Estructura e importe de datos

# <strong>Estructura e importe de datos</strong>
## <strong>Bioconductor</strong> para datos transcriptómicos de célula única (<strong>scRNA-seq</strong>) – <strong>CDSB2020</strong>
### <a href="http://lcolladotor.github.io/">Leonardo Collado-Torres</a>
### 2020-08-06

---

<a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.

]

.footnote[Descarga los materiales con `usethis::use_course('comunidadbioinfo/cdsb2020')` o revisalos en línea vía [**comunidadbioinfo.github.io/cdsb2020**](http://comunidadbioinfo.github.io/cdsb2020).]

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---

# Diapositivas de Peter Hickey

Ve las diapositivas [aquí](https://docs.google.com/presentation/d/1X9qP3wNlnn3BMUQhuZwAo4vCV76c33X_M-UnHxkPZpE/edit)

---

# Código de R

```r
library('scRNAseq')
sce.416b <- LunSpikeInData(which = "416b")
```

```
## snapshotDate(): 2020-04-27
```

```
## see ?scRNAseq and browseVignettes('scRNAseq') for documentation
```

```
## loading from cache
```

```
## see ?scRNAseq and browseVignettes('scRNAseq') for documentation
```

```
## loading from cache
```

```
## see ?scRNAseq and browseVignettes('scRNAseq') for documentation
```

```
## loading from cache
```

```
## snapshotDate(): 2020-04-27
```

```
## loading from cache
```

```r
# Carga el paquete SingleCellExperiment
library('SingleCellExperiment')
# Extrae la matriz de cuentas del set de datos de 416b
counts.416b <- counts(sce.416b)
# Construye un nuevo SCE de la matriz de cuentas
sce <- SingleCellExperiment(assays = list(counts = counts.416b))

# Revisa el objeto que acabamos de crear
sce
```

```
## class: SingleCellExperiment 
## dim: 46604 192 
## metadata(0):
## assays(1): counts
## rownames(46604): ENSMUSG00000102693 ENSMUSG00000064842 ...
##   ENSMUSG00000095742 CBFB-MYH11-mcherry
## rowData names(0):
## colnames(192): SLX-9555.N701_S502.C89V9ANXX.s_1.r_1
##   SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 ...
##   SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1
##   SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1
## colData names(0):
## reducedDimNames(0):
## altExpNames(0):
```

```r
## ¿Qué tan grande es el objeto de R?
pryr::object_size(sce)
```

```
## Registered S3 method overwritten by 'pryr':
##   method      from
##   print.bytes Rcpp
```

```
## 40.1 MB
```

```r
# Accesa la matriz de cuenta del compartimento (slot) "assays" 
# assays(sce, "counts")
# OJO: ¡esto puede inundar tu sesión de R!

# 1. El método general
assay(sce, "counts")[1:6, 1:3]
```

```
##                    SLX-9555.N701_S502.C89V9ANXX.s_1.r_1
## ENSMUSG00000102693                                    0
## ENSMUSG00000064842                                    0
## ENSMUSG00000051951                                    0
## ENSMUSG00000102851                                    0
## ENSMUSG00000103377                                    0
## ENSMUSG00000104017                                    0
##                    SLX-9555.N701_S503.C89V9ANXX.s_1.r_1
## ENSMUSG00000102693                                    0
## ENSMUSG00000064842                                    0
## ENSMUSG00000051951                                    0
## ENSMUSG00000102851                                    0
## ENSMUSG00000103377                                    0
## ENSMUSG00000104017                                    0
##                    SLX-9555.N701_S504.C89V9ANXX.s_1.r_1
## ENSMUSG00000102693                                    0
## ENSMUSG00000064842                                    0
## ENSMUSG00000051951                                    0
## ENSMUSG00000102851                                    0
## ENSMUSG00000103377                                    0
## ENSMUSG00000104017                                    0
```

```r
# 2. El método específico para accesar la matriz de cuentas "counts"
counts(sce)[1:6, 1:3]
```

```r
sce <- scater::logNormCounts(sce)
# Revisa el objeto que acabamos de actualizar
sce
```

```
## class: SingleCellExperiment 
## dim: 46604 192 
## metadata(0):
## assays(2): counts logcounts
## rownames(46604): ENSMUSG00000102693 ENSMUSG00000064842 ...
##   ENSMUSG00000095742 CBFB-MYH11-mcherry
## rowData names(0):
## colnames(192): SLX-9555.N701_S502.C89V9ANXX.s_1.r_1
##   SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 ...
##   SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1
##   SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1
## colData names(1): sizeFactor
## reducedDimNames(0):
## altExpNames(0):
```

```r
## ¿Qué tan grande es el objeto de R?
pryr::object_size(sce)
```

```
## 112 MB
```

```r
# 1. El método general
assay(sce, "logcounts")[1:6, 1:3]
```

```r
# 2. El método específico para accesar la matriz de cuentas
# transformadas "logcounts"
logcounts(sce)[1:6, 1:3]
```

```r
# Asigna una nueva matriz al compartimento (slot) de "assays"
assay(sce, "counts_100") <- assay(sce, "counts") + 100
# Enumera los "assays" en el objeto
assays(sce)
```

```
## List of length 3
## names(3): counts logcounts counts_100
```

```r
assayNames(sce)
```

```
## [1] "counts"     "logcounts"  "counts_100"
```

```r
## ¿Qué tan grande es el objeto de R?
pryr::object_size(sce)
```

```
## 183 MB
```

```r
# Extrae la información de las muestras (metadata) del set de datos de 416b
colData.416b <- colData(sce.416b)
# Agrega algo de esa información a nuestro objeto de SCE
colData(sce) <- colData.416b[, c("phenotype", "block")]
# Revisa el objeto que acabamos de actualizar
sce
```

```
## class: SingleCellExperiment 
## dim: 46604 192 
## metadata(0):
## assays(3): counts logcounts counts_100
## rownames(46604): ENSMUSG00000102693 ENSMUSG00000064842 ...
##   ENSMUSG00000095742 CBFB-MYH11-mcherry
## rowData names(0):
## colnames(192): SLX-9555.N701_S502.C89V9ANXX.s_1.r_1
##   SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 ...
##   SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1
##   SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1
## colData names(2): phenotype block
## reducedDimNames(0):
## altExpNames(0):
```

```r
# Accesa a la información de las muestras (metadata) en nuestro SCE
colData(sce)
```

```
## DataFrame with 192 rows and 2 columns
##                                                                    phenotype
##                                                                  <character>
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1                     wild type phenotype
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1                     wild type phenotype
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1                     wild type phenotype
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1  induced CBFB-MYH11 oncogene expression
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1  induced CBFB-MYH11 oncogene expression
## ...                                                                      ...
## SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1 induced CBFB-MYH11 oncogene expression
## SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1 induced CBFB-MYH11 oncogene expression
## SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1 induced CBFB-MYH11 oncogene expression
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1 induced CBFB-MYH11 oncogene expression
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1                    wild type phenotype
##                                           block
##                                       <integer>
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1   20160113
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1   20160113
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1   20160113
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1   20160113
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1   20160113
## ...                                         ...
## SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1  20160325
## SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1  20160325
## SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1  20160325
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1  20160325
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1  20160325
```

```r
# Accesa una columna específica de la información de las muestras (metadata)
table(sce$block)
```

```
## 
## 20160113 20160325 
##       96       96
```

```r
# Ejemplo de una función que agrega columnas nuevas al colData
sce <- scater::addPerCellQC(sce.416b)
# Accesa a la información de las muestras (metadata) en nuestro SCE actualizado
colData(sce)
```

```
## DataFrame with 192 rows and 22 columns
##                                                                 Source Name
##                                                                 <character>
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1   SLX-9555.N701_S502.C89V9ANXX.s_1.r_1
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1   SLX-9555.N701_S503.C89V9ANXX.s_1.r_1
