README.md
In jrufv/TFMjrufv: Framework para el análisis de datos ómicos
TFMjrufv
Este paquete pretende crear un pipeline que permita realizar un
análisis estadístico completo de diferentes tipos de datos ómicos.
Instalación
Puede instalar la versión en desarrollo desde
GitHub con:
install.packages("devtools")
#> Installing package into '/tmp/RtmpuK2pJA/temp_libpathbb25e2329c1'
#> (as 'lib' is unspecified)
library(devtools)
#> Loading required package: usethis
devtools::install_github("jrufv/TFMjrufv", upgrade = "always")
#> Downloading GitHub repo jrufv/TFMjrufv@HEAD
#>
#> checking for file ‘/tmp/RtmpRmZGiT/remotesd8547f6840d/jrufv-TFMjrufv-143ca40/DESCRIPTION’ ... ✓ checking for file ‘/tmp/RtmpRmZGiT/remotesd8547f6840d/jrufv-TFMjrufv-143ca40/DESCRIPTION’
#> ─ preparing ‘TFMjrufv’: (363ms)
#> ✓ checking DESCRIPTION meta-information
#> ─ checking for LF line-endings in source and make files and shell scripts
#> ─ checking for empty or unneeded directories
#> NB: this package now depends on R (>= 3.5.0)
#> WARNING: Added dependency on R >= 3.5.0 because serialized objects in
#> serialize/load version 3 cannot be read in older versions of R.
#> File(s) containing such objects:
#> ‘TFMjrufv/data/.RData’
#> ─ building ‘TFMjrufv_0.0.0.9000.tar.gz’
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#>
#> Installing package into '/tmp/RtmpuK2pJA/temp_libpathbb25e2329c1'
#> (as 'lib' is unspecified)
library(TFMjrufv)
Ejemplo 1. Datos de microarray
1.1 Carga de paquetes de Bioconductor
En primer lugar usamos la función ins_pack() para instalar (en caso
necesario) y cargar los paquetes de Bioconductor necesarios. Además
podemos añadir el paquete de anotaciones que usaremos durante el
análisis.
library(TFMjrufv)
ins_pack(annot_pack = "hgu133a.db")
#> Loading required package: AnnotationDbi
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#> Loading required package: parallel
#>
#> Attaching package: 'BiocGenerics'
#> The following objects are masked from 'package:parallel':
#>
#> clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
#> clusterExport, clusterMap, parApply, parCapply, parLapply,
#> parLapplyLB, parRapply, parSapply, parSapplyLB
#> The following objects are masked from 'package:stats':
#>
#> IQR, mad, sd, var, xtabs
#> The following objects are masked from 'package:base':
#>
#> anyDuplicated, append, as.data.frame, basename, cbind, colnames,
#> dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
#> grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
#> order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
#> rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
#> union, unique, unsplit, which.max, which.min
#> Loading required package: Biobase
#> Welcome to Bioconductor
#>
#> Vignettes contain introductory material; view with
#> 'browseVignettes()'. To cite Bioconductor, see
#> 'citation("Biobase")', and for packages 'citation("pkgname")'.
#> Loading required package: IRanges
#> Loading required package: S4Vectors
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#> expand.grid, I, unname
#> Loading required package: edgeR
#> Loading required package: limma
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#> plotMA
#> Loading required package: enrichplot
#>
#> Loading required package: genefilter
#> Loading required package: MSnbase
#> Loading required package: mzR
#> Loading required package: Rcpp
#> Loading required package: ProtGenerics
#>
#> Attaching package: 'ProtGenerics'
#> The following object is masked from 'package:stats':
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#> smooth
#>
#> This is MSnbase version 2.17.7
#> Visit https://lgatto.github.io/MSnbase/ to get started.
#>
#> Attaching package: 'MSnbase'
#> The following object is masked from 'package:base':
#>
#> trimws
#> Loading required package: oligo
#> Loading required package: oligoClasses
#> Welcome to oligoClasses version 1.53.0
#>
#> Attaching package: 'oligoClasses'
#> The following object is masked from 'package:mzR':
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#> manufacturer
#> Loading required package: Biostrings
#> Loading required package: XVector
#> Loading required package: GenomeInfoDb
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#> Attaching package: 'Biostrings'
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#> strsplit
#> ================================================================================
#> Welcome to oligo version 1.55.1
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#> Attaching package: 'oligo'
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#> intensity, MAplot
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#> intensity
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#> backgroundCorrect
#> Loading required package: pmp
#> Loading required package: POMA
#> Welcome to POMA!
