BioPlex 1.8.0
The BioPlex project uses affinity-purification mass spectrometry to profile protein-protein interactions (PPIs) in human cell lines.
To date, the BioPlex project has created two proteome-scale, cell-line-specific PPI networks. The first, BioPlex 3.0, results from affinity purification of 10,128 human proteins —- half the proteome —- in 293T cells and includes 118,162 interactions among 14,586 proteins. The second results from 5,522 immunoprecipitations in HCT116 cells and includes 70,966 interactions between 10,531 proteins.
For more information, please see:
The BioPlex R package implements access to the BioPlex protein-protein interaction networks and related resources from within R. Besides protein-protein interaction networks for 293T and HCT116 cells, this includes access to CORUM protein complex data, and transcriptome and proteome data for the two cell lines.
Functionality focuses on importing these data resources and storing them in dedicated Bioconductor data structures, as a foundation for integrative downstream analysis of the data. For a set of downstream analyses and applications, please see the BioPlexAnalysis package and analysis vignettes.
To install the package, start R and enter:
if(!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("BioPlex")After the installation, we proceed by loading the package and additional packages used in the vignette.
library(BioPlex)
library(AnnotationHub)
library(ExperimentHub)
library(graph)Connect to AnnotationHub:
ah <- AnnotationHub::AnnotationHub()Connect to ExperimentHub:
eh <- ExperimentHub::ExperimentHub()OrgDb package for human:
orgdb <- AnnotationHub::query(ah, c("orgDb", "Homo sapiens"))
orgdb <- orgdb[[1]]
orgdb
#> OrgDb object:
#> | DBSCHEMAVERSION: 2.1
#> | Db type: OrgDb
#> | Supporting package: AnnotationDbi
#> | DBSCHEMA: HUMAN_DB
#> | ORGANISM: Homo sapiens
#> | SPECIES: Human
#> | EGSOURCEDATE: 2023-Sep11
#> | EGSOURCENAME: Entrez Gene
#> | EGSOURCEURL: ftp://ftp.ncbi.nlm.nih.gov/gene/DATA
#> | CENTRALID: EG
#> | TAXID: 9606
#> | GOSOURCENAME: Gene Ontology
#> | GOSOURCEURL: http://current.geneontology.org/ontology/go-basic.obo
#> | GOSOURCEDATE: 2023-07-27
#> | GOEGSOURCEDATE: 2023-Sep11
#> | GOEGSOURCENAME: Entrez Gene
#> | GOEGSOURCEURL: ftp://ftp.ncbi.nlm.nih.gov/gene/DATA
#> | KEGGSOURCENAME: KEGG GENOME
#> | KEGGSOURCEURL: ftp://ftp.genome.jp/pub/kegg/genomes
#> | KEGGSOURCEDATE: 2011-Mar15
#> | GPSOURCENAME: UCSC Genome Bioinformatics (Homo sapiens)
#> | GPSOURCEURL:
#> | GPSOURCEDATE: 2023-Aug20
#> | ENSOURCEDATE: 2023-May10
#> | ENSOURCENAME: Ensembl
#> | ENSOURCEURL: ftp://ftp.ensembl.org/pub/current_fasta
#> | UPSOURCENAME: Uniprot
#> | UPSOURCEURL: http://www.UniProt.org/
#> | UPSOURCEDATE: Mon Sep 18 16:12:39 2023
keytypes(orgdb)
#> [1] "ACCNUM" "ALIAS" "ENSEMBL" "ENSEMBLPROT" "ENSEMBLTRANS"
#> [6] "ENTREZID" "ENZYME" "EVIDENCE" "EVIDENCEALL" "GENENAME"
#> [11] "GENETYPE" "GO" "GOALL" "IPI" "MAP"
#> [16] "OMIM" "ONTOLOGY" "ONTOLOGYALL" "PATH" "PFAM"
#> [21] "PMID" "PROSITE" "REFSEQ" "SYMBOL" "UCSCKG"
#> [26] "UNIPROT"Available networks include:
Let’s get the latest version of the 293T PPI network:
bp.293t <- getBioPlex(cell.line = "293T", version = "3.0")
#> Using cached version from 2023-04-25 14:56:45
head(bp.293t)
#> GeneA GeneB UniprotA UniprotB SymbolA SymbolB pW pNI
#> 1 100 728378 P00813 A5A3E0 ADA POTEF 6.881844e-10 0.0001176357
#> 2 222389 6137 Q8N7W2-2 P26373 BEND7 RPL13 1.340380e-18 0.2256644741
#> 3 222389 5928 Q8N7W2-2 Q09028-3 BEND7 RBBP4 7.221401e-21 0.0000641669
#> 4 222389 25873 Q8N7W2-2 Q9Y3U8 BEND7 RPL36 7.058372e-17 0.1281827343
#> 5 222389 6124 Q8N7W2-2 P36578 BEND7 RPL4 1.632313e-22 0.2006379109
#> 6 222389 6188 Q8N7W2-2 P23396 BEND7 RPS3 3.986270e-26 0.0010264311
#> pInt
#> 1 0.9998824
#> 2 0.7743355
#> 3 0.9999358
#> 4 0.8718173
#> 5 0.7993621
#> 6 0.9989736
nrow(bp.293t)
#> [1] 118162Each row corresponds to a PPI between a bait protein A and a prey protein B, for which
NCBI Entrez Gene IDs, Uniprot IDs, and gene symbols are annotated.
The last three columns reflect the likelihood that each interaction resulted
from either an incorrect protein identification (pW), background (pNI), or
a bona fide interacting partner (pInt) as determined using the
CompPASS algorithm.
