Normalización de datos

# <strong>Normalización 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/1_tCNLiEsQ_TgsqHHf9_1lzXSaM_LunEHxBq3k130dQI/edit#slide=id.g7cc450648d_0_118)

---

# Código de R

```r
library('scRNAseq')
```

```
## Loading required package: SingleCellExperiment
```

```
## Loading required package: SummarizedExperiment
```

```
## Loading required package: GenomicRanges
```

```
## 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, which.max, which.min
```

```
## Loading required package: S4Vectors
```

```
## 
## Attaching package: 'S4Vectors'
```

```
## The following object is masked from 'package:base':
## 
##     expand.grid
```

```
## Loading required package: IRanges
```

```
## Loading required package: GenomeInfoDb
```

```
## 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: DelayedArray
```

```
## Loading required package: matrixStats
```

```
## 
## Attaching package: 'matrixStats'
```

```
## The following objects are masked from 'package:Biobase':
## 
##     anyMissing, rowMedians
```

```
## 
## Attaching package: 'DelayedArray'
```

```
## The following objects are masked from 'package:matrixStats':
## 
##     colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges
```

```
## The following objects are masked from 'package:base':
## 
##     aperm, apply, rowsum
```

```r
sce.zeisel <- ZeiselBrainData(ensembl = TRUE)
```

```
## 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
```

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

```
## downloading 1 resources
```

```
## retrieving 1 resource
```

```
## loading from cache
```

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

```
## loading from cache
```

```
## require("ensembldb")
```

```
## Warning: Unable to map 1565 of 20006 requested IDs.
```

```r
# Control de calidad
library('scater')
```

```
## Loading required package: ggplot2
```

```r
is.mito <- which(rowData(sce.zeisel)$featureType == "mito")
stats <- perCellQCMetrics(sce.zeisel, subsets = list(Mt = is.mito))
qc <-
    quickPerCellQC(stats,
        percent_subsets = c("altexps_ERCC_percent", "subsets_Mt_percent"))
sce.zeisel <- sce.zeisel[, !qc$discard]
```

]

---

```r
# Estimar tamaños de librerías
lib.sf.zeisel <- librarySizeFactors(sce.zeisel)

# Examina la distribución de los tamaños de librerías
# que acabamos de estimar
summary(lib.sf.zeisel)
```

```
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.1754  0.5682  0.8669  1.0000  1.2758  4.0651
```

```r
hist(log10(lib.sf.zeisel), xlab = "Log10[Size factor]", col = "grey80")
```

![](04-normalization_files/figure-html/all_code2-1.png)

```r
ls.zeisel <- colSums(counts(sce.zeisel))
plot(
    ls.zeisel,
    lib.sf.zeisel,
    log = "xy",
    xlab = "Library size",
    ylab = "Size factor"
)
```

![](04-normalization_files/figure-html/all_code2-2.png)

]

---

# Ejercicio

* ¿Son idénticos `ls.zeisel` y `lib.sf.zeisel`?

* ¿Son proporcionales?

* Calcula `lib.sf.zeisel` de forma manual

---

# Soluciones

* Revisa los detalles (**Details**) en `?scater::librarySizeFactors`

* Calcula los tamaños de librería manualmente

```r
## Primero calcula las sumas
zeisel_sums <- colSums(counts(sce.zeisel))
identical(zeisel_sums, ls.zeisel)
```

```
## [1] TRUE
```

```r
## Ahora asegurate que su media sea 1 (unity mean)
zeisel_size_factors <- zeisel_sums/mean(zeisel_sums)
identical(zeisel_size_factors, lib.sf.zeisel)
```

```
## [1] TRUE
```

* Checa el [código fuente](https://github.com/LTLA/scuttle/blob/master/R/librarySizeFactors.R)

---

```r
# Normalización por decircunvolución (deconvolution)

library('scran')
# Pre-clustering
set.seed(100)
clust.zeisel <- quickCluster(sce.zeisel)
# Calcula factores de tamaño para la decircunvolución (deconvolution)
deconv.sf.zeisel <-
    calculateSumFactors(sce.zeisel, clusters = clust.zeisel, min.mean = 0.1)

# Examina la distribución de los factores de tamaño
summary(deconv.sf.zeisel)
```

```
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.1282  0.4859  0.8248  1.0000  1.3194  4.6521
```

```r
hist(log10(deconv.sf.zeisel), xlab = "Log10[Factor de tamaño]",
    col = "grey80")
```

![](04-normalization_files/figure-html/all_code3-1.png)

```r
plot(
    ls.zeisel,
    deconv.sf.zeisel,
    log = "xy",
    xlab = "Factores tamaño de librería",
    ylab = "Factores tamaño deconv"
)
```

![](04-normalization_files/figure-html/all_code3-2.png)

]

---

# Ejercicios

* ¿Cúantos clusters rápidos obtuvimos?

* ¿Cúantas células por cluster obtuvimos?

* ¿Cúantos clusters rápidos obtendríamos si cambiamos el tamaño mínimo a 200? Usa 100 como la semilla (seed).

* ¿Cúantas líneas ves en la gráfica?

???

* 12
* Desde 113 to 325, `sort(table(clust.zeisel))`
* 10 `set.seed(100); sort(table(quickCluster(sce.zeisel, min.size = 200)))` 
* Veo varias líneas cerca de la diagonal. Muy probablemente son 7 `table(factor(sce.zeisel$level1class))`

---

```r
# Factores de tamaño de librería vs 
# factor de tamaño vía circunvolución (convolution)

# Usa colores para los típos celulares que nos dieron
plot(
    lib.sf.zeisel,
    deconv.sf.zeisel,
    xlab = "Factores tamaño de librería",
    ylab = "Factores tamaño deconv",
    log = 'xy',
    pch = 16,
    col = as.integer(factor(sce.zeisel$level1class))
)
abline(a = 0, b = 1, col = "red")
```

![](04-normalization_files/figure-html/all_code4-1.png)

]

---

# ¡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                  
## 
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## 
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## [2] /Library/Frameworks/R.framework/Versions/4.0branch/Resources/library
```

]]