| Title: | Automatic RNA-Seq present/absent gene expression calls generation |
|---|---|
| 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). |
| Authors: | Julien Wollbrett [aut, cre], Sara Fonseca Costa [aut], Julien Roux [aut], Marc Robinson Rechavi [ctb], Frederic Bastian [aut] |
| Maintainer: | Julien Wollbrett <[email protected]> |
| License: | GPL-3 + file LICENSE |
| Version: | 1.23.0 |
| Built: | 2024-11-29 04:11:26 UTC |
| Source: | https://github.com/bioc/BgeeCall |
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.
txOutSimilar to tximport txOut parameter. Allows to keep abundance at transcript level if TRUE (default = FALSE)
ignoreTxVersionlogical used to remove transcript version in transcript ID if TRUE (default = FALSE)
cutoff_typeDefines 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.
cutoffnumeric 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_fileName 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_fileName 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_versionName 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_fileName 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_nameName 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_fileName 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_thresholdread size of the library below which transcript index is created using a smaller kmer size
transcript_id_headerName of the header of the column that contains transcript ID
count_headerName of the header of the column that contains count
abundance_headerName of the header of the column that contains abundance
eff_length_headerName of the header of the column that contains effective length
transcript_calls_file_namedefault name of file containing all transcript ids and calls (if calls created at transcript level)
gene_calls_file_namedefault name of file containing all gene ids and calls (if calls created at gene level)
transcript_cutoff_file_namedefault name of file containing summary of cutoff used to generate transcript expression calls (if calls created at transcript level)
gene_cutoff_file_namedefault name of file containing summary of cutoff used to generate gene expression calls (if calls created at gene level)
transcript_distribution_file_namedefault name of density plot file containing TPM distribution of all transcripts (if calls created at transcript level)
gene_distribution_file_namedefault name of density plot file containing TPM distribution of all genes (if calls created at gene level)
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).
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")
Julien Wollbrett
https://github.com/BgeeDB/BgeeCall
An S4 class that contains all information to retrieve intergenic regions generated by Bgee.
intergenic_releaseBgee intergenic release that will be used
all_releaseslist of all reference intergenic releases that can be used to generate your present/absent expression calls.
intergenic_prefixString used to generate an intergenic release specific output directory
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.
create_kallisto_index( myKallistoMetadata, myBgeeMetadata, myUserMetadata, transcriptome_path = "" )create_kallisto_index( myKallistoMetadata, myBgeeMetadata, myUserMetadata, transcriptome_path = "" )
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 |
create kallisto index and save it on the hard drive
Julien Wollbrett.
## 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)## 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)
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.
download_fasta_intergenic( myBgeeMetadata = new("BgeeMetadata"), myUserMetadata, intergenic_file )download_fasta_intergenic( myBgeeMetadata = new("BgeeMetadata"), myUserMetadata, intergenic_file )
myBgeeMetadata |
A Reference Class BgeeMetadata object (optional) |
myUserMetadata |
A Reference Class UserMetadata object. |
intergenic_file |
path where intergenic file will be saved |
download fasta intergenic from Bgee FTP or from the Zenodo community and save it locally
{ bgee_intergenic_file <- file.path(getwd(), 'intergenic.fasta') userMetadata <- new('UserMetadata', species_id = '7227') }{ bgee_intergenic_file <- file.path(getwd(), 'intergenic.fasta') userMetadata <- new('UserMetadata', species_id = '7227') }
Check your OS and download correct binary version of kallisto.
download_kallisto(myKallistoMetadata, myUserMetadata)download_kallisto(myKallistoMetadata, myUserMetadata)
myKallistoMetadata |
A Reference Class KallistoMetadata object. |
myUserMetadata |
A Reference Class UserMetadata object. |
save uncompressed executable of kallisto on the hard drive
Julien Wollbrett.
