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03-quality-control.html
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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>Control de calidad</title>
<meta charset="utf-8" />
<meta name="author" content="Leonardo Collado-Torres" />
<meta name="date" content="2020-08-05" />
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class: center, middle, inverse, title-slide
# <strong>Control de calidad</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>
### 2020-08-05
---
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>.
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]
.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;
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.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/1pIiA7fZd1GBxaKQpzT2sPn7C6fIZxEJ8NI4p3UtoIOo/edit#slide=id.p)
---
# Código de R
.scroll-output[
```r
## Datos
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.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
```
```
## require("ensembldb")
```
```r
sce.416b$block <- factor(sce.416b$block)
# Descarga los archivos de anotación de la base de datos de Ensembl
# correspondientes usando los recursos disponibles vía AnnotationHub
library('AnnotationHub')
```
```
## Loading required package: BiocFileCache
```
```
## Loading required package: dbplyr
```
```
##
## Attaching package: 'AnnotationHub'
```
```
## The following object is masked from 'package:Biobase':
##
## cache
```
```r
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
## # $tags: c("97", "AHEnsDbs", "Annotation", "EnsDb", "Ensembl", "Gene",
## # "Protein", "Transcript")
## # retrieve record with 'object[["AH73905"]]'
```
```r
# Obtén la posición del cromosoma para cada gen
ens.mm.v97 <- ah[["AH73905"]]
```
```
## loading from cache
```
```r
location <- mapIds(
ens.mm.v97,
keys = rownames(sce.416b),
keytype = "GENEID",
column = "SEQNAME"
)
```
```
## Warning: Unable to map 563 of 46604 requested IDs.
```
```r
# Identifica los genes mitocondriales
is.mito <- which(location == "MT")
library('scater')
```
```
## Loading required package: ggplot2
```
```r
sce.416b <- addPerCellQC(sce.416b,
subsets = list(Mito = is.mito))
```
]
---
# Preguntas
--
* ¿Qué cambió en nuestro objeto `sce` después de `addPerCellQC`?
--
* Haz una gráfica de _boxplots_ del número de genes por bloque (block) de células.
???
* Ahora tenemos más información en `colData(sce.416b)`
* `with(colData(sce.416b), boxplot(detected ~ block))`
---
# Gráficas sobre medidas de control de calidad (QC)
.scroll-output[
```r
plotColData(sce.416b, x = "block", y = "detected")
```
<!-- -->
```r
plotColData(sce.416b, x = "block", y = "detected") +
scale_y_log10()
```
<!-- -->
```r
plotColData(sce.416b,
x = "block",
y = "detected",
other_fields = "phenotype") +
scale_y_log10() +
facet_wrap( ~ phenotype)
```
<!-- -->
]
---
.scroll-output[
```r
# Valores de límite ejemplo
qc.lib <- sce.416b$sum < 100000
qc.nexprs <- sce.416b$detected < 5000
qc.spike <- sce.416b$altexps_ERCC_percent > 10
qc.mito <- sce.416b$subsets_Mito_percent > 10
discard <- qc.lib | qc.nexprs | qc.spike | qc.mito
# Obtenemos un resumen del número de células
# eliminadas por cada filtro
DataFrame(
LibSize = sum(qc.lib),
NExprs = sum(qc.nexprs),
SpikeProp = sum(qc.spike),
MitoProp = sum(qc.mito),
Total = sum(discard)
)
```
```
## DataFrame with 1 row and 5 columns
## LibSize NExprs SpikeProp MitoProp Total
## <integer> <integer> <integer> <integer> <integer>
## 1 3 0 19 14 33
```
```r
qc.lib2 <- isOutlier(sce.416b$sum, log = TRUE, type = "lower")
qc.nexprs2 <- isOutlier(sce.416b$detected, log = TRUE,
type = "lower")
qc.spike2 <- isOutlier(sce.416b$altexps_ERCC_percent,
type = "higher")
qc.mito2 <- isOutlier(sce.416b$subsets_Mito_percent,
type = "higher")
discard2 <- qc.lib2 | qc.nexprs2 | qc.spike2 | qc.mito2
# Extraemos los límites de valores (thresholds)
attr(qc.lib2, "thresholds")
```
```
## lower higher
## 434082.9 Inf
```
```r