Analgously, we can obtain the latest version of the HCT116 PPI network:
bp.hct116 <- getBioPlex(cell.line = "HCT116", version = "1.0")
#> Using cached version from 2023-04-25 14:57:35
head(bp.hct116)
#> GeneA GeneB UniprotA UniprotB SymbolA SymbolB pW pNI
#> 1 88455 50649 Q8IZ07 Q9NR80-4 ANKRD13A ARHGEF4 3.959215e-04 3.298003e-05
#> 2 88455 115106 Q8IZ07 Q96CS2 ANKRD13A HAUS1 4.488473e-02 1.934731e-03
#> 3 88455 23086 Q8IZ07 Q8NEV8-2 ANKRD13A EXPH5 7.402394e-05 9.296226e-04
#> 4 88455 54930 Q8IZ07 Q9H6D7 ANKRD13A HAUS4 9.180959e-07 1.278318e-04
#> 5 88455 79441 Q8IZ07 Q68CZ6 ANKRD13A HAUS3 8.709394e-07 1.495480e-03
#> 6 88455 93323 Q8IZ07 Q9BT25-2 ANKRD13A HAUS8 9.147659e-06 2.061483e-03
#> pInt
#> 1 0.9995711
#> 2 0.9531805
#> 3 0.9989964
#> 4 0.9998713
#> 5 0.9985036
#> 6 0.9979294
nrow(bp.hct116)
#> [1] 70966The protein-to-gene mappings from BioPlex (i.e. UNIPROT-to-SYMBOL and UNIPROT-to-ENTREZID) are based on the mappings available from Uniprot at the time of publication of the BioPlex 3.0 networks.
We can update those based on Bioc annotation functionality:
bp.293t.remapped <- getBioPlex(cell.line = "293T",
version = "3.0",
remap.uniprot.ids = TRUE)
#> Using cached version from 2023-04-25 14:56:45We can also represent a given version of the BioPlex PPI network for a given cell line as one big graph where bait and prey relationship are represented by directed edges from bait to prey.
bp.gr <- bioplex2graph(bp.293t)
bp.gr
#> A graphNEL graph with directed edges
#> Number of Nodes = 13689
#> Number of Edges = 115868
head(graph::nodeData(bp.gr))
#> $P00813
#> $P00813$ENTREZID
#> [1] "100"
#>
#> $P00813$SYMBOL
#> [1] "ADA"
#>
#> $P00813$ISOFORM
#> [1] "P00813"
#>
#>
#> $Q8N7W2
#> $Q8N7W2$ENTREZID
#> [1] "222389"
#>
#> $Q8N7W2$SYMBOL
#> [1] "BEND7"
#>
#> $Q8N7W2$ISOFORM
#> [1] "Q8N7W2-2"
#>
#>
#> $Q6ZMN8
#> $Q6ZMN8$ENTREZID
#> [1] "645121"
#>
#> $Q6ZMN8$SYMBOL
#> [1] "CCNI2"
#>
#> $Q6ZMN8$ISOFORM
#> [1] "Q6ZMN8"
#>
#>
#> $P20138
#> $P20138$ENTREZID
#> [1] "945"
#>
#> $P20138$SYMBOL
#> [1] "CD33"
#>
#> $P20138$ISOFORM
#> [1] "P20138"
#>
#>
#> $P55039
#> $P55039$ENTREZID
#> [1] "1819"
#>
#> $P55039$SYMBOL
#> [1] "DRG2"
#>
#> $P55039$ISOFORM
#> [1] "P55039"
#>
#>
#> $Q17R55
#> $Q17R55$ENTREZID
#> [1] "148109"
#>
#> $Q17R55$SYMBOL
#> [1] "FAM187B"
#>
#> $Q17R55$ISOFORM
#> [1] "Q17R55"
head(graph::edgeData(bp.gr))
#> $`P00813|A5A3E0`
#> $`P00813|A5A3E0`$weight
#> [1] 1
#>
#> $`P00813|A5A3E0`$pW
#> [1] 6.881844e-10
#>
#> $`P00813|A5A3E0`$pNI
#> [1] 0.0001176357
#>
#> $`P00813|A5A3E0`$pInt
#> [1] 0.9998824
#>
#>
#> $`Q8N7W2|P26373`
#> $`Q8N7W2|P26373`$weight
#> [1] 1
#>
#> $`Q8N7W2|P26373`$pW
#> [1] 1.34038e-18
#>
#> $`Q8N7W2|P26373`$pNI
#> [1] 0.2256645
#>
#> $`Q8N7W2|P26373`$pInt
#> [1] 0.7743355
#>
#>
#> $`Q8N7W2|Q09028`
#> $`Q8N7W2|Q09028`$weight
#> [1] 1
#>
#> $`Q8N7W2|Q09028`$pW
#> [1] 7.221401e-21
#>
#> $`Q8N7W2|Q09028`$pNI
#> [1] 6.41669e-05
#>
#> $`Q8N7W2|Q09028`$pInt
#> [1] 0.9999358
#>
#>
#> $`Q8N7W2|Q9Y3U8`
#> $`Q8N7W2|Q9Y3U8`$weight
#> [1] 1
#>
#> $`Q8N7W2|Q9Y3U8`$pW
#> [1] 7.058372e-17
#>
#> $`Q8N7W2|Q9Y3U8`$pNI
#> [1] 0.1281827
#>
#> $`Q8N7W2|Q9Y3U8`$pInt
#> [1] 0.8718173
#>
#>
#> $`Q8N7W2|P36578`
#> $`Q8N7W2|P36578`$weight
#> [1] 1
#>
#> $`Q8N7W2|P36578`$pW
#> [1] 1.632313e-22
#>
#> $`Q8N7W2|P36578`$pNI
#> [1] 0.2006379
#>
#> $`Q8N7W2|P36578`$pInt
#> [1] 0.7993621
#>
#>
#> $`Q8N7W2|P23396`
#> $`Q8N7W2|P23396`$weight
#> [1] 1
#>
#> $`Q8N7W2|P23396`$pW
#> [1] 3.98627e-26
#>
#> $`Q8N7W2|P23396`$pNI
#> [1] 0.001026431
#>
#> $`Q8N7W2|P23396`$pInt
#> [1] 0.9989736We can easily add PFAM domain annotations to the node metadata:
bp.gr <- annotatePFAM(bp.gr, orgdb)
head(graph::nodeData(bp.gr, graph::nodes(bp.gr), "PFAM"))
#> $P00813
#> [1] "PF00962"
#>
#> $Q8N7W2
#> [1] "PF10523"
#>
#> $Q6ZMN8
#> [1] "PF00134"
#>
#> $P20138
#> [1] "PF07686" "PF00047"
#>
#> $P55039
#> [1] "PF01926" "PF16897" "PF02824"
#>
#> $Q17R55
#> [1] NAObtain the complete set of human protein complexes from CORUM:
all <- getCorum(set = "all", organism = "Human")
dim(all)
#> [1] 2916 20
colnames(all)
#> [1] "ComplexID" "ComplexName"
#> [3] "Organism" "Synonyms"
#> [5] "Cell.line" "subunits.UniProt.IDs."
