Package 'BgeeCall'

AbundanceMetadata s4 class

Description

An S4 class that is the parent class of all abundance tool Classes. It contains information needed to all abundance tools. This class can be seen as an abstract class, you should never instanciate it.

Slots

txOut

Similar to tximport txOut parameter. Allows to keep abundance at transcript level if TRUE (default = FALSE)

ignoreTxVersion

logical used to remove transcript version in transcript ID if TRUE (default = FALSE)

cutoff_type

Defines the approach used to generate present/absent calls. default value is 'pValue', allowing calls to be generated using a pValue. Other possible values are 'intergenic' allowing to use a ratio of intergenic sequences considered as present as a threshold, or use qValue allowing calls to be generated from a qValue.

cutoff

numeric value of the cutoff used to generate the present/absent calls. If value of the slot cutoff_type is 'pValue' this cutoff will correspond to the highest pValue allowing to define a gene as present. If value of the slot cutoff_type is 'intergenic' this cutoff will correspond to the proportion of intergenic present divided by proportion of protein coding present. If value of the slot cutoff_type is 'qValue' this cutoff will correspond to the highest qValue allowing to define a gene as present. The qValue is calculated based on the proportion of intergenic/(intergenic + genic) at each unique abundance value (TPM). The default value is 0.05. Be careful when modifying this value as it could have a huge impact on present/absent calls.

full_transcriptome_file

Name of the fasta file containing both transcriptomic and intergenic regions. This file is created by the pipeline. You should edit this slot only if you already have such a file with a different name.

tx2gene_file

Name of the file containing the mapping between transcript IDs and gene IDs (See the tximport package vignette for more details). This file is created by the pipeline. You should edit this slot only if you already have such a file with a different name. This file must be store at get_species_path()

tx2gene_file_without_version

Name of the file containing the mapping between transcript IDs and gene IDs if ignoreTxVersion == TRUE (See the tximport package vignette for more details). This file is created by the pipeline. You should edit this slot only if you already have such a file with a different name. This file must be store at get_species_path()

gene2biotype_file

Name of the file containing the mapping between gene IDs and biotypes. This file is created by the pipeline. You should edit this slot only if you already have such a file with a different name.

tool_name

Name of the tool that will be use to generate transcript abundance estimation. All descendant of this class have to define a value for this slot (in the prototype section)

abundance_file

Name of the transcript-level abundance file. All descendant of this class have to define a value for this slot (in the prototype section)

read_size_kmer_threshold

read size of the library below which transcript index is created using a smaller kmer size

transcript_id_header

Name of the header of the column that contains transcript ID

count_header

Name of the header of the column that contains count

abundance_header

Name of the header of the column that contains abundance

eff_length_header

Name of the header of the column that contains effective length

transcript_calls_file_name

default name of file containing all transcript ids and calls (if calls created at transcript level)

gene_calls_file_name

default name of file containing all gene ids and calls (if calls created at gene level)

transcript_cutoff_file_name

default name of file containing summary of cutoff used to generate transcript expression calls (if calls created at transcript level)

gene_cutoff_file_name

default name of file containing summary of cutoff used to generate gene expression calls (if calls created at gene level)

transcript_distribution_file_name

default name of density plot file containing TPM distribution of all transcripts (if calls created at transcript level)

gene_distribution_file_name

default name of density plot file containing TPM distribution of all genes (if calls created at gene level)

---

generate gene expression calls with BgeeCall

Description

BgeeCall allows to generate present/absent gene expression calls without using an arbitrary cutoff like TPM<1. Calls are generated based on reference intergenic sequences. These sequences are generated based on expression of all RNA-Seq libraries of each species integrated in Bgee (https://bgee.org).

Details

Thes most important functions are :

• generate_calls_workflow : generate present/absent calls on a computer

• generate_slurm_indexes : generate kallisto indexes for a list of libraries on a cluster with slurm queuing system.

• generate_slurm_calls : generate present/absent calls for a list of libraries on a cluster with slurm queuing system. Indexes have to be generated first with the function 'generate_slurm_indexes'

• merging_libraries : merge calls from different libraries corresponding to the same condition. Extremely useful if different libraries correspond to same condition (e.g. same anatomical entity from same species)

For more details please have a look at the vignette with the command vignette("BgeeCall")

Author(s)

Julien Wollbrett

See Also

https://github.com/BgeeDB/BgeeCall

---

BgeeMetadata S4 class

Description

An S4 class that contains all information to retrieve intergenic regions generated by Bgee.

Slots

intergenic_release

Bgee intergenic release that will be used

all_releases

list of all reference intergenic releases that can be used to generate your present/absent expression calls.

intergenic_prefix

String used to generate an intergenic release specific output directory

---

Create kallisto indexes.

