BgeeDB is a collection of functions to import data from the Bgee database (http://bgee.org/) directly into R, and to facilitate downstream analyses, such as gene set enrichment test based on expression of genes in anatomical structures. Bgee provides annotated and processed expression data and expression calls from curated wild-type healthy samples, from human and many other animal species.
The package retrieves the annotation of RNA-seq or Affymetrix experiments integrated into the Bgee database, and downloads into R the quantitative data and expression calls produced by the Bgee pipeline. The package also allows to run GO-like enrichment analyses based on anatomical terms, where genes are mapped to anatomical terms by expression patterns, based on the topGO package. This is the same as the TopAnat web-service available at (http://bgee.org/?page=top_anat#/), but with more flexibility in the choice of parameters and developmental stages.
In summary, the BgeeDB package allows to: * 1. List annotation of RNA-seq and microarray data available the Bgee database * 2. Download the processed gene expression data available in the Bgee database * 3. Download the gene expression calls and use them to perform TopAnat analyses
The pipeline used to generate Bgee expression data is documented and publicly available at (https://github.com/BgeeDB/bgee_pipeline)
If you find a bug or have any issues to use BgeeDB please write a bug report in our own GitHub issues manager available at (https://github.com/BgeeDB/BgeeDB_R/issues)
In R:
#if (!requireNamespace("BiocManager", quietly=TRUE))
#install.packages("BiocManager")
#BiocManager::install("BgeeDB")In case BgeeDB is run on Windows please be sure your OS has curl installed. It is installed by default on Windows 10, version 1803 or later. If Git for Windows is installed on the OS then curl is already installed. If not installed please install it before running BgeeDB in order to avoid potential timeout errors when downloading files.
The listBgeeSpecies() function allows to retrieve available species in the Bgee database, and which data types are available for each species.
##
## Querying Bgee to get release information...
##
## Building URL to query species in Bgee release 14_1...
##
## Submitting URL to Bgee webservice... (https://r.bgee.org/bgee14_1/?page=r_package&action=get_all_species&display_type=tsv&source=BgeeDB_R_package&source_version=2.16.0)
##
## Query to Bgee webservice successful!## ID GENUS SPECIES_NAME COMMON_NAME AFFYMETRIX EST
## 1 6239 Caenorhabditis elegans nematode TRUE FALSE
## 2 7217 Drosophila ananassae FALSE FALSE
## 3 7227 Drosophila melanogaster fruit fly TRUE TRUE
## 4 7230 Drosophila mojavensis FALSE FALSE
## 5 7237 Drosophila pseudoobscura FALSE FALSE
## 6 7240 Drosophila simulans FALSE FALSE
## 7 7244 Drosophila virilis FALSE FALSE
## 8 7245 Drosophila yakuba FALSE FALSE
## 9 7955 Danio rerio zebrafish TRUE TRUE
## 10 8364 Xenopus tropicalis western clawed frog FALSE TRUE
## 11 9031 Gallus gallus chicken FALSE FALSE
## 12 9258 Ornithorhynchus anatinus platypus FALSE FALSE
## 13 9365 Erinaceus europaeus hedgehog FALSE FALSE
## 14 9544 Macaca mulatta macaque TRUE FALSE
## 15 9593 Gorilla gorilla gorilla FALSE FALSE
## 16 9597 Pan paniscus bonobo FALSE FALSE
## 17 9598 Pan troglodytes chimpanzee FALSE FALSE
## 18 9606 Homo sapiens human TRUE TRUE
## 19 9615 Canis lupus familiaris dog FALSE FALSE
## 20 9685 Felis catus cat FALSE FALSE
## 21 9796 Equus caballus horse FALSE FALSE
## 22 9823 Sus scrofa pig FALSE FALSE
## 23 9913 Bos taurus cattle FALSE FALSE
## 24 9986 Oryctolagus cuniculus rabbit FALSE FALSE
## 25 10090 Mus musculus mouse TRUE TRUE
## 26 10116 Rattus norvegicus rat TRUE FALSE
## 27 10141 Cavia porcellus guinea pig FALSE FALSE
## 28 13616 Monodelphis domestica opossum FALSE FALSE
## 29 28377 Anolis carolinensis green anole FALSE FALSE
## IN_SITU RNA_SEQ
## 1 TRUE TRUE
## 2 FALSE TRUE
## 3 TRUE TRUE
## 4 FALSE TRUE
## 5 FALSE TRUE
## 6 FALSE TRUE
## 7 FALSE TRUE
## 8 FALSE TRUE
## 9 TRUE TRUE
## 10 TRUE TRUE
## 11 FALSE TRUE
## 12 FALSE TRUE
## 13 FALSE TRUE
## 14 FALSE TRUE
## 15 FALSE TRUE
## 16 FALSE TRUE
## 17 FALSE TRUE
## 18 FALSE TRUE
## 19 FALSE TRUE
## 20 FALSE TRUE
## 21 FALSE TRUE
## 22 FALSE TRUE
## 23 FALSE TRUE
## 24 FALSE TRUE
## 25 TRUE TRUE
## 26 FALSE TRUE
## 27 FALSE TRUE
## 28 FALSE TRUE
## 29 FALSE TRUEIt is possible to list all species from a specific release of Bgee with the release argument (see listBgeeRelease() function), and order the species according to a specific columns with the ordering argument. For example:
