1 emtdata
The emtdata package is an ExperimentHub package for three data sets with an Epithelial to Mesenchymal Transition (EMT). This package provides pre-processed RNA-seq data where the epithelial to mesenchymal transition was induced on cell lines. These data come from three publications Cursons et al. (2015), Cursons etl al. (2018) and Foroutan et al. (2017). In each of these publications, EMT was induces across multiple cell lines following treatment by TGFb among other stimulants. This data will be useful in determining the regulatory programs modified in order to achieve an EMT. Data were processed by the Davis laboratory in the Bioinformatics division at WEHI.
This package can be installed using the code below:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("emtdata")
#> Bioconductor version 3.18 (BiocManager 1.30.22), R 4.3.1 (2023-06-16)
#> Warning: package(s) not installed when version(s) same as or greater than current; use
#> `force = TRUE` to re-install: 'emtdata'
#> Old packages: 'common', 'dbplyr', 'ggwordcloud', 'gridpattern'2 Download data from the emtdata R package
Data in this package can be downloaded using the ExperimentHub interface as shown below. To download the data, we first need to get a list of the data available in the emtdata package and determine the unique identifiers for each data. The query() function assists in getting this list.
eh = ExperimentHub()
query(eh , 'emtdata')
#> ExperimentHub with 3 records
#> # snapshotDate(): 2023-10-24
#> # $dataprovider: Walter and Eliza Hall Institute of Medical Research, Queens...
#> # $species: Homo sapiens
#> # $rdataclass: GSEABase::SummarizedExperiment
#> # additional mcols(): taxonomyid, genome, description,
#> # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
#> # rdatapath, sourceurl, sourcetype
#> # retrieve records with, e.g., 'object[["EH5439"]]'
#>
#> title
#> EH5439 | foroutan2017_se
#> EH5440 | cursons2018_se
#> EH5441 | cursons2015_seData can then be downloaded using the unique identifier.
eh[['EH5440']]
#> see ?emtdata and browseVignettes('emtdata') for documentation
#> loading from cache
#> class: SummarizedExperiment
#> dim: 27515 10
#> metadata(0):
#> assays(2): counts logRPKM
#> rownames(27515): ENSG00000223972 ENSG00000227232 ... ENSG00000276345
#> ENSG00000271254
#> rowData names(7): Chr Start ... gene_name gene_biotype
#> colnames(10): HMLE_polyAplus_rep1 HMLE_polyAplus_rep2 ...
#> mesHMLE_miR200c_polyAplus_rep1 mesHMLE_miR200c_polyAplus_rep2
#> colData names(14): group lib.size ... Organism SRA.StudyAlternatively, data can be downloaded using object name accessors in the emtdata package as below:
#metadata are displayed
cursons2018_se(metadata = TRUE)
#> ExperimentHub with 1 record
#> # snapshotDate(): 2023-10-24
#> # names(): EH5440
#> # package(): emtdata
#> # $dataprovider: Queensland University of Technology
#> # $species: Homo sapiens
#> # $rdataclass: GSEABase::SummarizedExperiment
#> # $rdatadateadded: 2021-03-30
#> # $title: cursons2018_se
#> # $description: Gene expression data from Cursons et al., Cell Syst 2018. Th...
#> # $taxonomyid: 9606
#> # $genome: NA
#> # $sourcetype: TXT
#> # $sourceurl: https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB25042
#> # $sourcesize: NA
#> # $tags: c("HMLE", "Homo_sapiens_Data")
#> # retrieve record with 'object[["EH5440"]]'
#data are loaded
cursons2018_se()
#> see ?emtdata and browseVignettes('emtdata') for documentation
#> loading from cache
#> class: SummarizedExperiment
#> dim: 27515 10
#> metadata(0):
#> assays(2): counts logRPKM
#> rownames(27515): ENSG00000223972 ENSG00000227232 ... ENSG00000276345
#> ENSG00000271254
#> rowData names(7): Chr Start ... gene_name gene_biotype
#> colnames(10): HMLE_polyAplus_rep1 HMLE_polyAplus_rep2 ...
