W)
minpack_common

minimum of pack that must be significant to tag a gene as significant
model_variables

custom model
numerics_as_factors

transform numeric values to factors. Default: TRUE
string_factors

string factors for WGCNA
numeric_factors

numeric factors for WGCNA
WGCNA_memory

see WGCNA package
WGCNA_norm_method

see WGCNA package
WGCNA_deepsplit

see WGCNA package
WGCNA_min_genes_cluster

see WGCNA package
WGCNA_detectcutHeight

see WGCNA package
WGCNA_mergecutHeight

see WGCNA package
WGCNA_all

see WGCNA package
WGCNA_blockwiseNetworkType

see WGCNA package
WGCNA_blockwiseTOMType

see WGCNA package
WGCNA_minCoreKME

see WGCNA package
WGCNA_minCoreKMESize

see WGCNA package
WGCNA_minKMEtoStay

see WGCNA package
WGCNA_corType

see WGCNA package
multifactorial

specify interaction/effect when multifactorial design
library_sizes

NULL or a dataframe with sample names and library sizes

Value

expression analysis result object with studies performed

Examples

data(toc)
data(target)
degh_out <- main_degenes_Hunter(raw=toc, target=target, modules="D")

Perform Functional analysis with different enrichment corpora.

Description

Perform Functional analysis with different enrichment corpora.

Usage

main_functional_hunter(
  hunter_results,
  model_organism,
  annot_table = NULL,
  organisms_table = get_organism_table(),
  input_gene_id = "ENSEMBL",
  custom = NULL,
  enrich_dbs = c("MF", "Reactome"),
  kegg_data_file = "",
  enrich_methods = "ORA",
  annotation_source = "orgdb",
  pthreshold = 0.1,
  qthreshold = 0.2,
  cores = 1,
  task_size = 1,
  output_files = "results",
  fc_colname = "mean_logFCs",
  universe = NULL,
  clean_parentals = FALSE,
  simplify = FALSE,
  top_categories = 50,
  sim_thr = NULL,
  summary_common_name = "ancestor",
  clusters_flag = FALSE
)

Arguments

hunter_results

output of main_degenes_hunter - list with DEG analysis results
model_organism

organisms which genes are being studied
annot_table

(OPTIONAL) annotation table to translate gene IDs
organisms_table

(OPTIONAL) configuration table for given organism. Use see get_organism_table()
input_gene_id

(OPTIONAL) type og gene IDs given. Allowed: ENSEMBL (E) , entrezgene (e), TAIR/Arabidopsis (T), Gene Names (G).
custom

(OPTIONAL) list of dataframes with GMT corpus
enrich_dbs

annotation dbs to be used: BP,MF,CC,Reactome,KEGG

  • MF and Reactome default

kegg_data_file

if KEGG included in enrich_dbs path needs
enrich_methods

enrichment methods to be used: ORA or GSEA
annotation_source

annote with orgdbs or Biomart? Currently on orgdbs
pthreshold

pvalue threshold
qthreshold

qvalue threshold
cores

cores to be used if parallel features are going to be used. Default: 1
task_size

number of elements per packages used
output_files

output folder
fc_colname

main logFC colname (into hunter_results dataframe)
universe

whether to use all genes as the background, or expressed only
clean_parentals

clean parental terms in merged enrichment
simplify

simplify the merged enrichments
top_categories

numbers of categories from each cluster to use for merge
sim_thr

value to use when combining similar categories in summary mode
summary_common_name

'significant' to use the most significant term to label each summarized group
clusters_flag

execute clusters enrichments

Value

functional result object with enrichments performed

Examples

# Load DE analysis result
# data(degh_output)
# NOT RUN
# func_results <- main_functional_hunter(hunter_results = degh_output,
# model_organism = "Mouse", enrich_dbs = "MF")
#

Perform gsea enrichment analysis of a list of genes

Description

Perform gsea enrichment analysis of a list of genes

Usage

multienricher_gsea(
  all_funsys = NULL,
  genes_list,
  organism_info,
  org_db = NULL,
  task_size = 1,
  workers = 1,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  kegg_file = NULL,
  custom_sets = NULL,
  ...
)

Arguments

all_funsys

vector of funsys to use (e.g. MF, Reatcome)
genes_list

vector of genes or list of vectors, to be enriched
organism_info

from the annotation table: infor on db names etc
org_db

org db file (optional) - normally obtained from organism_info
task_size

for parallelization
workers

for parallelization
pvalueCutoff

p-value finding enriched categories
pAdjustMethod

adjustment method for p-values
kegg_file

must be provided if kegg annotation required
custom_sets

custom set object (processed by load_and_parse_gmt)
...