Description

This function creates kallisto indexes. Two indexes can be created depending on the reads size (see 'AbundanceMetadata@read_size_kmer_threshold' and 'UserMetadata@reads_size' for more information). One with default kmer value (31 nt) and one with kmer size of 15 nt. In order to generate.

Usage

create_kallisto_index(
  myKallistoMetadata,
  myBgeeMetadata,
  myUserMetadata,
  transcriptome_path = ""
)

Arguments

myKallistoMetadata	A Reference Class KallistoMetadata object.
myBgeeMetadata	A Reference Class BgeeMetadata object.
myUserMetadata	A Reference Class UserMetadata object.
transcriptome_path	path to the transcriptome fasta file. If no path is provided the default path created using BgeeCall will be used. IMPORTANT : in BgeeCall the transcriptome used to generate present/absent calls contains both intergenic sequences downloaded from Bgee and the reference transcriptome. If this function is run to generate present/absent then 'transcriptome_path' has to be empty

Value

create kallisto index and save it on the hard drive

Author(s)

Julien Wollbrett.

Examples

## Not run: 
# first a transcriptome is needed. Here it is downloaded from AnnotationHub
library(AnnotationHub)
ah <- AnnotationHub()
ah_resources <- query(ah, c('Ensembl', 'Caenorhabditis elegans', '84'))

# kallisto can not deal with S4 objects. A Path to a transcriptome file is 
# required
transcriptome_object <- rtracklayer::import.2bit(ah_resources[['AH50453']])
transcriptome_path <- file.path(getwd(),'transcriptome.fa')
Biostrings::writeXStringSet(transcriptome_object, transcriptome_path)



# initialize objects needed to create destination folder
bgee <- new('BgeeMetadata')
user <- new('UserMetadata', species_id = '6239')
kallisto <- new('KallistoMetadata')

# generate transcriptome index
create_kallisto_index(kallisto, bgee, user, transcriptome_path)

## End(Not run)

---

Download fasta intergenic

Description

Check if reference intergenic fasta file has already been downloaded. If not the file is downloaded from Bgee FTP or from the community repository depending on myBgeeMetadata@intergenic_release. if myBgeeMetadata@intergenic_release == "community" then reference intergenic wil be downloaded from the Zenodo community repository. Otherwise Reference intergenic sequences will be downloaded from the official Bgee FTP. Be careful when using reference intergenic sequences generated by the community as the Bgee team do not deeply review them.

Usage

download_fasta_intergenic(
  myBgeeMetadata = new("BgeeMetadata"),
  myUserMetadata,
  intergenic_file
)

Arguments

myBgeeMetadata	A Reference Class BgeeMetadata object (optional)
myUserMetadata	A Reference Class UserMetadata object.
intergenic_file	path where intergenic file will be saved

Value

download fasta intergenic from Bgee FTP or from the Zenodo community and save it locally

Examples

{
bgee_intergenic_file <- file.path(getwd(), 'intergenic.fasta')
userMetadata <- new('UserMetadata', species_id = '7227')
}

---

Download binary version of kallisto.

Description

Check your OS and download correct binary version of kallisto.

Usage

download_kallisto(myKallistoMetadata, myUserMetadata)

Arguments

myKallistoMetadata	A Reference Class KallistoMetadata object.
myUserMetadata	A Reference Class UserMetadata object.

Value

save uncompressed executable of kallisto on the hard drive

Author(s)

Julien Wollbrett.

Examples

{
  kallisto <- new('KallistoMetadata')
  user <- new('UserMetadata')
  download_kallisto(kallisto, user)
}

---

generate present/absent calls

Description

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

Usage

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

Arguments

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')
checkTxVersion	boolean used to define if BgeeCall check rather transcript version should be removed. Default value is FALSE

Value

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

Author(s)

Julien Wollbrett

See Also

AbundanceMetadata, KallistoMetadata, BgeeMetadata, UserMetadata

Examples

## 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)

---

Generate presence absence

Description

Generate presence absence calls. It correponds to the last part of the generation of the expression calls workflow. It runs the last part of the workflow generating present/absent expression calls. This function should only be used by advanced user who already manually run all previous parts of the pipeline. If you are not an advanced user it is safer to run the function “generate_calls_workflow“ that run all steps of the worklow

Usage

generate_presence_absence(
  myAbundanceMetadata = new("KallistoMetadata"),
  myBgeeMetadata = new("BgeeMetadata"),
  myUserMetadata
)

Arguments

myAbundanceMetadata	A descendant object of the Class myAbundanceMetadata (optional).
myBgeeMetadata	A Class BgeeMetadata object (optional).
myUserMetadata	A Class UserMetadata object.