#> ✔ 1.1.15 version
#> ✔ POMAShiny app: https://github.com/pcastellanoescuder/POMAShiny
#> ℹ For more detailed package information please visit https://pcastellanoescuder.github.io/POMA/
#> Loading required package: ReactomePA
#> ReactomePA v1.35.1 For help: https://yulab-smu.top/biomedical-knowledge-mining-book/reactomepa.html
#>
#> If you use ReactomePA in published research, please cite:
#> Guangchuang Yu, Qing-Yu He. ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Molecular BioSystems 2016, 12(2):477-479
#> Loading required package: SummarizedExperiment
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#> colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#> colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#> colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#> colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
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#> colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
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#> rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#> rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#> rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#> rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#> rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#> rowWeightedSds, rowWeightedVars
#> The following objects are masked from 'package:genefilter':
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#> Attaching package: 'xcms'
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#> distance
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#> distance
#> The following object is masked from 'package:IRanges':
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#> distance
#> The following object is masked from 'package:stats':
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#> sigma
#> Loading required package: hgu133a.db
#> Loading required package: org.Hs.eg.db
#>
#>
1.2 Lectura de datos
Los datos los tenemos almacenados en archivos .CEL (uno por muestra).
Además debemos crear un archivo de texto en el que indicaremos las
características de cada muestra para poder realizar el análisis.
Nota: Se recomienda cambiar el directorio de trabajo a la carpeta del
paquete. De lo contrario deberá modificar los parámetros path y
targets para especificar la ruta de archivo específica.
data_microarray <- read_data(data_type = "microarray",
path = "./inst/extdata/microarray",
targets = "./inst/extdata/microarray/targets.csv",
sep_targ = ";",
gz_file = FALSE)
#> Loading required package: pd.hg.u133a
#> Loading required package: RSQLite
#> Loading required package: DBI
#> Platform design info loaded.
#> Reading in : ./inst/extdata/microarray/GSM23372.CEL
#> Reading in : ./inst/extdata/microarray/GSM23373.CEL
#> Reading in : ./inst/extdata/microarray/GSM23377.CEL
#> Reading in : ./inst/extdata/microarray/GSM23378.CEL
#> Reading in : ./inst/extdata/microarray/GSM23382.CEL
#> Reading in : ./inst/extdata/microarray/GSM23383.CEL
#> Reading in : ./inst/extdata/microarray/GSM23384.CEL
#> Warning in oligo::read.celfiles(files, phenoData = sampleinfo): 'channel'
#> automatically added to varMetadata in phenoData.
head(exprs(data_microarray))
#> UHR1 UHR2 UHR3 UHR4 CTHR1 CTHR2 CTHR3
#> 1 89.3 137.3 133.8 76.3 121.5 101.3 100.0
#> 2 6860.3 11210.5 14479.3 9876.5 10133.3 11418.8 9968.5
#> 3 97.8 178.8 124.8 100.0 144.3 114.0 112.5
#> 4 6809.0 10973.3 14421.0 10595.8 10417.5 11588.3 9872.0
#> 5 54.3 89.8 68.3 60.5 68.3 56.5 52.3
#> 6 93.0 130.5 114.8 83.5 129.5 120.3 109.3
pData(data_microarray)
#> ShortName Group
#> UHR1 UHR1 Untreated
#> UHR2 UHR2 Untreated
#> UHR3 UHR3 Untreated
#> UHR4 UHR4 Untreated
#> CTHR1 CTHR1 Treated
#> CTHR2 CTHR2 Treated
#> CTHR3 CTHR3 Treated
1.3 Visualización de los dato brutos
La función plotTFM() permite visualizar diferentes gráficos.
plotTFM(data_microarray, plot = "boxplot")
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning: Use of `meltdatacpm$samples` is discouraged. Use `samples` instead.
#> Warning: Use of `meltdatacpm$condition` is discouraged. Use `condition` instead.
plotTFM(data_microarray, plot = "density")
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning: Use of `meltdata$condition` is discouraged. Use `condition` instead.
plotTFM(data_microarray, plot = "PCA")
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning: Use of `pcdata$PC1` is discouraged. Use `PC1` instead.
#> Warning: Use of `pcdata$PC2` is discouraged. Use `PC2` instead.
#> Warning: Use of `pcdata$PC1` is discouraged. Use `PC1` instead.