#> [7] "subunits.Entrez.IDs." "Protein.complex.purification.method"
#> [9] "GO.ID" "GO.description"
#> [11] "FunCat.ID" "FunCat.description"
#> [13] "subunits.Gene.name." "Subunits.comment"
#> [15] "PubMed.ID" "Complex.comment"
#> [17] "Disease.comment" "SWISSPROT.organism"
#> [19] "subunits.Gene.name.syn." "subunits.Protein.name."
all[1:5, 1:5]
#> ComplexID ComplexName Organism
#> 1 1 BCL6-HDAC4 complex Human
#> 2 2 BCL6-HDAC5 complex Human
#> 3 3 BCL6-HDAC7 complex Human
#> 4 4 Multisubunit ACTR coactivator complex Human
#> 6 10 Condensin I complex Human
#> Synonyms Cell.line
#> 1 None None
#> 2 None None
#> 3 None None
#> 4 None None
#> 6 13S condensin complex NoneCore set of complexes:
core <- getCorum(set = "core", organism = "Human")
dim(core)
#> [1] 2417 20Complexes with splice variants:
splice <- getCorum(set = "splice", organism = "Human")
dim(splice)
#> [1] 44 20The protein-to-gene mappings from CORUM (i.e. UNIPROT-to-SYMBOL and UNIPROT-to-ENTREZID) might not be fully up-to-date.
We can update those based on Bioc annotation functionality:
core.remapped <- getCorum(set = "core",
organism = "Human",
remap.uniprot.ids = TRUE)We can represent the CORUM complexes as a list of character vectors. The names of the list are the complex IDs/names, and each element of the list is a vector of UniProt IDs for each complex.
core.list <- corum2list(core, subunit.id.type = "UNIPROT")
head(core.list)
#> $`CORUM1_BCL6-HDAC4_complex`
#> [1] "P41182" "P56524"
#>
#> $`CORUM2_BCL6-HDAC5_complex`
#> [1] "P41182" "Q9UQL6"
#>
#> $`CORUM3_BCL6-HDAC7_complex`
#> [1] "P41182" "Q8WUI4"
#>
#> $CORUM4_Multisubunit_ACTR_coactivator_complex
#> [1] "Q09472" "Q92793" "Q92831" "Q9Y6Q9"
#>
#> $`CORUM11_BLOC-3_(biogenesis_of_lysosome-related_organelles_complex_3)`
#> [1] "Q92902" "Q9NQG7"
#>
#> $`CORUM12_BLOC-2_(biogenesis_of_lysosome-related_organelles_complex_2)`
#> [1] "Q86YV9" "Q969F9" "Q9UPZ3"
length(core.list)
#> [1] 2417We can also represent the CORUM complexes as a list of graph instances, where all nodes of a complex are connected to all other nodes of that complex with undirected edges.
core.glist <- corum2graphlist(core, subunit.id.type = "UNIPROT")
head(core.glist)
#> $`CORUM1_BCL6-HDAC4_complex`
#> A graphNEL graph with undirected edges
#> Number of Nodes = 2
#> Number of Edges = 1
#>
#> $`CORUM2_BCL6-HDAC5_complex`
#> A graphNEL graph with undirected edges
#> Number of Nodes = 2
#> Number of Edges = 1
#>
#> $`CORUM3_BCL6-HDAC7_complex`
#> A graphNEL graph with undirected edges
#> Number of Nodes = 2
#> Number of Edges = 1
#>
#> $CORUM4_Multisubunit_ACTR_coactivator_complex
#> A graphNEL graph with undirected edges
#> Number of Nodes = 4
#> Number of Edges = 6
#>
#> $`CORUM11_BLOC-3_(biogenesis_of_lysosome-related_organelles_complex_3)`
#> A graphNEL graph with undirected edges
#> Number of Nodes = 2
#> Number of Edges = 1
#>
#> $`CORUM12_BLOC-2_(biogenesis_of_lysosome-related_organelles_complex_2)`
#> A graphNEL graph with undirected edges
#> Number of Nodes = 3
#> Number of Edges = 3
length(core.glist)
#> [1] 2417
core.glist[[1]]@graphData
#> $edgemode
#> [1] "undirected"
#>
#> $ComplexID
#> [1] 1
#>
#> $ComplexName
#> [1] "BCL6-HDAC4 complex"
#>
#> $GO.ID
#> [1] "GO:0006265" "GO:0045892" "GO:0051276" "GO:0030183" "GO:0005634"
#> [6] "GO:0016575"
#>
#> $PubMed.ID
#> [1] 11929873
graph::nodeData(core.glist[[1]])
#> $P41182
#> $P41182$ENTREZID
#> [1] "604"
#>
#> $P41182$SYMBOL
#> [1] "BCL6"
#>
#>
#> $P56524
#> $P56524$ENTREZID
#> [1] "9759"
#>
#> $P56524$SYMBOL
#> [1] "HDAC4"Note that we can easily convert a
graph object into an
igraph object using
igraph::graph_from_graphnel.
Genomic data from whole-genome sequencing for six different lineages of the human embryonic kidney HEK293 cell line can be obtained from hek293genome.org.
This includes copy number variation (CNV) data for the 293T cell line. Available CNV tracks include (i) CNV regions inferred from sequencing read-depth analysis, and (ii) CNV regions inferred from Illumina SNP arrays.
Here, we obtain CNV segments obtained from applying a hidden Markov model (HMM) to sequencing-inferred copy numbers in 2kbp windows. More details on how copy numbers were calculated can be obtained from the primary publication.
cnv.hmm <- getHEK293GenomeTrack(track = "cnv.hmm", cell.line = "293T")
#> Using cached version from 2023-04-25 14:57:00
cnv.hmm
#> GRanges object with 12382 ranges and 1 metadata column:
#> seqnames ranges strand | score
#> <Rle> <IRanges> <Rle> | <numeric>
#> [1] chr1 823231-829231 * | 3.26
#> [2] chr1 835231-913231 * | 3.08
#> [3] chr1 923231-1063231 * | 3.20
#> [4] chr1 1079231-1213231 * | 3.21
#> [5] chr1 1223231-1399231 * | 3.27
#> ... ... ... ... . ...