Value

path to the 4 output files

Author(s)

Julien Wollbrett

Julien Roux

Sara Fonseca Costa

See Also

generate_calls_workflow

Examples

{
# this example reuse data present in the directory 'extdata' of the package.
user <- new('UserMetadata', working_path = system.file('extdata', 
package = 'BgeeCall'), species_id = '6239', rnaseq_lib_path = system.file(
'extdata', 'SRX099901_subset', package = 'BgeeCall'), 
annotation_name = 'WBcel235_84', simple_arborescence = TRUE)
calls_output <- generate_presence_absence(myUserMetadata = user)

#
}

---

Generate present/absent calls on slurm queuing system

Description

This function is meant to be used with a cluster where the Slurm queuing system is installed. It processes all steps to generate present/absent calls at RNA-Seq library level. This function does not generate the kallisto indexes. If they are not already generated please run function “'generate_slurm_indexes“' first. Steps of present/absent gene expression calls generation are :

• Quantifying abundances of transcripts from RNA-Seq libraries

• Summarizing abundance at gene level

• generate present/absent expression calls

Usage

generate_slurm_calls(
  kallistoMetadata = new("KallistoMetadata"),
  bgeeMetadata = new("BgeeMetadata"),
  userMetadata = new("UserMetadata"),
  userFile,
  submit_sh_template = NULL,
  slurm_options = NULL,
  rscript_path = NULL,
  modules = NULL,
  submit = TRUE,
  nodes = 10,
  checkTxVersion = FALSE
)

Arguments

kallistoMetadata	A Reference Class KallistoMetadata object (optional) allowing to tune your gene quantification abundance analyze. If no object is provided a new one will be created with default values.
bgeeMetadata	A Reference Class BgeeMetadata object (optional) allowing to choose the version of reference intergenic sequences. If no object is provided a new one will be created with default values.
userMetadata	A Class UserMetadata object (optional). If no object is provided a new one will be created with default values.
userFile	Path to the file where each line corresponds to one abundance quantification to be run. The structure of the file is the same than the 'userFile' used as input of the 'generate_calls_workflow' function. A template of this file can be loaded with the command : “'inputFile <- read.table(system.file("userMetadataTemplate.tsv", package = "BgeeCall"), header = TRUE)“' It is important to keep the same column names.
submit_sh_template	A template of the bash script used to submit the jobs. By default the submition script provided by rslurm is used. Modify only if module dependancies have to be added (like kallisto or R)
slurm_options	A named list of options recognized by sbatch. More details in the documentation of the rslurm::slurm_apply function
rscript_path	The location of the Rscript command. If not specified, defaults to the location of Rscript within the R installation being run.
modules	A list of modules you want to load in the invironment. Should stay empty except if you need to load R and/or kallisto (e.g module add R)
submit	Whether or not to submit the job to the cluster with sbatch. Default value is TRUE
nodes	The (maximum) number of cluster nodes to spread the calculation over. slurm_apply automatically divides params in chunks of approximately equal size to send to each node. Less nodes are allocated if the parameter set is too small to use all CPUs on the requested nodes. By default this number is 10.
checkTxVersion	boolean used to define if BgeeCall check rather transcript version should be removed. Default value is FALSE

Value

generate calls

Examples

## Not run: 
# use function with all default values
userFile <- "/path/to/userList.tsv"
sjobs <- generate_slurm_calls(userFile = userFile)

## End(Not run)

---

Generate all indexes for the abundance quantification step

Description

Check all unique lines of the input file to check which indexes have to be generated beore running all abundance quantification. This function is meant to be used with a cluster where the Slurm queuing system is installed. This step has to be run before the quantification otherwise indexes will be created for each abundance quantification. This will slow down the abundance quantification and can generate errors when writting the same file at the same time from different nodes. This function also generate tx2gene and gene2biotype mapping files.

Usage

generate_slurm_indexes(
  kallistoMetadata = new("KallistoMetadata"),
  bgeeMetadata = new("BgeeMetadata"),
  userMetadata = new("UserMetadata"),
  userFile,
  submit_sh_template = NULL,
  slurm_options = NULL,
  rscript_path = NULL,
  modules = NULL,
  submit = TRUE,
  nodes = 10
)

