Digital Expression Explorer 2 (or DEE2 for short) is a repository of processed RNA-seq data in the form of counts. It was designed so that researchers could undertake re-analysis and meta-analysis of published RNA-seq studies quickly and easily. As of April 2020, over 1 million SRA runs have been processed.
For further information about the resource, refer to the journal article and project homepage.
This package provides an interface to access these expression data programmatically.
The first step is to download the list of accession numbers of available datasets with the getDEE2Metadata function, specifying a species name. options for species currently are:
| Scientific name | Common name | DEE2 name | Organism group |
|---|---|---|---|
| Caenorhabditis elegans | Roundworm | celegans | animals |
| Drosophila melanogaster | Fruitfly | dmelanogaster | animals |
| Danio rerio | Zebrafish | drerio | animals |
| Homo sapiens | Human | hsapiens | animals |
| Mus musculus | Mouse | mmusculus | animals |
| Rattus norvegicus | Rat | rnorvegicus | animals |
| Arabidopsis thaliana | Thale cress | athaliana | plants |
| Brachypodium distachyon | Purple false brome (grass) | bdistachyon | plants |
| Glycine max | Soybean | gmax | plants |
| Hordeum vulgare | Barley | hvulgare | plants |
| Oryza sativa | Rice | osativa | plants |
| Populus trichocarpa | Black cottonwood tree | ptrichocarpa | plants |
| Sorghum bicolor | Sorghum | sbicolor | plants |
| Solanum lycopersicum | Tomato | slycopersicum | plants |
| Solanum tuberosum | Potato | stuberosum | plants |
| Triticum aestivum | Wheat | taestivum | plants |
| Vitis vinifera | Grapevine | vvinifera | plants |
| Escherichia coli | E. coli | ecoli | microbes |
| Saccharomyces cerevisiae | Yeast | scerevisiae | microbes |
If the species name is incorrect, an error will be thrown.
## SRR_accession QC_summary SRX_accession SRS_accession SRP_accession
## 1 DRR003389 WARN(2,6) DRX002713 DRS002797 DRP000898
## 2 DRR003390 WARN(2,6,7) DRX002714 DRS002798 DRP000898
## 3 DRR024071 WARN(5,7) DRX021821 DRS023187 DRP002871
## 4 DRR024072 WARN(5,7) DRX021822 DRS002797 DRP002871
## 5 DRR031524 FAIL(3,6) DRX028492 DRS092197 DRP004944
## 6 DRR031525 FAIL(3,6) DRX028493 DRS092198 DRP004944
## Experiment_title GEO_series
## 1 KH1668:smg-2(yb979)
## 2 KH1857:smg-2(yb979) unc-75 (yb1701)
## 3 Illumina Genome Analyzer IIx sequencing of SAMD00021020
## 4 Illumina Genome Analyzer IIx sequencing of SAMD00015748
## 5 Illumina HiSeq 2500 sequencing of SAMD00027920
## 6 Illumina HiSeq 2500 sequencing of SAMD00027921If you have a SRA project accession number in mind already (eg: SRP009256) then we can see if the datasets are present.
## SRR_accession QC_summary SRX_accession SRS_accession SRP_accession
## 15319 SRR363796 FAIL(2,3,4,6,7) SRX105188 SRS270025 SRP009256
## 15320 SRR363797 FAIL(3,4,6,7) SRX105189 SRS270026 SRP009256
## 15321 SRR363798 FAIL(2,3,4,6,7) SRX105190 SRS270027 SRP009256
## 15322 SRR363799 FAIL(3,4,6,7) SRX105191 SRS270028 SRP009256
## Experiment_title GEO_series
## 15319 GSM829554: 4SU_GLD1_PARCLIP_1 GSE33543
## 15320 GSM829555: 4SU_GLD1_PARCLIP_2 GSE33543
## 15321 GSM829556: 4SU_GLD1_PARCLIP_3 GSE33543
## 15322 GSM829557: 6SG_GLD1_PARCLIP_1 GSE33543DEE2 data is centred around SRA run accessions numbers, these SRR_accessions can be obtained like this:
mdat1 <- mdat[which(mdat$SRP_accession %in% "SRP009256"),]
SRRvec <- as.vector(mdat1$SRR_accession)
SRRvec## [1] "SRR363796" "SRR363797" "SRR363798" "SRR363799"The general syntax for obtaining DEE2 data is this:
getDEE2(species,SRRvec,metadata,outfile="NULL",counts="GeneCounts")
First, the function queries the metadata to make sure that the requested datasets are present. If metadata is not specified, then it will download a ‘fresh’ copy of the metadata. It then fetches the requested expression data and constructs a SummarizedExperiment object. The ‘counts’ parameter controls the type of counts provided:
GeneCounts STAR gene level counts (this is the default)
TxCounts Kallisto transcript level counts
Tx2Gene transcript counts aggregated (sum) to the gene level.