other arguments passed to the enrichment function

Perform ORA enrichment analysis of a list of genes

Description

Perform ORA enrichment analysis of a list of genes

Usage

multienricher_ora(
  all_funsys = NULL,
  genes_list,
  universe = NULL,
  organism_info,
  org_db = NULL,
  task_size = 1,
  workers = 1,
  pvalueCutoff = 0.05,
  qvalueCutoff = 0.2,
  pAdjustMethod = "BH",
  kegg_file = NULL,
  custom_sets = NULL,
  readable = TRUE,
  return_all = FALSE,
  ...
)

Arguments

all_funsys

vector of funsys to use (e.g. MF, Reatcome)
genes_list

vector of genes or list of vectors, to be enriched
universe

background of genes to use as universe
organism_info

from the annotation table: infor on db names etc
org_db

org db file (optional) - normally obtained from organism_info
task_size

for parallelization
workers

for parallelization
pvalueCutoff

p-value finding enriched categories
qvalueCutoff

q-value finding enriched categories
pAdjustMethod

adjustment method for p-values
kegg_file

must be provided if kegg annotation required
custom_sets

custom set object (processed by load_and_parse_gmt)
readable

Whether output should include gene symbols
return_all

Whether to remove list items with no enrichment
...

other arguments passed to the enrichment function

Perform topGO enrichment analysis of a list of genes

Description

Perform topGO enrichment analysis of a list of genes

Usage

multienricher_topGO(
  all_funsys,
  genes_list,
  universe = NULL,
  organism_info,
  gene_id = "entrez",
  algorithm = "classic",
  statistic = "fisher",
  nodeSize = 5,
  task_size = 1,
  workers = 1
)

Arguments

all_funsys

vector of funsys to use (e.g. MF, Reatcome)
genes_list

vector of genes or list of vectors, to be enriched
universe

background of genes to use as universe
organism_info

from the annotation table: infor on db names etc
gene_id

what identifier do the genes use - must be entrez for
algorithm

alrgorithm for decting enriched functions - see topGO docs
statistic

method for detecting enriched functions
nodeSize

related to the creation of the GOdata object
task_size

for parallelization
workers

for parallelization

Parallelize function execution in list with multiple elements

Description

Parallelize function execution in list with multiple elements

Usage

parallel_list(X, FUNC, workers = 2, task_size = 1, ...)

Arguments

X

R list
FUNC

Function to execute in each element of the list
workers

Number of process to use in parallel execution
task_size

Number of list elements to be executed on each BiocParallel task
...

Additional arguments to BiocParallel or to FUNC

Value

list

main_parse_pseudocounts Functions that loads pseudommappers results and complite it into a table of counts

Description

main_parse_pseudocounts Functions that loads pseudommappers results and complite it into a table of counts

Usage

parse_pseudocounts(input_folder, mapper, output_type, annotation_file)

Arguments

input_folder

folder with pseudocounts files
mapper

string pseudomapping program
output_type

string indicate i fthe output must be 'G' genes, 'T' transcripts or both
annotation_file

gtf or gff path, requested for parsin salmon results

Examples

## Not run: 
	parse_pseudocounts(input_folder = "Path_to_folder",
						mapper = "salmon",
						annotation_file = "path_to_gtf")	
 
## End(Not run)

Calculates Positive Predictive Value from a measures dataframe

Description

Calculates Positive Predictive Value from a measures dataframe

Usage

ppv(df)

Arguments

df

dataframe with measures

Value

calculated metric

Examples

df <- data.frame(TP = 4, FP = 3, TN = 2, FN = 1)
df$Precision <- ppv(df)

Calculates Recall from a measures dataframe

Description

Calculates Recall from a measures dataframe

Usage

recall(df)

Arguments

df

dataframe with measures

Value

calculated metric

Examples

df <- data.frame(TP = 4, FP = 3, TN = 2, FN = 1)
df$Recall <- recall(df)

Calculates emasures from result tables

Description

Calculates emasures from result tables

Usage

rtable2measures(
  htfile,
  realprediction,
  aucfile = NULL,
  experiment = NULL,
  logFC_threshold = 1,
  pval_threshold = 0.01
)

Arguments

htfile

hunter table file
realprediction

real predictions file
aucfile

output AUC plot file
experiment

experiment info file to be loaded
logFC_threshold

logFC threshold
pval_threshold

p-value threshold

Value

metrics table

Examples

htfile <- system.file("extData", 
"hunter_results_table.txt", package = "ExpHunterSuite")
realprediction <- system.file("extData", 
"ground_truth_DE.txt", package = "ExpHunterSuite")
rtable2measures(htfile, realprediction)

Scale a matrix using minimum-maximum method

Description

Scale a matrix using minimum-maximum method

Usage

scale_data_matrix(data_matrix, norm_by_col = FALSE)