Arguments

kallistoMetadata	A Reference Class KallistoMetadata object (optional) allowing to tune your gene quantification abundance analyze. If no object is provided a new one will be created with default values.
bgeeMetadata	A Reference Class BgeeMetadata object (optional) allowing to choose the version of reference intergenic sequences. If no object is provided a new one will be created with default values.
userMetadata	A Class UserMetadata object (optional). If no object is provided a new one will be created with default values.
userFile	Path to the file where each line corresponds to one abundance quantification to be run. The structure of the file is the same than the 'userFile' used as input of the 'generate_calls_workflow' function. A template of this file can be loaded with the command : “'inputFile <- read.table(system.file("userMetadataTemplate.tsv", package = "BgeeCall"), header = TRUE)“' It is important to keep the same column names.
submit_sh_template	A template of the bash script used to submit the jobs. By default the submition script provided by rslurm is used. Modify only if module dependancies have to be added (like kallisto or R)
slurm_options	A named list of options recognized by sbatch. More details in the documentation of the rslurm::slurm_apply function
rscript_path	The location of the Rscript command. If not specified, defaults to the location of Rscript within the R installation being run.
modules	A list of modules you want to load in the invironment. Should stay empty except if you need to load R and/or kallisto (e.g module add R)
submit	Whether or not to submit the job to the cluster with sbatch. Default value is TRUE
nodes	The (maximum) number of cluster nodes to spread the calculation over. slurm_apply automatically divides params in chunks of approximately equal size to send to each node. Less nodes are allocated if the parameter set is too small to use all CPUs on the requested nodes. By default this number is 10.

Value

generate index files

Examples

## Not run: 
# use function with all default values
userFile <- "/path/to/userList.tsv"
sjobs <- generate_slurm_indexes(userFile = userFile)

## End(Not run)

---

Gather statistical information

Description

Collect the statistics provided by the gene_cutoff_info_file from each individual library, in order to generate a global summary file.

Usage

get_summary_stats(userFile, outDir)

Arguments

userFile	A data frame containing all information of each library
outDir	Output directory where the generated file should be saved

Value

A tsv file

Author(s)

Sara Fonseca Costa

---

'intergenic_prefix' Getter

Description

Get value of the 'intergenic_prefix' slot

Usage

getIntergenicPrefix(bgeeObject)

## S4 method for signature 'BgeeMetadata'
getIntergenicPrefix(bgeeObject)

Arguments

bgeeObject

The BgeeMetadata object

Value

the value of the 'intergenic_prefix' slot of the object

Examples

{
bgee <- new("BgeeMetadata")
intergenic_prefix <- getIntergenicPrefix(bgee)
}

---

'intergenic_release' Getter

Description

Get value of the 'intergenic_release' slot

Usage

getIntergenicRelease(bgeeObject)

## S4 method for signature 'BgeeMetadata'
getIntergenicRelease(bgeeObject)

Arguments

bgeeObject

The BgeeMetadata object

Value

the value of the 'intergenic_release' slot of the object

Examples

{
bgee <- new("BgeeMetadata")
intergenic_release <- getIntergenicRelease(bgee)
}

---

'run_ids' Getter

Description

Get value of the 'run_ids' slot

Usage

getRunIds(userObject)

## S4 method for signature 'UserMetadata'
getRunIds(userObject)

Arguments

userObject

The UserMetadata object

Value

the value of the 'run_ids' slot of the object

Examples

{
user <- new("UserMetadata")
run_ids <- getRunIds(user)
}

---

'simple_arborescence' Getter

Description

Get value of the 'simple_arborescence' slot

Usage

getSimpleArborescence(userObject)

## S4 method for signature 'UserMetadata'
getSimpleArborescence(userObject)

Arguments

userObject

The UserMetadata object

Value

the value of the 'simple_arborescence' slot of the object

Examples

{
user <- new("UserMetadata")
simple_arborescence <- getSimpleArborescence(user)
}

---

'working_path' Getter

Description

Get value of the 'working_path' slot

Usage

getWorkingPath(userObject)

## S4 method for signature 'UserMetadata'
getWorkingPath(userObject)

Arguments

userObject

The UserMetadata object

Value

the value of the 'working_path' slot of the object

Examples

{
user <- new("UserMetadata")
working_path <- getWorkingPath(user)
}

---

KallistoMetadata S4 class

Description

An S4 class that is the desendant of the AbundanceMetadata class. It contains all metadata needed to run kallisto analysis. All slots of this class have a default value. You do not need to edit them to run the package

Slots

download_kallisto

A logical allowing to use an already installed version of kallisto or to download a version that will be used only by this package

kallisto_windows_url

URL to the binary of kallisto for windows

kallisto_linux_url

URL to the binary of kallisto for linux

kallisto_osx_url

URL to the binary of kallisto for MacOS

kallisto_windows_dir

Name of the directory where kallisto will be installed on windows

kallisto_linux_dir

Name of the directory where kallisto will be installed on linux

kallisto_osx_dir

Name of the directory where kallisto will be installed on Mac

unix_kallisto_name

Name of the kallisto executable in linux and macOS

windows_kallisto_name

Name of the kallisto executable in windows

index_file

Name of index file generated by kallisto with default kmer size. It will be generated using the fasta file that contains both transcriptomic and intergenic regions. Do not use an index you generated outside of this package. This file is created by the pipeline. You should edit this slot only if you already have such a file with a different name. This file must be store at get_tool_path()

k15_index_file

same as index_file. This index is generated with smallest kmers and will be used only for libraries containing reads smallest than 50nt.