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1   SLX-9555.N701_S504.C89V9ANXX.s_1.r_1
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1   SLX-9555.N701_S505.C89V9ANXX.s_1.r_1
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1   SLX-9555.N701_S506.C89V9ANXX.s_1.r_1
## ...                                                                     ...
## SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1 SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1
## SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1 SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1
## SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1 SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1 SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1 SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1
##                                         cell line           cell type
##                                       <character>         <character>
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1         416B embryonic stem cell
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1         416B embryonic stem cell
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1         416B embryonic stem cell
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1         416B embryonic stem cell
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1         416B embryonic stem cell
## ...                                           ...                 ...
## SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1        416B embryonic stem cell
## SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1        416B embryonic stem cell
## SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1        416B embryonic stem cell
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1        416B embryonic stem cell
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1        416B embryonic stem cell
##                                       single cell well quality
##                                                    <character>
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1                        OK
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1                        OK
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1                        OK
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1                        OK
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1                        OK
## ...                                                        ...
## SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1                       OK
## SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1                       OK
## SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1                       OK
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1                       OK
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1                       OK
##                                                                        genotype
##                                                                     <character>
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1  Doxycycline-inducible CBFB-MYH11 oncogene
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1  Doxycycline-inducible CBFB-MYH11 oncogene
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1  Doxycycline-inducible CBFB-MYH11 oncogene
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1  Doxycycline-inducible CBFB-MYH11 oncogene
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1  Doxycycline-inducible CBFB-MYH11 oncogene
## ...                                                                         ...
## SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1 Doxycycline-inducible CBFB-MYH11 oncogene
## SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1 Doxycycline-inducible CBFB-MYH11 oncogene
## SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1 Doxycycline-inducible CBFB-MYH11 oncogene
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1 Doxycycline-inducible CBFB-MYH11 oncogene
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1 Doxycycline-inducible CBFB-MYH11 oncogene
##                                                                    phenotype
##                                                                  <character>
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1                     wild type phenotype
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1                     wild type phenotype
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1                     wild type phenotype
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1  induced CBFB-MYH11 oncogene expression
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1  induced CBFB-MYH11 oncogene expression
## ...                                                                      ...
## SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1 induced CBFB-MYH11 oncogene expression
## SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1 induced CBFB-MYH11 oncogene expression
## SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1 induced CBFB-MYH11 oncogene expression
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1 induced CBFB-MYH11 oncogene expression
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1                    wild type phenotype
##                                            strain spike-in addition     block
##                                       <character>       <character> <integer>
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1     B6D2F1-J         ERCC+SIRV  20160113
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1     B6D2F1-J         ERCC+SIRV  20160113
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1     B6D2F1-J         ERCC+SIRV  20160113
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1     B6D2F1-J         ERCC+SIRV  20160113
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1     B6D2F1-J         ERCC+SIRV  20160113
## ...                                           ...               ...       ...
## SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1    B6D2F1-J          Premixed  20160325
## SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1    B6D2F1-J          Premixed  20160325
## SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1    B6D2F1-J          Premixed  20160325
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1    B6D2F1-J          Premixed  20160325
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1    B6D2F1-J          Premixed  20160325
##                                             sum  detected percent_top_50
##                                       <integer> <integer>      <numeric>
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1     865936      7618        26.7218
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1    1076277      7521        29.4043
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1    1180138      8306        27.3454
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1    1342593      8143        35.8092
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1    1668311      7154        34.1198
## ...                                         ...       ...            ...
## SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1    776622      8174        45.9362
## SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1   1299950      8956        38.0829
## SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1   1800696      9530        30.6675
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1     46731      6649        32.2998
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1   1866692     10964        26.6632
##                                       percent_top_100 percent_top_200
##                                             <numeric>       <numeric>
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1          32.2773         39.7208
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1          35.0354         42.2581
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1          32.4770         39.3296
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1          40.2666         46.2460
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1          39.0901         45.6660
## ...                                               ...             ...
## SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1         49.7010         54.6101
## SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1         42.8930         49.0622
## SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1         35.5839         41.8550
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1         37.9149         44.5999
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1         31.2584         37.5608
##                                       percent_top_500 altexps_ERCC_sum
##                                             <numeric>        <integer>
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1          52.9038            65278