##
## Querying Bgee to get release information...
##
## Building URL to query species in Bgee release 13_2...
##
## Submitting URL to Bgee webservice... (https://r.bgee.org/bgee13/?page=species&display_type=tsv&source=BgeeDB_R_package&source_version=2.16.0)
##
## Query to Bgee webservice successful!## ID GENUS SPECIES_NAME COMMON_NAME AFFYMETRIX EST IN_SITU
## 17 28377 Anolis carolinensis anolis FALSE FALSE FALSE
## 13 9913 Bos taurus cow FALSE FALSE FALSE
## 1 6239 Caenorhabditis elegans c.elegans TRUE FALSE TRUE
## 3 7955 Danio rerio zebrafish TRUE TRUE TRUE
## 2 7227 Drosophila melanogaster fruitfly TRUE TRUE TRUE
## 5 9031 Gallus gallus chicken FALSE FALSE FALSE
## 8 9593 Gorilla gorilla gorilla FALSE FALSE FALSE
## 11 9606 Homo sapiens human TRUE TRUE FALSE
## 7 9544 Macaca mulatta macaque FALSE FALSE FALSE
## 16 13616 Monodelphis domestica opossum FALSE FALSE FALSE
## 14 10090 Mus musculus mouse TRUE TRUE TRUE
## 6 9258 Ornithorhynchus anatinus platypus FALSE FALSE FALSE
## 9 9597 Pan paniscus bonobo FALSE FALSE FALSE
## 10 9598 Pan troglodytes chimpanzee FALSE FALSE FALSE
## 15 10116 Rattus norvegicus rat FALSE FALSE FALSE
## 12 9823 Sus scrofa pig FALSE FALSE FALSE
## 4 8364 Xenopus tropicalis xenopus FALSE TRUE TRUE
## RNA_SEQ
## 17 TRUE
## 13 TRUE
## 1 TRUE
## 3 FALSE
## 2 FALSE
## 5 TRUE
## 8 TRUE
## 11 TRUE
## 7 TRUE
## 16 TRUE
## 14 TRUE
## 6 TRUE
## 9 TRUE
## 10 TRUE
## 15 TRUE
## 12 TRUE
## 4 TRUEIn the following example we will choose to focus on mouse (“Mus_musculus”) RNA-seq. Species can be specified using their name or their NCBI taxonomic IDs. To specify that RNA-seq data want to be downloaded, the dataType argument is set to “rna_seq”. To download Affymetrix microarray data, set this argument to “affymetrix”.
##
## Querying Bgee to get release information...
##
## Building URL to query species in Bgee release 14_1...
##
## Submitting URL to Bgee webservice... (https://r.bgee.org/bgee14_1/?page=r_package&action=get_all_species&display_type=tsv&source=BgeeDB_R_package&source_version=2.16.0)
##
## Query to Bgee webservice successful!
##
## API key built: 3fe87320130948088207ebd4acd1a7639f7b35b41e815d808675e552b0a66e36ea51adc79754706197a2b148890fce891ae232735ff9c5eeccc6bda5af816172Note 1: It is possible to work with data from a specific release of Bgee by specifying the release argument, see listBgeeRelease() function.
Note 2: The functions of the package will store the downloaded files in a versioned folder created by default in the working directory. These cache files allow faster re-access to the data. The directory where data are stored can be changed with the pathToData argument.
The getAnnotation() function will output the list of RNA-seq experiments and libraries available in Bgee for mouse.
##
## Saved annotation files in /tmp/RtmpWj2bWw/Rbuild6b1368972677/BgeeDB/vignettes/Mus_musculus_Bgee_14_1 folder.## $sample.annotation
## Experiment.ID Library.ID Anatomical.entity.ID Anatomical.entity.name
## 1 ERP104395 ERX2187486 UBERON:0000082 adult mammalian kidney
## 2 ERP104395 ERX2187487 UBERON:0000082 adult mammalian kidney
## 3 ERP104395 ERX2187488 UBERON:0000082 adult mammalian kidney
## 4 ERP104395 ERX2187498 UBERON:0000945 stomach
## 5 ERP104395 ERX2187499 UBERON:0000945 stomach
## 6 ERP104395 ERX2187500 UBERON:0000945 stomach
## Stage.ID Stage.name Sex Strain Platform.ID
## 1 UBERON:0000113 post-juvenile adult stage male C57BL/6J Illumina HiSeq 2000
## 2 UBERON:0000113 post-juvenile adult stage male C57BL/6J Illumina HiSeq 2000
## 3 UBERON:0000113 post-juvenile adult stage male C57BL/6J Illumina HiSeq 2000
## 4 UBERON:0000113 post-juvenile adult stage male C57BL/6J Illumina HiSeq 2000
## 5 UBERON:0000113 post-juvenile adult stage male C57BL/6J Illumina HiSeq 2000
## 6 UBERON:0000113 post-juvenile adult stage male C57BL/6J Illumina HiSeq 2000
## Library.type Library.orientation TMM.normalization.factor
## 1 single NA 1.183867
## 2 single NA 1.211967
## 3 single NA 1.150353
## 4 single NA 1.152138
## 5 single NA 1.009166
## 6 single NA 1.101729
## TPM.expression.threshold FPKM.expression.threshold Read.count
## 1 0.043961 0.036158 22187319
## 2 0.046119 0.038656 29584966
## 3 0.032360 0.026583 25448291
## 4 0.036161 0.032999 25212673
## 5 0.024721 0.025872 25083482
## 6 0.030623 0.027480 23784442
## Mapped.read.count Min.read.length Max.read.length All.genes.percent.present
## 1 19888728 50 50 43.22
## 2 26170492 50 50 45.06
## 3 23512513 50 50 43.28
## 4 22594646 50 50 45.31
## 5 22776315 50 50 44.15
## 6 21418113 50 50 43.96
## Protein.coding.genes.percent.present Intergenic.regions.percent.present
## 1 71.69 15.55
## 2 72.45 16.37
## 3 72.02 15.31
## 4 75.26 16.87
## 5 73.94 15.40
## 6 73.67 15.72
## Run.IDs Data.source
## 1 ERR2130634 SRA
## 2 ERR2130635 SRA
## 3 ERR2130636 SRA
## 4 ERR2130646 SRA
## 5 ERR2130647 SRA
## 6 ERR2130648 SRA
## Data.source.URL
## 1 http://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?cmd=viewer&m=data&s=viewer&run=ERX2187486
## 2 http://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?cmd=viewer&m=data&s=viewer&run=ERX2187487
## 3 http://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?cmd=viewer&m=data&s=viewer&run=ERX2187488
## 4 http://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?cmd=viewer&m=data&s=viewer&run=ERX2187498
## 5 http://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?cmd=viewer&m=data&s=viewer&run=ERX2187499
## 6 http://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?cmd=viewer&m=data&s=viewer&run=ERX2187500
## Bgee.normalized.data.URL
## 1 ftp://ftp.bgee.org/bgee_v14_1/download/processed_expr_values/rna_seq/Mus_musculus/Mus_musculus_RNA-Seq_read_counts_TPM_FPKM_ERP104395.tsv.tar.gz