single_end_parameters

kallisto parameters used to run a single end mapping

pair_end_parameters

kallisto parameters used to run a pair end mapping

overwrite_index

logical allowing to overwrite already existing index. FALSE by default. Then by default already existing index files will not be generated again.

overwrite_quant

logical allowing to overwrite already existing abundance.txt files. FALSE by default. Then by default already existing quantitfdication files will not be generated again.

overwrite_calls

logical allowing to overwrite already existing present/absent calls. FALSE by default. Then by default already generated calls will not be generated again.

---

List species having Bgee reference intergenic sequences

Description

Return information related to species having Bgee reference intergenic sequences available for the selected Bgee intergenic release:

• speciesId the NCBI species ID of the species

• specieName scientific species name

• numberOfLibraries number of libraries used to generate these reference intergenic sequences

• genomeVersion version of the genome used to generate the reference intergenic sequences

If a BgeeMetadata object is provided this function retrieve the list of species using BgeeMetadata@intergenic_release. If only a 'release' is provided it will use it to retrieve the list of species. If none of them are provided the default Bgee reference intergenic release will be used.

Usage

list_bgee_ref_intergenic_species(myBgeeMetadata = NULL, release = NULL)

Arguments

myBgeeMetadata	A Reference Class BgeeMetadata object
release	A Bgee reference intergenic release name

Value

list all species having reference intergenic sequences available in the selected release

Author(s)

Julien Wollbrett

Examples

{
bgee <- new("BgeeMetadata")
list_bgee_ref_intergenic_species(myBgeeMetadata = bgee)
list_bgee_ref_intergenic_species(release = '0.2')
}

---

List species having reference intergenic sequences created by the BgeeCall community

Description

Return information related to species having reference intergenic sequences created by the BgeeCall community - speciesId : the NCBI species ID of the species - url : url to the reference intergenic fasta file - numberOfLibraries : number of libraries used to generate these reference intergenic sequences

Usage

list_community_ref_intergenic_species()

Value

list all species having reference intergenic sequences created by the community

Author(s)

Julien Wollbrett

Examples

{
list_community_ref_intergenic_species()
}

---

List reference intergenic releases usable with the BgeeCall package

Description

Returns information on available Bgee intergenic releases, the access URL for FTP, and the date of release

Usage

list_intergenic_release(release = NULL)

Arguments

release

A character specifying a targeted release number (e.g., '0.1'). If not specified, all available releases are shown.

Value

A data frame with information on Bgee intergenic releases available to use with the BgeeCall package.

Author(s)

Julien Wollbrett

Examples

{
 list_intergenic_release()
}

---

Merge transcriptome file provided by the user with the Bgee intergenic fasta file.

Description

This function will create a file corresponding to the concatenation of the transcriptome fasta file provided by the user and the corresponding intergenic fasta file created by Bgee.

Usage

merge_transcriptome_and_intergenic(
  myKallistoMetadata,
  myBgeeMetadata,
  myUserMetadata
)

Arguments

myKallistoMetadata	A Reference Class KallistoMetadata object.
myBgeeMetadata	A Reference Class BgeeMetadata object.
myUserMetadata	A Reference Class UserMetadata object.

Value

save merged file on the hard drive

Author(s)

Julien Wollbrett.

Examples

{
bgee <- new('BgeeMetadata', intergenic_release = '0.1')
user <- new ('UserMetadata', species_id = '6239')
kallisto <- new('KallistoMetadata')
user <- setTranscriptomeFromFile(user, system.file("extdata", 
"transcriptome.fa", package = "BgeeCall"), 'WBcel235')
merge_transcriptome_and_intergenic(kallisto, bgee, user)
}

---

Calls of expression in combined libraries

Description

Merging/combine libraries based in a condition specified by the user. The merging can be done using the p-values of the libraries, by applying the BH method, or using the q-values of the libraries using the fdr_inverse method.

Usage

merging_libraries(
  userFile = NULL,
  approach = "BH",
  condition = "species_id",
  cutoff = 0.05,
  outDir = NULL
)

Arguments

userFile	A file provided by the user with correspondent conditions
approach	Approach used to do the merging of libraries
condition	Condition/s where the merging should be done
cutoff	Cutoff that should be applied to call Present/Absent genes
outDir	Directory where the output files should be saved

Value

A dataframe containing the minimum quantitative value (p-value or q-value) and the calls to each gene id for the referent condition.