#> Warning: Use of `pcdata$PC2` is discouraged. Use `PC2` instead.
plotTFM(data_microarray, plot = "heatmap")
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
plotTFM(data_microarray, plot = "heatmapS")
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
1.4 Preprocesamiento y normalizacion
La función prep_norm_microarray() permite realizar el filtraje no
específico de los datos mediante los argumentos varFilter y
featureFilter, y posteriormente realiza la normalización de los datos.
norm_data_microarray <- prep_norm_microarray(data_microarray,
varFilter = TRUE,
featureFilter = TRUE,
annot_pack = "hgu133a.db")
#> Background correcting
#> Normalizing
#> Calculating Expression
head(exprs(norm_data_microarray))
#> UHR1 UHR2 UHR3 UHR4 CTHR1 CTHR2
#> 203440_at 10.075796 10.477572 10.744096 10.614318 9.721993 10.366111
#> 207079_s_at 8.019684 7.581823 7.817177 7.755407 6.335449 7.375170
#> 209027_s_at 7.766735 7.840724 7.403147 7.144601 5.975719 6.529452
#> 202382_s_at 8.149254 7.915265 8.315253 8.248825 8.453436 8.637004
#> 209451_at 4.877752 5.655935 4.877752 4.530698 4.707170 4.885731
#> 217087_at 6.902545 7.097501 6.843267 7.214645 7.280983 7.431862
#> CTHR3
#> 203440_at 10.264942
#> 207079_s_at 7.118590
#> 209027_s_at 6.452251
#> 202382_s_at 8.962548
#> 209451_at 5.290542
#> 217087_at 7.099811
1.5 Visualización de los datos normalizados
Una vez se han normalizado los datos se pueden comparar los gráficos
antes y después de la normalización.
plotTFM(norm_data_microarray, plot = "boxplot")
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning: Use of `meltdatacpm$samples` is discouraged. Use `samples` instead.
#> Warning: Use of `meltdatacpm$condition` is discouraged. Use `condition` instead.
plotTFM(norm_data_microarray, plot = "density")
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning: Use of `meltdata$condition` is discouraged. Use `condition` instead.
plotTFM(norm_data_microarray, plot = "PCA")
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning: Use of `pcdata$PC1` is discouraged. Use `PC1` instead.
#> Warning: Use of `pcdata$PC2` is discouraged. Use `PC2` instead.
#> Warning: Use of `pcdata$PC1` is discouraged. Use `PC1` instead.
#> Warning: Use of `pcdata$PC2` is discouraged. Use `PC2` instead.
plotTFM(norm_data_microarray, plot = "heatmap")
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
plotTFM(norm_data_microarray, plot = "heatmapS")
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
1.6 Análisis de expresión diferencial
La función dea() realiza un análisis de expresión diferencial basado
en modelos lineales del paquete limma. El retorno de la funcion consta
de una lista con un marco de datos para cada contraste realizado, una
tabla que muestra la cantidad de analitos sobreexpresados e
infraexpresados por contraste y un objeto TestResults que mantiene los
analitos que han mostrado expresión diferencial en algún contraste.
dea_microarray <- dea(norm_data_microarray, cont = "Treated-Untreated",
name = "TRvsUN", maxanal = 1000, adjmethod = "fdr",
pvalcoff = 0.1, dtmethod = "separate")
head(dea_microarray[[1]])
#> logFC AveExpr t P.Value adj.P.Val B
#> 36711_at 4.208272 5.701037 23.78321 1.814076e-18 1.124546e-14 31.48386
#> 202672_s_at 2.914235 8.530236 17.11506 3.732774e-15 1.156973e-11 24.54060
#> 220952_s_at -3.109164 6.473018 -16.28228 1.154659e-14 2.385909e-11 23.47338
#> 212653_s_at -2.710004 6.764173 -15.44444 3.780226e-14 5.858405e-11 22.34393
#> 209409_at -2.600283 8.302325 -14.71795 1.104226e-13 1.369019e-10 21.31613
#> 218611_at 2.576729 8.995985 14.58962 1.340343e-13 1.384798e-10 21.12967
dea_microarray[[2]]
#> TRvsUN
#> Down 1887
#> NotSig 3229
#> Up 1083
dea_microarray[[3]]
#> TestResults matrix
#> Contrasts
#> TRvsUN
#> 207079_s_at -1
#> 209027_s_at -1
#> 202382_s_at 1
#> 221847_at 1
#> 217746_s_at -1
#> 2965 more rows ...
1.7 Anotación de resultados
A continuacion se añaden las anotaciones de los genes seleccionados en
cada contraste (en nuestro caso un unico contraste).