{ kallisto <- new('KallistoMetadata') user <- new('UserMetadata') download_kallisto(kallisto, user) }{ kallisto <- new('KallistoMetadata') user <- new('UserMetadata') download_kallisto(kallisto, user) }
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, checkTxVersion = FALSE )generate_calls_workflow( abundanceMetadata = new("KallistoMetadata"), bgeeMetadata = new("BgeeMetadata"), userMetadata = NULL, userDataFrame = NULL, userFile = NULL, checkTxVersion = FALSE )
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 :
|
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 |
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)## 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 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
generate_presence_absence( myAbundanceMetadata = new("KallistoMetadata"), myBgeeMetadata = new("BgeeMetadata"), myUserMetadata )generate_presence_absence( myAbundanceMetadata = new("KallistoMetadata"), myBgeeMetadata = new("BgeeMetadata"), myUserMetadata )
myAbundanceMetadata |
A descendant object of the Class myAbundanceMetadata (optional). |
myBgeeMetadata |
A Class BgeeMetadata object (optional). |
myUserMetadata |
A Class UserMetadata object. |
path to the 4 output files
Julien Wollbrett
Julien Roux
Sara Fonseca Costa
generate_calls_workflow
{ # 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) # }{ # 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) # }
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
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 )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 )
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 |
generate calls
## Not run: # use function with all default values userFile <- "/path/to/userList.tsv" sjobs <- generate_slurm_calls(userFile = userFile) ## End(Not run)## Not run: # use function with all default values userFile <- "/path/to/userList.tsv" sjobs <- generate_slurm_calls(userFile = userFile) ## End(Not run)
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.
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 )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 )
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. |
generate index files
## Not run: # use function with all default values userFile <- "/path/to/userList.tsv" sjobs <- generate_slurm_indexes(userFile = userFile) ## End(Not run)## Not run: # use function with all default values userFile <- "/path/to/userList.tsv" sjobs <- generate_slurm_indexes(userFile = userFile) ## End(Not run)
Collect the statistics provided by the gene_cutoff_info_file from each individual library, in order to generate a global summary file.
get_summary_stats(userFile, outDir)get_summary_stats(userFile, outDir)
userFile |
A data frame containing all information of each library |
outDir |
Output directory where the generated file should be saved |
A tsv file
Sara Fonseca Costa
Get value of the 'intergenic_prefix' slot
getIntergenicPrefix(bgeeObject) ## S4 method for signature 'BgeeMetadata' getIntergenicPrefix(bgeeObject)getIntergenicPrefix(bgeeObject) ## S4 method for signature 'BgeeMetadata' getIntergenicPrefix(bgeeObject)
bgeeObject |
The BgeeMetadata object |
the value of the 'intergenic_prefix' slot of the object
{ bgee <- new("BgeeMetadata") intergenic_prefix <- getIntergenicPrefix(bgee) }{ bgee <- new("BgeeMetadata") intergenic_prefix <- getIntergenicPrefix(bgee) }
Get value of the 'intergenic_release' slot
getIntergenicRelease(bgeeObject) ## S4 method for signature 'BgeeMetadata' getIntergenicRelease(bgeeObject)getIntergenicRelease(bgeeObject) ## S4 method for signature 'BgeeMetadata' getIntergenicRelease(bgeeObject)
bgeeObject |
The BgeeMetadata object |
the value of the 'intergenic_release' slot of the object
{ bgee <- new("BgeeMetadata") intergenic_release <- getIntergenicRelease(bgee) }{ bgee <- new("BgeeMetadata") intergenic_release <- getIntergenicRelease(bgee) }
Get value of the 'run_ids' slot
getRunIds(userObject) ## S4 method for signature 'UserMetadata' getRunIds(userObject)getRunIds(userObject) ## S4 method for signature 'UserMetadata' getRunIds(userObject)
userObject |
The UserMetadata object |
the value of the 'run_ids' slot of the object
{ user <- new("UserMetadata") run_ids <- getRunIds(user) }{ user <- new("UserMetadata") run_ids <- getRunIds(user) }
Get value of the 'simple_arborescence' slot
getSimpleArborescence(userObject) ## S4 method for signature 'UserMetadata' getSimpleArborescence(userObject)getSimpleArborescence(userObject) ## S4 method for signature 'UserMetadata' getSimpleArborescence(userObject)
userObject |
The UserMetadata object |
the value of the 'simple_arborescence' slot of the object
{ user <- new("UserMetadata") simple_arborescence <- getSimpleArborescence(user) }{ user <- new("UserMetadata") simple_arborescence <- getSimpleArborescence(user) }
Get value of the 'working_path' slot
getWorkingPath(userObject) ## S4 method for signature 'UserMetadata' getWorkingPath(userObject)getWorkingPath(userObject) ## S4 method for signature 'UserMetadata' getWorkingPath(userObject)
userObject |
The UserMetadata object |
the value of the 'working_path' slot of the object
{ user <- new("UserMetadata") working_path <- getWorkingPath(user) }{ user <- new("UserMetadata") working_path <- getWorkingPath(user) }
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
download_kallistoA 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_urlURL to the binary of kallisto for windows
kallisto_linux_urlURL to the binary of kallisto for linux
kallisto_osx_urlURL to the binary of kallisto for MacOS
kallisto_windows_dirName of the directory where kallisto will be installed on windows
kallisto_linux_dirName of the directory where kallisto will be installed on linux
kallisto_osx_dirName of the directory where kallisto will be installed on Mac
unix_kallisto_nameName of the kallisto executable in linux and macOS
windows_kallisto_nameName of the kallisto executable in windows
index_fileName 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_filesame as index_file. This index is generated with smallest kmers and will be used only for libraries containing reads smallest than 50nt.