Arguments

data_matrix

to be scaled
norm_by_col

boolean flag: if true scaling will be performed by columns intead of by rows. Default: FALSE

Value

scaled matrix

Funtion used to scale a vector of numeric values from (x_min,x_max) range to (y_min,y_max) range. f : R -> R (x_min, x_max) -> (y_min, y_max) vect - x_min f(vect,y_min,y_max) = ————- x (y_max - y_min) + y_min x_max - x_min

Description

Funtion used to scale a vector of numeric values from (x_min,x_max) range to (y_min,y_max) range. f : R -> R (x_min, x_max) -> (y_min, y_max) vect - x_min f(vect,y_min,y_max) = ————- x (y_max - y_min) + y_min x_max - x_min

Usage

scale_range(vect, nmin, nmax)

Arguments

vect

vector to be transformated
nmin

new minimum
nmax

new maximum

Value

transformated vector to new range

Calculates specificity from a measures dataframe

Description

Calculates specificity from a measures dataframe

Usage

spc(df)

Arguments

df

dataframe with measures

Value

calculated metric

Examples

df <- data.frame(TP = 4, FP = 3, TN = 2, FN = 1)
spc(df)

Function to Simulate Treatment-Control paired experiments for genes expression count tables. Pipeline inspired on TCC simulateReadCounts function

Description

Function to Simulate Treatment-Control paired experiments for genes expression count tables. Pipeline inspired on TCC simulateReadCounts function

Usage

STC(
  Ngene = 1000,
  PDEG = 0.2,
  DEG.foldchange = 2,
  replicates = 3,
  bcount = NULL,
  group = NULL
)

Arguments

Ngene

number of genes to be included in the experiment
PDEG

Percentage of Differentially Expressed Genes. If one number is specified, only Up-regulated genes will be simulated. If a vector of two number is given, proportions will be applied for Up and Down regualted simulation respectively
DEG.foldchange

foldchange to be applied. Can be a number or a function which must accept only a vector and return a vector of the same length with the fold-change values. Warning: NOT LOG FOLD CHANGE
replicates

to be ge generated of each group (Treatment and Control)
bcount

matrix of genes counts used to simulate. If NULL, toc dataset will be loaded
group

columns to be used as group replicates. If NULL, all columns will be used.

Value

a list with COUNT) a simulated gene counts matrix with simulated genes and sample names and TRUEDEG) a vector with real up/down regulated genes

Author(s)

Fernando Moreno Jabato <jabato(at)uma(dot)es>

Target data containing sample information for the toc data

Description

Target data containing sample information for the toc data

Usage

data(target)

Format

A data.frame of sample information. Rows are samples, columns are different experimental variables/factors

Examples

data(target)

Function to generate targets file Load target file if it exists, otherwise use the -C and -T flags. Note target takes precedence over target.

Description

Function to generate targets file Load target file if it exists, otherwise use the -C and -T flags. Note target takes precedence over target.

Usage

target_generation(from_file = NULL, ctrl_samples = NULL, treat_samples = NULL)

Arguments

from_file

input targets file
ctrl_samples

control samples
treat_samples

target samples

Value

targets info loaded

Examples

# To generate target or load from file. 
# Example shows generation based on names of treament/control samples.
# These should match the column names in the table of counts.
case_sample_names <- paste("case", seq(5), sep="_")
control_sample_names <- paste("control", seq(5), sep="_")
target <- target_generation(ctrl_samples=control_sample_names, 
                            treat_samples=case_sample_names)

Table of counts dummy dataset for DEgeneHunter examples

Description

Table of counts dummy dataset for DEgeneHunter examples

Usage

data(toc)

Format

A truncated data.frame of aligned read counts for genes (rows) and samples (columns)

Examples

data(toc)

Translates a given gene ID using a dictionary. Note: one unknown ID can corresponds to many known ids.

Description

Translates a given gene ID using a dictionary. Note: one unknown ID can corresponds to many known ids.

Usage

translate_from_table(ids_to_translate, annot_table)

Arguments

ids_to_translate

set of IDs to be translated
annot_table

dictionary to translate IDs

Value

translated IDs or NA if it's not possible to translate

if pairwise_termsim throws an error, remove dup cat descriptions Only seems to be a problem with Reactome - to study further Further investigation re: number of cats also required

Description

if pairwise_termsim throws an error, remove dup cat descriptions Only seems to be a problem with Reactome - to study further Further investigation re: number of cats also required

Usage

trycatch_pairwise_termsim(enr)

Arguments

enr

enrichment object to be studied

Value

enrichment object after add termsim info

Write Main clusters to enrichment output This function allows you to report the Functional analysis.

Description

Write Main clusters to enrichment output This function allows you to report the Functional analysis.