## 2 ftp://ftp.bgee.org/bgee_v14_1/download/processed_expr_values/rna_seq/Mus_musculus/Mus_musculus_RNA-Seq_read_counts_TPM_FPKM_ERP104395.tsv.tar.gz
## 3 ftp://ftp.bgee.org/bgee_v14_1/download/processed_expr_values/rna_seq/Mus_musculus/Mus_musculus_RNA-Seq_read_counts_TPM_FPKM_ERP104395.tsv.tar.gz
## 4 ftp://ftp.bgee.org/bgee_v14_1/download/processed_expr_values/rna_seq/Mus_musculus/Mus_musculus_RNA-Seq_read_counts_TPM_FPKM_ERP104395.tsv.tar.gz
## 5 ftp://ftp.bgee.org/bgee_v14_1/download/processed_expr_values/rna_seq/Mus_musculus/Mus_musculus_RNA-Seq_read_counts_TPM_FPKM_ERP104395.tsv.tar.gz
## 6 ftp://ftp.bgee.org/bgee_v14_1/download/processed_expr_values/rna_seq/Mus_musculus/Mus_musculus_RNA-Seq_read_counts_TPM_FPKM_ERP104395.tsv.tar.gz
## Raw.file.URL
## 1 https://trace.ncbi.nlm.nih.gov/Traces/study/?acc=ERX2187486
## 2 https://trace.ncbi.nlm.nih.gov/Traces/study/?acc=ERX2187487
## 3 https://trace.ncbi.nlm.nih.gov/Traces/study/?acc=ERX2187488
## 4 https://trace.ncbi.nlm.nih.gov/Traces/study/?acc=ERX2187498
## 5 https://trace.ncbi.nlm.nih.gov/Traces/study/?acc=ERX2187499
## 6 https://trace.ncbi.nlm.nih.gov/Traces/study/?acc=ERX2187500
##
## $experiment.annotation
## Experiment.ID
## 1 ERP104395
## 2 GSE30617
## 3 SRP028336
## 4 GSE41637
## 5 GSE30352
## 6 SRP061644
## Experiment.name
## 1 An RNASeq normal tissue atlas for mouse and rat
## 2 [E-MTAB-599] Mouse Transcriptome
## 3 Large-scale multi-species survey of metabolome and lipidome
## 4 Evolutionary dynamics of gene and isoform regulation in mammalian tissues
## 5 The evolution of gene expression levels in mammalian organs
## 6 The relationship between gene network structure and expression variation among individuals and species
## Library.count Condition.count Organ.stage.count Organ.count Stage.count
## 1 38 13 13 13 1
## 2 36 6 6 6 1
## 3 30 10 5 5 1
## 4 26 26 9 9 1
## 5 17 17 6 6 1
## 6 17 6 6 2 3
## Sex.count Strain.count Data.source
## 1 1 2 SRA
## 2 0 1 GEO
## 3 2 1 SRA
## 4 1 6 GEO
## 5 2 2 GEO
## 6 0 1 SRA
## Data.source.URL
## 1 http://www.ncbi.nlm.nih.gov/sra/ERP104395
## 2 http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30617
## 3 http://www.ncbi.nlm.nih.gov/sra/SRP028336
## 4 http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41637
## 5 http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30352
## 6 http://www.ncbi.nlm.nih.gov/sra/SRP061644
## Bgee.normalized.data.URL
## 1 ftp://ftp.bgee.org/bgee_v14_1/download/processed_expr_values/rna_seq/Mus_musculus/Mus_musculus_RNA-Seq_read_counts_TPM_FPKM_ERP104395.tsv.tar.gz
## 2 ftp://ftp.bgee.org/bgee_v14_1/download/processed_expr_values/rna_seq/Mus_musculus/Mus_musculus_RNA-Seq_read_counts_TPM_FPKM_GSE30617.tsv.tar.gz
## 3 ftp://ftp.bgee.org/bgee_v14_1/download/processed_expr_values/rna_seq/Mus_musculus/Mus_musculus_RNA-Seq_read_counts_TPM_FPKM_SRP028336.tsv.tar.gz
## 4 ftp://ftp.bgee.org/bgee_v14_1/download/processed_expr_values/rna_seq/Mus_musculus/Mus_musculus_RNA-Seq_read_counts_TPM_FPKM_GSE41637.tsv.tar.gz
## 5 ftp://ftp.bgee.org/bgee_v14_1/download/processed_expr_values/rna_seq/Mus_musculus/Mus_musculus_RNA-Seq_read_counts_TPM_FPKM_GSE30352.tsv.tar.gz
## 6 ftp://ftp.bgee.org/bgee_v14_1/download/processed_expr_values/rna_seq/Mus_musculus/Mus_musculus_RNA-Seq_read_counts_TPM_FPKM_SRP061644.tsv.tar.gz
## Experiment.description
## 1 The function of a gene is closely connected to its expression specificity across tissues and cell types. RNA-Seq is a powerful quantitative tool to explore genome wide expression. The aim of the present study is to provide a comprehensive RNA-Seq dataset across the same 13 tissues for mouse and rat, two of the most relevant species for biomedical research. The dataset provides the transcriptome across tissues from three male C57BL6 mice and three male Han Wistar rats. We also describe our bioinformatics pipeline to process and technically validate the data. Principal component analysis shows that tissue samples from both species cluster similarly. By comparative genomics we show that many genes with high sequence identity with respect to their human orthologues have also a highly correlated tissue distribution profile and are in agreement with manually curated literature data for human. These results make us confident that the present study provides a unique resource for comparative genomics and will facilitate the analysis of tissue specificity and cross-species conservation in higher organisms.
## 2 Sequencing the transcriptome of DBAxC57BL/6J mice. To study the regulation of transcription, splicing and RNA turnover we have sequenced the transcriptomes of tissues collected DBAxC57BL/6J mice.
## 3 This dataset was generated with the goal of comparative study of gene expression in three brain regions and two non-neural tissues of humans, chimpanzees, macaque monkeys and mice. Using this dataset, we performed studies of gene expression and gene splicing evolution across species and search of tissue-specific gene expression and splicing patterns. We also used the gene expression information of genes encoding metabolic enzymes in this dataset to support a larger comparative study of metabolome evolution in the same set of tissues and species. Overall design: 120 tissue samples of prefrontal cortex (PFC), primary visual cortex (VC), cerebellar cortex (CBC), kidney and skeletal muscle of humans, chimpanzees, macaques and mice. The data accompanies a large set of metabolite measurements of the same tissue samples. Enzyme expression was used to validate metabolite measurement variation among species.