single_end_parameterskallisto parameters used to run a single end mapping
pair_end_parameterskallisto parameters used to run a pair end mapping
overwrite_indexlogical allowing to overwrite already existing index. FALSE by default. Then by default already existing index files will not be generated again.
overwrite_quantlogical allowing to overwrite already existing abundance.txt files. FALSE by default. Then by default already existing quantitfdication files will not be generated again.
overwrite_callslogical allowing to overwrite already existing present/absent calls. FALSE by default. Then by default already generated calls will not be generated again.
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.
list_bgee_ref_intergenic_species(myBgeeMetadata = NULL, release = NULL)list_bgee_ref_intergenic_species(myBgeeMetadata = NULL, release = NULL)
myBgeeMetadata |
A Reference Class BgeeMetadata object |
release |
A Bgee reference intergenic release name |
list all species having reference intergenic sequences available in the selected release
Julien Wollbrett
{ bgee <- new("BgeeMetadata") list_bgee_ref_intergenic_species(myBgeeMetadata = bgee) list_bgee_ref_intergenic_species(release = '0.2') }{ bgee <- new("BgeeMetadata") list_bgee_ref_intergenic_species(myBgeeMetadata = bgee) list_bgee_ref_intergenic_species(release = '0.2') }
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
list_community_ref_intergenic_species()list_community_ref_intergenic_species()
list all species having reference intergenic sequences created by the community
Julien Wollbrett
{ list_community_ref_intergenic_species() }{ list_community_ref_intergenic_species() }
Returns information on available Bgee intergenic releases, the access URL for FTP, and the date of release
list_intergenic_release(release = NULL)list_intergenic_release(release = NULL)
release |
A character specifying a targeted release number (e.g., '0.1'). If not specified, all available releases are shown. |
A data frame with information on Bgee intergenic releases available to use with the BgeeCall package.
Julien Wollbrett
{ list_intergenic_release() }{ list_intergenic_release() }
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.
merge_transcriptome_and_intergenic( myKallistoMetadata, myBgeeMetadata, myUserMetadata )merge_transcriptome_and_intergenic( myKallistoMetadata, myBgeeMetadata, myUserMetadata )
myKallistoMetadata |
A Reference Class KallistoMetadata object. |
myBgeeMetadata |
A Reference Class BgeeMetadata object. |
myUserMetadata |
A Reference Class UserMetadata object. |
save merged file on the hard drive
Julien Wollbrett.
{ 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) }{ 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) }
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.
merging_libraries( userFile = NULL, approach = "BH", condition = "species_id", cutoff = 0.05, outDir = NULL )merging_libraries( userFile = NULL, approach = "BH", condition = "species_id", cutoff = 0.05, outDir = NULL )
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 |
A dataframe containing the minimum quantitative value (p-value or q-value) and the calls to each gene id for the referent condition.
Sara Fonseca Costa
## 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)## 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 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
run_kallisto( myKallistoMetadata, myBgeeMetadata, myUserMetadata, transcriptome_path = "" )run_kallisto( myKallistoMetadata, myBgeeMetadata, myUserMetadata, transcriptome_path = "" )
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. |
create kallisto output files and save them on the hard drive
Julien Wollbrett.
## 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)## 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. Will summarize abundance estimation from transcript level to gene level if 'myAbundanceMetadata@txout == FALSE'. Otherwise keep abundance estimation at transcript level.
run_tximport( myAbundanceMetadata = new("KallistoMetadata"), myBgeeMetadata = new("BgeeMetadata"), myUserMetadata, abundanceFile = "" )run_tximport( myAbundanceMetadata = new("KallistoMetadata"), myBgeeMetadata = new("BgeeMetadata"), myUserMetadata, abundanceFile = "" )
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' |
a tximport object
Julien Wollbrett
{ 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) }{ 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) }
Method of the class UserMetadata. Set annotation_object of one UserMetadata object by providing the path to a fasta transcriptome file.