#> mesHMLE_miR200c_polyAplus_rep1 mesHMLE_miR200c_polyAplus_rep2
#> colData names(14): group lib.size ... Organism SRA.Study3 Accessing SummarizedExperiment object
cursons2018_se = eh[['EH5440']]
#> see ?emtdata and browseVignettes('emtdata') for documentation
#> loading from cache
#read counts
assay(cursons2018_se)[1:5, 1:5]
#> HMLE_polyAplus_rep1 HMLE_polyAplus_rep2 HMLE_polyAplus_rep3
#> ENSG00000223972 13 6 16
#> ENSG00000227232 449 282 567
#> ENSG00000278267 14 5 2
#> ENSG00000240361 7 0 0
#> ENSG00000186092 19 0 0
#> mesHMLE_polyAplus_rep1 mesHMLE_polyAplus_rep2
#> ENSG00000223972 1 24
#> ENSG00000227232 243 239
#> ENSG00000278267 8 13
#> ENSG00000240361 17 21
#> ENSG00000186092 0 16
#genes
rowData(cursons2018_se)
#> DataFrame with 27515 rows and 7 columns
#> Chr Start
#> <character> <character>
#> ENSG00000223972 1;1;1;1;1;1;1;1;1 11869;12010;12179;12..
#> ENSG00000227232 1;1;1;1;1;1;1;1;1;1;1 14404;15005;15796;16..
#> ENSG00000278267 1 17369
#> ENSG00000240361 1;1;1;1 57598;58700;62916;62..
#> ENSG00000186092 1;1;1;1 65419;65520;69037;69..
#> ... ... ...
#> ENSG00000278384 GL000218.1 51867
#> ENSG00000278633 KI270731.1 10598
#> ENSG00000278066 KI270731.1;KI270731.1 26533;26671
#> ENSG00000276345 KI270721.1;KI270721... 2585;6094;7322;7977;..
#> ENSG00000271254 KI270711.1;KI270711... 4612;6101;6101;6102;..
#> End Strand Length
#> <character> <character> <integer>
#> ENSG00000223972 12227;12057;12227;12.. +;+;+;+;+;+;+;+;+ 1735
#> ENSG00000227232 14501;15038;15947;16.. -;-;-;-;-;-;-;-;-;-;- 1351
#> ENSG00000278267 17436 - 68
#> ENSG00000240361 57653;58856;64116;63.. +;+;+;+ 1414
#> ENSG00000186092 65433;65573;71585;70.. +;+;+;+ 2618
#> ... ... ... ...
#> ENSG00000278384 54893 - 3027
#> ENSG00000278633 13001 - 2404
#> ENSG00000278066 26667;27138 -;- 603
#> ENSG00000276345 2692;6216;7404;8050;.. +;+;+;+;+ 740
#> ENSG00000271254 6370;6370;6370;6370;.. -;-;-;-;-;-;-;-;-;-;.. 4520
#> gene_name gene_biotype
#> <character> <character>
#> ENSG00000223972 DDX11L1 transcribed_unproces..
#> ENSG00000227232 WASH7P unprocessed_pseudogene
#> ENSG00000278267 MIR6859-1 miRNA
#> ENSG00000240361 OR4G11P transcribed_unproces..
#> ENSG00000186092 OR4F5 protein_coding
#> ... ... ...
#> ENSG00000278384 AL354822.1 protein_coding
#> ENSG00000278633 AC023491.2 protein_coding
#> ENSG00000278066 AC023491.1 pseudogene
#> ENSG00000276345 AC004556.3 protein_coding
#> ENSG00000271254 AC240274.1 protein_coding
#sample information
colData(cursons2018_se)
#> DataFrame with 10 rows and 14 columns
#> group lib.size norm.factors Run
#> <factor> <numeric> <numeric> <character>
#> HMLE_polyAplus_rep1 1 92190427 0.992509 ERR2306893
#> HMLE_polyAplus_rep2 1 52695983 0.875815 ERR2306894
#> HMLE_polyAplus_rep3 1 103842038 0.697357 ERR2306895
#> mesHMLE_polyAplus_rep1 1 72789081 1.125205 ERR2306896
#> mesHMLE_polyAplus_rep2 1 59117276 1.176159 ERR2306897
#> mesHMLE_polyAplus_rep3 1 60576244 0.958298 ERR2306898
#> mesHMLE_QKI5kd_polyAplus_rep1 1 63260143 1.074816 ERR2306899
#> mesHMLE_QKI5kd_polyAplus_rep2 1 54287166 1.024172 ERR2306900
#> mesHMLE_miR200c_polyAplus_rep1 1 46864487 1.115876 ERR2306901
#> mesHMLE_miR200c_polyAplus_rep2 1 47131679 1.058954 ERR2306902
#> Sample.Name Subline Treatment