anot_dea_microarray <- annotated(dea_microarray, maPackage = "hgu133a.db",
ID = "PROBEID")
#> 'select()' returned 1:1 mapping between keys and columns
head(anot_dea_microarray[[1]])
#> PROBEID ENTREZID SYMBOL GENENAME
#> 1 36711_at 23764 MAFF MAF bZIP transcription factor F
#> 2 202672_s_at 467 ATF3 activating transcription factor 3
#> 3 220952_s_at 54477 PLEKHA5 pleckstrin homology domain containing A5
#> 4 212653_s_at 23301 EHBP1 EH domain binding protein 1
#> 5 209409_at 2887 GRB10 growth factor receptor bound protein 10
#> 6 218611_at 51278 IER5 immediate early response 5
#> logFC AveExpr t P.Value adj.P.Val B
#> 1 4.208272 5.701037 23.78321 1.814076e-18 1.124546e-14 31.48386
#> 2 2.914235 8.530236 17.11506 3.732774e-15 1.156973e-11 24.54060
#> 3 -3.109164 6.473018 -16.28228 1.154659e-14 2.385909e-11 23.47338
#> 4 -2.710004 6.764173 -15.44444 3.780226e-14 5.858405e-11 22.34393
#> 5 -2.600283 8.302325 -14.71795 1.104226e-13 1.369019e-10 21.31613
#> 6 2.576729 8.995985 14.58962 1.340343e-13 1.384798e-10 21.12967
1.8 Análisis de significación biológica
Finalmente podemos realizar el análisis de enriquecimiento para
encontrar las vías que se expresan diferencialmente en cada contraste.
enRes_microarray <- bsa(anot_dea_microarray, GOPackage = org.Hs.egGO,
PATHPackage = org.Hs.egPATH, organism = "human",
pvcoff = 0.05, padmethod = "BH")
enRes_microarray
#> $TRvsUN
#> #
#> # over-representation test
#> #
#> #...@organism human
#> #...@ontology Reactome
#> #...@keytype ENTREZID
#> #...@gene chr [1:1000] "23764" "467" "54477" "23301" "2887" "51278" "10614" "116039" ...
#> #...pvalues adjusted by 'BH' with cutoff <0.05
#> #...122 enriched terms found
#> 'data.frame': 122 obs. of 9 variables:
#> $ ID : chr "R-HSA-2500257" "R-HSA-141424" "R-HSA-141444" "R-HSA-68877" ...
#> $ Description: chr "Resolution of Sister Chromatid Cohesion" "Amplification of signal from the kinetochores" "Amplification of signal from unattached kinetochores via a MAD2 inhibitory signal" "Mitotic Prometaphase" ...
#> $ GeneRatio : chr "32/680" "26/680" "26/680" "39/680" ...
#> $ BgRatio : chr "124/10818" "94/10818" "94/10818" "201/10818" ...
#> $ pvalue : num 3.22e-12 6.62e-11 6.62e-11 1.85e-10 3.24e-10 ...
#> $ p.adjust : num 4.01e-09 2.75e-08 2.75e-08 5.75e-08 8.07e-08 ...
#> $ qvalue : num 3.21e-09 2.20e-08 2.20e-08 4.60e-08 6.46e-08 ...
#> $ geneID : chr "BUB1B/PDS5B/NDC80/STAG2/TUBB2A/MAD1L1/PPP2R5E/STAG1/PAFAH1B1/BUB1/PPP2R5C/CENPE/PDS5A/XPO1/SPC25/CENPI/CENPF/CL"| __truncated__ "BUB1B/NDC80/MAD1L1/PPP2R5E/PAFAH1B1/BUB1/PPP2R5C/CENPE/XPO1/SPC25/CENPI/CENPF/CLIP1/CLASP2/CKAP5/PLK1/KIF2A/KIF"| __truncated__ "BUB1B/NDC80/MAD1L1/PPP2R5E/PAFAH1B1/BUB1/PPP2R5C/CENPE/XPO1/SPC25/CENPI/CENPF/CLIP1/CLASP2/CKAP5/PLK1/KIF2A/KIF"| __truncated__ "BUB1B/PDS5B/NDC80/STAG2/TUBB2A/TUBGCP3/MAD1L1/AKAP9/PPP2R5E/STAG1/PAFAH1B1/BUB1/PPP2R5C/CENPE/PDS5A/XPO1/SPC25/"| __truncated__ ...
#> $ Count : int 32 26 26 39 28 27 51 17 40 40 ...
#> #...Citation
#> Guangchuang Yu, Qing-Yu He. ReactomePA: an R/Bioconductor package for
#> reactome pathway analysis and visualization. Molecular BioSystems
#> 2016, 12(2):477-479
jrufv/TFMjrufv documentation built on Dec. 21, 2021, 3:15 a.m.
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TFMjrufv
Este paquete pretende crear un pipeline que permita realizar un análisis estadístico completo de diferentes tipos de datos ómicos.
