scRNAseq/03-quality-control.html

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# <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>
### 2020-08-06

---


class: inverse

.center[

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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).]

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---

# 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 &lt;- 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$block &lt;- factor(sce.416b$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 &lt;- 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 &lt;- ah[["AH73905"]]
```

```
## loading from cache
```

```r
location &lt;- 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 &lt;- which(location == "MT")

library('scater')
```

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

```r
sce.416b &lt;- 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")
```

![](03-quality-control_files/figure-html/qc_metrics-1.png)&lt;!-- --&gt;

```r
plotColData(sce.416b, x = "block", y = "detected") +
    scale_y_log10()
```

![](03-quality-control_files/figure-html/qc_metrics-2.png)&lt;!-- --&gt;

```r
plotColData(sce.416b,
    x = "block",
    y = "detected",
    other_fields = "phenotype") +
    scale_y_log10() +
    facet_wrap( ~ phenotype)
```

![](03-quality-control_files/figure-html/qc_metrics-3.png)&lt;!-- --&gt;

]

---

.scroll-output[


```r
# Valores de límite ejemplo
qc.lib &lt;- sce.416b$sum &lt; 100000
qc.nexprs &lt;- sce.416b$detected &lt; 5000
qc.spike &lt;- sce.416b$altexps_ERCC_percent &gt; 10
qc.mito &lt;- sce.416b$subsets_Mito_percent &gt; 10
discard &lt;- 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
##   &lt;integer&gt; &lt;integer&gt; &lt;integer&gt; &lt;integer&gt; &lt;integer&gt;
## 1         3         0        19        14        33
```

```r
qc.lib2 &lt;- isOutlier(sce.416b$sum, log = TRUE, type = "lower")
qc.nexprs2 &lt;- isOutlier(sce.416b$detected, log = TRUE,
    type = "lower")
qc.spike2 &lt;- isOutlier(sce.416b$altexps_ERCC_percent,
    type = "higher")
qc.mito2 &lt;- isOutlier(sce.416b$subsets_Mito_percent,
    type = "higher")
discard2 &lt;- 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
##   &lt;integer&gt; &lt;integer&gt; &lt;integer&gt; &lt;integer&gt; &lt;integer&gt;
## 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)
```

![](03-quality-control_files/figure-html/all_code_part2-1.png)&lt;!-- --&gt;

```r
batch &lt;- paste0(sce.416b$phenotype, "-", sce.416b$block)
qc.lib3 &lt;- isOutlier(sce.416b$sum,
    log = TRUE,
    type = "lower",
    batch = batch)
qc.nexprs3 &lt;- isOutlier(sce.416b$detected,
    log = TRUE,
    type = "lower",
    batch = batch)
qc.spike3 &lt;- isOutlier(sce.416b$altexps_ERCC_percent,
    type = "higher",
    batch = batch)
qc.mito3 &lt;- isOutlier(sce.416b$subsets_Mito_percent,
    type = "higher",
    batch = batch)
discard3 &lt;- 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
##   &lt;integer&gt; &lt;integer&gt; &lt;integer&gt; &lt;integer&gt; &lt;integer&gt;
## 1         5         4         6         2         9
```

]

---

# Preguntas

--

* ¿Fue necesario `qc.lib` para crear `discard`?

--

* ¿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 &lt;- GrunPancreasData()
```

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

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

```
## loading from cache
```

```r
sce.grun &lt;- 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).
```

![](03-quality-control_files/figure-html/use_case-1.png)&lt;!-- --&gt;

```r
discard.ercc &lt;- 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 &lt;- 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).
```

![](03-quality-control_files/figure-html/use_case-2.png)&lt;!-- --&gt;

```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).
```

![](03-quality-control_files/figure-html/use_case-3.png)&lt;!-- --&gt;

```r
# Agregamos información sobre que células
# tienen valores extremos
sce.416b$discard &lt;- 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()
```

![](03-quality-control_files/figure-html/use_case-4.png)&lt;!-- --&gt;

```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)
```

![](03-quality-control_files/figure-html/use_case-5.png)&lt;!-- --&gt;

]


---

.scroll-output[


```r
library('BiocFileCache')
bfc &lt;- BiocFileCache()
raw.path &lt;-
    bfcrpath(
        bfc,
        file.path(
            "http://cf.10xgenomics.com/samples",
            "cell-exp/2.1.0/pbmc4k/pbmc4k_raw_gene_bc_matrices.tar.gz"
        )
    )
untar(raw.path, exdir = file.path(tempdir(), "pbmc4k"))

library('DropletUtils')
library('Matrix')
fname &lt;- file.path(tempdir(), "pbmc4k/raw_gene_bc_matrices/GRCh38")
sce.pbmc &lt;- read10xCounts(fname, col.names = TRUE)

bcrank &lt;- barcodeRanks(counts(sce.pbmc))

# Mostremos solo los puntos únicos para acelerar
# el proceso de hacer esta gráfica
uniq &lt;- !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 &lt;= 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
)
```

![](03-quality-control_files/figure-html/use_case_pbmc-1.png)&lt;!-- --&gt;

```r
set.seed(100)
e.out &lt;- emptyDrops(counts(sce.pbmc))

# Revisa ?emptyDrops para una explicación de porque hay valores NA
summary(e.out$FDR &lt;= 0.001)
```

```
##    Mode   FALSE    TRUE    NA's 
## logical    1056    4233  731991
```

```r
set.seed(100)
limit &lt;- 100
all.out &lt;-
    emptyDrops(counts(sce.pbmc), lower = limit, test.ambient = TRUE)
# Idealmente, este histograma debería 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 &lt;= limit &amp;
        all.out$Total &gt; 0],
    xlab = "P-value",
    main = "",
    col = "grey80")
```

![](03-quality-control_files/figure-html/use_case_pbmc-2.png)&lt;!-- --&gt;

```r
sce.pbmc &lt;- sce.pbmc[, which(e.out$FDR &lt;= 0.001)]

is.mito &lt;- grep("^MT-", rowData(sce.pbmc)$Symbol)
sce.pmbc &lt;- addPerCellQC(sce.pbmc, subsets = list(MT = is.mito))
discard.mito &lt;-
    isOutlier(sce.pmbc$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")
```

![](03-quality-control_files/figure-html/use_case_pbmc-3.png)&lt;!-- --&gt;

]

---

# 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 &lt;- sce.416b[,!discard2]
# Alternativamente, podemos marcar
# las células de baja calidad
marked &lt;- sce.416b
marked$discard &lt;- 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/).

&lt;a href="https://www.libd.org"&gt;&lt;img src="img/LIBD_logo.jpg" style="width: 20%" /&gt;&lt;/a&gt;

]

.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)                    
##  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)                    
##  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)                    
##  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)                    
##  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)                    
##  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)                    
##  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)                    
##  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                      
##  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)                    
##  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)                    
##  rhdf5                    2.32.2   2020-07-03 [1] Bioconductor                      
##  Rhdf5lib                 1.10.1   2020-07-09 [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)                    
##  Rsamtools                2.4.0    2020-04-27 [1] Bioconductor                      
##  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                      
##  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                      
##  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                      
##  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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