Author(s)

Sara Fonseca Costa

Examples

## Not run: 
callsMerging_species <- merging_libraries(userFile = 'PATH_USER_FILE', approach = 'BH', 
condition = 'species_id', cutoff = 0.05, outDir = 'PATH_OUTPUT')
callsMerging_species_sex <- merging_libraries(userFile = 'PATH_USER_FILE', approach = 'fdr_inverse', 
condition = c(species_id, sex), cutoff = 0.01, outDir = 'PATH_OUTPUT')
callsMerging_all <- merging_libraries(userFile = 'PATH_USER_FILE', approach = 'fdr_inverse', 
condition = c(species_id, anatEntity, devStage, sex, strain), cutoff = 0.05, outDir = 'PATH_OUTPUT')

## End(Not run)

---

Run one kallisto abundance analyse

Description

Run kallisto and all preliminary steps if needed like : - creation of transcriptome with intergenic (if needed) - installation of kallisto (if needed) - index creation (if needed) - run kallisto quantification

Usage

run_kallisto(
  myKallistoMetadata,
  myBgeeMetadata,
  myUserMetadata,
  transcriptome_path = ""
)

Arguments

myKallistoMetadata	A Reference Class KallistoMetadata object.
myBgeeMetadata	A Reference Class BgeeMetadata object.
myUserMetadata	A Reference Class UserMetadata object. This object has to be edited before running kallisto @seealso UserMetadata.R
transcriptome_path	path to the transcriptome fasta file. If no path is provided the default path created using BgeeCall will be used. IMPORTANT : in BgeeCall the transcriptome used to generate present/absent calls contains both intergenic sequences downloaded from Bgee and the reference transcriptome.

Value

create kallisto output files and save them on the hard drive

Author(s)

Julien Wollbrett.

Examples

## Not run: 
# first a transcriptome is needed. Here it is downloaded from AnnotationHub
library(AnnotationHub)
ah <- AnnotationHub()
ah_resources <- query(ah, c('Ensembl', 'Caenorhabditis elegans', '84'))

# kallisto can not deal with S4 objects. Path to transcriptome file is 
# required
transcriptome_object <- rtracklayer::import.2bit(ah_resources[['AH50453']])
transcriptome_path <- file.path(getwd(),'transcriptome.fa')
Biostrings::writeXStringSet(transcriptome_object, transcriptome_path)

# initialize objects needed to create destination folder
bgee <- new('BgeeMetadata')
user <- new('UserMetadata', species_id = '6239')
user <- setRNASeqLibPath(user, system.file( 
                     'extdata', 'SRX099901_subset', 
                     package = 'BgeeCall'))
kallisto <- new('KallistoMetadata')

# generate transcriptome index
run_kallisto(kallisto, bgee, user, transcriptome_path)

## End(Not run)

---

Run tximport

Description

Run tximport. Will summarize abundance estimation from transcript level to gene level if 'myAbundanceMetadata@txout == FALSE'. Otherwise keep abundance estimation at transcript level.

Usage

run_tximport(
  myAbundanceMetadata = new("KallistoMetadata"),
  myBgeeMetadata = new("BgeeMetadata"),
  myUserMetadata,
  abundanceFile = ""
)

Arguments

myAbundanceMetadata	A descendant object of the Class myAbundanceMetadata.
myBgeeMetadata	A Reference Class BgeeMetadata object.
myUserMetadata	A Reference Class UserMetadata object.
abundanceFile	(Optional) Path to the abundance file. NULL by default. If not NULL, the file located at 'abundanceFile' will be used to run tximport. Otherwise (Default) the path to the abundance file is deduced fom attributes of classes 'BgeeMetadata', 'UserMetadata' and 'AbundanceMetadata'

Value

a tximport object

Author(s)

Julien Wollbrett

Examples

{
user <- new("UserMetadata", working_path = system.file("extdata", 
    package = "BgeeCall"), species_id = "6239", 
  rnaseq_lib_path = system.file("extdata", 
    "SRX099901_subset", package = "BgeeCall"), 
  annotation_name = "WBcel235_84", simple_arborescence = TRUE)
abundance_file <- system.file('extdata', 'abundance.tsv', package = 'BgeeCall')
tx_import <- run_tximport(myUserMetadata = user, 
abundanceFile = abundance_file)
}

---

Set annotation_object of one UserMetadata object

Description

Method of the class UserMetadata. Set annotation_object of one UserMetadata object by providing the path to a fasta transcriptome file.

Usage

setAnnotationFromFile(userObject, annotationPath, annotationName)

## S4 method for signature 'UserMetadata,character,missing'
setAnnotationFromFile(userObject, annotationPath, annotationName)

## S4 method for signature 'UserMetadata,character,character'
setAnnotationFromFile(userObject, annotationPath, annotationName)

Arguments

userObject	The UserMetadata object
annotationPath	Absolute path to the annotation file
annotationName	(optional) Name of the annotation. Will be used to create folders.