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1          55.7454            74748
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1          51.9337            60878
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1          57.1210            60073
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1          58.2004           136810
## ...                                               ...              ...
## SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1         64.4249            61575
## SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1         60.6675            94982
## SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1         53.6781           113707
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1         56.5235             7580
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1         48.9489            48664
##                                       altexps_ERCC_detected
##                                                   <integer>
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1                     39
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1                     40
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1                     42
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1                     42
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1                     44
## ...                                                     ...
## SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1                    39
## SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1                    41
## SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1                    40
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1                    44
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1                    39
##                                       altexps_ERCC_percent altexps_SIRV_sum
##                                                  <numeric>        <integer>
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1               6.80658            27828
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1               6.28030            39173
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1               4.78949            30058
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1               4.18567            32542
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1               7.28887            71850
## ...                                                    ...              ...
## SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1              7.17620            19848
## SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1              6.65764            31729
## SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1              5.81467            41116
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1             13.48898             1883
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1              2.51930            16289
##                                       altexps_SIRV_detected
##                                                   <integer>
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1                      7
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1                      7
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1                      7
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1                      7
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1                      7
## ...                                                     ...
## SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1                     7
## SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1                     7
## SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1                     7
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1                     7
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1                     7
##                                       altexps_SIRV_percent     total
##                                                  <numeric> <integer>
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1               2.90165    959042
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1               3.29130   1190198
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1               2.36477   1271074
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1               2.26741   1435208
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1               3.82798   1876971
## ...                                                    ...       ...
## SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1             2.313165    858045
## SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1             2.224004   1426661
## SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1             2.102562   1955519
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1             3.350892     56194
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1             0.843271   1931645
```

```r
# Revisa el objeto que acabamos de actualizar
sce
```

```
## class: SingleCellExperiment 
## dim: 46604 192 
## metadata(0):
## assays(1): counts
## rownames(46604): ENSMUSG00000102693 ENSMUSG00000064842 ...
##   ENSMUSG00000095742 CBFB-MYH11-mcherry
## rowData names(1): Length
## colnames(192): SLX-9555.N701_S502.C89V9ANXX.s_1.r_1
##   SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 ...
##   SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1
##   SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1
## colData names(22): Source Name cell line ... altexps_SIRV_percent total
## reducedDimNames(0):
## altExpNames(2): ERCC SIRV
```

```r
## ¿Qué tan grande es el objeto de R?
pryr::object_size(sce)
```

```
## 41.4 MB
```

```r
## Agrega las cuentas normalizadas (lognorm) de nuevo
sce <- scater::logNormCounts(sce)

## ¿Qué tan grande es el objeto de R?
pryr::object_size(sce)
```

```
## 113 MB
```

```r
# Ejemplo: obtén el subconjunto de células de fenotipo "wild type"
# Acuérdate que las células son columnas del SCE
sce[, sce$phenotype == "wild type phenotype"]
```

```
## class: SingleCellExperiment 
## dim: 46604 96 
## metadata(0):
## assays(2): counts logcounts
## rownames(46604): ENSMUSG00000102693 ENSMUSG00000064842 ...
##   ENSMUSG00000095742 CBFB-MYH11-mcherry
## rowData names(1): Length
## colnames(96): SLX-9555.N701_S502.C89V9ANXX.s_1.r_1
##   SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 ...
##   SLX-11312.N712_S504.H5H5YBBXX.s_8.r_1
##   SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1
## colData names(23): Source Name cell line ... total sizeFactor
## reducedDimNames(0):
## altExpNames(2): ERCC SIRV
```

```r
# Accesa la información de los genes de nuestro SCE
# ¡Está vació actualmente!
rowData(sce)
```

```
## DataFrame with 46604 rows and 1 column
##                       Length
##                    <integer>
## ENSMUSG00000102693      1070
## ENSMUSG00000064842       110
## ENSMUSG00000051951      6094
## ENSMUSG00000102851       480
## ENSMUSG00000103377      2819
## ...                      ...
## ENSMUSG00000094621       121
## ENSMUSG00000098647        99
## ENSMUSG00000096730      3077
## ENSMUSG00000095742       243
## CBFB-MYH11-mcherry      2998
```

```r
# Ejemplo de una función que agrega campos nuevos en el rowData
sce <- scater::addPerFeatureQC(sce)
# Accesa a la información de las muestras (metadata) en nuestro SCE actualizado
rowData(sce)
```

```
## DataFrame with 46604 rows and 3 columns
##                       Length      mean  detected
##                    <integer> <numeric> <numeric>
## ENSMUSG00000102693      1070 0.0000000  0.000000
## ENSMUSG00000064842       110 0.0000000  0.000000
## ENSMUSG00000051951      6094 0.0000000  0.000000
## ENSMUSG00000102851       480 0.0000000  0.000000
## ENSMUSG00000103377      2819 0.0104167  0.520833
## ...                      ...       ...       ...
## ENSMUSG00000094621       121       0.0         0
## ENSMUSG00000098647        99       0.0         0
## ENSMUSG00000096730      3077       0.0         0
## ENSMUSG00000095742       243       0.0         0
## CBFB-MYH11-mcherry      2998   50375.7       100
```

```r
## ¿Qué tan grande es el objeto de R?
pryr::object_size(sce)
```

```
## 113 MB
```

```r
# Descarga los archivos de anotación de la base de datos de Ensembl
# correspondientes usando los recursos disponibles vía AnnotationHub
library('AnnotationHub')
ah <- AnnotationHub()
```

```
## snapshotDate(): 2020-04-27
```

```r
query(ah, c("Mus musculus", "Ensembl", "v97"))
```

```
## AnnotationHub with 1 record
## # snapshotDate(): 2020-04-27
## # names(): AH73905
## # $dataprovider: Ensembl
## # $species: Mus musculus
## # $rdataclass: EnsDb
## # $rdatadateadded: 2019-05-02
## # $title: Ensembl 97 EnsDb for Mus musculus
## # $description: Gene and protein annotations for Mus musculus based on Ensem...
## # $taxonomyid: 10090
## # $genome: GRCm38
## # $sourcetype: ensembl
## # $sourceurl: http://www.ensembl.org
## # $sourcesize: NA
## # $tags: c("97", "AHEnsDbs", "Annotation", "EnsDb", "Ensembl", "Gene",
## #   "Protein", "Transcript") 
## # retrieve record with 'object[["AH73905"]]'
```