## 4 Most mammalian genes produce multiple distinct mRNAs through alternative splicing, but the extent of splicing conservation is not clear. To assess tissue-specific transcriptome variation across mammals, we sequenced cDNA from 9 tissues from 4 mammals and one bird in biological triplicate, at unprecedented depth. We find that while tissue-specific gene expression programs are largely conserved, alternative splicing is well conserved in only a subset of tissues and is frequently lineage-specific. Thousands of novel, lineage-specific and conserved alternative exons were identified; widely conserved alternative exons had signatures of binding by MBNL, PTB, RBFOX, STAR and TIA family splicing factors, implicating them as ancestral mammalian splicing regulators. Our data also indicates that alternative splicing is often used to alter protein phosphorylatability, delimiting the scope of kinase signaling.
## 5 Changes in gene expression are thought to underlie many of the phenotypic differences between species. However, large-scale analyses of gene expression evolution were until recently prevented by technological limitations. Here we report the sequencing of polyadenylated RNA from six organs across ten species that represent all major mammalian lineages (placentals, marsupials and monotremes) and birds (the evolutionary outgroup), with the goal of understanding the dynamics of mammalian transcriptome evolution. We show that the rate of gene expression evolution varies among organs, lineages and chromosomes, owing to differences in selective pressures: transcriptome change was slow in nervous tissues and rapid in testes, slower in rodents than in apes and monotremes, and rapid for the X chromosome right after its formation. Although gene expression evolution in mammals was strongly shaped by purifying selection, we identify numerous potentially selectively driven expression switches, which occurred at different rates across lineages and tissues and which probably contributed to the specific organ biology of various mammals. Our transcriptome data provide a valuable resource for functional and evolutionary analyses of mammalian genomes.
## 6 Variation among individuals is a prerequisite of evolution by natural selection. As such, identifying the origins of variation is a fundamental goal of biology. We investigated the link between gene interactions and variation in gene expression among individuals and species, using the mammalian limb as a model system. We first built interaction networks for key genes regulating early (outgrowth; E9.5-11) and late (expansion and elongation; E11-13) limb development in mouse. This resulted in an Early (ESN) and Late (LSN) Stage Network. Computational perturbations of these networks suggest that the ESN is more robust. We then quantified levels of the same key genes among mouse individuals, and found that they vary less at earlier limb stages and that variation in gene expression is heritable. Finally, we quantified variation in gene expression levels among four mammals with divergent limbs (bat, opossum, mouse and pig), and found that levels vary less among species at earlier limb stages. We also found that variation in gene expression levels among individuals and species are correlated for earlier and later limb development. In conclusion, results are consistent with the robustness of the ESN buffering among-individual variation in gene expression levels early in mammalian limb development, and constraining the evolution of early limb development among mammalian species. Overall design: Bat, mouse, opossum, and pig mRNA profiles at early and late developmental stages on each species fore and hind-limbs . Various replicates of each library were generated by single-end sequencing using Illumina HiSeq 2500. Please note that the De novo transcriptome assembly for bat (Trinity.fasta) was generated from pooled RNA-seq data of fore and hind-limbs at various embryonic developmental stages; Beginning stage (Wanek stage 2: 3 FL and 3 HL samples), early-stage (Wanek stage 3/4: 2 FL and 2 HL samples), and late_stage (Wanke stage 6: 2 FL and 2 HL samples).The getData() function will download processed RNA-seq data from all mouse experiments in Bgee as a data frame.
##
## NOTE: annotation files for this species were found in the download directory /tmp/RtmpWj2bWw/Rbuild6b1368972677/BgeeDB/vignettes/Mus_musculus_Bgee_14_1. Data will not be redownloaded.## downloading data from Bgee FTP...## You tried to download more than 15 experiments, because of that all the Bgee data for this species will be downloaded.## Downloading all expression data for species Mus_musculus## Saved expression data file in/tmp/RtmpWj2bWw/Rbuild6b1368972677/BgeeDB/vignettes/Mus_musculus_Bgee_14_1folder. Now untar file...## Finished uncompress tar files## Save data in local sqlite database## Load queried data. The query is : SELECT * from rna_seq## [1] 16## $Experiment.ID
## [1] "SRP007655" "SRP007655" "SRP007655" "SRP007655" "SRP007655" "SRP007655"
##
## $Library.ID
## [1] "SRX088978" "SRX088978" "SRX088978" "SRX088978" "SRX088978" "SRX088978"
##
## $Library.type
## [1] "paired" "paired" "paired" "paired" "paired" "paired"
##
## $Gene.ID
## [1] "ENSMUSG00000000001" "ENSMUSG00000000003" "ENSMUSG00000000028"
## [4] "ENSMUSG00000000031" "ENSMUSG00000000037" "ENSMUSG00000000049"
##
## $Anatomical.entity.ID
## [1] "UBERON:0003902" "UBERON:0003902" "UBERON:0003902" "UBERON:0003902"
## [5] "UBERON:0003902" "UBERON:0003902"
##
## $Anatomical.entity.name
## [1] "\"retinal neural layer\"" "\"retinal neural layer\""
## [3] "\"retinal neural layer\"" "\"retinal neural layer\""
## [5] "\"retinal neural layer\"" "\"retinal neural layer\""
##
## $Stage.ID
## [1] "MmusDv:0000062" "MmusDv:0000062" "MmusDv:0000062" "MmusDv:0000062"
## [5] "MmusDv:0000062" "MmusDv:0000062"
##
## $Stage.name
## [1] "\"2 month-old stage (mouse)\"" "\"2 month-old stage (mouse)\""
## [3] "\"2 month-old stage (mouse)\"" "\"2 month-old stage (mouse)\""
## [5] "\"2 month-old stage (mouse)\"" "\"2 month-old stage (mouse)\""
##
## $Sex
## [1] "mixed" "mixed" "mixed" "mixed" "mixed" "mixed"
##
## $Strain
## [1] "\"C57BL/6J - 129SvEvTac\"" "\"C57BL/6J - 129SvEvTac\""
## [3] "\"C57BL/6J - 129SvEvTac\"" "\"C57BL/6J - 129SvEvTac\""
## [5] "\"C57BL/6J - 129SvEvTac\"" "\"C57BL/6J - 129SvEvTac\""
##
## $Read.count
## [1] 744.00000 0.00000 87.99996 10.00001 42.00001 4.00000
##
## $TPM
## [1] 26.445908 0.000000 5.613535 0.738085 1.626799 0.401370
##
## $FPKM
## [1] 14.988300 0.000000 3.181488 0.418312 0.921993 0.227478
##
## $Detection.flag
## [1] "present" "absent" "present" "present" "present" "present"
##
## $Detection.quality
## [1] "high quality" "high quality" "high quality" "high quality" "high quality"
## [6] "high quality"
##
## $State.in.Bgee
## [1] "Part of a call" "Part of a call" "Part of a call" "Part of a call"
## [5] "Part of a call" "Part of a call"The result of the getData() function is a data frame. Each row is a gene and the expression levels are displayed as raw read counts, RPKMs (up to Bgee 13.2), TPMs (from Bgee 14.0), or FPKMs (from Bgee 14.0). A detection flag indicates if the gene is significantly expressed above background level of expression.