Details

If no annotationName is provided the name of the annotation file will be used to create folders.

Value

An object of the class UserMetadata

Examples

{
# path to gtf annotation file
annotation_file <- system.file("extdata", "annotation.gtf", package = "BgeeCall")
user <- new("UserMetadata")
user <- setAnnotationFromFile(user, annotation_file,
                             "annotation_name")
}

---

Set annotation_object of one UserMetadata object

Description

Method of the class UserMetadata. Set annotation_object of one UserMetadata object by using one GRanges object as input.

Usage

setAnnotationFromObject(userObject, annotationObject, annotationName)

## S4 method for signature 'UserMetadata,GRanges,character'
setAnnotationFromObject(userObject, annotationObject, annotationName = "")

Arguments

userObject	The UserMetadata object
annotationObject	object of thr GRanges S4 class
annotationName	(optional) Name of the annotation. Will be used to create folders.

Details

If no annotationName is provided the name of the file is used to create folders.

Value

An object of the class UserMetadata

Examples

{
user <- new("UserMetadata")
annotation_object <- rtracklayer::import(system.file("extdata",
"annotation.gtf", package = "BgeeCall"))
user <- setAnnotationFromObject(user, annotation_object,
                                 "annotation_name")
}

---

'intergenic_release' Setter

Description

Set value of the 'intergenic_release' slot

Usage

setIntergenicRelease(bgeeObject, intergenicRelease)

## S4 method for signature 'BgeeMetadata,character'
setIntergenicRelease(bgeeObject, intergenicRelease)

Arguments

bgeeObject	The BgeeMetadata object
intergenicRelease	character corresponding to the 'intergenic_release'

Value

An object of the class BgeeMetadata with new 'intergenic_release' value

Examples

{
bgee <- new("BgeeMetadata")
bgee <- setIntergenicRelease(bgee, "0.1")
}

---

'output_dir' Setter

Description

Set value of the 'output_dir' slot

Usage

setOutputDir(userObject, outputDir)

## S4 method for signature 'UserMetadata,character'
setOutputDir(userObject, outputDir)

Arguments

userObject	The UserMetadata object
outputDir	path to the directory wanted as 'output_dir'

Value

An object of the class UserMetadata with new 'output_dir' value

Examples

{
user <- new("UserMetadata")
user <- setOutputDir(user, getwd())
}

---

'rnaseq_lib_path' Setter

Description

Set value of the 'rnaseq_lib_path' slot

Usage

setRNASeqLibPath(userObject, rnaSeqLibPath)

## S4 method for signature 'UserMetadata,character'
setRNASeqLibPath(userObject, rnaSeqLibPath)

Arguments

userObject	The UserMetadata object
rnaSeqLibPath	path to the directory wanted as 'rnaseq_lib_path'

Value

An object of the class UserMetadata with new 'rnaseq_lib_path' value

Examples

{
user <- new("UserMetadata")
user <- setRNASeqLibPath(user, getwd())
}

---

'run_ids' Setter

Description

Method of the class UserMetadata. Set run_ids of one UserMetadata object by providing the id of all wanted runs

Usage

setRunIds(userObject, runIds)

## S4 method for signature 'UserMetadata,character'
setRunIds(userObject, runIds)

Arguments

userObject	The UserMetadata object
runIds	id of all wanted runs

Value

An object of the class UserMetadata

Examples

{
user <- new("UserMetadata")
user <- setRunIds(user, c("RUN_1", "RUN_2"))
}

---

'simple_arborescence' Setter

Description

Set value of the 'simple_arborescence' slot

Usage

setSimpleArborescence(userObject, simpleArborescence)

## S4 method for signature 'UserMetadata,logical'
setSimpleArborescence(userObject, simpleArborescence)

Arguments

userObject	The UserMetadata object
simpleArborescence	boolean defining if output files will be created a simple arborescence (TRUE) or not (FALSE)

Value

An object of the class UserMetadata with new 'simple_arborescence' value

Examples

{
user <- new("UserMetadata")
user <- setSimpleArborescence(user, FALSE)
}

---

Set transcriptome_object of one UserMetadata object

Description

Method of the class UserMetadata. Set transcriptome_object of one UserMetadata object by providing the path to a fasta transcriptome file.