```r
# Obtén la posición del cromosoma para cada gen
ensdb <- ah[["AH73905"]]
```

```
## loading from cache
```

```r
chromosome <- mapIds(ensdb,
    keys = rownames(sce),
    keytype = "GENEID",
    column = "SEQNAME")
```

```
## Warning: Unable to map 563 of 46604 requested IDs.
```

```r
rowData(sce)$chromosome <- chromosome

# Accesa a la información de las muestras (metadata) en nuestro SCE actualizado
rowData(sce)
```

```
## DataFrame with 46604 rows and 4 columns
##                       Length      mean  detected  chromosome
##                    <integer> <numeric> <numeric> <character>
## ENSMUSG00000102693      1070 0.0000000  0.000000           1
## ENSMUSG00000064842       110 0.0000000  0.000000           1
## ENSMUSG00000051951      6094 0.0000000  0.000000           1
## ENSMUSG00000102851       480 0.0000000  0.000000           1
## ENSMUSG00000103377      2819 0.0104167  0.520833           1
## ...                      ...       ...       ...         ...
## ENSMUSG00000094621       121       0.0         0  GL456372.1
## ENSMUSG00000098647        99       0.0         0  GL456381.1
## ENSMUSG00000096730      3077       0.0         0  JH584292.1
## ENSMUSG00000095742       243       0.0         0  JH584295.1
## CBFB-MYH11-mcherry      2998   50375.7       100          NA
```

```r
## ¿Qué tan grande es el objeto de R?
pryr::object_size(sce)
```

```
## 114 MB
```

```r
# Ejemplo: obtén el subconjunto de datos donde los genes están en el
# cromosoma 3
# NOTA: which() fue necesario para lidear con los nombres de cromosoma
# que son NA
sce[which(rowData(sce)$chromosome == "3"), ]
```

```
## class: SingleCellExperiment 
## dim: 2876 192 
## metadata(0):
## assays(2): counts logcounts
## rownames(2876): ENSMUSG00000098982 ENSMUSG00000098307 ...
##   ENSMUSG00000105990 ENSMUSG00000075903
## rowData names(4): Length mean detected chromosome
## colnames(192): SLX-9555.N701_S502.C89V9ANXX.s_1.r_1
##   SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 ...
##   SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1
##   SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1
## colData names(23): Source Name cell line ... total sizeFactor
## reducedDimNames(0):
## altExpNames(2): ERCC SIRV
```

```r
# Accesa la información de nuestro experimento usando metadata()
# ¡Está vació actualmente!
metadata(sce)
```

```
## list()
```

```r
# La información en el metadata() es como Vegas - todo se vale
metadata(sce) <- list(favourite_genes = c("Shh", "Nck1", "Diablo"),
    analyst = c("Pete"))

# Accesa la información de nuestro experimento usando metadata() de
# nuestro objeto actualizado
metadata(sce)
```

```
## $favourite_genes
## [1] "Shh"    "Nck1"   "Diablo"
## 
## $analyst
## [1] "Pete"
```

```r
# Ejemplo: agrega los componentes principales (PCs) de las logcounts
# NOTA: aprenderemos más sobre análisis de componentes principales (PCA) después
sce <- scater::runPCA(sce)
# Revisa el objeto que acabamos de actualizar
sce
```

```
## class: SingleCellExperiment 
## dim: 46604 192 
## metadata(2): favourite_genes analyst
## assays(2): counts logcounts
## rownames(46604): ENSMUSG00000102693 ENSMUSG00000064842 ...
##   ENSMUSG00000095742 CBFB-MYH11-mcherry
## rowData names(4): Length mean detected chromosome
## colnames(192): SLX-9555.N701_S502.C89V9ANXX.s_1.r_1
##   SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 ...
##   SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1
##   SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1
## colData names(23): Source Name cell line ... total sizeFactor
## reducedDimNames(1): PCA
## altExpNames(2): ERCC SIRV
```

```r
# Accesa la matriz de PCA del componente (slot) reducedDims
reducedDim(sce, "PCA")[1:6, 1:3]
```

```
##                                             PC1        PC2       PC3
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1  18.717668 -27.598132  5.939654
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1   2.480705 -27.564583  4.916567
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1  42.034018  -7.552435 12.126964
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1  -8.494303  31.833727 15.760853
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1 -49.737390   4.226795  6.123169
## SLX-9555.N701_S507.C89V9ANXX.s_1.r_1 -44.528081  -3.215503 10.384939
```

```r
# Ejemplo, agrega una representación de los logcounts en t-SNE
# NOTA: aprenderemos más sobre t-SNE después
sce <- scater::runTSNE(sce)
# Revisa el objeto que acabamos de actualizar
sce
```

```r
# Accesa a la matriz de t-SNE en el componente (slot) de reducedDims
head(reducedDim(sce, "TSNE"))
```

```
##                                            [,1]       [,2]
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1  3.6325953 -3.1664239
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1  0.7872262 -1.9856767
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1  7.9233587  1.4333125
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1  2.7673456  4.0043258
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1 -8.7769468  0.4735364
## SLX-9555.N701_S507.C89V9ANXX.s_1.r_1 -8.8302294  2.1605382
```

```r
# Ejemplo: agrega una representación 'manual' de los logcounts en UMAP
# NOTA: aprenderemos más sobre UMAP después y de una forma más sencilla de
#       calcularla
u <- uwot::umap(t(logcounts(sce)), n_components = 2)

# Agrega la matriz de UMAP al componente (slot) reducedDims
reducedDim(sce, "UMAP") <- u

# Accesa a la matriz de UMAP desde el componente (slot) reducedDims
head(reducedDim(sce, "UMAP"))
```

```
##                                             [,1]      [,2]
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1 -3.08790954 -1.790366
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 -1.83529409 -1.405215
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1 -3.18092569 -1.682470
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1 -0.56800970 -1.440291
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1  0.02311221 -1.839899
## SLX-9555.N701_S507.C89V9ANXX.s_1.r_1 -0.17816242 -1.628937
```

```r
# Enumera los resultados de reducción de dimensiones en nuestro objeto SCE
reducedDims(sce)
```

```
## List of length 3
## names(3): PCA TSNE UMAP
```

```r
# Extrae la información de ERCC de nuestro SCE para el set de datos de 416b
ercc.sce.416b <- altExp(sce.416b, "ERCC")
# Inspecciona el SCE para los datos de ERCC
ercc.sce.416b
```

```
## class: SingleCellExperiment 
## dim: 92 192 
## metadata(0):
## assays(1): counts
## rownames(92): ERCC-00002 ERCC-00003 ... ERCC-00170 ERCC-00171
## rowData names(1): Length
## colnames(192): SLX-9555.N701_S502.C89V9ANXX.s_1.r_1
##   SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 ...
##   SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1
##   SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1
## colData names(0):
## reducedDimNames(0):
## altExpNames(0):
```

```r
# Agrega el SCE de ERCC como un experimento alternativo a nuestro SCE
altExp(sce, "ERCC") <- ercc.sce.416b
# Revisa el objeto que acabamos de actualizar
sce
```

```r
## ¿Qué tan grande es el objeto de R?
pryr::object_size(sce)
```

```
## 114 MB
```

```r
# Enumera los experimentos alternativos almacenados en nuestro objeto
altExps(sce)
```

```
## List of length 2
## names(2): ERCC SIRV
```

```r
# El crear un subconjunto del SCE por muestra (célula) automáticamente
# obtiene el subconjunto de los experimentos alternativos
sce.subset <- sce[, 1:10]
ncol(sce.subset)
```

```
## [1] 10
```

```r
ncol(altExp(sce.subset))
```

```
## [1] 10
```

```r
## ¿Qué tan grande es el objeto de R?
pryr::object_size(sce.subset)
```

```
## 12.6 MB
```

```r
# Extrae los factores de tamaño (size factors)
# Estos fueron añadidos a nuestro objeto cuando corrimos
# scater::logNormCounts(sce)
head(sizeFactors(sce))
```

```
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1 SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 
##                            0.7427411                            0.9231573 
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1 SLX-9555.N701_S505.C89V9ANXX.s_1.r_1 
##                            1.0122422                            1.1515851 
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1 SLX-9555.N701_S507.C89V9ANXX.s_1.r_1 
##                            1.4309639                            0.8713409
```

```r
# "Automáticamente" reemplaza los factores de tamaño
sce <- scran::computeSumFactors(sce)
head(sizeFactors(sce))
```

```
## [1] 0.6961756 0.8834223 0.9704247 0.9804890 1.2446699 0.7922620
```

```r
# "Manualmente" reemplaza los factores de tamaño
sizeFactors(sce) <- scater::librarySizeFactors(sce)
head(sizeFactors(sce))
```