Note: If microarray data are downloaded, rows corresponding to probesets and log2 of expression intensities are available instead of read counts/RPKMs/TPMs/FPKMs.
Alternatively, you can choose to download data from one RNA-seq experiment from Bgee with the experimentId parameter:
##
## NOTE: annotation files for this species were found in the download directory /tmp/RtmpWj2bWw/Rbuild6b1368972677/BgeeDB/vignettes/Mus_musculus_Bgee_14_1. Data will not be redownloaded.## Load queried data. The query is : SELECT * from rna_seq WHERE [Experiment.ID] = "GSE30617"It is possible to download data by combining filters : * experimentId : one or more experimentId, * sampleId : one or more sampleId (i.e libraryId for RNA-Seq and ChipId for Affymetrix), * anatEntityId : one or more anatomical entity ID from the UBERON ontology (https://uberon.github.io/) * stageId : one or more developmental stage ID from the developmental stage ontologies (https://github.com/obophenotype/developmental-stage-ontologies)
# Examples of data downloading using different filtering combination
# retrieve mouse RNA-Seq data for heart or brain
data_bgee_mouse_filters <- getData(bgee, anatEntityId = c("UBERON:0000955","UBERON:0000948"))
# retrieve mouse RNA-Seq data for heart (UBERON:0000955) or brain (UBERON:0000948) part of the experiment GSE30617
data_bgee_mouse_filters <- getData(bgee, experimentId = "GSE30617", anatEntityId = c("UBERON:0000955","UBERON:0000948"))
# retrieve mouse RNA-Seq data for heart (UBERON:0000955) or brain (UBERON:0000948) from post-embryonic stage (UBERON:0000092)
data_bgee_mouse_filters <- getData(bgee, stageId = "UBERON:0000092", anatEntityId = c("UBERON:0000955","UBERON:0000948"))It is sometimes easier to work with data organized as a matrix, where rows represent genes or probesets and columns represent different samples. The formatData() function reformats the data into an ExpressionSet object including: * An expression data matrix, with genes or probesets as rows, and samples as columns (assayData slot). The stats argument allows to choose if the matrix should be filled with read counts, RPKMs (up to Bgee 13.2), FPKMs (from Bgee 14.0), or TPMs (from Bgee 14.0) for RNA-seq data. For microarray data the matrix is filled with log2 expression intensities. * A data frame listing the samples and their anatomical structure and developmental stage annotation (phenoData slot) * For microarray data, the mapping from probesets to Ensembl genes (featureData slot)
The callType argument allows to retain only actively expressed genes or probesets, if set to “present” or “present high quality”. Genes or probesets that are absent in a given sample are given NA values.
# use only present calls and fill expression matric with FPKM values
gene.expression.mouse.fpkm <- formatData(bgee, data_bgee_mouse_gse30617, callType = "present", stats = "fpkm")##
## Extracting expression data matrix...
## Keeping only present genes.
##
## Extracting features information...
##
## Extracting samples information...## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 47729 features, 36 samples
## element names: exprs
## protocolData: none
## phenoData
## sampleNames: ERX012344 ERX012345 ... ERX012379 (36 total)
## varLabels: Library.ID Anatomical.entity.ID ... Stage.name (5 total)
## varMetadata: labelDescription
## featureData
## featureNames: ENSMUSG00000000001 ENSMUSG00000000003 ...
## ENSMUSG00000109578 (47729 total)
## fvarLabels: Gene.ID
## fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
## Annotation:For some documentation on the TopAnat analysis, please refer to our publications, or to the web-tool page (http://bgee.org/?page=top_anat#/).
Similarly to the quantitative data download example above, the first step of a topAnat analysis is to built an object from the Bgee class. For this example, we will focus on zebrafish:
##
## NOTE: You did not specify any data type. The argument dataType will be set to c("rna_seq","affymetrix","est","in_situ") for the next steps.
##
## Querying Bgee to get release information...
##
## NOTE: the file describing Bgee species information for release 14_1 was found in the download directory /tmp/RtmpWj2bWw/Rbuild6b1368972677/BgeeDB/vignettes. Data will not be redownloaded.
##
## API key built: 3fe87320130948088207ebd4acd1a7639f7b35b41e815d808675e552b0a66e36ea51adc79754706197a2b148890fce891ae232735ff9c5eeccc6bda5af816172Note : We are free to specify any data type of interest using the dataType argument among rna_seq, affymetrix, est or in_situ, or even a combination of data types. If nothing is specified, as in the above example, all data types available for the targeted species are used. This equivalent to specifying dataType=c("rna_seq","affymetrix","est","in_situ").
The loadTopAnatData() function loads a mapping from genes to anatomical structures based on calls of expression in anatomical structures. It also loads the structure of the anatomical ontology and the names of anatomical structures.