Usage

setTranscriptomeFromFile(userObject, transcriptomePath, transcriptomeName)

## S4 method for signature 'UserMetadata,character,missing'
setTranscriptomeFromFile(userObject, transcriptomePath, transcriptomeName)

## S4 method for signature 'UserMetadata,character,character'
setTranscriptomeFromFile(userObject, transcriptomePath, transcriptomeName)

Arguments

userObject	The UserMetadata object
transcriptomePath	Absolute path to the transcriptome file
transcriptomeName	(optional) Name of the trancriptome. Will be used to create folders.

Details

If no transcriptomeName is provided the name of the transcriptome file will be used to create folders.

Value

An object of the class UserMetadata

Examples

{
transcriptome_path <- system.file("extdata", "transcriptome.fa", package = "BgeeCall")
user <- new("UserMetadata")
user <- setTranscriptomeFromFile(user, transcriptome_path,
                                 "transcriptome_name")
}

---

Set transcriptome_object of one UserMetadata object

Description

Method of the class UserMetadata. Set transcriptome_object of one UserMetadata object by using one DNAStringSet object as input.

Usage

setTranscriptomeFromObject(userObject, transcriptomeObject, transcriptomeName)

## S4 method for signature 'UserMetadata,DNAStringSet,character'
setTranscriptomeFromObject(userObject, transcriptomeObject, transcriptomeName)

Arguments

userObject	UserMetadata object
transcriptomeObject	Object of the DNAStringSet S4 class
transcriptomeName	Name of the transcriptome. Will be used to create transcriptome folders.

Details

Please use a DNAStringSet object as input. This class is defined in the Biostrings package

Value

an object of UserMetadata

Examples

{
user <- new("UserMetadata")
transcriptome_object <- Biostrings::readDNAStringSet(
    system.file("extdata", "transcriptome.fa", package = "BgeeCall"))
user <- setTranscriptomeFromObject(user,
                 transcriptome_object,
                 "transcriptome_name")
}

---

'working_path' Setter

Description

Set value of the 'working_path' slot

Usage

setWorkingPath(userObject, workingPath)

## S4 method for signature 'UserMetadata,character'
setWorkingPath(userObject, workingPath)

Arguments

userObject	The UserMetadata object
workingPath	path to the directory wanted as 'working_path'

Value

An object of the class UserMetadata with new 'working_path' value

Examples

{
user <- new("UserMetadata")
user <- setWorkingPath(user, getwd())
}

---

UserMetadata S4 class

Description

An S4 class containing all metadata that have to be provided by the user It is mandatory to edit 'species_id', 'rnaseq_lib_path', 'transcriptome_path', 'annotation_name', 'annotation_object' and potentialy 'run_ids' before using the package.

Slots

species_id

The NCBI Taxon Id of the species

run_ids

A vector of charater. Has to be provided only if a subset of runs present in UserMetadata@rnaseq_lib_path has to be run. If empty, all fastq files present in the rnaseq_lib_path will be considered as technical replicates and merged to run one transcript expression estimation analyse.

reads_size

The size of the reads. If smaller than 'KallistoMetadata@read_size_kmer_threshold', an index with a kmer size of 15 bp will be used.

rnaseq_lib_path

Path to the directory of the RNA-Seq library that contains fastq files. The extension of the fastq files name must be .fq, .fastq, .fq.gz, or .fastq.gz

transcriptome_name

Name of the transcriptome used to generate arborescence of output repositories.

transcriptome_object

Object containing transcriptome

annotation_name

Name of the annotation used to generate arborescence of output repositories.

annotation_object

Object containing annotations from GTF or GFF file

working_path

Working directory. By default the working directory is defined with the 'getwd()' function.

gtf_source

The source name from where the gtf file comes from. By default is ensembl.

simple_arborescence

logical allowing to create a simple arborescence of directory. If 'TRUE' (default), all results will be on the same directory (working_path/intergenic_release/all_results/libraryId). Use 'FALSE' if you plan to generate expression calls for the same library using different transcriptomes or gene annotations, otherwise you will overwrite previous results. When 'FALSE' the path to result folder looks like : working_path/intergenic_release/speciesId/kallisto/transcriptome_name/annotation_name/libraryId

output_dir

(optional) Allows to manually define your output directory. By default the path to output directory is created automatically from the working_path (working_path/intergenic_release/all_results/libraryId/).

verbose

logical allowing to use the verbose mode. TRUE by default.

custom_intergenic_path

path to a local version of reference intergenic fasta file. If NULL (by default) the reference intergenic fasta file will be downloaded. If not NULL BgeeCall will merge this local reference intergenic file with the transcriptome. Except if you generated your own intergenic regions always keep it NULL.

encrypted_pattern

Allows to manage encrypted libraries. If a fastq file with the suffix .enc is found for a run, this slot will allow to use a string pattern to decrypt it. . This encrypted_pattern needs to contain the string FASTQ_PATH that will be transformed to the actual path to the fastq file.

---