#> [12378] chrX 154750237-154762237 * | 3.96
#> [12379] chrX 154778237-154780237 * | 4.02
#> [12380] chrX 154802237-154822237 * | 3.79
#> [12381] chrX 154842237-154846237 * | 3.85
#> [12382] chrM 0-12000 * | 8.82
#> -------
#> seqinfo: 24 sequences from hg18 genome; no seqlengthsSee also the data checks vignette, Section 5 for an exploration of the agreement between inferred copy numbers from both assay types (SNP arrays vs. sequencing).
Obtain transcriptome data for 293T cells from GEO dataset: GSE122425.
se <- getGSE122425()
#> Using cached version from 2023-04-25 14:57:15
se
#> class: SummarizedExperiment
#> dim: 57905 6
#> metadata(0):
#> assays(2): raw rpkm
#> rownames(57905): ENSG00000223972 ENSG00000227232 ... ENSG00000231514
#> ENSG00000235857
#> rowData names(4): SYMBOL KO GO length
#> colnames(6): GSM3466389 GSM3466390 ... GSM3466393 GSM3466394
#> colData names(41): title geo_accession ... passages.ch1 strain.ch1
head(assay(se, "raw"))
#> GSM3466389 GSM3466390 GSM3466391 GSM3466392 GSM3466393
#> ENSG00000223972 1 2 2 0 0
#> ENSG00000227232 732 690 804 705 812
#> ENSG00000243485 0 0 2 0 0
#> ENSG00000237613 0 0 0 0 0
#> ENSG00000268020 0 0 0 0 0
#> ENSG00000240361 0 0 0 0 0
#> GSM3466394
#> ENSG00000223972 2
#> ENSG00000227232 1121
#> ENSG00000243485 0
#> ENSG00000237613 0
#> ENSG00000268020 0
#> ENSG00000240361 1
head(assay(se, "rpkm"))
#> GSM3466389 GSM3466390 GSM3466391 GSM3466392 GSM3466393
#> ENSG00000223972 0.01 0.01 0.01 0.00 0.00
#> ENSG00000227232 5.43 5.07 5.39 4.77 5.21
#> ENSG00000243485 0.00 0.00 0.04 0.00 0.00
#> ENSG00000237613 0.00 0.00 0.00 0.00 0.00
#> ENSG00000268020 0.00 0.00 0.00 0.00 0.00
#> ENSG00000240361 0.00 0.00 0.00 0.00 0.00
#> GSM3466394
#> ENSG00000223972 0.01
#> ENSG00000227232 6.80
#> ENSG00000243485 0.00
#> ENSG00000237613 0.00
#> ENSG00000268020 0.00
#> ENSG00000240361 0.02
colData(se)
#> DataFrame with 6 rows and 41 columns
#> title geo_accession status submission_date
#> <character> <character> <character> <character>
#> GSM3466389 WT rep1 GSM3466389 Public on Nov 16 2018 Nov 12 2018
#> GSM3466390 WT rep2 GSM3466390 Public on Nov 16 2018 Nov 12 2018
#> GSM3466391 WT rep3 GSM3466391 Public on Nov 16 2018 Nov 12 2018
#> GSM3466392 NSUN2-KO rep1 GSM3466392 Public on Nov 16 2018 Nov 12 2018
#> GSM3466393 NSUN2-KO rep2 GSM3466393 Public on Nov 16 2018 Nov 12 2018
#> GSM3466394 NSUN2-KO rep3 GSM3466394 Public on Nov 16 2018 Nov 12 2018
#> last_update_date type channel_count source_name_ch1
#> <character> <character> <character> <character>
#> GSM3466389 Nov 16 2018 SRA 1 kidney
#> GSM3466390 Nov 16 2018 SRA 1 kidney
#> GSM3466391 Nov 16 2018 SRA 1 kidney
#> GSM3466392 Nov 16 2018 SRA 1 kidney
#> GSM3466393 Nov 16 2018 SRA 1 kidney
#> GSM3466394 Nov 16 2018 SRA 1 kidney
#> organism_ch1 characteristics_ch1 characteristics_ch1.1
#> <character> <character> <character>
#> GSM3466389 Homo sapiens strain: HEK293 passages: 8-12
#> GSM3466390 Homo sapiens strain: HEK293 passages: 8-12
#> GSM3466391 Homo sapiens strain: HEK293 passages: 8-12
#> GSM3466392 Homo sapiens strain: HEK293 passages: 8-12
#> GSM3466393 Homo sapiens strain: HEK293 passages: 8-12
#> GSM3466394 Homo sapiens strain: HEK293 passages: 8-12
#> treatment_protocol_ch1 growth_protocol_ch1 molecule_ch1
#> <character> <character> <character>
#> GSM3466389 The NSUN2-deficient .. Cells were grown in .. polyA RNA
#> GSM3466390 The NSUN2-deficient .. Cells were grown in .. polyA RNA
#> GSM3466391 The NSUN2-deficient .. Cells were grown in .. polyA RNA
#> GSM3466392 The NSUN2-deficient .. Cells were grown in .. polyA RNA
#> GSM3466393 The NSUN2-deficient .. Cells were grown in .. polyA RNA
#> GSM3466394 The NSUN2-deficient .. Cells were grown in .. polyA RNA
#> extract_protocol_ch1 extract_protocol_ch1.1 taxid_ch1
#> <character> <character> <character>
#> GSM3466389 Total RNA of cells w.. Next generation sequ.. 9606
#> GSM3466390 Total RNA of cells w.. Next generation sequ.. 9606
#> GSM3466391 Total RNA of cells w.. Next generation sequ.. 9606
#> GSM3466392 Total RNA of cells w.. Next generation sequ.. 9606
#> GSM3466393 Total RNA of cells w.. Next generation sequ.. 9606
#> GSM3466394 Total RNA of cells w.. Next generation sequ.. 9606
#> data_processing data_processing.1 data_processing.2
#> <character> <character> <character>
#> GSM3466389 Bcl2fastq (v2.17.1.1.. 3’ adaptor-trimming .. The high quality tri..
#> GSM3466390 Bcl2fastq (v2.17.1.1.. 3’ adaptor-trimming .. The high quality tri..
#> GSM3466391 Bcl2fastq (v2.17.1.1.. 3’ adaptor-trimming .. The high quality tri..
#> GSM3466392 Bcl2fastq (v2.17.1.1.. 3’ adaptor-trimming .. The high quality tri..
#> GSM3466393 Bcl2fastq (v2.17.1.1.. 3’ adaptor-trimming .. The high quality tri..
