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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>Normalización de datos</title>
<meta charset="utf-8" />
<meta name="author" content="Leonardo Collado-Torres" />
<meta name="date" content="2020-08-06" />
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class: center, middle, inverse, title-slide
# <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
---
class: inverse
.center[
<a href="https://osca.bioconductor.org/"><img src="https://raw.githubusercontent.com/Bioconductor/OrchestratingSingleCellAnalysis-release/master/images/cover.png" style="width: 30%"/></a>
<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>.
<a href='https://clustrmaps.com/site/1b5pl' title='Visit tracker'><img src='//clustrmaps.com/map_v2.png?cl=ffffff&w=150&t=n&d=rP3KLyAMuzVNcJFL-_C-B0XnLNVy8Sp6a8HDaKEnSzc'/></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).]
<style type="text/css">
/* From https://github.com/yihui/xaringan/issues/147 */
.scroll-output {
height: 80%;
overflow-y: scroll;
}
/* https://stackoverflow.com/questions/50919104/horizontally-scrollable-output-on-xaringan-slides */
pre {
max-width: 100%;
overflow-x: scroll;
}
/* From https://github.com/yihui/xaringan/wiki/Font-Size */
.tiny{
font-size: 40%
}
/* From https://github.com/yihui/xaringan/wiki/Title-slide */
.title-slide {
background-image: url(https://raw.githubusercontent.com/Bioconductor/OrchestratingSingleCellAnalysis/master/images/Workflow.png);
background-size: 33%;
background-position: 0% 100%
}
</style>
---
# 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
.scroll-output[
```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]
```
]
---
.scroll-output[
```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")
```
<!-- -->
```r
ls.zeisel <- colSums(counts(sce.zeisel))
plot(
ls.zeisel,
lib.sf.zeisel,
log = "xy",
xlab = "Library size",
ylab = "Size factor"
)
```
<!-- -->
]
---
# 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)
---
.scroll-output[
```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")
```
<!-- -->
```r
plot(
ls.zeisel,
deconv.sf.zeisel,
log = "xy",
xlab = "Factores tamaño de librería",
ylab = "Factores tamaño deconv"
)
```
<!-- -->
]
---
# 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$level1class))`
---
.scroll-output[
```r
# Factores de tamaño de librería vs
# factor de tamaño vía circunvolución (convolution)
# Usa colores para los típos celulares que nos dieron
plot(
lib.sf.zeisel,
deconv.sf.zeisel,
xlab = "Factores tamaño de librería",
ylab = "Factores tamaño deconv",
log = 'xy',
pch = 16,
col = as.integer(factor(sce.zeisel$level1class))
)
abline(a = 0, b = 1, col = "red")
```
<!-- -->
]
---
class: middle
.center[
# ¡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/).
<a href="https://www.libd.org"><img src="img/LIBD_logo.jpg" style="width: 20%" /></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).]
---
# Detalles de la sesión de R
.scroll-output[
.tiny[
```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
## 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)
## 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)
## 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
## 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)
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## htmltools 0.5.0 2020-06-16 [1] CRAN (R 4.0.2)
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## igraph 1.2.5 2020-03-19 [1] CRAN (R 4.0.0)
## interactiveDisplayBase 1.26.3 2020-06-02 [1] Bioconductor
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## 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)
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## 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)
## memoise 1.1.0 2017-04-21 [1] CRAN (R 4.0.0)
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## promises 1.1.1 2020-06-09 [1] CRAN (R 4.0.2)
## ProtGenerics 1.20.0 2020-04-27 [1] Bioconductor
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## 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)
## 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)
## 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)
## Rsamtools 2.4.0 2020-04-27 [1] Bioconductor
## RSQLite 2.2.0 2020-01-07 [1] CRAN (R 4.0.0)
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## rtracklayer 1.48.0 2020-04-27 [1] Bioconductor
## 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
## 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)
## 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
```
]]
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