]

---

# Ejercicios con el objeto `sce`

* ¿Qué función define la clase del objeto `sce`?

* ¿Cuáles son los tipos de tablas que debe siempre contenter el objeto `sce`?

* ¿Donde usamos los `colnames(sce)`?

* Similarmente, ¿donde usamos los `rownames(sce)`?

* ¿Cuántos componentes principales calculamos?

* ¿Cúales son los 3 cromosomas que tienen la media de expresión más alta?

???

* `SingleCellExperiment::SingleCellExperiment`
* `colData()`, `assays()` y `rowData()` con `reducedDims()` siendo opcional
* `rownames(colData())` y `colnames(assays())`
* `rownames(rowData())` y `rownames(assays())`
* `ncol(reducedDim(sce, 'PCA'))`
* `sort(with(rowData(sce), tapply(mean, chromosome, mean)), decreasing = TRUE)`

---

# Ejercicio con los datos de ERCC

* Crea una gráfica para cada célula mostrando el número de cuentas de ERCC esperadas vs las observadas.

* [Archivo de ERCC con las cuentas esperadas](https://tools.thermofisher.com/content/sfs/manuals/cms_095046.txt)

---

# Solución ERCC

```r
## Lee los datos de ERCC de la red
ercc_info <-
    read.delim(
        'https://tools.thermofisher.com/content/sfs/manuals/cms_095046.txt',
        as.is = TRUE,
        row.names = 2,
        check.names = FALSE
    )

## Pon los datos de ERCC en el mismo orden
m <- match(rownames(altExp(sce, "ERCC")), rownames(ercc_info))
ercc_info <- ercc_info[m, ]

## Normaliza las cuentas de ERCC
altExp(sce, "ERCC") <- scater::logNormCounts(altExp(sce, "ERCC"))
```
---

```r
for (i in seq_len(2)) {
    plot(
        log2(10 * ercc_info[, "concentration in Mix 1 (attomoles/ul)"] + 1) ~
            log2(counts(altExp(sce, "ERCC"))[, i] +
                    1),
        xlab = "cuentas log norm",
        ylab = "Mezcla 1: log2(10 * Concentración + 1)",
        main = colnames(altExp(sce, "ERCC"))[i],
        xlim = c(min(logcounts(
            altExp(sce, "ERCC")
        )), max(logcounts(
            altExp(sce, "ERCC")
        )))
    )
    abline(0, 1, lty = 2, col = 'red')
}
```

![](02-data-infrastructure-and-import_files/figure-html/ercc_solution_plots-1.png)![](02-data-infrastructure-and-import_files/figure-html/ercc_solution_plots-2.png)

]

