3 Objetos de Bioconductor para datos de expresión

3.1 SummarizedExperiment

rnaseqGene workflow
SummarizedExperiment
- 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
GenomicRanges
rtracklayer
- 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

## Número de genes y muestras
dim(rse)

## [1] 200   6

## IDs de nuestros genes y muestras
dimnames(rse)

## [[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"

## Nombres de tablas de cuentas que tenemos (RPKM, CPM, counts, logcounts, etc)
assayNames(rse)

## [1] "counts"

## El inicio de nuestra tabla de cuentas
head(assay(rse))

##               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

## Información de los genes en un objeto de Bioconductor
rowRanges(rse)

## 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

## Tabla con información de los genes
rowData(rse) # es idéntico a 'mcols(rowRanges(rse))'

## 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

## Tabla con información de las muestras
colData(rse)

## DataFrame with 6 rows and 1 column
##     Treatment
##   <character>
## A        ChIP
## B       Input
## C        ChIP
## D       Input
## E        ChIP
## F       Input

3.2 Ejercicio

Explica que sucede en las siguientes líneas de código de R.

## Comando 1
rse[1:2, ]

## 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

## Comando 2
rse[, c("A", "D", "F")]

## 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): Treatment

3.3 iSEE

## Explora el objeto rse de forma interactiva
library("iSEE")
iSEE::iSEE(rse)

3.4 Ejercicio con spatialLIBD

Vamos a descargar datos de un objeto SingleCellExperiment que es similar a uno de SummarizedExperiment.

## Descarguemos unos datos de spatialLIBD
sce_layer <- spatialLIBD::fetch_data("sce_layer")

## snapshotDate(): 2021-10-19

## adding rname 'https://www.dropbox.com/s/bg8xwysh2vnjwvg/Human_DLPFC_Visium_processedData_sce_scran_sce_layer_spatialLIBD.Rdata?dl=1'

## 2022-02-04 17:18:34 loading file /github/home/.cache/R/BiocFileCache/48c2ec1cfa8_Human_DLPFC_Visium_processedData_sce_scran_sce_layer_spatialLIBD.Rdata%3Fdl%3D1

sce_layer

## 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):

## Revisemos el tamaño de este objeto
lobstr::obj_size(sce_layer) / 1024^2 ## Convertir a MB

## 32.41268 B

Al igual que nuestro objeto rse podemos usar iSEE::iSEE() para explorar los datos.

iSEE::iSEE(sce_layer)

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, MBP y PCP4. 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
ENSG00000197971

3.5 Comunidad

Autores de iSEE:

https://twitter.com/KevinRUE67
https://twitter.com/FedeBioinfo
https://twitter.com/CSoneson
Aaron Lun https://orcid.org/0000-0002-3564-4813
Otro ejemplo de exploración de datos usando SummarizedExperiment y iSEE:

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

📔 https://t.co/iUQHE0xqRc
🗞️ https://t.co/qhAdXbhY9c #rstats @Bioconductor https://t.co/OXTukByhoo
— LIBD rstats club (@LIBDrstats) February 12, 2021