#> <character> <character> <character>
#> HMLE_polyAplus_rep1 HMLE_polyAplus_rep1 HMLE Control
#> HMLE_polyAplus_rep2 HMLE_polyAplus_rep2 HMLE Control
#> HMLE_polyAplus_rep3 HMLE_polyAplus_rep3 HMLE Control
#> mesHMLE_polyAplus_rep1 mesHMLE_polyAplus_rep1 mesHMLE Control
#> mesHMLE_polyAplus_rep2 mesHMLE_polyAplus_rep2 mesHMLE Control
#> mesHMLE_polyAplus_rep3 mesHMLE_polyAplus_rep3 mesHMLE Control
#> mesHMLE_QKI5kd_polyAplus_rep1 mesHMLE_QKI5kd_polyA.. mesHMLE QKI5kd
#> mesHMLE_QKI5kd_polyAplus_rep2 mesHMLE_QKI5kd_polyA.. mesHMLE QKI5kd
#> mesHMLE_miR200c_polyAplus_rep1 mesHMLE_miR200c_poly.. mesHMLE miR200c
#> mesHMLE_miR200c_polyAplus_rep2 mesHMLE_miR200c_poly.. mesHMLE miR200c
#> BioProject BioSample
#> <character> <character>
#> HMLE_polyAplus_rep1 PRJEB25042 SAMEA104599608
#> HMLE_polyAplus_rep2 PRJEB25042 SAMEA104599609
#> HMLE_polyAplus_rep3 PRJEB25042 SAMEA104599610
#> mesHMLE_polyAplus_rep1 PRJEB25042 SAMEA104599611
#> mesHMLE_polyAplus_rep2 PRJEB25042 SAMEA104599612
#> mesHMLE_polyAplus_rep3 PRJEB25042 SAMEA104599613
#> mesHMLE_QKI5kd_polyAplus_rep1 PRJEB25042 SAMEA104599614
#> mesHMLE_QKI5kd_polyAplus_rep2 PRJEB25042 SAMEA104599615
#> mesHMLE_miR200c_polyAplus_rep1 PRJEB25042 SAMEA104599616
#> mesHMLE_miR200c_polyAplus_rep2 PRJEB25042 SAMEA104599617
#> Center.Name Experiment Cell.Line
#> <character> <character> <character>
#> HMLE_polyAplus_rep1 CENTRE FOR CANCER BI.. ERX2358203 HMLE
#> HMLE_polyAplus_rep2 CENTRE FOR CANCER BI.. ERX2358204 HMLE
#> HMLE_polyAplus_rep3 CENTRE FOR CANCER BI.. ERX2358205 HMLE
#> mesHMLE_polyAplus_rep1 CENTRE FOR CANCER BI.. ERX2358206 HMLE
#> mesHMLE_polyAplus_rep2 CENTRE FOR CANCER BI.. ERX2358207 HMLE
#> mesHMLE_polyAplus_rep3 CENTRE FOR CANCER BI.. ERX2358208 HMLE
#> mesHMLE_QKI5kd_polyAplus_rep1 CENTRE FOR CANCER BI.. ERX2358209 HMLE
#> mesHMLE_QKI5kd_polyAplus_rep2 CENTRE FOR CANCER BI.. ERX2358210 HMLE
#> mesHMLE_miR200c_polyAplus_rep1 CENTRE FOR CANCER BI.. ERX2358211 HMLE
#> mesHMLE_miR200c_polyAplus_rep2 CENTRE FOR CANCER BI.. ERX2358212 HMLE
#> Organism SRA.Study
#> <character> <character>
#> HMLE_polyAplus_rep1 Homo sapiens ERP106922
#> HMLE_polyAplus_rep2 Homo sapiens ERP106922
#> HMLE_polyAplus_rep3 Homo sapiens ERP106922
#> mesHMLE_polyAplus_rep1 Homo sapiens ERP106922
#> mesHMLE_polyAplus_rep2 Homo sapiens ERP106922
#> mesHMLE_polyAplus_rep3 Homo sapiens ERP106922
#> mesHMLE_QKI5kd_polyAplus_rep1 Homo sapiens ERP106922
#> mesHMLE_QKI5kd_polyAplus_rep2 Homo sapiens ERP106922
#> mesHMLE_miR200c_polyAplus_rep1 Homo sapiens ERP106922
#> mesHMLE_miR200c_polyAplus_rep2 Homo sapiens ERP1069224 Exploratory analysis and visualization
Below we demonstrate how the SummarizedExperiment object can be interacted with. A simple MDS analyis is demonstrated for each of the datasets within this package. This transcriptomic data can be used for differential expression (DE) analyis and co-expression analysis to better understand the processes underlying EMT or MET.
4.1 cursons2018
This gene expression data comes from the human mammary epithelial (HMLE) cell line. A mesenchymal HMLE (mesHMLE) phenotype was induced following treatment with TGFb. The mesHMLE subline was then treated with mir200c to reinduce an epithelial phenotype.