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)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)
userObject |
The UserMetadata object |
annotationPath |
Absolute path to the annotation file |
annotationName |
(optional) Name of the annotation. Will be used to create folders. |
If no annotationName is provided the name of the annotation file will be used to create folders.
An object of the class UserMetadata
{ # path to gtf annotation file annotation_file <- system.file("extdata", "annotation.gtf", package = "BgeeCall") user <- new("UserMetadata") user <- setAnnotationFromFile(user, annotation_file, "annotation_name") }{ # path to gtf annotation file annotation_file <- system.file("extdata", "annotation.gtf", package = "BgeeCall") user <- new("UserMetadata") user <- setAnnotationFromFile(user, annotation_file, "annotation_name") }
Method of the class UserMetadata. Set annotation_object of one UserMetadata object by using one GRanges object as input.
setAnnotationFromObject(userObject, annotationObject, annotationName) ## S4 method for signature 'UserMetadata,GRanges,character' setAnnotationFromObject(userObject, annotationObject, annotationName = "")setAnnotationFromObject(userObject, annotationObject, annotationName) ## S4 method for signature 'UserMetadata,GRanges,character' setAnnotationFromObject(userObject, annotationObject, annotationName = "")
userObject |
The UserMetadata object |
annotationObject |
object of thr GRanges S4 class |
annotationName |
(optional) Name of the annotation. Will be used to create folders. |
If no annotationName is provided the name of the file is used to create folders.
An object of the class UserMetadata
{ user <- new("UserMetadata") annotation_object <- rtracklayer::import(system.file("extdata", "annotation.gtf", package = "BgeeCall")) user <- setAnnotationFromObject(user, annotation_object, "annotation_name") }{ user <- new("UserMetadata") annotation_object <- rtracklayer::import(system.file("extdata", "annotation.gtf", package = "BgeeCall")) user <- setAnnotationFromObject(user, annotation_object, "annotation_name") }
Set value of the 'intergenic_release' slot
setIntergenicRelease(bgeeObject, intergenicRelease) ## S4 method for signature 'BgeeMetadata,character' setIntergenicRelease(bgeeObject, intergenicRelease)setIntergenicRelease(bgeeObject, intergenicRelease) ## S4 method for signature 'BgeeMetadata,character' setIntergenicRelease(bgeeObject, intergenicRelease)
bgeeObject |
The BgeeMetadata object |
intergenicRelease |
character corresponding to the 'intergenic_release' |
An object of the class BgeeMetadata with new 'intergenic_release' value
{ bgee <- new("BgeeMetadata") bgee <- setIntergenicRelease(bgee, "0.1") }{ bgee <- new("BgeeMetadata") bgee <- setIntergenicRelease(bgee, "0.1") }
Set value of the 'output_dir' slot
setOutputDir(userObject, outputDir) ## S4 method for signature 'UserMetadata,character' setOutputDir(userObject, outputDir)setOutputDir(userObject, outputDir) ## S4 method for signature 'UserMetadata,character' setOutputDir(userObject, outputDir)
userObject |
The UserMetadata object |
outputDir |
path to the directory wanted as 'output_dir' |
An object of the class UserMetadata with new 'output_dir' value
{ user <- new("UserMetadata") user <- setOutputDir(user, getwd()) }{ user <- new("UserMetadata") user <- setOutputDir(user, getwd()) }
Set value of the 'rnaseq_lib_path' slot
setRNASeqLibPath(userObject, rnaSeqLibPath) ## S4 method for signature 'UserMetadata,character' setRNASeqLibPath(userObject, rnaSeqLibPath)setRNASeqLibPath(userObject, rnaSeqLibPath) ## S4 method for signature 'UserMetadata,character' setRNASeqLibPath(userObject, rnaSeqLibPath)
userObject |
The UserMetadata object |
rnaSeqLibPath |
path to the directory wanted as 'rnaseq_lib_path' |
An object of the class UserMetadata with new 'rnaseq_lib_path' value
{ user <- new("UserMetadata") user <- setRNASeqLibPath(user, getwd()) }{ user <- new("UserMetadata") user <- setRNASeqLibPath(user, getwd()) }
Method of the class UserMetadata. Set run_ids of one UserMetadata object by providing the id of all wanted runs
setRunIds(userObject, runIds) ## S4 method for signature 'UserMetadata,character' setRunIds(userObject, runIds)setRunIds(userObject, runIds) ## S4 method for signature 'UserMetadata,character' setRunIds(userObject, runIds)
userObject |
The UserMetadata object |
runIds |
id of all wanted runs |
An object of the class UserMetadata
{ user <- new("UserMetadata") user <- setRunIds(user, c("RUN_1", "RUN_2")) }{ user <- new("UserMetadata") user <- setRunIds(user, c("RUN_1", "RUN_2")) }
Set value of the 'simple_arborescence' slot
setSimpleArborescence(userObject, simpleArborescence) ## S4 method for signature 'UserMetadata,logical' setSimpleArborescence(userObject, simpleArborescence)setSimpleArborescence(userObject, simpleArborescence) ## S4 method for signature 'UserMetadata,logical' setSimpleArborescence(userObject, simpleArborescence)
userObject |
The UserMetadata object |
simpleArborescence |
boolean defining if output files will be created a simple arborescence (TRUE) or not (FALSE) |
An object of the class UserMetadata with new 'simple_arborescence' value
{ user <- new("UserMetadata") user <- setSimpleArborescence(user, FALSE) }{ user <- new("UserMetadata") user <- setSimpleArborescence(user, FALSE) }
Method of the class UserMetadata. Set transcriptome_object of one UserMetadata object by providing the path to a fasta transcriptome file.