---

# Importar datos

```r
# Descarga datos de ejemplo procesados con CellRanger
# Paréntesis: al usar BiocFileCache solo tenemos que descargar
#             los datos una vez.
library('BiocFileCache')
bfc <- BiocFileCache()
pbmc.url <-
    paste0(
        "http://cf.10xgenomics.com/samples/cell-vdj/",
        "3.1.0/vdj_v1_hs_pbmc3/",
        "vdj_v1_hs_pbmc3_filtered_feature_bc_matrix.tar.gz"
    )
pbmc.data <- bfcrpath(bfc, pbmc.url)

# Extrae los archivos en un directorio temporal
untar(pbmc.data, exdir = tempdir())

# Enumera los archivos que descargamos y que extrajimos
# Estos son los archivos típicos de CellRanger
pbmc.dir <- file.path(tempdir(),
    "filtered_feature_bc_matrix")
list.files(pbmc.dir)
```

```
## [1] "barcodes.tsv.gz" "features.tsv.gz" "matrix.mtx.gz"
```

```r
# Importa los datos como un objeto de tipo SingleCellExperiment
library('DropletUtils')
sce.pbmc <- read10xCounts(pbmc.dir)
# Revisa el objeto que acabamos de construir
sce.pbmc
```

```
## class: SingleCellExperiment 
## dim: 33555 7231 
## metadata(1): Samples
## assays(1): counts
## rownames(33555): ENSG00000243485 ENSG00000237613 ... CD127 CD15
## rowData names(3): ID Symbol Type
## colnames: NULL
## colData names(2): Sample Barcode
## reducedDimNames(0):
## altExpNames(0):
```

```r
## ¿Qué tan grande es el objeto de R?
pryr::object_size(sce.pbmc)
```

```
## 130 MB
```

```r
# Almacena la información de CITE-seq como un experimento alternativo
sce.pbmc <- splitAltExps(sce.pbmc, rowData(sce.pbmc)$Type)
# Revisa el objeto que acabamos de actualizar
sce.pbmc
```

```
## class: SingleCellExperiment 
## dim: 33538 7231 
## metadata(1): Samples
## assays(1): counts
## rownames(33538): ENSG00000243485 ENSG00000237613 ... ENSG00000277475
##   ENSG00000268674
## rowData names(3): ID Symbol Type
## colnames: NULL
## colData names(2): Sample Barcode
## reducedDimNames(0):
## altExpNames(1): Antibody Capture
```

```r
## ¿Qué tan grande es el objeto de R?
pryr::object_size(sce.pbmc)
```

```
## 131 MB
```

```r
# Descarga datos de ejemplo procesados con scPipe
library('BiocFileCache')
bfc <- BiocFileCache()
sis_seq.url <-
    "https://github.com/LuyiTian/SIS-seq_script/archive/master.zip"
sis_seq.data <- bfcrpath(bfc, sis_seq.url)

# Extrae los archivos en un directorio temporal
unzip(sis_seq.data, exdir = tempdir())

# Enumera (algunos de) los archivos que descargamos y extrajimos
# Estos son los archivos típicos de scPipe
sis_seq.dir <- file.path(tempdir(),
    "SIS-seq_script-master",
    "data",
    "BcorKO_scRNAseq",
    "RPI10")
list.files(sis_seq.dir)
```

```
## [1] "gene_count.csv" "stat"
```

```r
# Importa los datos como un objeto de tipo SingleCellExperiment
library('scPipe')
sce.sis_seq <- create_sce_by_dir(sis_seq.dir)
```

```
## organism/gene_id_type not provided. Make a guess: mmusculus_gene_ensembl / ensembl_gene_id
```

```r
# Revisa el objeto que acabamos de construir
sce.sis_seq
```

```
## class: SingleCellExperiment 
## dim: 19232 383 
## metadata(2): scPipe Biomart
## assays(1): counts
## rownames(19232): ENSMUSG00000079140 ENSMUSG00000081587 ...
##   ENSMUSG00000036880 ENSMUSG00000106872
## rowData names(0):
## colnames(383): A1 A10 ... P8 P9
## colData names(7): unaligned aligned_unmapped ... mapped_to_ERCC
##   mapped_to_MT
## reducedDimNames(0):
## altExpNames(0):
```

```r
## ¿Qué tan grande es el objeto de R?
pryr::object_size(sce.sis_seq)
```

```
## 31.3 MB
```

```r
# Descarga un ejemplo de un montón de archivos
library('BiocFileCache')
bfc <- BiocFileCache()
lun_counts.url <-
    paste0(
        "https://www.ebi.ac.uk/arrayexpress/files/",
        "E-MTAB-5522/E-MTAB-5522.processed.1.zip"
    )
lun_counts.data <- bfcrpath(bfc, lun_counts.url)
lun_coldata.url <-
    paste0("https://www.ebi.ac.uk/arrayexpress/files/",
        "E-MTAB-5522/E-MTAB-5522.sdrf.txt")
lun_coldata.data <- bfcrpath(bfc, lun_coldata.url)

# Extrae los archivos en un directorio temporal
lun_counts.dir <- tempfile("lun_counts.")
unzip(lun_counts.data, exdir = lun_counts.dir)

# Enumera los archivos que descargamos y extrajimos
list.files(lun_counts.dir)
```

```
## [1] "counts_Calero_20160113.tsv" "counts_Calero_20160325.tsv"
## [3] "counts_Liora_20160906.tsv"  "counts_Liora_20170201.tsv"
```

```r
# Lee la matriz de cuentas (para una placa)
lun.counts <- read.delim(
    file.path(lun_counts.dir, "counts_Calero_20160113.tsv"),
    header = TRUE,
    row.names = 1,
    check.names = FALSE
)
# Almacena la información de la longitud de los genes para después
gene.lengths <- lun.counts$Length
# Convierte los datos de cuentas de genez a una matriz
lun.counts <- as.matrix(lun.counts[, -1])

# Lee la información de las muestras (células)
lun.coldata <- read.delim(lun_coldata.data,
    check.names = FALSE,
    stringsAsFactors = FALSE)
library('S4Vectors')
lun.coldata <- as(lun.coldata, "DataFrame")

# Pon en orden la información de las muestras para que
# sea idéntico al orden en la matriz de cuentas
m <- match(colnames(lun.counts),
    lun.coldata$`Source Name`)
lun.coldata <- lun.coldata[m,]

# Construye la tabla de información de los genes
lun.rowdata <- DataFrame(Length = gene.lengths)

# Construye el objeto de SingleCellExperiment
lun.sce <- SingleCellExperiment(
    assays = list(assays = lun.counts),
    colData = lun.coldata,
    rowData = lun.rowdata
)
# Revisa el objeto que acabamos de construir
lun.sce
```

```
## class: SingleCellExperiment 
## dim: 46703 96 
## metadata(0):
## assays(1): assays
## rownames(46703): ENSMUSG00000102693 ENSMUSG00000064842 ... SIRV7
##   CBFB-MYH11-mcherry
## rowData names(1): Length
## colnames(96): SLX-9555.N701_S502.C89V9ANXX.s_1.r_1
##   SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 ...
##   SLX-9555.N712_S508.C89V9ANXX.s_1.r_1
##   SLX-9555.N712_S517.C89V9ANXX.s_1.r_1
## colData names(50): Source Name Comment[ENA_SAMPLE] ... Factor
##   Value[phenotype] Factor Value[block]
## reducedDimNames(0):
## altExpNames(0):
```

```r
## ¿Qué tan grande es el objeto de R?
pryr::object_size(lun.sce)
```

```
## 22.5 MB
```

]

---

# ¡Gracias!

Las diapositivias fueron hechas con el paquete de R [**xaringan**](https://github.com/yihui/xaringan) y configuradas con [**xaringanthemer**](https://github.com/gadenbuie/xaringanthemer).

Este curso está basado en el libro [**Orchestrating Single Cell Analysis with Bioconductor**](https://osca.bioconductor.org/) de [Aaron Lun](https://www.linkedin.com/in/aaron-lun-869b5894/), [Robert Amezquita](https://robertamezquita.github.io/), [Stephanie Hicks](https://www.stephaniehicks.com/) y [Raphael Gottardo](http://rglab.org), además del [**curso de scRNA-seq para WEHI**](https://drive.google.com/drive/folders/1cn5d-Ey7-kkMiex8-74qxvxtCQT6o72h) creado por [Peter Hickey](https://www.peterhickey.org/).

Puedes encontrar los archivos para este taller en  [comunidadbioinfo/cdsb2020](https://github.com/comunidadbioinfo/cdsb2020).

Instructor: [**Leonardo Collado-Torres**](http://lcolladotor.github.io/).

]