If ‘outfile’ is defined, then files will be downloaded to the specified path. If it is not defined, then the files are downloaded to a temporary directory and deleted immediately after use.
The SRR numbers need to exactly match those in SRA.
Here is an example of using the SRR vector as defined above.
suppressPackageStartupMessages(library("SummarizedExperiment"))
x <- getDEE2("celegans",SRRvec,metadata=mdat,counts="GeneCounts")## For more information about DEE2 QC metrics, visit
## https://github.com/markziemann/dee2/blob/master/qc/qc_metrics.md## class: SummarizedExperiment
## dim: 46739 4
## metadata(0):
## assays(1): counts
## rownames(46739): WBGene00197333 WBGene00198386 ... WBGene00010967
## WBGene00014473
## rowData names(0):
## colnames(4): SRR363796 SRR363797 SRR363798 SRR363799
## colData names(50): QC_summary SRX_accession ... Kallisto_MapRate
## QC_SUMMARY## DataFrame with 4 rows and 7 columns
## QC_summary SRX_accession SRS_accession SRP_accession
## <character> <character> <character> <character>
## SRR363796 FAIL(2,3,4,6,7) SRX105188 SRS270025 SRP009256
## SRR363797 FAIL(3,4,6,7) SRX105189 SRS270026 SRP009256
## SRR363798 FAIL(2,3,4,6,7) SRX105190 SRS270027 SRP009256
## SRR363799 FAIL(3,4,6,7) SRX105191 SRS270028 SRP009256
## Experiment_title GEO_series sample_alias
## <character> <character> <character>
## SRR363796 GSM829554: 4SU_GLD1_.. GSE33543 GSM829554
## SRR363797 GSM829555: 4SU_GLD1_.. GSE33543 GSM829555
## SRR363798 GSM829556: 4SU_GLD1_.. GSE33543 GSM829556
## SRR363799 GSM829557: 6SG_GLD1_.. GSE33543 GSM829557## SRR363796 SRR363797 SRR363798 SRR363799
## WBGene00197333 0 0 0 0
## WBGene00198386 0 0 0 0
## WBGene00015153 4 16 6 4
## WBGene00002061 44 100 217 77
## WBGene00255704 0 5 1 1
## WBGene00235314 0 0 0 1You can directly specify the SRR accessions in the command line, but be sure to type them correctly. In case SRR accessions are not present in the database, there will be a warning message.
x <- getDEE2("celegans",c("SRR363798","SRR363799","SRR3581689","SRR3581692"),
metadata=mdat,counts="GeneCounts")## For more information about DEE2 QC metrics, visit
## https://github.com/markziemann/dee2/blob/master/qc/qc_metrics.md## Warning, datasets not found: 'SRR3581689,SRR3581692'In this case the accessions SRR3581689 and SRR3581692 are A. thaliana accessions and therefore not present in the C. elegans accession list.
DEE2 data are perfectly suitable for downstream analysis with edgeR, DESeq2, and many other gene expression and pathway enrichment tools. For more information about working with SummarizedExperiment refer to the rnaseqGene package which describes a workflow for differential gene expression of SummarizedExperiment objects.
The function to obtain DEE2 in the legacy format is provided for completeness but is no longer recommended. It gives DEE2 data in the form of a list object with slots for gene counts, transcript counts, gene length, transcript length, quality control data, sample metadata summary, sample metadata (full) and any absent datasets.