attr(qc.nexprs2, "thresholds")
```
```
## lower higher
## 5231.468 Inf
```
```r
# Obtenemos un resumen del número de células
# eliminadas por cada filtro
DataFrame(
LibSize = sum(qc.lib2),
NExprs = sum(qc.nexprs2),
SpikeProp = sum(qc.spike2),
MitoProp = sum(qc.mito2),
Total = sum(discard2)
)
```
```
## DataFrame with 1 row and 5 columns
## LibSize NExprs SpikeProp MitoProp Total
## <integer> <integer> <integer> <integer> <integer>
## 1 4 0 1 2 6
```
```r
## Más pruebas
plotColData(sce.416b,
x = "block",
y = "detected",
other_fields = "phenotype") +
scale_y_log10() +
facet_wrap( ~ phenotype)
```
<!-- -->
```r
batch <- paste0(sce.416b$phenotype, "-", sce.416b$block)
qc.lib3 <- isOutlier(sce.416b$sum,
log = TRUE,
type = "lower",
batch = batch)
qc.nexprs3 <- isOutlier(sce.416b$detected,
log = TRUE,
type = "lower",
batch = batch)
qc.spike3 <- isOutlier(sce.416b$altexps_ERCC_percent,
type = "higher",
batch = batch)
qc.mito3 <- isOutlier(sce.416b$subsets_Mito_percent,
type = "higher",
batch = batch)
discard3 <- qc.lib3 | qc.nexprs3 | qc.spike3 | qc.mito3
# Extraemos los límites de valores (thresholds)
attr(qc.lib3, "thresholds")
```
```
## induced CBFB-MYH11 oncogene expression-20160113
## lower 461073.1
## higher Inf
## induced CBFB-MYH11 oncogene expression-20160325
## lower 399133.7
## higher Inf
## wild type phenotype-20160113 wild type phenotype-20160325
## lower 599794.9 370316.5
## higher Inf Inf
```
```r
attr(qc.nexprs3, "thresholds")
```
```
## induced CBFB-MYH11 oncogene expression-20160113
## lower 5399.24
## higher Inf
## induced CBFB-MYH11 oncogene expression-20160325
## lower 6519.74
## higher Inf
## wild type phenotype-20160113 wild type phenotype-20160325
## lower 7215.887 7586.402
## higher Inf Inf
```
```r
# Obtenemos un resumen del número de células
# eliminadas por cada filtro
DataFrame(
LibSize = sum(qc.lib3),
NExprs = sum(qc.nexprs3),
SpikeProp = sum(qc.spike3),
MitoProp = sum(qc.mito3),
Total = sum(discard3)
)
```
```
## DataFrame with 1 row and 5 columns
## LibSize NExprs SpikeProp MitoProp Total
## <integer> <integer> <integer> <integer> <integer>
## 1 5 4 6 2 9
```
]
---
# Preguntas
--
* ¿Fue necesario `qc.lib` para crear `discord`?
--
* ¿Cúal filtro fue más estricto? ¿`discard` o `discard2`?
--
* Al considerar el grupo de cada muestra (batch), ¿descartamos más células usando un valor de límite automático?
???
* Sí, usando `table(qc.lib , qc.spike)` y `table(qc.lib , qc.mito)`
* `discard` de `table(discard, discard2)`
* Sí, usando `table(discard, discard2, discard3)`
---
.scroll-output[
```r
sce.grun <- GrunPancreasData()
```
```
## snapshotDate(): 2020-04-27
```
```
## see ?scRNAseq and browseVignettes('scRNAseq') for documentation
```
```
## loading from cache
```
```r
sce.grun <- addPerCellQC(sce.grun)
plotColData(sce.grun, x = "donor", y = "altexps_ERCC_percent")
```
```
## Warning: Removed 10 rows containing non-finite values (stat_ydensity).
```
```
## Warning: Removed 10 rows containing missing values (position_quasirandom).
```
<!-- -->
```r
discard.ercc <- isOutlier(sce.grun$altexps_ERCC_percent,
type = "higher",
batch = sce.grun$donor)
```
```
## Warning in isOutlier(sce.grun$altexps_ERCC_percent, type = "higher", batch =
## sce.grun$donor): missing values ignored during outlier detection
```
```r
discard.ercc2 <- isOutlier(
sce.grun$altexps_ERCC_percent,
type = "higher",
batch = sce.grun$donor,
subset = sce.grun$donor %in% c("D17", "D2", "D7")
)
```
```
## Warning in isOutlier(sce.grun$altexps_ERCC_percent, type = "higher", batch =
## sce.grun$donor, : missing values ignored during outlier detection
```
```r
plotColData(
sce.grun,
x = "donor",
y = "altexps_ERCC_percent",
colour_by = data.frame(discard = discard.ercc)
)
```
```
## Warning: Removed 10 rows containing non-finite values (stat_ydensity).
## Warning: Removed 10 rows containing missing values (position_quasirandom).
```
<!-- -->
```r
plotColData(
sce.grun,
x = "donor",
y = "altexps_ERCC_percent",
colour_by = data.frame(discard = discard.ercc2)
)