#> GSM3466394 Bcl2fastq (v2.17.1.1.. 3’ adaptor-trimming .. The high quality tri..
#> data_processing.3 data_processing.4 data_processing.5
#> <character> <character> <character>
#> GSM3466389 Reads Per Kilobase o.. Differential express.. Genome_build: HG19
#> GSM3466390 Reads Per Kilobase o.. Differential express.. Genome_build: HG19
#> GSM3466391 Reads Per Kilobase o.. Differential express.. Genome_build: HG19
#> GSM3466392 Reads Per Kilobase o.. Differential express.. Genome_build: HG19
#> GSM3466393 Reads Per Kilobase o.. Differential express.. Genome_build: HG19
#> GSM3466394 Reads Per Kilobase o.. Differential express.. Genome_build: HG19
#> data_processing.6 platform_id contact_name contact_institute
#> <character> <character> <character> <character>
#> GSM3466389 Supplementary_files_.. GPL11154 Zhen,,Sun Yangzhou University
#> GSM3466390 Supplementary_files_.. GPL11154 Zhen,,Sun Yangzhou University
#> GSM3466391 Supplementary_files_.. GPL11154 Zhen,,Sun Yangzhou University
#> GSM3466392 Supplementary_files_.. GPL11154 Zhen,,Sun Yangzhou University
#> GSM3466393 Supplementary_files_.. GPL11154 Zhen,,Sun Yangzhou University
#> GSM3466394 Supplementary_files_.. GPL11154 Zhen,,Sun Yangzhou University
#> contact_address contact_city contact_zip.postal_code contact_country
#> <character> <character> <character> <character>
#> GSM3466389 Wenhui Yangzhou 225009 China
#> GSM3466390 Wenhui Yangzhou 225009 China
#> GSM3466391 Wenhui Yangzhou 225009 China
#> GSM3466392 Wenhui Yangzhou 225009 China
#> GSM3466393 Wenhui Yangzhou 225009 China
#> GSM3466394 Wenhui Yangzhou 225009 China
#> data_row_count instrument_model library_selection library_source
#> <character> <character> <character> <character>
#> GSM3466389 0 Illumina HiSeq 2000 cDNA transcriptomic
#> GSM3466390 0 Illumina HiSeq 2000 cDNA transcriptomic
#> GSM3466391 0 Illumina HiSeq 2000 cDNA transcriptomic
#> GSM3466392 0 Illumina HiSeq 2000 cDNA transcriptomic
#> GSM3466393 0 Illumina HiSeq 2000 cDNA transcriptomic
#> GSM3466394 0 Illumina HiSeq 2000 cDNA transcriptomic
#> library_strategy relation relation.1
#> <character> <character> <character>
#> GSM3466389 RNA-Seq BioSample: https://w.. SRA: https://www.ncb..
#> GSM3466390 RNA-Seq BioSample: https://w.. SRA: https://www.ncb..
#> GSM3466391 RNA-Seq BioSample: https://w.. SRA: https://www.ncb..
#> GSM3466392 RNA-Seq BioSample: https://w.. SRA: https://www.ncb..
#> GSM3466393 RNA-Seq BioSample: https://w.. SRA: https://www.ncb..
#> GSM3466394 RNA-Seq BioSample: https://w.. SRA: https://www.ncb..
#> supplementary_file_1 passages.ch1 strain.ch1
#> <character> <character> <character>
#> GSM3466389 NONE 8-12 HEK293
#> GSM3466390 NONE 8-12 HEK293
#> GSM3466391 NONE 8-12 HEK293
#> GSM3466392 NONE 8-12 HEK293
#> GSM3466393 NONE 8-12 HEK293
#> GSM3466394 NONE 8-12 HEK293
rowData(se)
#> DataFrame with 57905 rows and 4 columns
#> SYMBOL KO GO length
#> <character> <character> <character> <integer>
#> ENSG00000223972 DDX11L1 K11273 _ 2544
#> ENSG00000227232 WASH7P K18461 _ 15444
#> ENSG00000243485 MIR1302-10 _ _ 1556
#> ENSG00000237613 FAM138A _ _ 1528
#> ENSG00000268020 OR4G4P K04257 _ 2464
#> ... ... ... ... ...
#> ENSG00000224240 CYCSP49 K08738 _ 319
#> ENSG00000227629 SLC25A15P1 K15101 _ 4960
#> ENSG00000237917 PARP4P1 K10798 _ 39802
#> ENSG00000231514 FAM58CP _ _ 640
#> ENSG00000235857 CTBP2P1 K04496 _ 245The dataset includes three wild type samples and three NSUN2 knockout samples.
See also the data checks vignette, Section 7 for an exploration of the relationship between expression level and the frequency of a protein being detected as prey.
RNA-seq data for 934 cancer cell lines (incl. HCT116) from the Cancer Cell Line Encyclopedia is available from the ArrayExpress-ExpressionAtlas (Accession: E-MTAB-2770).
The data can be obtained as a SummarizedExperiment using the
ExpressionAtlas package.
ccle.trans <- ExpressionAtlas::getAtlasExperiment("E-MTAB-2770")See also the Transcriptome-Proteome analysis vignette for further exploration of the correlation between CCLE HCT116 transcript and protein expression.
RNA-seq data of 675 commonly used human cancer cell lines (incl. HCT116) from Klijn et al., 2015 is available from the ArrayExpress-ExpressionAtlas (Accession: E-MTAB-2706)
The data can be obtained as a SummarizedExperiment using the
ExpressionAtlas package.
klijn <- ExpressionAtlas::getAtlasExperiment("E-MTAB-2706")See also the Transcriptome-Proteome analysis vignette for further exploration of differential transcript and protein expression between 293T and HCT116 cells.
For the inference of differential exon usage between cell lines, raw RNA-seq read counts on exon level can be obtained from ExperimentHub.
RNA-seq data for 293T cells was obtained from GEO accession GSE122633 and RNA-seq data for HCT116 cells was obtained from GEO accession GSE52429.
The data can be obtained as a DEXSeqDataSet which is a
SummarizedExperiment-derivative and can be accessed and manipulated very much
like a DESeqDataSet.
AnnotationHub::query(eh, c("BioPlex"))
#> ExperimentHub with 14 records
#> # snapshotDate(): 2023-10-24
#> # $dataprovider: Department of Cell Biology, Harvard Medical School, Munich ...