##
## Building URLs to retrieve organ relationships from Bgee.........
## URL successfully built (https://r.bgee.org/bgee14_1/?page=r_package&action=get_anat_entity_relations&display_type=tsv&species_list=7955&attr_list=SOURCE_ID&attr_list=TARGET_ID&api_key=3fe87320130948088207ebd4acd1a7639f7b35b41e815d808675e552b0a66e36ea51adc79754706197a2b148890fce891ae232735ff9c5eeccc6bda5af816172&source=BgeeDB_R_package&source_version=2.16.0)
## Submitting URL to Bgee webservice (can be long)
## Got results from Bgee webservice. Files are written in "/tmp/RtmpWj2bWw/Rbuild6b1368972677/BgeeDB/vignettes/Danio_rerio_Bgee_14_1"
##
## Building URLs to retrieve organ names from Bgee.................
## URL successfully built (https://r.bgee.org/bgee14_1/?page=r_package&action=get_anat_entities&display_type=tsv&species_list=7955&attr_list=ID&attr_list=NAME&api_key=3fe87320130948088207ebd4acd1a7639f7b35b41e815d808675e552b0a66e36ea51adc79754706197a2b148890fce891ae232735ff9c5eeccc6bda5af816172&source=BgeeDB_R_package&source_version=2.16.0)
## Submitting URL to Bgee webservice (can be long)
## Got results from Bgee webservice. Files are written in "/tmp/RtmpWj2bWw/Rbuild6b1368972677/BgeeDB/vignettes/Danio_rerio_Bgee_14_1"
##
## Building URLs to retrieve mapping of gene to organs from Bgee...
## URL successfully built (https://r.bgee.org/bgee14_1/?page=r_package&action=get_expression_calls&display_type=tsv&species_list=7955&attr_list=GENE_ID&attr_list=ANAT_ENTITY_ID&api_key=3fe87320130948088207ebd4acd1a7639f7b35b41e815d808675e552b0a66e36ea51adc79754706197a2b148890fce891ae232735ff9c5eeccc6bda5af816172&source=BgeeDB_R_package&source_version=2.16.0&data_qual=SILVER)
## Submitting URL to Bgee webservice (can be long)
## Got results from Bgee webservice. Files are written in "/tmp/RtmpWj2bWw/Rbuild6b1368972677/BgeeDB/vignettes/Danio_rerio_Bgee_14_1"
##
## Parsing the results.............................................
##
## Adding BGEE:0 as unique root of all terms of the ontology.......
##
## Done.The strigency on the quality of expression calls can be changed with the confidence argument. Finally, if you are interested in expression data coming from a particular developmental stage or a group of stages, please specify the a Uberon stage Id in the stage argument.
## Loading silver and gold expression calls from affymetrix data made on embryonic samples only
## This is just given as an example, but is not run in this vignette because only few data are returned
bgee <- Bgee$new(species = "Danio_rerio", dataType="affymetrix")
myTopAnatData <- loadTopAnatData(bgee, stage="UBERON:0000068", confidence="silver")Note 1: As mentioned above, the downloaded data files are stored in a versioned folder that can be set with the pathToData argument when creating the Bgee class object (default is the working directory). If you query again Bgee with the exact same parameters, these cached files will be read instead of querying the web-service again. It is thus important, if you plan to reuse the same data for multiple parallel topAnat analyses, to plan to make use of these cached files instead of redownloading them for each analysis. The cached files also give the possibility to repeat analyses offline.
Note 2: In releases up to Bgee 13.2 allowed confidence`` values were `low_quality` or or `high_quality`. From Bgee 14.0confidence``values aregoldorsilver`.
First we need to prepare a list of genes in the foreground and in the background. The input format is the same as the gene list required to build a topGOdata object in the topGO package: a vector with background genes as names, and 0 or 1 values depending if a gene is in the foreground or not. In this example we will look at genes with an annotated phenotype related to “pectoral fin” . We use the biomaRt package to retrieve this list of genes. We expect them to be enriched for expression in male tissues, notably the testes. The background list of genes is set to all genes annotated to at least one Gene Ontology term, allowing to account for biases in which types of genes are more likely to receive Gene Ontology annotation.
# if (!requireNamespace("BiocManager", quietly=TRUE))
# install.packages("BiocManager")
# BiocManager::install("biomaRt")
library(biomaRt)
ensembl <- useMart("ENSEMBL_MART_ENSEMBL", dataset="drerio_gene_ensembl", host="mar2016.archive.ensembl.org")
# get the mapping of Ensembl genes to phenotypes. It will corresponds to the background genes
universe <- getBM(filters=c("phenotype_source"), value=c("ZFIN"), attributes=c("ensembl_gene_id","phenotype_description"), mart=ensembl)
# select phenotypes related to pectoral fin
phenotypes <- grep("pectoral fin", unique(universe$phenotype_description), value=T)
# Foreground genes are those with an annotated phenotype related to "pectoral fin"
myGenes <- unique(universe$ensembl_gene_id[universe$phenotype_description %in% phenotypes])
# Prepare the gene list vector
geneList <- factor(as.integer(unique(universe$ensembl_gene_id) %in% myGenes))
names(geneList) <- unique(universe$ensembl_gene_id)
summary(geneList)
# Prepare the topGO object
myTopAnatObject <- topAnat(myTopAnatData, geneList)The above code using the biomaRt package is not executed in this vignette to prevent building issues of our package in case of biomaRt downtime. Instead we use a geneList object saved in the data/ folder that we built using pre-downloaded data.
##
## Checking topAnatData object.............
##
## Checking gene list......................
##
## Building most specific Ontology terms... ( 1174 Ontology terms found. )
##
## Building DAG topology................... ( 2036 Ontology terms and 3883 relations. )
##
## Annotating nodes (Can be long).......... ( 3005 genes annotated to the Ontology terms. )Warning: This can be long, especially if the gene list is large, since the Uberon anatomical ontology is large and expression calls will be propagated through the whole ontology (e.g., expression in the forebrain will also be counted as expression in parent structures such as the brain, nervous system, etc). Consider running a script in batch mode if you have multiple analyses to do.
For this step, see the vignette of the topGO package for more details, as you have to directly use the tests implemented in the topGO package, as shown in this example:
##
## -- Weight Algorithm --
##
## The algorithm is scoring 1007 nontrivial nodes
## parameters:
## test statistic: fisher : ratio##
## Level 27: 1 nodes to be scored.##
## Level 26: 1 nodes to be scored.##
## Level 25: 1 nodes to be scored.##
## Level 24: 4 nodes to be scored.##
## Level 23: 4 nodes to be scored.##
## Level 22: 5 nodes to be scored.##
## Level 21: 4 nodes to be scored.##
## Level 20: 8 nodes to be scored.##
## Level 19: 23 nodes to be scored.##
## Level 18: 23 nodes to be scored.##
## Level 17: 27 nodes to be scored.##
## Level 16: 39 nodes to be scored.##
## Level 15: 63 nodes to be scored.##
## Level 14: 63 nodes to be scored.##
## Level 13: 74 nodes to be scored.##
## Level 12: 95 nodes to be scored.##
## Level 11: 120 nodes to be scored.##
## Level 10: 115 nodes to be scored.##
## Level 9: 92 nodes to be scored.##
## Level 8: 75 nodes to be scored.##
## Level 7: 67 nodes to be scored.##
## Level 6: 44 nodes to be scored.##
## Level 5: 27 nodes to be scored.##
## Level 4: 21 nodes to be scored.##
## Level 3: 6 nodes to be scored.##
## Level 2: 4 nodes to be scored.##
## Level 1: 1 nodes to be scored.Warning: This can be long because of the size of the ontology. Consider running scripts in batch mode if you have multiple analyses to do.