## For more information about DEE2 QC metrics, visit
## https://github.com/markziemann/dee2/blob/master/qc/qc_metrics.md## [1] "GeneCounts" "TxCounts" "GeneInfo" "TxInfo"
## [5] "QcMx" "MetadataSummary" "MetadataFull" "absent"## SRR363796 SRR363797 SRR363798 SRR363799
## WBGene00197333 0 0 0 0
## WBGene00198386 0 0 0 0
## WBGene00015153 4 16 6 4
## WBGene00002061 44 100 217 77
## WBGene00255704 0 5 1 1
## WBGene00235314 0 0 0 1## SRR363796 SRR363797 SRR363798 SRR363799
## Y110A7A.10 11 23 48 45
## F27C8.1 0 0 0 0
## F07C3.7 0 2 0 21
## F52H2.2a 0 0 7 0
## F52H2.2b 0 4 0 5
## T13A10.10a 0 0 0 0## SRR363796 SRR363797 SRR363798
## SequenceFormat SE SE SE
## QualityEncoding Sanger/Illumina1.9 Sanger/Illumina1.9 Sanger/Illumina1.9
## Read1MinimumLength 36 36 36
## Read1MedianLength 36 36 36
## Read1MaxLength 36 36 36
## Read2MinimumLength NULL NULL NULL
## SRR363799
## SequenceFormat SE
## QualityEncoding Sanger/Illumina1.9
## Read1MinimumLength 36
## Read1MedianLength 36
## Read1MaxLength 36
## Read2MinimumLength NULL## GeneSymbol mean median longest_isoform merged
## WBGene00197333 cTel3X.2 150 150 150 150
## WBGene00198386 cTel3X.3 150 150 150 150
## WBGene00015153 B0348.5 988 988 1178 1178
## WBGene00002061 ife-3 1024 1023 1107 1107
## WBGene00255704 B0348.10 363 363 363 363
## WBGene00235314 B0348.9 220 220 220 220## GeneID GeneSymbol TxLength
## Y110A7A.10 WBGene00000001 aap-1 1787
## F27C8.1 WBGene00000002 aat-1 1940
## F07C3.7 WBGene00000003 aat-2 1728
## F52H2.2a WBGene00000004 aat-3 1739
## F52H2.2b WBGene00000004 aat-3 1840
## T13A10.10a WBGene00000005 aat-4 1734The DEE2 webpage has processed many projects containing dozens to thousands of runs (available here). These large project datasets are easiest to access with the “bundles” functionality described here. The three functions are:
list_bundles downloads a list of available bundles for a species
query_bundles checks whether a particular SRA project or GEO series accession number is available
getDEE2_bundle fetches the expression data for a particular accession and loads it as a SummarizedExperiment object
In this first example, we search for a dataset with SRA project accession number SRP058781 and load the gene level counts.
## file_name date_added time_added file_size SRP_accession GSE_accession
## 1 DRP001761_NA.zip 2023-02-21 04:05 3.2M DRP001761 NA
## 2 DRP002301_NA.zip 2023-02-21 04:05 2.2M DRP002301 NA
## 3 DRP003066_NA.zip 2023-02-21 04:05 1.8M DRP003066 NA
## 4 DRP003416_NA.zip 2023-02-21 04:05 3.5M DRP003416 NA
## 5 DRP003686_NA.zip 2023-02-21 04:05 5.1M DRP003686 NA
## 6 DRP003759_NA.zip 2023-02-21 04:05 2.4M DRP003759 NA## $present
## [1] "SRP058781"
##
## $absent
## character(0)## For more information about DEE2 QC metrics, visit
## https://github.com/markziemann/dee2/blob/master/qc/qc_metrics.md## SRR2042819 SRR2042820 SRR2042821 SRR2042822
## AT1G01010 26 47 101 97
## AT1G01020 115 105 187 130
## AT1G03987 0 0 1 2
## AT1G01030 22 32 38 30
## AT1G01040 628 591 1227 890
## AT1G03993 0 0 0 0Similarly, it is possible to search with GEO series numbers, as in the next example.
## For more information about DEE2 QC metrics, visit
## https://github.com/markziemann/dee2/blob/master/qc/qc_metrics.md## SRR6268134 SRR6268135 SRR6268136 SRR6268137
## ENSDARG00000104632 0 0 0 0
## ENSDARG00000100660 51 41 36 38
## ENSDARG00000098417 0 0 0 0
## ENSDARG00000100422 11 5 4 9
## ENSDARG00000102128 0 0 0 0
## ENSDARG00000103095 0 0 0 0## R version 4.5.0 (2025-04-11 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
##
## Matrix products: default
## LAPACK version 3.12.1
##
## locale:
## [1] LC_COLLATE=C
## [2] LC_CTYPE=English_United States.utf8
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.utf8
##
## time zone: America/New_York
## tzcode source: internal
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] SummarizedExperiment_1.39.0 Biobase_2.69.0
## [3] GenomicRanges_1.61.0 GenomeInfoDb_1.45.4
## [5] IRanges_2.43.0 S4Vectors_0.47.0
## [7] BiocGenerics_0.55.0 generics_0.1.4
## [9] MatrixGenerics_1.21.0 matrixStats_1.5.0
## [11] getDEE2_1.19.3
##
## loaded via a namespace (and not attached):
## [1] crayon_1.5.3 httr_1.4.7 cli_3.6.5
## [4] knitr_1.50 rlang_1.1.6 xfun_0.52
## [7] UCSC.utils_1.5.0 DelayedArray_0.35.1 jsonlite_2.0.0
## [10] htmltools_0.5.8.1 sass_0.4.10 rmarkdown_2.29
## [13] grid_4.5.0 abind_1.4-8 evaluate_1.0.3
## [16] jquerylib_0.1.4 fastmap_1.2.0 yaml_2.3.10
## [19] lifecycle_1.0.4 compiler_4.5.0 XVector_0.49.0
## [22] lattice_0.22-7 digest_0.6.37 R6_2.6.1
## [25] SparseArray_1.9.0 htm2txt_2.2.2 Matrix_1.7-3
## [28] bslib_0.9.0 tools_4.5.0 S4Arrays_1.9.1
## [31] cachem_1.1.0