Instalación
Puede instalar la versión en desarrollo desde GitHub con:
install.packages("devtools")
#> Installing package into '/tmp/RtmpuK2pJA/temp_libpathbb25e2329c1'
#> (as 'lib' is unspecified)
library(devtools)
#> Loading required package: usethis
devtools::install_github("jrufv/TFMjrufv", upgrade = "always")
#> Downloading GitHub repo jrufv/TFMjrufv@HEAD
#>
#> checking for file ‘/tmp/RtmpRmZGiT/remotesd8547f6840d/jrufv-TFMjrufv-143ca40/DESCRIPTION’ ... ✓ checking for file ‘/tmp/RtmpRmZGiT/remotesd8547f6840d/jrufv-TFMjrufv-143ca40/DESCRIPTION’
#> ─ preparing ‘TFMjrufv’: (363ms)
#> ✓ checking DESCRIPTION meta-information
#> ─ checking for LF line-endings in source and make files and shell scripts
#> ─ checking for empty or unneeded directories
#> NB: this package now depends on R (>= 3.5.0)
#> WARNING: Added dependency on R >= 3.5.0 because serialized objects in
#> serialize/load version 3 cannot be read in older versions of R.
#> File(s) containing such objects:
#> ‘TFMjrufv/data/.RData’
#> ─ building ‘TFMjrufv_0.0.0.9000.tar.gz’
#>
#>
#> Installing package into '/tmp/RtmpuK2pJA/temp_libpathbb25e2329c1'
#> (as 'lib' is unspecified)
library(TFMjrufv)
Ejemplo 1. Datos de microarray
1.1 Carga de paquetes de Bioconductor
En primer lugar usamos la función ins_pack() para instalar (en caso
necesario) y cargar los paquetes de Bioconductor necesarios. Además
podemos añadir el paquete de anotaciones que usaremos durante el
análisis.
library(TFMjrufv)
ins_pack(annot_pack = "hgu133a.db")
#> Loading required package: AnnotationDbi
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#> Loading required package: parallel
#>
#> Attaching package: 'BiocGenerics'
#> The following objects are masked from 'package:parallel':
#>
#> clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
#> clusterExport, clusterMap, parApply, parCapply, parLapply,
#> parLapplyLB, parRapply, parSapply, parSapplyLB
#> The following objects are masked from 'package:stats':
#>
#> IQR, mad, sd, var, xtabs
#> The following objects are masked from 'package:base':
#>
#> anyDuplicated, append, as.data.frame, basename, cbind, colnames,
#> dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
#> grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
#> order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
#> rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
#> union, unique, unsplit, which.max, which.min
#> Loading required package: Biobase
#> Welcome to Bioconductor
#>
#> Vignettes contain introductory material; view with
#> 'browseVignettes()'. To cite Bioconductor, see
#> 'citation("Biobase")', and for packages 'citation("pkgname")'.
#> Loading required package: IRanges
#> Loading required package: S4Vectors
#>
#> Attaching package: 'S4Vectors'
#> The following objects are masked from 'package:base':
#>
#> expand.grid, I, unname
#> Loading required package: edgeR
#> Loading required package: limma
#>
#> Attaching package: 'limma'
#> The following object is masked from 'package:BiocGenerics':
#>
#> plotMA
#> Loading required package: enrichplot
#>
#> Loading required package: genefilter
#> Loading required package: MSnbase
#> Loading required package: mzR
#> Loading required package: Rcpp
#> Loading required package: ProtGenerics
#>
#> Attaching package: 'ProtGenerics'
#> The following object is masked from 'package:stats':
#>
#> smooth
#>
#> This is MSnbase version 2.17.7
#> Visit https://lgatto.github.io/MSnbase/ to get started.
#>
#> Attaching package: 'MSnbase'
#> The following object is masked from 'package:base':
#>
#> trimws
#> Loading required package: oligo
#> Loading required package: oligoClasses
#> Welcome to oligoClasses version 1.53.0
#>
#> Attaching package: 'oligoClasses'
#> The following object is masked from 'package:mzR':
#>
#> manufacturer
#> Loading required package: Biostrings
#> Loading required package: XVector
#> Loading required package: GenomeInfoDb
#>
#> Attaching package: 'Biostrings'
#> The following object is masked from 'package:base':
#>
#> strsplit
#> ================================================================================
#> Welcome to oligo version 1.55.1
#> ================================================================================
#>
#> Attaching package: 'oligo'
#> The following objects are masked from 'package:MSnbase':
#>
#> intensity, MAplot
#> The following object is masked from 'package:ProtGenerics':
#>
#> intensity
#> The following object is masked from 'package:limma':
#>
#> backgroundCorrect
#> Loading required package: pmp
#> Loading required package: POMA
#> Welcome to POMA!