The makeTable function allows to filter and format the test results, and calculate FDR values.
# Display results sigificant at a 1% FDR threshold
tableOver <- makeTable(myTopAnatData, myTopAnatObject, results, cutoff = 0.01)##
## Building the results table for the 27 significant terms at FDR threshold of 0.01...
## Ordering results by pValue column in increasing order...
## Done## organId organName annotated significant
## UBERON:0000151 UBERON:0000151 pectoral fin 439 79
## UBERON:0004357 UBERON:0004357 paired limb/fin bud 198 48
## UBERON:2000040 UBERON:2000040 median fin fold 59 20
## UBERON:0003051 UBERON:0003051 ear vesicle 391 49
## UBERON:0005729 UBERON:0005729 pectoral appendage field 20 11
## UBERON:0004376 UBERON:0004376 fin bone 34 12
## expected foldEnrichment pValue FDR
## UBERON:0000151 21.48 3.677840 1.358300e-27 1.471039e-24
## UBERON:0004357 9.69 4.953560 5.187251e-23 2.808896e-20
## UBERON:2000040 2.89 6.920415 9.370662e-13 3.382809e-10
## UBERON:0003051 19.13 2.561422 5.501734e-11 1.489595e-08
## UBERON:0005729 0.98 11.224490 3.052286e-10 6.611252e-08
## UBERON:0004376 1.66 7.228916 2.603199e-08 4.698774e-06At the time of building this vignette (June 2018), there was 27 significant anatomical structures. The first term is “pectoral fin”, and the second “paired limb/fin bud”. Other terms in the list, especially those with high enrichment folds, are clearly related to pectoral fins or substructures of fins. This analysis shows that genes with phenotypic effects on pectoral fins are specifically expressed in or next to these structures
By default results are sorted by p-value, but this can be changed with the ordering parameter by specifying which column should be used to order the results (preceded by a “-” sign to indicate that ordering should be made in decreasing order). For example, it is often convenient to sort significant structures by decreasing enrichment fold, using ordering = -6. The full table of results can be obtained using cutoff = 1.
Of note, it is possible to retrieve for a particular tissue the significant genes that were mapped to it.
# In order to retrieve significant genes mapped to the term " paired limb/fin bud"
term <- "UBERON:0004357"
termStat(myTopAnatObject, term) ## Annotated Significant Expected
## UBERON:0004357 198 48 9.69## $`UBERON:0004357`
## [1] "ENSDARG00000001057" "ENSDARG00000001785" "ENSDARG00000002445"
## [4] "ENSDARG00000002795" "ENSDARG00000002933" "ENSDARG00000002952"
## [7] "ENSDARG00000003293" "ENSDARG00000003399" "ENSDARG00000004954"
## [10] "ENSDARG00000005479" "ENSDARG00000005645" "ENSDARG00000005762"
## [13] "ENSDARG00000006120" "ENSDARG00000006514" "ENSDARG00000007407"
## [16] "ENSDARG00000007438" "ENSDARG00000007641" "ENSDARG00000008305"
## [19] "ENSDARG00000008886" "ENSDARG00000009534" "ENSDARG00000011027"
## [22] "ENSDARG00000011407" "ENSDARG00000011618" "ENSDARG00000012078"
## [25] "ENSDARG00000012422" "ENSDARG00000012824" "ENSDARG00000013409"
## [28] "ENSDARG00000013853" "ENSDARG00000013881" "ENSDARG00000014091"
## [31] "ENSDARG00000014259" "ENSDARG00000014329" "ENSDARG00000014626"
## [34] "ENSDARG00000014634" "ENSDARG00000014796" "ENSDARG00000015554"
## [37] "ENSDARG00000015674" "ENSDARG00000016022" "ENSDARG00000016454"
## [40] "ENSDARG00000016858" "ENSDARG00000017219" "ENSDARG00000018025"
## [43] "ENSDARG00000018426" "ENSDARG00000018460" "ENSDARG00000018492"
## [46] "ENSDARG00000018693" "ENSDARG00000018902" "ENSDARG00000019260"
## [49] "ENSDARG00000019353" "ENSDARG00000019579" "ENSDARG00000019838"
## [52] "ENSDARG00000019995" "ENSDARG00000020527" "ENSDARG00000021389"
## [55] "ENSDARG00000021442" "ENSDARG00000021938" "ENSDARG00000022280"
## [58] "ENSDARG00000024561" "ENSDARG00000024894" "ENSDARG00000025081"
## [61] "ENSDARG00000025147" "ENSDARG00000025375" "ENSDARG00000025641"
## [64] "ENSDARG00000025891" "ENSDARG00000028071" "ENSDARG00000029764"
## [67] "ENSDARG00000030110" "ENSDARG00000030756" "ENSDARG00000030932"
## [70] "ENSDARG00000031222" "ENSDARG00000031809" "ENSDARG00000031894"
## [73] "ENSDARG00000031952" "ENSDARG00000033327" "ENSDARG00000033616"
## [76] "ENSDARG00000034375" "ENSDARG00000035559" "ENSDARG00000035648"
## [79] "ENSDARG00000036254" "ENSDARG00000036558" "ENSDARG00000037109"
## [82] "ENSDARG00000037556" "ENSDARG00000037675" "ENSDARG00000037677"
## [85] "ENSDARG00000038006" "ENSDARG00000038428" "ENSDARG00000038672"
## [88] "ENSDARG00000038879" "ENSDARG00000038990" "ENSDARG00000038991"
## [91] "ENSDARG00000040534" "ENSDARG00000040764" "ENSDARG00000041430"
## [94] "ENSDARG00000041609" "ENSDARG00000041706" "ENSDARG00000041799"
## [97] "ENSDARG00000042233" "ENSDARG00000042296" "ENSDARG00000043130"
## [100] "ENSDARG00000043559" "ENSDARG00000043923" "ENSDARG00000044511"
## [103] "ENSDARG00000044574" "ENSDARG00000052131" "ENSDARG00000052139"
## [106] "ENSDARG00000052344" "ENSDARG00000052494" "ENSDARG00000052652"
