| Title: | Comparative Analysis with Annotation-Based Genomic Components |
|---|---|
| Description: | A first-principle, phylogeny-aware comparative genomics tool for investigating associations between terms used to annotate genomic components (e.g., Pfam IDs, Gene Ontology terms,) with quantitative or rank variables such as number of cell types, genome size, or density of specific genomic elements. See the project website for more information, documentation and examples, and <doi:10.1016/j.patter.2023.100728> for the full paper. |
| Authors: | Francisco Lobo [aut], Felipe Campelo [aut, cre], Jorge Augusto Hongo [aut], Giovanni Marques de Castro [aut], Gabriel Almeida [sad, dnc] |
| Maintainer: | Felipe Campelo <[email protected]> |
| License: | GPL-2 |
| Version: | 1.0.20 |
| Built: | 2024-11-03 05:08:26 UTC |
| Source: | https://github.com/LABpackages/CALANGO |
This function installs the latest versions of all Bioconductor packages required for the report generation, namely:
AnnotationDbi
KEGGREST
GO.db
install_bioc_dependencies(bioc.args = list(), force = FALSE)install_bioc_dependencies(bioc.args = list(), force = FALSE)
bioc.args |
list containing further arguments to be passed down to 'BiocManager::install()'. |
force |
logical: reinstall already-installed packages? |
It is essential that these Bioconductor packages be installed for CALANGO to work properly. It uses 'BiocManager::install()' for installing Bioconductor packages. Further arguments to this function are passed as a list.
No return value, called for side effects.
## Not run: install_bioc_dependencies() ## End(Not run)## Not run: install_bioc_dependencies() ## End(Not run)
This script generates the HTML5 report based on an enriched 'CALANGO'-type list output by 'run_CALANGO()'.
make_report(defs, render.report = TRUE)make_report(defs, render.report = TRUE)
defs |
an enriched 'CALANGO'-type list generated by 'run_CALANGO()' |
render.report |
logical: should the HTML5 report be generated (for internal use only) |
Updated 'defs' list, containing:
All input parameters originally passed (see [run_CALANGO()] for details).
Derived fields calculated for displaying the results, including several statistical summaries of the data (including correlations, contrasts, covariances, p-values).
This function is mainly called for its side effect, namely the generation of an HTML5 report of the analysis, which is saved to the folder indicated in 'defs$output.dir'.
This script downloads the *calanguize_genomes.pl* Perl script from the repository, together with associated README instructions for using the script, managing dependencies, etc. It will extract the data into a folder containing everything that is needed for preparing data for using CALANGO.
retrieve_calanguize_genomes( target.dir, method = "auto", unzip = getOption("unzip"), overwrite = TRUE )retrieve_calanguize_genomes( target.dir, method = "auto", unzip = getOption("unzip"), overwrite = TRUE )
target.dir |
path to the folder where the files will be saved ( accepts relative and absolute paths) |
method |
Method to be used for downloading files. Current download
methods are "internal", "wininet" (Windows only) "libcurl", "wget" and
"curl", and there is a value "auto": see _Details_ and _Note_ in the
documentation of |
unzip |
The unzip method to be used. See the documentation of
|
overwrite |
If TRUE, overwrite existing files in target the folder (the equivalent of unzip -o), otherwise ignore such files (the equivalent of unzip -n). |
If the 'target.dir' provided does not exist it is created (recursively) by the function.
No return value, called for side effects (see Description).
## Not run: CALANGO::retrieve_calanguize_genomes(target.dir = "./data") ## End(Not run)## Not run: CALANGO::retrieve_calanguize_genomes(target.dir = "./data") ## End(Not run)
This script downloads relevant data files from the CALANGO project repository. It will extract the data into a folder containing directories related to dictionary files, Gene Ontology annotation files, tree files, etc. Note: you may need to edit the file paths in the example scripts contained under the 'parameters' subfolder of 'target.dir', or pass an appropriate base path using parameter 'basedir' in [run_CALANGO()].
retrieve_data_files( target.dir, method = "auto", unzip = getOption("unzip"), ... )retrieve_data_files( target.dir, method = "auto", unzip = getOption("unzip"), ... )
target.dir |
path to the folder where the files will be saved ( accepts relative and absolute paths) |
method |
Method to be used for downloading files. Current download
methods are "internal", "wininet" (Windows only) "libcurl", "wget" and
"curl", and there is a value "auto": see _Details_ and _Note_ in the
documentation of |
unzip |
The unzip method to be used. See the documentation of
|
... |
additional attributes (currently ignored) |
If the 'target.dir' provided does not exist it is created (recursively) by the function.