#> # $species: Homo sapiens
#> # $rdataclass: data.frame, SummarizedExperiment, GRanges, DEXSeqDataSet
#> # additional mcols(): taxonomyid, genome, description,
#> # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
#> # rdatapath, sourceurl, sourcetype
#> # retrieve records with, e.g., 'object[["EH7563"]]'
#>
#> title
#> EH7563 | HEK293T_HCT116_exon_counts
#> EH8094 | BioPlex_293T_1
#> EH8095 | BioPlex_293T_2
#> EH8096 | BioPlex_293T_3
#> EH8097 | BioPlex_HCT116_1
#> ... ...
#> EH8102 | HEK293Genome_cnv_hmm
#> EH8103 | HEK293Genome_cnv_snp
#> EH8104 | E-MTAB-2706
#> EH8105 | E-MTAB-2770
#> EH8106 | BioPlex_proteome
dex <- eh[["EH7563"]]
dex
#> class: DEXSeqDataSet
#> dim: 607086 10
#> metadata(1): version
#> assays(1): counts
#> rownames(607086): ENSG00000000003:E001 ENSG00000000003:E002 ...
#> ENSG00000288723:E005 ENSG00000288723:E006
#> rowData names(8): featureID groupID ... chromosome biotype
#> colnames: NULL
#> colData names(9): sample sampleName ... exon sizeFactorWe take a closer look at the sample annotation, the counts for each exon for both cell lines, and the genomic coordinates and additional annotation for each exon.
DEXSeq::sampleAnnotation(dex)
#> DataFrame with 5 rows and 8 columns
#> sample sampleName GEO_ID Platform_ID End
#> <factor> <character> <character> <character> <character>
#> 1 GSM1266733 HCT116 RNA-seq repPF GSE52429 GPL11154 paired
#> 2 GSM1266734 HCT116 RNA-seq repPJ GSE52429 GPL11154 paired
#> 3 GSM3476820 UnTfx1_S7_R1_001 GSE122633 GPL18573 single
#> 4 GSM3476821 UnTfx2_S8_R1_001 GSE122633 GPL18573 single
#> 5 GSM3476822 UnTfx3_S9_R1_001 GSE122633 GPL18573 single
#> Stranded cellLine sizeFactor
#> <character> <character> <numeric>
#> 1 unstranded HCT116 1.336168
#> 2 unstranded HCT116 1.383283
#> 3 stranded HEK293T 0.860023
#> 4 stranded HEK293T 0.803370
#> 5 stranded HEK293T 0.848761
head(DEXSeq::featureCounts(dex))
#> GSM1266733 GSM1266734 GSM3476820 GSM3476821 GSM3476822
#> ENSG00000000003:E001 2 0 2 0 2
#> ENSG00000000003:E002 1249 939 2713 3321 3323
#> ENSG00000000003:E003 399 287 907 1050 1039
#> ENSG00000000003:E004 5 2 6 3 0
#> ENSG00000000003:E005 313 239 386 486 476
#> ENSG00000000003:E006 243 200 190 239 235
rowRanges(dex)
#> GRanges object with 607086 ranges and 8 metadata columns:
#> seqnames ranges strand | featureID
#> <Rle> <IRanges> <Rle> | <character>
#> ENSG00000000003:E001 X 100627108 - | E001
#> ENSG00000000003:E002 X 100627109-100629986 - | E002
#> ENSG00000000003:E003 X 100630759-100630866 - | E003
#> ENSG00000000003:E004 X 100632063-100632068 - | E004
#> ENSG00000000003:E005 X 100632485-100632540 - | E005
#> ... ... ... ... . ...
#> ENSG00000288723:E002 1 241723858-241723938 - | E002
#> ENSG00000288723:E003 1 241728735-241728834 - | E003
#> ENSG00000288723:E004 1 241742059-241742106 - | E004
#> ENSG00000288723:E005 1 241765062-241765184 - | E005
#> ENSG00000288723:E006 1 241847969-241848128 - | E006
#> groupID exonBaseMean exonBaseVar
#> <character> <numeric> <numeric>
#> ENSG00000000003:E001 ENSG00000000003 1.2 1.2
#> ENSG00000000003:E002 ENSG00000000003 2309.0 1304014.0
#> ENSG00000000003:E003 ENSG00000000003 736.4 133703.8
#> ENSG00000000003:E004 ENSG00000000003 3.2 5.7
#> ENSG00000000003:E005 ENSG00000000003 380.0 11214.5
#> ... ... ... ...
#> ENSG00000288723:E002 ENSG00000288723 1.0 1.5
#> ENSG00000288723:E003 ENSG00000288723 1.8 3.7
#> ENSG00000288723:E004 ENSG00000288723 2.0 6.0
#> ENSG00000288723:E005 ENSG00000288723 1.8 6.2
#> ENSG00000288723:E006 ENSG00000288723 0.2 0.2
#> transcripts symbol chromosome
#> <list> <character> <character>
#> ENSG00000000003:E001 ENST00000373020 TSPAN6 X
#> ENSG00000000003:E002 ENST00000373020,ENST00000612152 TSPAN6 X
#> ENSG00000000003:E003 ENST00000373020,ENST00000612152 TSPAN6 X
#> ENSG00000000003:E004 ENST00000614008 TSPAN6 X
#> ENSG00000000003:E005 ENST00000373020,ENST00000614008 TSPAN6 X
#> ... ... ... ...
#> ENSG00000288723:E002 ENST00000684005 1
#> ENSG00000288723:E003 ENST00000684005 1
#> ENSG00000288723:E004 ENST00000684005 1
#> ENSG00000288723:E005 ENST00000684005 1
#> ENSG00000288723:E006 ENST00000684005 1
#> biotype
#> <character>
#> ENSG00000000003:E001 protein_coding
#> ENSG00000000003:E002 protein_coding
#> ENSG00000000003:E003 protein_coding
#> ENSG00000000003:E004 protein_coding
#> ENSG00000000003:E005 protein_coding
#> ... ...
#> ENSG00000288723:E002 lncRNA
#> ENSG00000288723:E003 lncRNA
#> ENSG00000288723:E004 lncRNA
#> ENSG00000288723:E005 lncRNA
#> ENSG00000288723:E006 lncRNA
#> -------
#> seqinfo: 47 sequences from an unspecified genome; no seqlengthsPull the CCLE proteome data from ExperimentHub. The dataset profiles 12,755 proteins by mass spectrometry across 375 cancer cell lines.