---

# Detalles de la sesión de R

```r
options(width = 120)
sessioninfo::session_info()
```

```
## ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 4.0.2 (2020-06-22)
##  os       macOS Catalina 10.15.5      
##  system   x86_64, darwin17.0          
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  ctype    en_US.UTF-8                 
##  tz       America/New_York            
##  date     2020-08-05                  
## 
## ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────
##  package                * version  date       lib source                            
##  AnnotationDbi          * 1.50.3   2020-07-25 [1] Bioconductor                      
##  AnnotationFilter       * 1.12.0   2020-04-27 [1] Bioconductor                      
##  AnnotationHub          * 2.20.0   2020-04-27 [1] Bioconductor                      
##  askpass                  1.1      2019-01-13 [1] CRAN (R 4.0.0)                    
##  assertthat               0.2.1    2019-03-21 [1] CRAN (R 4.0.0)                    
##  beeswarm                 0.2.3    2016-04-25 [1] CRAN (R 4.0.0)                    
##  Biobase                * 2.48.0   2020-04-27 [1] Bioconductor                      
##  BiocFileCache          * 1.12.0   2020-04-27 [1] Bioconductor                      
##  BiocGenerics           * 0.34.0   2020-04-27 [1] Bioconductor                      
##  BiocManager              1.30.10  2019-11-16 [1] CRAN (R 4.0.0)                    
##  BiocNeighbors            1.6.0    2020-04-27 [1] Bioconductor                      
##  BiocParallel             1.22.0   2020-04-27 [1] Bioconductor                      
##  BiocSingular             1.4.0    2020-04-27 [1] Bioconductor                      
##  BiocVersion              3.11.1   2020-04-07 [1] Bioconductor                      
##  biomaRt                  2.44.1   2020-06-17 [1] Bioconductor                      
##  Biostrings               2.56.0   2020-04-27 [1] Bioconductor                      
##  bit                      4.0.3    2020-07-30 [1] CRAN (R 4.0.2)                    
##  bit64                    4.0.2    2020-07-30 [1] CRAN (R 4.0.2)                    
##  bitops                   1.0-6    2013-08-17 [1] CRAN (R 4.0.0)                    
##  blob                     1.2.1    2020-01-20 [1] CRAN (R 4.0.0)                    
##  cli                      2.0.2    2020-02-28 [1] CRAN (R 4.0.0)                    
##  codetools                0.2-16   2018-12-24 [1] CRAN (R 4.0.2)                    
##  colorout               * 1.2-2    2020-03-16 [1] Github (jalvesaq/colorout@726d681)
##  colorspace               1.4-1    2019-03-18 [1] CRAN (R 4.0.0)                    
##  crayon                   1.3.4    2017-09-16 [1] CRAN (R 4.0.0)                    
##  curl                     4.3      2019-12-02 [1] CRAN (R 4.0.0)                    
##  DBI                      1.1.0    2019-12-15 [1] CRAN (R 4.0.0)                    
##  dbplyr                 * 1.4.4    2020-05-27 [1] CRAN (R 4.0.2)                    
##  DelayedArray           * 0.14.1   2020-07-14 [1] Bioconductor                      
##  DelayedMatrixStats       1.10.1   2020-07-03 [1] Bioconductor                      
##  DEoptimR                 1.0-8    2016-11-19 [1] CRAN (R 4.0.0)                    
##  digest                   0.6.25   2020-02-23 [1] CRAN (R 4.0.0)                    
##  dplyr                    1.0.1    2020-07-31 [1] CRAN (R 4.0.2)                    
##  dqrng                    0.2.1    2019-05-17 [1] CRAN (R 4.0.0)                    
##  DropletUtils           * 1.8.0    2020-04-27 [1] Bioconductor                      
##  edgeR                    3.30.3   2020-06-02 [1] Bioconductor                      
##  ellipsis                 0.3.1    2020-05-15 [1] CRAN (R 4.0.0)                    
##  ensembldb              * 2.12.1   2020-05-06 [1] Bioconductor                      
##  evaluate                 0.14     2019-05-28 [1] CRAN (R 4.0.0)                    
##  ExperimentHub            1.14.0   2020-04-27 [1] Bioconductor                      
##  fansi                    0.4.1    2020-01-08 [1] CRAN (R 4.0.0)                    
##  fastmap                  1.0.1    2019-10-08 [1] CRAN (R 4.0.0)                    
##  FNN                      1.1.3    2019-02-15 [1] CRAN (R 4.0.0)                    
##  generics                 0.0.2    2018-11-29 [1] CRAN (R 4.0.0)                    
##  GenomeInfoDb           * 1.24.2   2020-06-15 [1] Bioconductor                      
##  GenomeInfoDbData         1.2.3    2020-04-16 [1] Bioconductor                      
##  GenomicAlignments        1.24.0   2020-04-27 [1] Bioconductor                      
##  GenomicFeatures        * 1.40.1   2020-07-14 [1] Bioconductor                      
##  GenomicRanges          * 1.40.0   2020-04-27 [1] Bioconductor                      
##  GGally                   2.0.0    2020-06-06 [1] CRAN (R 4.0.2)                    
##  ggbeeswarm               0.6.0    2017-08-07 [1] CRAN (R 4.0.0)                    
##  ggplot2                * 3.3.2    2020-06-19 [1] CRAN (R 4.0.2)                    
##  glue                     1.4.1    2020-05-13 [1] CRAN (R 4.0.0)                    
##  gridExtra                2.3      2017-09-09 [1] CRAN (R 4.0.0)                    
##  gtable                   0.3.0    2019-03-25 [1] CRAN (R 4.0.0)                    
##  HDF5Array                1.16.1   2020-06-16 [1] Bioconductor                      
##  hms                      0.5.3    2020-01-08 [1] CRAN (R 4.0.0)                    
##  htmltools                0.5.0    2020-06-16 [1] CRAN (R 4.0.2)                    
##  httpuv                   1.5.4    2020-06-06 [1] CRAN (R 4.0.2)                    
##  httr                     1.4.2    2020-07-20 [1] CRAN (R 4.0.2)                    
##  igraph                   1.2.5    2020-03-19 [1] CRAN (R 4.0.0)                    
##  interactiveDisplayBase   1.26.3   2020-06-02 [1] Bioconductor                      
##  IRanges                * 2.22.2   2020-05-21 [1] Bioconductor                      
##  irlba                    2.3.3    2019-02-05 [1] CRAN (R 4.0.0)                    
##  knitr                    1.29     2020-06-23 [1] CRAN (R 4.0.0)                    
##  later                    1.1.0.1  2020-06-05 [1] CRAN (R 4.0.2)                    
##  lattice                  0.20-41  2020-04-02 [1] CRAN (R 4.0.2)                    
##  lazyeval                 0.2.2    2019-03-15 [1] CRAN (R 4.0.0)                    
##  lifecycle                0.2.0    2020-03-06 [1] CRAN (R 4.0.0)                    
##  limma                    3.44.3   2020-06-12 [1] Bioconductor                      
##  