#> ✔ 1.1.15 version
#> ✔ POMAShiny app: https://github.com/pcastellanoescuder/POMAShiny
#> ℹ For more detailed package information please visit https://pcastellanoescuder.github.io/POMA/
#> Loading required package: ReactomePA
#> ReactomePA v1.35.1 For help: https://yulab-smu.top/biomedical-knowledge-mining-book/reactomepa.html
#>
#> If you use ReactomePA in published research, please cite:
#> Guangchuang Yu, Qing-Yu He. ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Molecular BioSystems 2016, 12(2):477-479
#> Loading required package: SummarizedExperiment
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#>
#> Attaching package: 'matrixStats'
#> The following objects are masked from 'package:genefilter':
#>
#> rowSds, rowVars
#> The following objects are masked from 'package:Biobase':
#>
#> anyMissing, rowMedians
#>
#> Attaching package: 'MatrixGenerics'
#> The following objects are masked from 'package:matrixStats':
#>
#> colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#> colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#> colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#> colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#> colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#> colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#> colWeightedMeans, colWeightedMedians, colWeightedSds,
#> colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#> rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#> rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#> rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#> rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#> rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#> rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#> rowWeightedSds, rowWeightedVars
#> The following objects are masked from 'package:genefilter':
#>
#> rowSds, rowVars
#> The following object is masked from 'package:Biobase':
#>
#> rowMedians
#> Loading required package: GenomicRanges
#> Loading required package: xcms
#> Loading required package: BiocParallel
#>
#> This is xcms version 3.13.8
#>
#> Attaching package: 'xcms'
#> The following object is masked from 'package:SummarizedExperiment':
#>
#> distance
#> The following object is masked from 'package:GenomicRanges':
#>
#> distance
#> The following object is masked from 'package:IRanges':
#>
#> distance
#> The following object is masked from 'package:stats':
#>
#> sigma
#> Loading required package: hgu133a.db
#> Loading required package: org.Hs.eg.db
#>
#>
1.2 Lectura de datos
Los datos los tenemos almacenados en archivos .CEL (uno por muestra). Además debemos crear un archivo de texto en el que indicaremos las características de cada muestra para poder realizar el análisis.
Nota: Se recomienda cambiar el directorio de trabajo a la carpeta del
paquete. De lo contrario deberá modificar los parámetros path y
targets para especificar la ruta de archivo específica.
data_microarray <- read_data(data_type = "microarray",
path = "./inst/extdata/microarray",
targets = "./inst/extdata/microarray/targets.csv",
sep_targ = ";",
gz_file = FALSE)
#> Loading required package: pd.hg.u133a
#> Loading required package: RSQLite
#> Loading required package: DBI
#> Platform design info loaded.
#> Reading in : ./inst/extdata/microarray/GSM23372.CEL
#> Reading in : ./inst/extdata/microarray/GSM23373.CEL
#> Reading in : ./inst/extdata/microarray/GSM23377.CEL
#> Reading in : ./inst/extdata/microarray/GSM23378.CEL
#> Reading in : ./inst/extdata/microarray/GSM23382.CEL
#> Reading in : ./inst/extdata/microarray/GSM23383.CEL
#> Reading in : ./inst/extdata/microarray/GSM23384.CEL
#> Warning in oligo::read.celfiles(files, phenoData = sampleinfo): 'channel'
#> automatically added to varMetadata in phenoData.
head(exprs(data_microarray))
#> UHR1 UHR2 UHR3 UHR4 CTHR1 CTHR2 CTHR3
#> 1 89.3 137.3 133.8 76.3 121.5 101.3 100.0
#> 2 6860.3 11210.5 14479.3 9876.5 10133.3 11418.8 9968.5
#> 3 97.8 178.8 124.8 100.0 144.3 114.0 112.5
#> 4 6809.0 10973.3 14421.0 10595.8 10417.5 11588.3 9872.0
#> 5 54.3 89.8 68.3 60.5 68.3 56.5 52.3
#> 6 93.0 130.5 114.8 83.5 129.5 120.3 109.3
pData(data_microarray)
#> ShortName Group
#> UHR1 UHR1 Untreated
#> UHR2 UHR2 Untreated
#> UHR3 UHR3 Untreated
#> UHR4 UHR4 Untreated
#> CTHR1 CTHR1 Treated
#> CTHR2 CTHR2 Treated
#> CTHR3 CTHR3 Treated
1.3 Visualización de los dato brutos
La función plotTFM() permite visualizar diferentes gráficos.
plotTFM(data_microarray, plot = "boxplot")
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning: Use of `meltdatacpm$samples` is discouraged. Use `samples` instead.
#> Warning: Use of `meltdatacpm$condition` is discouraged. Use `condition` instead.
plotTFM(data_microarray, plot = "density")
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning: Use of `meltdata$condition` is discouraged. Use `condition` instead.
plotTFM(data_microarray, plot = "PCA")
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning: Use of `pcdata$PC1` is discouraged. Use `PC1` instead.
#> Warning: Use of `pcdata$PC2` is discouraged. Use `PC2` instead.