AnnotationHub::query(eh, c("gygi", "depmap"))
#> ExperimentHub with 1 record
#> # snapshotDate(): 2023-10-24
#> # names(): EH3459
#> # package(): depmap
#> # $dataprovider: Broad Institute
#> # $species: Homo sapiens
#> # $rdataclass: tibble
#> # $rdatadateadded: 2020-05-19
#> # $title: proteomic_20Q2
#> # $description: Quantitative profiling of 12399 proteins in 375 cell lines, ...
#> # $taxonomyid: 9606
#> # $genome:
#> # $sourcetype: CSV
#> # $sourceurl: https://gygi.med.harvard.edu/sites/gygi.med.harvard.edu/files/...
#> # $sourcesize: NA
#> # $tags: c("ExperimentHub", "ExperimentData", "ReproducibleResearch",
#> # "RepositoryData", "AssayDomainData", "CopyNumberVariationData",
#> # "DiseaseModel", "CancerData", "BreastCancerData", "ColonCancerData",
#> # "KidneyCancerData", "LeukemiaCancerData", "LungCancerData",
#> # "OvarianCancerData", "ProstateCancerData", "OrganismData",
#> # "Homo_sapiens_Data", "PackageTypeData", "SpecimenSource",
#> # "CellCulture", "Genome", "Proteome", "StemCell", "Tissue")
#> # retrieve record with 'object[["EH3459"]]'
ccle.prot <- eh[["EH3459"]]
ccle.prot <- as.data.frame(ccle.prot)Explore the data:
dim(ccle.prot)
#> [1] 4821390 12
colnames(ccle.prot)
#> [1] "depmap_id" "gene_name" "entrez_id"
#> [4] "protein" "protein_expression" "protein_id"
#> [7] "desc" "group_id" "uniprot"
#> [10] "uniprot_acc" "TenPx" "cell_line"
head(ccle.prot)
#> depmap_id gene_name entrez_id protein protein_expression
#> 1 ACH-000849 SLC12A2 6558 MDAMB468_BREAST_TenPx01 2.11134846
#> 2 ACH-000441 SLC12A2 6558 SH4_SKIN_TenPx01 0.07046807
#> 3 ACH-000248 SLC12A2 6558 AU565_BREAST_TenPx01 -0.46392793
#> 4 ACH-000684 SLC12A2 6558 KMRC1_KIDNEY_TenPx01 -0.88364548
#> 5 ACH-000856 SLC12A2 6558 CAL51_BREAST_TenPx01 0.78856534
#> 6 ACH-000348 SLC12A2 6558 RPMI7951_SKIN_TenPx01 -0.91235198
#> protein_id desc group_id
#> 1 sp|P55011|S12A2_HUMAN S12A2_HUMAN Solute carrier family 12 member 2 0
#> 2 sp|P55011|S12A2_HUMAN S12A2_HUMAN Solute carrier family 12 member 2 0
#> 3 sp|P55011|S12A2_HUMAN S12A2_HUMAN Solute carrier family 12 member 2 0
#> 4 sp|P55011|S12A2_HUMAN S12A2_HUMAN Solute carrier family 12 member 2 0
#> 5 sp|P55011|S12A2_HUMAN S12A2_HUMAN Solute carrier family 12 member 2 0
#> 6 sp|P55011|S12A2_HUMAN S12A2_HUMAN Solute carrier family 12 member 2 0
#> uniprot uniprot_acc TenPx cell_line
#> 1 S12A2_HUMAN P55011 TenPx01 MDAMB468_BREAST
#> 2 S12A2_HUMAN P55011 TenPx01 SH4_SKIN
#> 3 S12A2_HUMAN P55011 TenPx01 AU565_BREAST
#> 4 S12A2_HUMAN P55011 TenPx01 KMRC1_KIDNEY
#> 5 S12A2_HUMAN P55011 TenPx01 CAL51_BREAST
#> 6 S12A2_HUMAN P55011 TenPx01 RPMI7951_SKINRestrict to HCT116:
ccle.prot.hct116 <- subset(ccle.prot, cell_line == "HCT116_LARGE_INTESTINE")
dim(ccle.prot.hct116)
#> [1] 12755 12
head(ccle.prot.hct116)
#> depmap_id gene_name entrez_id protein
#> 28 ACH-000971 SLC12A2 6558 HCT116_LARGE_INTESTINE_TenPx04
#> 406 ACH-000971 HOXD13 3239 HCT116_LARGE_INTESTINE_TenPx04
#> 784 ACH-000971 KDM1A 23028 HCT116_LARGE_INTESTINE_TenPx04
#> 1162 ACH-000971 SOX1 6656 HCT116_LARGE_INTESTINE_TenPx04
#> 1540 ACH-000971 SOX2 6657 HCT116_LARGE_INTESTINE_TenPx04
#> 1918 ACH-000971 SOX3 6658 HCT116_LARGE_INTESTINE_TenPx04
#> protein_expression protein_id
#> 28 -0.2422502 sp|P55011|S12A2_HUMAN
#> 406 NA sp|P35453|HXD13_HUMAN
#> 784 -0.1941110 sp|O60341|KDM1A_HUMAN
#> 1162 NA sp|O00570|SOX1_HUMAN
#> 1540 -1.5306584 sp|P48431|SOX2_HUMAN
#> 1918 NA sp|P41225|SOX3_HUMAN
#> desc group_id uniprot
#> 28 S12A2_HUMAN Solute carrier family 12 member 2 0 S12A2_HUMAN
#> 406 HXD13_HUMAN Homeobox protein Hox-D13 1 HXD13_HUMAN
#> 784 KDM1A_HUMAN Lysine-specific histone demethylase 1A 2 KDM1A_HUMAN
#> 1162 SOX1_HUMAN Transcription factor SOX-1 4 SOX1_HUMAN
#> 1540 SOX2_HUMAN Transcription factor SOX-2 4 SOX2_HUMAN
#> 1918 SOX3_HUMAN Transcription factor SOX-3 4 SOX3_HUMAN
#> uniprot_acc TenPx cell_line
#> 28 P55011 TenPx04 HCT116_LARGE_INTESTINE
#> 406 P35453 TenPx04 HCT116_LARGE_INTESTINE
#> 784 O60341 TenPx04 HCT116_LARGE_INTESTINE
#> 1162 O00570 TenPx04 HCT116_LARGE_INTESTINE
#> 1540 P48431 TenPx04 HCT116_LARGE_INTESTINE
#> 1918 P41225 TenPx04 HCT116_LARGE_INTESTINEOr turn into a SummarizedExperiment for convenience (we can restrict
this to selected cell lines, but here we keep all cell lines):
se <- ccleProteome2SummarizedExperiment(ccle.prot, cell.line = NULL)
assay(se)[1:5, 1:5]
#> 22RV1 697 769P 786O 8305C
#> P55011 1.8112046 -0.01482998 -0.5658598 -1.2205591 -0.1713740
#> P35453 NA NA -2.5433313 NA NA
#> O60341 -0.3379936 0.37121437 -0.8170886 -0.9183874 0.1141192
#> O00570 NA NA NA -0.5043593 NA
#> P48431 -1.2617033 NA NA -3.4006509 NA
assay(se)[1:5, "HCT116"]
#> P55011 P35453 O60341 O00570 P48431
#> -0.2422502 NA -0.1941110 NA -1.5306584
rowData(se)
#> DataFrame with 12755 rows and 2 columns
#> SYMBOL ENTREZID
#> <character> <numeric>
#> P55011 SLC12A2 6558
#> P35453 HOXD13 3239
#> O60341 KDM1A 23028
#> O00570 SOX1 6656
#> P48431 SOX2 6657
#> ... ... ...