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)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)
userObject |
The UserMetadata object |
transcriptomePath |
Absolute path to the transcriptome file |
transcriptomeName |
(optional) Name of the trancriptome. Will be used to create folders. |
If no transcriptomeName is provided the name of the transcriptome file will be used to create folders.
An object of the class UserMetadata
{ transcriptome_path <- system.file("extdata", "transcriptome.fa", package = "BgeeCall") user <- new("UserMetadata") user <- setTranscriptomeFromFile(user, transcriptome_path, "transcriptome_name") }{ transcriptome_path <- system.file("extdata", "transcriptome.fa", package = "BgeeCall") user <- new("UserMetadata") user <- setTranscriptomeFromFile(user, transcriptome_path, "transcriptome_name") }
Method of the class UserMetadata. Set transcriptome_object of one UserMetadata object by using one DNAStringSet object as input.
setTranscriptomeFromObject(userObject, transcriptomeObject, transcriptomeName) ## S4 method for signature 'UserMetadata,DNAStringSet,character' setTranscriptomeFromObject(userObject, transcriptomeObject, transcriptomeName)setTranscriptomeFromObject(userObject, transcriptomeObject, transcriptomeName) ## S4 method for signature 'UserMetadata,DNAStringSet,character' setTranscriptomeFromObject(userObject, transcriptomeObject, transcriptomeName)
userObject |
UserMetadata object |
transcriptomeObject |
Object of the DNAStringSet S4 class |
transcriptomeName |
Name of the transcriptome. Will be used to create transcriptome folders. |
Please use a DNAStringSet object as input. This class is defined in the Biostrings package
an object of UserMetadata
{ user <- new("UserMetadata") transcriptome_object <- Biostrings::readDNAStringSet( system.file("extdata", "transcriptome.fa", package = "BgeeCall")) user <- setTranscriptomeFromObject(user, transcriptome_object, "transcriptome_name") }{ user <- new("UserMetadata") transcriptome_object <- Biostrings::readDNAStringSet( system.file("extdata", "transcriptome.fa", package = "BgeeCall")) user <- setTranscriptomeFromObject(user, transcriptome_object, "transcriptome_name") }
Set value of the 'working_path' slot
setWorkingPath(userObject, workingPath) ## S4 method for signature 'UserMetadata,character' setWorkingPath(userObject, workingPath)setWorkingPath(userObject, workingPath) ## S4 method for signature 'UserMetadata,character' setWorkingPath(userObject, workingPath)
userObject |
The UserMetadata object |
workingPath |
path to the directory wanted as 'working_path' |
An object of the class UserMetadata with new 'working_path' value
{ user <- new("UserMetadata") user <- setWorkingPath(user, getwd()) }{ user <- new("UserMetadata") user <- setWorkingPath(user, getwd()) }
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.
species_idThe NCBI Taxon Id of the species
run_idsA 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_sizeThe 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_pathPath 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_nameName of the transcriptome used to generate arborescence of output repositories.
transcriptome_objectObject containing transcriptome
annotation_nameName of the annotation used to generate arborescence of output repositories.
annotation_objectObject containing annotations from GTF or GFF file
working_pathWorking directory. By default the working directory is defined with the 'getwd()' function.
gtf_sourceThe source name from where the gtf file comes from. By default is ensembl.
simple_arborescencelogical 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/).
verboselogical allowing to use the verbose mode. TRUE by default.
custom_intergenic_pathpath 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_patternAllows 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.