## [109] "ENSDARG00000053479" "ENSDARG00000053493" "ENSDARG00000054026"
## [112] "ENSDARG00000054030" "ENSDARG00000054619" "ENSDARG00000055026"
## [115] "ENSDARG00000055027" "ENSDARG00000055381" "ENSDARG00000055398"
## [118] "ENSDARG00000056995" "ENSDARG00000057830" "ENSDARG00000058115"
## [121] "ENSDARG00000058543" "ENSDARG00000058822" "ENSDARG00000059073"
## [124] "ENSDARG00000059233" "ENSDARG00000059276" "ENSDARG00000059279"
## [127] "ENSDARG00000059437" "ENSDARG00000060397" "ENSDARG00000060808"
## [130] "ENSDARG00000061328" "ENSDARG00000061345" "ENSDARG00000061600"
## [133] "ENSDARG00000062824" "ENSDARG00000068365" "ENSDARG00000068567"
## [136] "ENSDARG00000068732" "ENSDARG00000069105" "ENSDARG00000069473"
## [139] "ENSDARG00000069763" "ENSDARG00000069922" "ENSDARG00000070069"
## [142] "ENSDARG00000070670" "ENSDARG00000071336" "ENSDARG00000071560"
## [145] "ENSDARG00000071699" "ENSDARG00000073814" "ENSDARG00000074378"
## [148] "ENSDARG00000074597" "ENSDARG00000075713" "ENSDARG00000076010"
## [151] "ENSDARG00000076554" "ENSDARG00000076566" "ENSDARG00000076856"
## [154] "ENSDARG00000077121" "ENSDARG00000077353" "ENSDARG00000077473"
## [157] "ENSDARG00000078696" "ENSDARG00000078784" "ENSDARG00000078864"
## [160] "ENSDARG00000079027" "ENSDARG00000079570" "ENSDARG00000079922"
## [163] "ENSDARG00000079964" "ENSDARG00000080453" "ENSDARG00000087196"
## [166] "ENSDARG00000089805" "ENSDARG00000090820" "ENSDARG00000091161"
## [169] "ENSDARG00000092136" "ENSDARG00000092809" "ENSDARG00000095743"
## [172] "ENSDARG00000095859" "ENSDARG00000098359" "ENSDARG00000099088"
## [175] "ENSDARG00000099175" "ENSDARG00000099458" "ENSDARG00000099996"
## [178] "ENSDARG00000100236" "ENSDARG00000100312" "ENSDARG00000100725"
## [181] "ENSDARG00000101076" "ENSDARG00000101199" "ENSDARG00000101209"
## [184] "ENSDARG00000101244" "ENSDARG00000101701" "ENSDARG00000101766"
## [187] "ENSDARG00000101831" "ENSDARG00000102470" "ENSDARG00000102750"
## [190] "ENSDARG00000102824" "ENSDARG00000102995" "ENSDARG00000103432"
## [193] "ENSDARG00000103515" "ENSDARG00000103754" "ENSDARG00000104353"
## [196] "ENSDARG00000104815" "ENSDARG00000105230" "ENSDARG00000105357"# 48 significant genes mapped to this term for Bgee 14.0 and Ensembl 84
annotated <- genesInTerm(myTopAnatObject, term)[["UBERON:0004357"]]
annotated[annotated %in% sigGenes(myTopAnatObject)]## [1] "ENSDARG00000002445" "ENSDARG00000002952" "ENSDARG00000003293"
## [4] "ENSDARG00000008305" "ENSDARG00000011407" "ENSDARG00000012824"
## [7] "ENSDARG00000013853" "ENSDARG00000013881" "ENSDARG00000014091"
## [10] "ENSDARG00000018426" "ENSDARG00000018693" "ENSDARG00000018902"
## [13] "ENSDARG00000019260" "ENSDARG00000019353" "ENSDARG00000019838"
## [16] "ENSDARG00000021389" "ENSDARG00000024894" "ENSDARG00000028071"
## [19] "ENSDARG00000030932" "ENSDARG00000031894" "ENSDARG00000036254"
## [22] "ENSDARG00000037677" "ENSDARG00000038006" "ENSDARG00000038672"
## [25] "ENSDARG00000041799" "ENSDARG00000042233" "ENSDARG00000042296"
## [28] "ENSDARG00000043559" "ENSDARG00000043923" "ENSDARG00000053493"
## [31] "ENSDARG00000054619" "ENSDARG00000058543" "ENSDARG00000060397"
## [34] "ENSDARG00000068567" "ENSDARG00000069473" "ENSDARG00000071336"
## [37] "ENSDARG00000073814" "ENSDARG00000076856" "ENSDARG00000077121"
## [40] "ENSDARG00000077353" "ENSDARG00000079027" "ENSDARG00000079570"
## [43] "ENSDARG00000087196" "ENSDARG00000095859" "ENSDARG00000099088"
## [46] "ENSDARG00000100312" "ENSDARG00000101831" "ENSDARG00000105357"Warning: it is debated whether FDR correction is appropriate on enrichment test results, since tests on different terms of the ontologies are not independent. A nice discussion can be found in the vignette of the topGO package.
Since version 2.14.0 (Bioconductor 3.11) BgeeDB store downloaded expression data in a local RSQLite database. The advantages of this approach compared to the one used in the previous BgeeDB versions are: * do not anymore need a server with lot of memory to access to subset of huge dataset (e.g GTeX dataset) * more granular filtering using arguments in the getData() function * do not download twice the same data * fast access to data once integrated in the local database
This approach comes with some drawbacks : * the SQLite local database use more disk space than the previously conpressed .rds approach * first access to a dataset takes more time (integration to SQLite local database is time consuming)
It is possible to remove .rds files generated in previous versions of BgeeDB and not used anymore since version 2.14.0. The function below delete all .rds files for the selected species and for all datatype.
bgee <- Bgee$new(species="Mus_musculus", release = "14.1")
# delete all old .rds files of species Mus musculus
deleteOldData(bgee)As the new SQLite approach use more disk space it is now possible to delete all local data of one species from one release of Bgee.