#> Q9Y258 CCL26 10344
#> P20292 ALOX5AP 241
#> Q9H1C7 CYSTM1 84418
#> Q99735 MGST2 4258
#> Q9P003 CNIH4 29097The BioPlex 3.0 publication, Supplementary Table S4A, provides relative protein expression data comparing 293T and HCT116 cells based on tandem mass tag analysis.
bp.prot <- getBioplexProteome()
#> Using cached version from 2023-04-25 14:57:24
assay(bp.prot)[1:5,1:5]
#> HCT1 HCT2 HCT3 HCT4 HCT5
#> P0CG40 1.526690 3.479370 2.223500 2.258470 2.923410
#> Q8IXZ3-4 1.758790 1.610220 2.004360 1.800270 1.371470
#> P55011 12.570100 11.637500 12.495700 11.374500 12.377400
#> O60341 8.914910 9.677760 8.397850 8.745780 8.746410
#> O14654 0.196305 0.277787 0.425389 0.199624 0.491558
colData(bp.prot)
#> DataFrame with 10 rows and 1 column
#> cell.line
#> <character>
#> HCT1 HCT116
#> HCT2 HCT116
#> HCT3 HCT116
#> HCT4 HCT116
#> HCT5 HCT116
#> HEK1 293T
#> HEK2 293T
#> HEK3 293T
#> HEK4 293T
#> HEK5 293T
rowData(bp.prot)
#> DataFrame with 9604 rows and 5 columns
#> ENTREZID SYMBOL nr.peptides log2ratio adj.pvalue
#> <character> <character> <integer> <numeric> <numeric>
#> P0CG40 100131390 SP9 1 -2.819071 6.66209e-08
#> Q8IXZ3-4 221833 SP8 3 -3.419888 6.94973e-07
#> P55011 6558 SLC12A2 4 0.612380 4.85602e-06
#> O60341 23028 KDM1A 7 -0.319695 5.08667e-04
#> O14654 8471 IRS4 4 -5.951096 1.45902e-06
#> ... ... ... ... ... ...
#> Q9H6X4 80194 TMEM134 2 -0.379342 7.67195e-05
#> Q9BS91 55032 SLC35A5 1 -2.237634 8.75523e-05
#> Q9UKJ5 26511 CHIC2 1 -0.614932 1.78756e-03
#> Q9H3S5 93183 PIGM 1 -1.011397 8.91589e-06
#> Q8WYQ3 400916 CHCHD10 1 0.743852 1.17163e-03The data contains 5 replicates each for 293T and for HCT116 cells. As a result of the data collection process, the data represent relative protein abundance scaled to add up to 100% in each row.
See also the data checks vignette, Section 8 for a basic exploration of the annotated differential expression measures.
Note that calling functions like getCorum or getBioPlex with argument
cache = FALSE will automatically overwrite the corresponding object in your
cache. It is thus typically not required for a user to interact with the cache.
For more extended control of the cache, use from within R:
cache.dir <- tools::R_user_dir("BioPlex", which = "cache")
bfc <- BiocFileCache::BiocFileCache(cache.dir)and then proceed as described in the
BiocFileCache vignette, Section 1.10
either via cleanbfc() to clean or removebfc() to remove your cache.
To do a hard reset (use with caution!):
BiocFileCache::removebfc(bfc)sessionInfo()
#> R version 4.3.1 (2023-06-16)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.3 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] depmap_1.16.0 dplyr_1.1.3
#> [3] DEXSeq_1.48.0 RColorBrewer_1.1-3
#> [5] DESeq2_1.42.0 BiocParallel_1.36.0
#> [7] ExperimentHub_2.10.0 GenomicFeatures_1.54.0
#> [9] graph_1.80.0 AnnotationHub_3.10.0
#> [11] BiocFileCache_2.10.0 dbplyr_2.3.4
#> [13] AnnotationDbi_1.64.0 BioPlex_1.8.0
#> [15] SummarizedExperiment_1.32.0 Biobase_2.62.0
#> [17] GenomicRanges_1.54.0 GenomeInfoDb_1.38.0
#> [19] IRanges_2.36.0 S4Vectors_0.40.0
#> [21] BiocGenerics_0.48.0 MatrixGenerics_1.14.0
#> [23] matrixStats_1.0.0 BiocStyle_2.30.0
#>
#> loaded via a namespace (and not attached):
#> [1] DBI_1.1.3 bitops_1.0-7
#> [3] biomaRt_2.58.0 rlang_1.1.1
#> [5] magrittr_2.0.3 compiler_4.3.1
#> [7] RSQLite_2.3.1 png_0.1-8
#> [9] vctrs_0.6.4 stringr_1.5.0
#> [11] pkgconfig_2.0.3 crayon_1.5.2
#> [13] fastmap_1.1.1 XVector_0.42.0
#> [15] ellipsis_0.3.2 utf8_1.2.4
#> [17] Rsamtools_2.18.0 promises_1.2.1
#> [19] rmarkdown_2.25 purrr_1.0.2
#> [21] bit_4.0.5 xfun_0.40
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