See help page ?cursons2018_se for further reference
library(edgeR)
#> Loading required package: limma
#>
#> Attaching package: 'limma'
#> The following object is masked from 'package:BiocGenerics':
#>
#> plotMA
library(RColorBrewer)
cursons2018_dge <- asDGEList(cursons2018_se)
cursons2018_dge <- calcNormFactors(cursons2018_dge)
plotMDS(cursons2018_dge)
4.2 cursons2015
This gene expression data comes from the PMC42-ET, PMC42-LA and MDA-MB-468 cell lines. Mesenchymal phenotype was induced in PMC42 cell lines with EGF treatment and in MDA-MB-468 with either EGF treatment or kept under Hypoxia.
See help page ?cursons2015_se for further reference.
cursons2015_se = eh[['EH5441']]
#> see ?emtdata and browseVignettes('emtdata') for documentation
#> loading from cache
cursons2015_dge <- asDGEList(cursons2015_se)
cursons2015_dge <- calcNormFactors(cursons2015_dge)
colours <- brewer.pal(7, name = "Paired")
plotMDS(cursons2015_dge, dim.plot = c(2,3), col=rep(colours, each = 3)) 
4.3 foroutan2017
This gene expression data comes from multiple different studies (microarary and RNA-seq), with cell lines treated using TGFb to induce a mesenchymal shift. Data were combined using SVA and ComBat to remove batch effects.
See help page ?foroutan2017_se for further reference
foroutan2017_se = eh[['EH5439']]
#> see ?emtdata and browseVignettes('emtdata') for documentation
#> loading from cache
foroutan2017_dge <- asDGEList(foroutan2017_se, assay_name = "logExpr")
foroutan2017_dge <- calcNormFactors(foroutan2017_dge)
tgfb_col <- as.numeric(foroutan2017_dge$samples$Treatment %in% 'TGFb') + 1
plotMDS(foroutan2017_dge, labels = foroutan2017_dge$samples$Treatment, col = tgfb_col)
5 Session information
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] RColorBrewer_1.1-3 edgeR_4.0.0
#> [3] limma_3.58.0 SummarizedExperiment_1.32.0
#> [5] Biobase_2.62.0 GenomicRanges_1.54.0
#> [7] GenomeInfoDb_1.38.0 IRanges_2.36.0
#> [9] S4Vectors_0.40.0 MatrixGenerics_1.14.0
#> [11] matrixStats_1.0.0 ExperimentHub_2.10.0
#> [13] AnnotationHub_3.10.0 BiocFileCache_2.10.0
#> [15] dbplyr_2.3.4 BiocGenerics_0.48.0
#> [17] emtdata_1.10.0
#>
#> loaded via a namespace (and not attached):
#> [1] tidyselect_1.2.0 dplyr_1.1.3
#> [3] blob_1.2.4 filelock_1.0.2
#> [5] Biostrings_2.70.1 bitops_1.0-7
#> [7] fastmap_1.1.1 RCurl_1.98-1.12
#> [9] promises_1.2.1 digest_0.6.33
#> [11] mime_0.12 lifecycle_1.0.3
#> [13] ellipsis_0.3.2 statmod_1.5.0
#> [15] KEGGREST_1.42.0 interactiveDisplayBase_1.40.0
#> [17] RSQLite_2.3.1 magrittr_2.0.3
#> [19] compiler_4.3.1 rlang_1.1.1
#> [21] sass_0.4.7 tools_4.3.1
#> [23] utf8_1.2.4 yaml_2.3.7
#> [25] knitr_1.44 S4Arrays_1.2.0
#> [27] bit_4.0.5 curl_5.1.0
#> [29] DelayedArray_0.28.0 abind_1.4-5
#> [31] withr_2.5.1 purrr_1.0.2
#> [33] grid_4.3.1 fansi_1.0.5
#> [35] xtable_1.8-4 cli_3.6.1
#> [37] rmarkdown_2.25 crayon_1.5.2
#> [39] generics_0.1.3 httr_1.4.7
#> [41] DBI_1.1.3 cachem_1.0.8
#> [43] zlibbioc_1.48.0 AnnotationDbi_1.64.0
#> [45] BiocManager_1.30.22 XVector_0.42.0
#> [47] vctrs_0.6.4 Matrix_1.6-1.1
#> [49] jsonlite_1.8.7 bit64_4.0.5
#> [51] locfit_1.5-9.8 jquerylib_0.1.4
#> [53] glue_1.6.2 BiocVersion_3.18.0
#> [55] later_1.3.1 tibble_3.2.1
#> [57] pillar_1.9.0 rappdirs_0.3.3
#> [59] htmltools_0.5.6.1 GenomeInfoDbData_1.2.11
#> [61] R6_2.5.1 evaluate_0.22
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