3 Objetos de Bioconductor para datos de expresión
3.1 SummarizedExperiment
rnaseqGeneworkflowSummarizedExperiment- https://www.nature.com/articles/nmeth.3252 Figure 2
- http://bioconductor.org/packages/release/bioc/html/SummarizedExperiment.html
- Introducción a
SummarizedExperiment: - http://bioconductor.org/packages/release/bioc/vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.html - Extendiendo
SummarizedExperiment: http://bioconductor.org/packages/release/bioc/vignettes/SummarizedExperiment/inst/doc/Extensions.html
GenomicRangesrtracklayer- http://bioconductor.org/packages/release/bioc/html/rtracklayer.html
- http://bioconductor.org/packages/release/bioc/vignettes/rtracklayer/inst/doc/rtracklayer.pdf
- Formato de archivos BED https://genome.ucsc.edu/FAQ/FAQformat.html#format1
- Formato de archivos GTF https://useast.ensembl.org/info/website/upload/gff.html
## Lets build our first SummarizedExperiment object
library("SummarizedExperiment")
## ?SummarizedExperiment
## De los ejemplos en la ayuda oficial
## Creamos los datos para nuestro objeto de tipo SummarizedExperiment
## para 200 genes a lo largo de 6 muestras
nrows <- 200
ncols <- 6
## Números al azar de cuentas
set.seed(20210223)
counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
## Información de nuestros genes
rowRanges <- GRanges(
rep(c("chr1", "chr2"), c(50, 150)),
IRanges(floor(runif(200, 1e5, 1e6)), width = 100),
strand = sample(c("+", "-"), 200, TRUE),
feature_id = sprintf("ID%03d", 1:200)
)
names(rowRanges) <- paste0("gene_", seq_len(length(rowRanges)))
## Información de nuestras muestras
colData <- DataFrame(
Treatment = rep(c("ChIP", "Input"), 3),
row.names = LETTERS[1:6]
)
## Juntamos ahora toda la información en un solo objeto de R
rse <- SummarizedExperiment(
assays = SimpleList(counts = counts),
rowRanges = rowRanges,
colData = colData
)
## Exploremos el objeto resultante
rse## class: RangedSummarizedExperiment
## dim: 200 6
## metadata(0):
## assays(1): counts
## rownames(200): gene_1 gene_2 ... gene_199 gene_200
## rowData names(1): feature_id
## colnames(6): A B ... E F
## colData names(1): Treatment## [1] 200 6## [[1]]
## [1] "gene_1" "gene_2" "gene_3" "gene_4" "gene_5" "gene_6" "gene_7" "gene_8" "gene_9" "gene_10"
## [11] "gene_11" "gene_12" "gene_13" "gene_14" "gene_15" "gene_16" "gene_17" "gene_18" "gene_19" "gene_20"
## [21] "gene_21" "gene_22" "gene_23" "gene_24" "gene_25" "gene_26" "gene_27" "gene_28" "gene_29" "gene_30"
## [31] "gene_31" "gene_32" "gene_33" "gene_34" "gene_35" "gene_36" "gene_37" "gene_38" "gene_39" "gene_40"
## [41] "gene_41" "gene_42" "gene_43" "gene_44" "gene_45" "gene_46" "gene_47" "gene_48" "gene_49" "gene_50"
## [51] "gene_51" "gene_52" "gene_53" "gene_54" "gene_55" "gene_56" "gene_57" "gene_58" "gene_59" "gene_60"
## [61] "gene_61" "gene_62" "gene_63" "gene_64" "gene_65" "gene_66" "gene_67" "gene_68" "gene_69" "gene_70"
## [71] "gene_71" "gene_72" "gene_73" "gene_74" "gene_75" "gene_76" "gene_77" "gene_78" "gene_79" "gene_80"
## [81] "gene_81" "gene_82" "gene_83" "gene_84" "gene_85" "gene_86" "gene_87" "gene_88" "gene_89" "gene_90"
## [91] "gene_91" "gene_92" "gene_93" "gene_94" "gene_95" "gene_96" "gene_97" "gene_98" "gene_99" "gene_100"
## [101] "gene_101" "gene_102" "gene_103" "gene_104" "gene_105" "gene_106" "gene_107" "gene_108" "gene_109" "gene_110"
## [111] "gene_111" "gene_112" "gene_113" "gene_114" "gene_115" "gene_116" "gene_117" "gene_118" "gene_119" "gene_120"
## [121] "gene_121" "gene_122" "gene_123" "gene_124" "gene_125" "gene_126" "gene_127" "gene_128" "gene_129" "gene_130"
## [131] "gene_131" "gene_132" "gene_133" "gene_134" "gene_135" "gene_136" "gene_137" "gene_138" "gene_139" "gene_140"
## [141] "gene_141" "gene_142" "gene_143" "gene_144" "gene_145" "gene_146" "gene_147" "gene_148" "gene_149" "gene_150"
## [151] "gene_151" "gene_152" "gene_153" "gene_154" "gene_155" "gene_156" "gene_157" "gene_158" "gene_159" "gene_160"
## [161] "gene_161" "gene_162" "gene_163" "gene_164" "gene_165" "gene_166" "gene_167" "gene_168" "gene_169" "gene_170"
## [171] "gene_171" "gene_172" "gene_173" "gene_174" "gene_175" "gene_176" "gene_177" "gene_178" "gene_179" "gene_180"
## [181] "gene_181" "gene_182" "gene_183" "gene_184" "gene_185" "gene_186" "gene_187" "gene_188" "gene_189" "gene_190"
## [191] "gene_191" "gene_192" "gene_193" "gene_194" "gene_195" "gene_196" "gene_197" "gene_198" "gene_199" "gene_200"
##
## [[2]]
## [1] "A" "B" "C" "D" "E" "F"## [1] "counts"## A B C D E F
## gene_1 2577.960 8526.615 2226.3070 3615.8967 1723.8851 3267.954
## gene_2 7793.183 3462.579 478.2716 7688.3839 295.2813 2698.921
## gene_3 9571.769 5280.564 9772.1671 9916.0076 2621.2085 6880.067
## gene_4 4641.969 2784.091 6670.6757 258.5218 2771.8970 8737.586
## gene_5 6436.758 7053.276 9978.8199 3588.1194 1447.9821 7290.890
## gene_6 6845.704 1502.045 4383.5686 9750.8286 3529.3153 2192.060## GRanges object with 200 ranges and 1 metadata column:
## seqnames ranges strand | feature_id
## <Rle> <IRanges> <Rle> | <character>
## gene_1 chr1 286235-286334 + | ID001
## gene_2 chr1 586770-586869 - | ID002
## gene_3 chr1 577897-577996 + | ID003
## gene_4 chr1 494350-494449 + | ID004
## gene_5 chr1 686692-686791 - | ID005
## ... ... ... ... . ...
## gene_196 chr2 804998-805097 - | ID196
## gene_197 chr2 177462-177561 - | ID197
## gene_198 chr2 649993-650092 - | ID198
## gene_199 chr2 275940-276039 - | ID199
## gene_200 chr2 487418-487517 + | ID200
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengths## DataFrame with 200 rows and 1 column
## feature_id
## <character>
## gene_1 ID001
## gene_2 ID002
## gene_3 ID003
## gene_4 ID004
## gene_5 ID005
## ... ...
## gene_196 ID196
## gene_197 ID197
## gene_198 ID198
## gene_199 ID199
## gene_200 ID200## DataFrame with 6 rows and 1 column
## Treatment
## <character>
## A ChIP
## B Input
## C ChIP
## D Input
## E ChIP
## F Input3.2 Ejercicio
Explica que sucede en las siguientes líneas de código de R.
## class: RangedSummarizedExperiment
## dim: 2 6
## metadata(0):
## assays(1): counts
## rownames(2): gene_1 gene_2
## rowData names(1): feature_id
## colnames(6): A B ... E F
## colData names(1): Treatment## class: RangedSummarizedExperiment
## dim: 200 3
## metadata(0):
## assays(1): counts
## rownames(200): gene_1 gene_2 ... gene_199 gene_200
## rowData names(1): feature_id
## colnames(3): A D F
## colData names(1): Treatment3.4 Ejercicio con spatialLIBD
- Vamos a descargar datos de un objeto
SingleCellExperimentque es similar a uno deSummarizedExperiment.
## adding rname 'https://www.dropbox.com/s/bg8xwysh2vnjwvg/Human_DLPFC_Visium_processedData_sce_scran_sce_layer_spatialLIBD.Rdata?dl=1'## 2024-02-02 14:20:08.973406 loading file /github/home/.cache/R/BiocFileCache/76f6bf67856_Human_DLPFC_Visium_processedData_sce_scran_sce_layer_spatialLIBD.Rdata%3Fdl%3D1## class: SingleCellExperiment
## dim: 22331 76
## metadata(0):
## assays(2): counts logcounts
## rownames(22331): ENSG00000243485 ENSG00000238009 ... ENSG00000278384 ENSG00000271254
## rowData names(10): source type ... is_top_hvg is_top_hvg_sce_layer
## colnames(76): 151507_Layer1 151507_Layer2 ... 151676_Layer6 151676_WM
## colData names(13): sample_name layer_guess ... layer_guess_reordered_short spatialLIBD
## reducedDimNames(6): PCA TSNE_perplexity5 ... UMAP_neighbors15 PCAsub
## mainExpName: NULL
## altExpNames(0):## 33.99 MB- Al igual que nuestro objeto
rsepodemos usariSEE::iSEE()para explorar los datos.
- Descarga un PDF que reproduzca la imagen del lado derecho de la siguiente diapositiva. Incluye ese PDF en tu repositorio de notas del curso.
- Explora en con un heatmap la expresión de los genes
MOBP,MBPyPCP4. Si hacemos un clustering (agrupamos los genes), ¿cúales genes se parecen más? - ¿En qué capas se expresan más los genes MOBP y MBP?
ENSG00000168314
ENSG00000183036
ENSG000001979713.5 Comunidad
Autores de iSEE:
- Kévin Rue-Albrecht https://twitter.com/KevinRUE67
- Federico Marini https://twitter.com/FedeBioinfo
- Charlotte Soneson https://bsky.app/profile/csoneson.bsky.social
- Aaron Lun https://twitter.com/realAaronLun
- Otro ejemplo de exploración de datos usando
SummarizedExperimentyiSEE:
Today we explored RNA-seq data from @StefanoBerto83 et al who made it easy to re-use. Thank you! ^^@lcolladotor used #shiny + #ggpubr as well as #iSEE
— LIBD rstats club (@LIBDrstats) February 12, 2021
📔 https://t.co/iUQHE0xqRc
🗞️ https://t.co/qhAdXbhY9c#rstats @Bioconductorhttps://t.co/OXTukByhoo