#> Warning: Use of `pcdata$PC1` is discouraged. Use `PC1` instead.
#> Warning: Use of `pcdata$PC2` is discouraged. Use `PC2` instead.
plotTFM(data_microarray, plot = "heatmap")
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
plotTFM(data_microarray, plot = "heatmapS")
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
1.4 Preprocesamiento y normalizacion
La función prep_norm_microarray() permite realizar el filtraje no
específico de los datos mediante los argumentos varFilter y
featureFilter, y posteriormente realiza la normalización de los datos.
norm_data_microarray <- prep_norm_microarray(data_microarray,
varFilter = TRUE,
featureFilter = TRUE,
annot_pack = "hgu133a.db")
#> Background correcting
#> Normalizing
#> Calculating Expression
head(exprs(norm_data_microarray))
#> UHR1 UHR2 UHR3 UHR4 CTHR1 CTHR2
#> 203440_at 10.075796 10.477572 10.744096 10.614318 9.721993 10.366111
#> 207079_s_at 8.019684 7.581823 7.817177 7.755407 6.335449 7.375170
#> 209027_s_at 7.766735 7.840724 7.403147 7.144601 5.975719 6.529452
#> 202382_s_at 8.149254 7.915265 8.315253 8.248825 8.453436 8.637004
#> 209451_at 4.877752 5.655935 4.877752 4.530698 4.707170 4.885731
#> 217087_at 6.902545 7.097501 6.843267 7.214645 7.280983 7.431862
#> CTHR3
#> 203440_at 10.264942
#> 207079_s_at 7.118590
#> 209027_s_at 6.452251
#> 202382_s_at 8.962548
#> 209451_at 5.290542
#> 217087_at 7.099811
1.5 Visualización de los datos normalizados
Una vez se han normalizado los datos se pueden comparar los gráficos antes y después de la normalización.
plotTFM(norm_data_microarray, plot = "boxplot")
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning: Use of `meltdatacpm$samples` is discouraged. Use `samples` instead.
#> Warning: Use of `meltdatacpm$condition` is discouraged. Use `condition` instead.
plotTFM(norm_data_microarray, plot = "density")
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning: Use of `meltdata$condition` is discouraged. Use `condition` instead.
plotTFM(norm_data_microarray, plot = "PCA")
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning: Use of `pcdata$PC1` is discouraged. Use `PC1` instead.
#> Warning: Use of `pcdata$PC2` is discouraged. Use `PC2` instead.
#> Warning: Use of `pcdata$PC1` is discouraged. Use `PC1` instead.
#> Warning: Use of `pcdata$PC2` is discouraged. Use `PC2` instead.
plotTFM(norm_data_microarray, plot = "heatmap")
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
plotTFM(norm_data_microarray, plot = "heatmapS")
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by the
#> caller; using TRUE
1.6 Análisis de expresión diferencial
La función dea() realiza un análisis de expresión diferencial basado
en modelos lineales del paquete limma. El retorno de la funcion consta
de una lista con un marco de datos para cada contraste realizado, una
tabla que muestra la cantidad de analitos sobreexpresados e
infraexpresados por contraste y un objeto TestResults que mantiene los
analitos que han mostrado expresión diferencial en algún contraste.
dea_microarray <- dea(norm_data_microarray, cont = "Treated-Untreated",
name = "TRvsUN", maxanal = 1000, adjmethod = "fdr",
pvalcoff = 0.1, dtmethod = "separate")
head(dea_microarray[[1]])
#> logFC AveExpr t P.Value adj.P.Val B
#> 36711_at 4.208272 5.701037 23.78321 1.814076e-18 1.124546e-14 31.48386
#> 202672_s_at 2.914235 8.530236 17.11506 3.732774e-15 1.156973e-11 24.54060
#> 220952_s_at -3.109164 6.473018 -16.28228 1.154659e-14 2.385909e-11 23.47338
#> 212653_s_at -2.710004 6.764173 -15.44444 3.780226e-14 5.858405e-11 22.34393
#> 209409_at -2.600283 8.302325 -14.71795 1.104226e-13 1.369019e-10 21.31613
#> 218611_at 2.576729 8.995985 14.58962 1.340343e-13 1.384798e-10 21.12967
dea_microarray[[2]]
#> TRvsUN
#> Down 1887
#> NotSig 3229
#> Up 1083
dea_microarray[[3]]
#> TestResults matrix
#> Contrasts
#> TRvsUN
#> 207079_s_at -1
#> 209027_s_at -1
#> 202382_s_at 1
#> 221847_at 1
#> 217746_s_at -1
#> 2965 more rows ...