No return value, called for side effects (see Description).
## Not run: CALANGO::retrieve_data_files(target.dir = "./data") ## End(Not run)## Not run: CALANGO::retrieve_data_files(target.dir = "./data") ## End(Not run)
This function runs the complete workflow of CALANGO and generates the HTML5 output pages and export files.
run_CALANGO( defs, type = "correlation", cores = NULL, render.report = TRUE, basedir = "" )run_CALANGO( defs, type = "correlation", cores = NULL, render.report = TRUE, basedir = "" )
defs |
either a CALANGO-type list object or a path to a text file containing the required definitions (see Details). |
type |
type of analysis to perform. Currently only "correlation" is supported. |
cores |
positive integer, how many CPU cores to use. Setting this parameter overrides any 'cores' field from 'defs'. |
render.report |
logical: should a HTML5 report be generated? |
basedir |
path to base folder to which all relative paths in 'defs' refer to. |
The script expects a 'CALANGO'-type list, passed either as an actual list object or as a file path. In the latter case, notice that the file must be a text file with a 'field = value' format. Blank likes and lines starting with '#' are ignored. The function expects the input list to have the following fields:
annotation.files.dir (required, string) - Folder where
annotation files are located.
output.dir (required, string) - output folder for results
dataset.info (required, string) - genome metadata file, it
should contain at least:
File names. Please notice this information should be the first column in metadata file;
Phenotype data (numeric, this is the value CALANGO uses to rank species when searching for associations)
Normalization data (numeric, this is the value CALANGO uses as a denominator to compute annotation term frequencies to remove potential biases caused by, for instance, over annotation of model organisms or large differences in the counts of genomic elements). Please notice that CALANGO does not require normalization data for GO, as it computes the total number of GO terms per species and uses it as a normalizing factor.
x.column (required, numeric) - which column in "dataset.info"
contains the phenotype data?
ontology (required, string) - which dictionary data type to
use? Possible values are "GO" and "other". For GO, CALANGO can compute
normalization data.
dict.path (required, string) - file for dictionary file
(two-column file containing annotation IDs and their descriptions. Not
needed for GO.
column (required, string) - which column in annotation files
should be used (column name)
denominator.column (optional, numeric) - which column contains
normalization data (column number)
tree.path (required, string) - path for tree file in either
newick or nexus format
tree.type (required, string) - tree file type (either "nexus"
or "newick")
cores (optional, numeric) - how many cores to use? If not
provided the function defaults to 1.
linear.model.cutoff (required, numeric) - parameter that
regulates how much graphical output is produced. We configure it to
generate plots only for annotation terms with corrected q-values for
phylogenetically independent contrasts (standard: smaller than 0.5).
MHT.method (optional, string) - type of multiple hypothesis
correction to be used. Accepts all methods listed by
'stats::p.adjust.methods()'. If not provided the function defaults to
"BH".
Updated 'defs' list, containing:
All input parameters originally passed or read from a 'defs' file (see **Details**).
Derived fields loaded and preprocessed from the files indicated in 'defs'.
Several statistical summaries of the data (used to render the report), including correlations, contrasts, covariances, p-values and other summary statistics.
Results are also saved to files under 'defs$output.dir'.
## Not run: ## Install any missing BioConductor packages for report generation ## (only needs to be done once) # CALANGO::install_bioc_dependencies() # Retrieve example files basedir <- tempdir() retrieve_data_files(target.dir = paste0(basedir, "/data")) defs <- paste0(basedir, "/data/parameters/parameters_domain2GO_count_less_phages.txt") # Run CALANGO res <- run_CALANGO(defs, cores = 2) ## End(Not run)## Not run: ## Install any missing BioConductor packages for report generation ## (only needs to be done once) # CALANGO::install_bioc_dependencies() # Retrieve example files basedir <- tempdir() retrieve_data_files(target.dir = paste0(basedir, "/data")) defs <- paste0(basedir, "/data/parameters/parameters_domain2GO_count_less_phages.txt") # Run CALANGO res <- run_CALANGO(defs, cores = 2) ## End(Not run)