```
```
## Warning: Removed 10 rows containing non-finite values (stat_ydensity).
## Warning: Removed 10 rows containing missing values (position_quasirandom).
```
<!-- -->
```r
# Agregamos información sobre que células
# tienen valores extremos
sce.416b$discard <- discard2
# Haz esta gráfica para cada medida de
# control de calidad (QC)
plotColData(
sce.416b,
x = "block",
y = "sum",
colour_by = "discard",
other_fields = "phenotype"
) +
facet_wrap( ~ phenotype) +
scale_y_log10()
```
<!-- -->
```r
# Otra gráfica de diagnóstico útil
plotColData(
sce.416b,
x = "sum",
y = "subsets_Mito_percent",
colour_by = "discard",
other_fields = c("block", "phenotype")
) +
facet_grid(block ~ phenotype)
```
<!-- -->
]
---
.scroll-output[
```r
library('BiocFileCache')
bfc <- BiocFileCache()
raw.path <-
bfcrpath(
bfc,
file.path(
"http://cf.10xgenomics.com/samples",
"cell-exp/2.1.0/pbmc4k/pbmc4k_raw_gene_bc_matrices.tar.gz"
)
)
```
```
## adding rname 'http://cf.10xgenomics.com/samples/cell-exp/2.1.0/pbmc4k/pbmc4k_raw_gene_bc_matrices.tar.gz'
```
```r
untar(raw.path, exdir = file.path(tempdir(), "pbmc4k"))
library('DropletUtils')
library('Matrix')
```
```
##
## Attaching package: 'Matrix'
```
```
## The following object is masked from 'package:S4Vectors':
##
## expand
```
```r
fname <- file.path(tempdir(), "pbmc4k/raw_gene_bc_matrices/GRCh38")
sce.pbmc <- read10xCounts(fname, col.names = TRUE)
bcrank <- barcodeRanks(counts(sce.pbmc))
# Mostremos solo los puntos únicos para acelerar
# el proceso de hacer esta gráfica
uniq <- !duplicated(bcrank$rank)
plot(
bcrank$rank[uniq],
bcrank$total[uniq],
log = "xy",
xlab = "Rank",
ylab = "Total UMI count",
cex.lab = 1.2
)
```
```
## Warning in xy.coords(x, y, xlabel, ylabel, log): 1 y value <= 0 omitted from
## logarithmic plot
```
```r
abline(h = metadata(bcrank)$inflection,
col = "darkgreen",
lty = 2)
abline(h = metadata(bcrank)$knee,
col = "dodgerblue",
lty = 2)
legend(
"bottomleft",
legend = c("Inflection", "Knee"),
col = c("darkgreen", "dodgerblue"),
lty = 2,
cex = 1.2
)
```
<!-- -->
```r
set.seed(100)
e.out <- emptyDrops(counts(sce.pbmc))
# Revisa ?emptyDrops para una explicación de porqué hay valores NA
summary(e.out$FDR <= 0.001)
```
```
## Mode FALSE TRUE NA's
## logical 1056 4233 731991
```
```r
set.seed(100)
limit <- 100
all.out <-
emptyDrops(counts(sce.pbmc), lower = limit, test.ambient = TRUE)
# Idealmente, este histograma ddebería verse uniforme.
# Picos grandes cerca de cero indican que los _barcodes_
# con un número total de cuentas menor a "lower" no son
# de origen ambiental.
hist(all.out$PValue[all.out$Total <= limit &
all.out$Total > 0],
xlab = "P-value",
main = "",
col = "grey80")
```
<!-- -->
```r
sce.pbmc <- sce.pbmc[, which(e.out$FDR <= 0.001)]
is.mito <- grep("^MT-", rowData(sce.pbmc)$Symbol)
sce.pmbc <- addPerCellQC(sce.pbmc, subsets = list(MT = is.mito))
discard.mito <-
isOutlier(sce.pmbc$subsets_MT_percent, type = "higher")
plot(
sce.pmbc$sum,
sce.pmbc$subsets_MT_percent,
log = "x",
xlab = "Total count",
ylab = "Mitochondrial %"
)
abline(h = attr(discard.mito, "thresholds")["higher"], col = "red")
```
<!-- -->
]
---
# Ejercicios
--
* ¿Por qué `emptyDrops()` regresa valores `NA`?
--
* ¿Los valores p son iguales entre `e.out` y `all.out`?
--
* ¿Son iguales si obtienes el subconjunto de valores que no son `NA`?
???
* Debajo de `lower` son considerados _droplets_ vacíos. Solo se usan para la correción estadística de pruebas múltiples.
* No, debido a los `NA`s.
* Sí: `identical(e.out$PValue[!is.na(e.out$FDR)], all.out$PValue[!is.na(e.out$FDR)])`
---
```r
# Eliminemos las células de calidad baja
# al quedarnos con las columnas del objeto sce que NO
# queremos descartar (eso hace el !)
filtered <- sce.416b[,!discard2]
# Alternativamente, podemos marcar
# las células de baja calidad
marked <- sce.416b
marked$discard <- discard2
```
--
* ¿Cúal de estos objetos es más grande?
--
* ¿Cúal prefieres usar?
???
* `marked` es más grande que `filtered`
* Yo prefiero usar `marked` si tengo suficiente memoria para usarlo.
---
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 2020-08-04
##
## ─ 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)
## cowplot 1.0.0 2019-07-11 [1] CRAN (R 4.0.2)
## 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)
## DropletUtils * 1.8.0 2020-04-27 [1] Bioconductor
## edgeR 3.30.3 2020-06-02 [1] Bioconductor