1.7 Anotación de resultados
A continuacion se añaden las anotaciones de los genes seleccionados en cada contraste (en nuestro caso un unico contraste).
anot_dea_microarray <- annotated(dea_microarray, maPackage = "hgu133a.db",
ID = "PROBEID")
#> 'select()' returned 1:1 mapping between keys and columns
head(anot_dea_microarray[[1]])
#> PROBEID ENTREZID SYMBOL GENENAME
#> 1 36711_at 23764 MAFF MAF bZIP transcription factor F
#> 2 202672_s_at 467 ATF3 activating transcription factor 3
#> 3 220952_s_at 54477 PLEKHA5 pleckstrin homology domain containing A5
#> 4 212653_s_at 23301 EHBP1 EH domain binding protein 1
#> 5 209409_at 2887 GRB10 growth factor receptor bound protein 10
#> 6 218611_at 51278 IER5 immediate early response 5
#> logFC AveExpr t P.Value adj.P.Val B
#> 1 4.208272 5.701037 23.78321 1.814076e-18 1.124546e-14 31.48386
#> 2 2.914235 8.530236 17.11506 3.732774e-15 1.156973e-11 24.54060
#> 3 -3.109164 6.473018 -16.28228 1.154659e-14 2.385909e-11 23.47338
#> 4 -2.710004 6.764173 -15.44444 3.780226e-14 5.858405e-11 22.34393
#> 5 -2.600283 8.302325 -14.71795 1.104226e-13 1.369019e-10 21.31613
#> 6 2.576729 8.995985 14.58962 1.340343e-13 1.384798e-10 21.12967
1.8 Análisis de significación biológica
Finalmente podemos realizar el análisis de enriquecimiento para encontrar las vías que se expresan diferencialmente en cada contraste.
enRes_microarray <- bsa(anot_dea_microarray, GOPackage = org.Hs.egGO,
PATHPackage = org.Hs.egPATH, organism = "human",
pvcoff = 0.05, padmethod = "BH")
enRes_microarray
#> $TRvsUN
#> #
#> # over-representation test
#> #
#> #...@organism human
#> #...@ontology Reactome
#> #...@keytype ENTREZID
#> #...@gene chr [1:1000] "23764" "467" "54477" "23301" "2887" "51278" "10614" "116039" ...
#> #...pvalues adjusted by 'BH' with cutoff <0.05
#> #...122 enriched terms found
#> 'data.frame': 122 obs. of 9 variables:
#> $ ID : chr "R-HSA-2500257" "R-HSA-141424" "R-HSA-141444" "R-HSA-68877" ...
#> $ Description: chr "Resolution of Sister Chromatid Cohesion" "Amplification of signal from the kinetochores" "Amplification of signal from unattached kinetochores via a MAD2 inhibitory signal" "Mitotic Prometaphase" ...
#> $ GeneRatio : chr "32/680" "26/680" "26/680" "39/680" ...
#> $ BgRatio : chr "124/10818" "94/10818" "94/10818" "201/10818" ...
#> $ pvalue : num 3.22e-12 6.62e-11 6.62e-11 1.85e-10 3.24e-10 ...
#> $ p.adjust : num 4.01e-09 2.75e-08 2.75e-08 5.75e-08 8.07e-08 ...
#> $ qvalue : num 3.21e-09 2.20e-08 2.20e-08 4.60e-08 6.46e-08 ...
#> $ geneID : chr "BUB1B/PDS5B/NDC80/STAG2/TUBB2A/MAD1L1/PPP2R5E/STAG1/PAFAH1B1/BUB1/PPP2R5C/CENPE/PDS5A/XPO1/SPC25/CENPI/CENPF/CL"| __truncated__ "BUB1B/NDC80/MAD1L1/PPP2R5E/PAFAH1B1/BUB1/PPP2R5C/CENPE/XPO1/SPC25/CENPI/CENPF/CLIP1/CLASP2/CKAP5/PLK1/KIF2A/KIF"| __truncated__ "BUB1B/NDC80/MAD1L1/PPP2R5E/PAFAH1B1/BUB1/PPP2R5C/CENPE/XPO1/SPC25/CENPI/CENPF/CLIP1/CLASP2/CKAP5/PLK1/KIF2A/KIF"| __truncated__ "BUB1B/PDS5B/NDC80/STAG2/TUBB2A/TUBGCP3/MAD1L1/AKAP9/PPP2R5E/STAG1/PAFAH1B1/BUB1/PPP2R5C/CENPE/PDS5A/XPO1/SPC25/"| __truncated__ ...
#> $ Count : int 32 26 26 39 28 27 51 17 40 40 ...
#> #...Citation
#> Guangchuang Yu, Qing-Yu He. ReactomePA: an R/Bioconductor package for
#> reactome pathway analysis and visualization. Molecular BioSystems
#> 2016, 12(2):477-479
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