Usage

write_clusters_to_enrichment(
  output_path = "results",
  output_file = "results",
  mode = "PR",
  enrichments_ORA = NULL,
  task_size = 1,
  workers = 1,
  template_folder = file.path(find.package("ExpHunterSuite"), "templates"),
  top_categories = NULL,
  group_results = FALSE,
  n_category = 30,
  sim_thr = 0.7,
  summary_common_name = "ancestor",
  pvalcutoff = 0.1,
  gene_attributes = NULL,
  gene_attribute_name = NULL,
  max_genes = 200,
  simplify = FALSE,
  clean_parentals = FALSE
)

Arguments

output_path

output folder
output_file

output file name for heatmaps
mode

type of output to produce - P for plots, R for reports, and S for summarized heatmaps
enrichments_ORA

list of enrich results for all clusters
task_size

number of elements per packages used
workers

(OPTIONAL) cores for parallel features
template_folder

(OPTIONAL) RMD templates folder
top_categories

numbers of categories from each cluster to use for merge
group_results

experimental - whether to group results in the emap plot
n_category

number of categories in the figures (per cluster)
sim_thr

value to use when combining similar categories in summary mode
summary_common_name

'significant' to use the most significant term to label each summarized group 'ancestor' to use the common ancestor of the group"
pvalcutoff

used to select terms for summarizing
gene_attributes

named list of attributes e.g. FCs for emap plot coloured nodes (genes)
gene_attribute_name

name for the legend in the emap plot for the nodes (genes)
max_genes

maximum number of genes to plot in cnet plot
simplify

Activate for process ClusterProfiler results with simplify function
clean_parentals

Activate to reduce significant terms in GO by removin the parentals

Value

void

Function to write expression package results. Write to disk the results of each diff expression package

Description

Function to write expression package results. Write to disk the results of each diff expression package

Usage

write_df_list_as_tables(df_list, prefix, root = getwd())

Arguments

df_list

dataframes list
prefix

prefix concatenated to output file name
root

path where write

Value

void

Examples

# Typically used with the @all_counts_for_plotting slot of the main output
deg_res <- list(aa=data.frame(a=seq(5),b=seq(5)), 
                bb=data.frame(a=seq(6,10),b=seq(6,10)))
write_df_list_as_tables(deg_res, prefix="test", root=tempdir())

Write enrichment files related to functional_hunter results list

Description

Write enrichment files related to functional_hunter results list

Usage

write_enrich_files(func_results, output_path = getwd())

Arguments

func_results

functional enrichment results
output_path

output folder path

Value

void

Examples

# Use a @functional_hunter or @multienricher result object to 
# create real files
write_enrich_files(list(),"./")

Write Main DEgenes Hunter report

Description

This function allows you to report the DEG analysis.

Usage

write_expression_report(
  exp_results,
  output_files = getwd(),
  template_folder = NULL,
  opt = NULL
)

Arguments

exp_results

DEG analysis results object
output_files

output file path
template_folder

RMD templates folder
opt

option list

Value

void

Examples

# Load DE analysis results
degh_output <- list() # data(degh_output)
write_expression_report(degh_output)

Write Main DEgenes Hunter functional report This function allows you to report the Functional analysis.

Description

Write Main DEgenes Hunter functional report This function allows you to report the Functional analysis.

Usage

write_functional_report(
  hunter_results,
  func_results,
  output_files = getwd(),
  fc_colname = "mean_logFCs",
  organisms_table = NULL,
  template_folder = file.path(find.package("ExpHunterSuite"), "templates"),
  cores = 2,
  task_size = 1,
  report = "fc",
  showCategories = 30,
  group_results = FALSE,
  max_genes = 200,
  corr_threshold = 0.8,
  pvalcutoff = 0.05
)

Arguments

hunter_results

DEG analysis results
func_results

functional results
output_files

output folder.
fc_colname

(OPTIONAL) main logFC colname (into hunter_results dataframe)
organisms_table

(OPTIONAL) configuration table for given organism. Use see get_organism_table()
template_folder

(OPTIONAL) RMD templates folder
cores

(OPTIONAL) cores for parallel features
task_size

number of elements per packages used
report

string with reports to be written. Allowed: clusters (c)
showCategories

number of categories in the figures (per cluster)
group_results

experimental - whether to group results in the emap plot and functional (f). Default = "fc"
max_genes

maximum number of genes to plot in cnet plot
corr_threshold

minimun module eigengene-trait vector absolute Pearson R value
pvalcutoff

maximum module eigengene-trait vector correlation P value

Value

void

Examples

# Load func and DE results
# data(degh_output)
# func_results <- list() 
# func_results <- main_functional_hunter(degh_output, "Mouse")
# write_functional_report(degh_output, func_results)