absent gene expression calls generation

Description Usage Arguments Value Author(s) See Also Examples

Main function running the workflow that generates present/absent calls from a file, a data.frame, or objects of the classe UserMetadata (please choose only 1 out of the 3). This workflow is highly tunable by editing default values of the slots of S4 objects. For more information on how to tune the workflow please have a look at the vignette and the documentation of the classes KallistoMetadata, AbundanceMetadata, UserMetadata and BgeeMetadata

generate_calls_workflow(
  abundanceMetadata = new("KallistoMetadata"),
  bgeeMetadata = new("BgeeMetadata"),
  userMetadata = NULL,
  userDataFrame = NULL,
  userFile = NULL
)

`abundanceMetadata`	A Class AbundanceMetadata object (optional) allowing to tune your gene quantification abundance analyze
`bgeeMetadata`	A Class BgeeMetadata object (optional) allowing to choose the version of reference intergenic sequences
`userMetadata`	A Class UserMetadata object (optional). generate present/absent calls using slots of the UserMetadata class.
`userDataFrame`	a data.frame comtaining all information to generate present/absent calls. Each line of this data.frame will generate calls for one RNA-Seq library. This data.frame must contains between 4 and 8 columns : species_id : The ensembl species ID run_ids : (optional) allows to generate calls for a subpart of all runs of the library. must be a character or a list of characters reads_size (optional) the size of the reads of the library (Default = 51) if the reads size is lower than 51 abundance quantification will be run from an index generated with a smaller kmer size rnaseq_lib_path : path to RNA-Seq library directory transcriptome_path : path to transcriptome file annotation_path : path to annotation file output_dir : (optional)root of the directory where results will be written custom_intergenic_path : (optional) To use if the "custom" reference intergenic release has been selected. Provide the path to the reference intergenic file
`userFile`	path to a tsv file containing between 4 and 8 columns. these columns are the same than for userDataFrame (see above). a template of this file is available at the root of the package and accessible with the command system.file('userMetadataTemplate.tsv', package = 'BgeeCall')

paths to the 5 results files (see vignette for more details)

Julien Wollbrett

AbundanceMetadata, KallistoMetadata, BgeeMetadata, UserMetadata

## Not run: 
# import gene annotation and transcriptome from AnnotationHub
library(AnnotationHub)
ah <- AnnotationHub()
ah_resources <- query(ah, c('Ensembl', 'Caenorhabditis elegans', '84'))
annotation_object <- ah_resources[['AH50789']]
transcriptome_object <- rtracklayer::import.2bit(ah_resources[['AH50453']])

# instanciate BgeeCall object
# add annotation and transcriptome in the user_BgeeCall object
# it is possible to import them using an S4 object (GRanges, DNAStringSet)
# or a file (gtf, fasta) with methods setAnnotationFromFile() and
# setTranscriptomeFromFile()
user_BgeeCall <- setAnnotationFromObject(user_BgeeCall,
                                         annotation_object,
                                         'WBcel235_84')
user_BgeeCall <- setTranscriptomeFromObject(user_BgeeCall,
                                          transcriptome_object,
                                          'WBcel235')
# provide path to the directory of your RNA-Seq library
user_BgeeCall <- setRNASeqLibPath(user_BgeeCall,
                 system.file('extdata', 'SRX099901_subset',
                 package = 'BgeeCall'))

# run the full BgeeCall workflow
calls_output <- generate_calls_workflow(
             userMetadata = user_BgeeCall)

## End(Not run)