locfit                   1.5-9.4  2020-03-25 [1] CRAN (R 4.0.0)                    
##  magrittr                 1.5      2014-11-22 [1] CRAN (R 4.0.0)                    
##  Matrix                   1.2-18   2019-11-27 [1] CRAN (R 4.0.2)                    
##  matrixStats            * 0.56.0   2020-03-13 [1] CRAN (R 4.0.0)                    
##  mclust                   5.4.6    2020-04-11 [1] CRAN (R 4.0.0)                    
##  memoise                  1.1.0    2017-04-21 [1] CRAN (R 4.0.0)                    
##  mime                     0.9      2020-02-04 [1] CRAN (R 4.0.0)                    
##  munsell                  0.5.0    2018-06-12 [1] CRAN (R 4.0.0)                    
##  openssl                  1.4.2    2020-06-27 [1] CRAN (R 4.0.1)                    
##  org.Hs.eg.db             3.11.4   2020-07-27 [1] Bioconductor                      
##  org.Mm.eg.db             3.11.4   2020-08-04 [1] Bioconductor                      
##  pillar                   1.4.6    2020-07-10 [1] CRAN (R 4.0.2)                    
##  pkgconfig                2.0.3    2019-09-22 [1] CRAN (R 4.0.0)                    
##  plyr                     1.8.6    2020-03-03 [1] CRAN (R 4.0.0)                    
##  prettyunits              1.1.1    2020-01-24 [1] CRAN (R 4.0.0)                    
##  progress                 1.2.2    2019-05-16 [1] CRAN (R 4.0.0)                    
##  promises                 1.1.1    2020-06-09 [1] CRAN (R 4.0.2)                    
##  ProtGenerics             1.20.0   2020-04-27 [1] Bioconductor                      
##  pryr                     0.1.4    2018-02-18 [1] CRAN (R 4.0.2)                    
##  purrr                    0.3.4    2020-04-17 [1] CRAN (R 4.0.0)                    
##  R.methodsS3              1.8.0    2020-02-14 [1] CRAN (R 4.0.0)                    
##  R.oo                     1.23.0   2019-11-03 [1] CRAN (R 4.0.0)                    
##  R.utils                  2.9.2    2019-12-08 [1] CRAN (R 4.0.0)                    
##  R6                       2.4.1    2019-11-12 [1] CRAN (R 4.0.0)                    
##  rappdirs                 0.3.1    2016-03-28 [1] CRAN (R 4.0.0)                    
##  RColorBrewer             1.1-2    2014-12-07 [1] CRAN (R 4.0.2)                    
##  Rcpp                     1.0.5    2020-07-06 [1] CRAN (R 4.0.2)                    
##  RCurl                    1.98-1.2 2020-04-18 [1] CRAN (R 4.0.0)                    
##  reshape                  0.8.8    2018-10-23 [1] CRAN (R 4.0.0)                    
##  rhdf5                    2.32.2   2020-07-03 [1] Bioconductor                      
##  Rhdf5lib                 1.10.1   2020-07-09 [1] Bioconductor                      
##  Rhtslib                  1.20.0   2020-04-27 [1] Bioconductor                      
##  rlang                    0.4.7    2020-07-09 [1] CRAN (R 4.0.2)                    
##  rmarkdown                2.3      2020-06-18 [1] CRAN (R 4.0.0)                    
##  robustbase               0.93-6   2020-03-23 [1] CRAN (R 4.0.0)                    
##  Rsamtools                2.4.0    2020-04-27 [1] Bioconductor                      
##  RSpectra                 0.16-0   2019-12-01 [1] CRAN (R 4.0.0)                    
##  RSQLite                  2.2.0    2020-01-07 [1] CRAN (R 4.0.0)                    
##  rstudioapi               0.11     2020-02-07 [1] CRAN (R 4.0.0)                    
##  rsvd                     1.0.3    2020-02-17 [1] CRAN (R 4.0.0)                    
##  rtracklayer              1.48.0   2020-04-27 [1] Bioconductor                      
##  Rtsne                    0.15     2018-11-10 [1] CRAN (R 4.0.2)                    
##  S4Vectors              * 0.26.1   2020-05-16 [1] Bioconductor                      
##  scales                   1.1.1    2020-05-11 [1] CRAN (R 4.0.0)                    
##  scater                   1.16.2   2020-06-26 [1] Bioconductor                      
##  scPipe                 * 1.10.0   2020-04-27 [1] Bioconductor                      
##  scran                    1.16.0   2020-04-27 [1] Bioconductor                      
##  scRNAseq               * 2.2.0    2020-05-07 [1] Bioconductor                      
##  sessioninfo              1.1.1    2018-11-05 [1] CRAN (R 4.0.2)                    
##  shiny                    1.5.0    2020-06-23 [1] CRAN (R 4.0.2)                    
##  showtext                 0.8-1    2020-05-25 [1] CRAN (R 4.0.2)                    
##  showtextdb               3.0      2020-06-04 [1] CRAN (R 4.0.2)                    
##  SingleCellExperiment   * 1.10.1   2020-04-28 [1] Bioconductor                      
##  statmod                  1.4.34   2020-02-17 [1] CRAN (R 4.0.0)                    
##  stringi                  1.4.6    2020-02-17 [1] CRAN (R 4.0.0)                    
##  stringr                  1.4.0    2019-02-10 [1] CRAN (R 4.0.0)                    
##  SummarizedExperiment   * 1.18.2   2020-07-14 [1] Bioconductor                      
##  sysfonts                 0.8.1    2020-05-08 [1] CRAN (R 4.0.0)                    
##  tibble                   3.0.3    2020-07-10 [1] CRAN (R 4.0.2)                    
##  tidyselect               1.1.0    2020-05-11 [1] CRAN (R 4.0.2)                    
##  uwot                     0.1.8    2020-03-16 [1] CRAN (R 4.0.2)                    
##  vctrs                    0.3.2    2020-07-15 [1] CRAN (R 4.0.2)                    
##  vipor                    0.4.5    2017-03-22 [1] CRAN (R 4.0.0)                    
##  viridis                  0.5.1    2018-03-29 [1] CRAN (R 4.0.0)                    
##  viridisLite              0.3.0    2018-02-01 [1] CRAN (R 4.0.0)                    
##  whisker                  0.4      2019-08-28 [1] CRAN (R 4.0.0)                    
##  withr                    2.2.0    2020-04-20 [1] CRAN (R 4.0.0)                    
##  xaringan                 0.16     2020-03-31 [1] CRAN (R 4.0.0)                    
##  xaringanthemer         * 0.3.0    2020-05-04 [1] CRAN (R 4.0.0)                    
##  xfun                     0.16     2020-07-24 [1] CRAN (R 4.0.2)                    
##  XML                      3.99-0.5 2020-07-23 [1] CRAN (R 4.0.2)                    
##  xtable                   1.8-4    2019-04-21 [1] CRAN (R 4.0.0)                    
##  XVector                  0.28.0   2020-04-27 [1] Bioconductor                      
##  yaml                     2.2.1    2020-02-01 [1] CRAN (R 4.0.0)                    
##  zlibbioc                 1.34.0   2020-04-27 [1] Bioconductor                      
## 
## [1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library
## [2] /Library/Frameworks/R.framework/Versions/4.0branch/Resources/library
```

]]