Quality Control

# <strong>Quality Control</strong>
## Analyzing <strong>scRNA-seq</strong> data with <strong>Bioconductor</strong> for <strong>LCG-EJ-UNAM</strong> March 2020
### <a href="http://lcolladotor.github.io/">Leonardo Collado-Torres</a>
### 2020-03-23

---

<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[ Download the materials for this course with `usethis::use_course('lcolladotor/osca_LIIGH_UNAM_2020')` or view online at [**lcolladotor.github.io/osca_LIIGH_UNAM_2020**](http://lcolladotor.github.io/osca_LIIGH_UNAM_2020).]

/* 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>

---

# Slides by Peter Hickey

View them [here](https://docs.google.com/presentation/d/1pIiA7fZd1GBxaKQpzT2sPn7C6fIZxEJ8NI4p3UtoIOo/edit#slide=id.p)

---

# Code and output

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

```
## snapshotDate(): 2019-10-22
```

```
## 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
```

```r
sce.416b$block <- factor(sce.416b$block)

# Download the relevant Ensembl annotation database
# using AnnotationHub resources
library('AnnotationHub')
ah <- AnnotationHub()
```

```
## snapshotDate(): 2019-10-29
```

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

```
## AnnotationHub with 1 record
## # snapshotDate(): 2019-10-29 
## # 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
# Annotate each gene with its chromosome location
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
# Identify the mitochondrial genes
is.mito <- which(location == "MT")

library('scater')
sce.416b <- addPerCellQC(sce.416b,
    subsets = list(Mito = is.mito))
```

]

---

# Questions

* What changed in our `sce` object after `addPerCellQC`?

* Plot boxplots of the number of detected genes by the sample block.

???

* We now have more information at `colData(sce.416b)`
* `with(colData(sce.416b), boxplot(detected ~ block))`

---

# QC metrics plots

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

```
## Warning: partial match of 'val' to 'value'

## Warning: partial match of 'val' to 'value'
```

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

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

```
## Warning: partial match of 'val' to 'value'

## Warning: partial match of 'val' to 'value'
```

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

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

```
## Warning: partial match of 'val' to 'value'

## Warning: partial match of 'val' to 'value'
```

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

]

---

```r
# Example thresholds
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

# Summarize the number of cells removed for each reason
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

# Extract the thresholds
attr(qc.lib2, "thresholds")
```

```
##    lower   higher 
## 434082.9      Inf
```

```r
attr(qc.nexprs2, "thresholds")
```

```
##    lower   higher 
## 5231.468      Inf
```

```r
# Summarize the number of cells removed for each reason.
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
## More checks
plotColData(sce.416b,
    x = "block",
    y = "detected",
    other_fields = "phenotype") +
    scale_y_log10() +
    facet_wrap( ~ phenotype)
```

```
## Warning: partial match of 'val' to 'value'

## Warning: partial match of 'val' to 'value'
```

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

```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

# Extract the 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
# Summarize the number of cells removed for each reason
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
```

]

---

# Questions

* Was `qc.lib` necessary for creating `discord`?

* Which filter discarded more cells? `discard` or `discard2`?

* By considering the sample batch, did we discard more cells using automatic threshold detection?

???

* Yes from `table(qc.lib , qc.spike)` and `table(qc.lib , qc.mito)`
* `discard` from `table(discard, discard2)`
* Yes from `table(discard, discard2, discard3)`

---

```r
sce.grun <- GrunPancreasData()
```

```
## snapshotDate(): 2019-10-22
```

```
## 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: partial match of 'val' to 'value'

## Warning: partial match of 'val' to 'value'
```

```
## 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)

```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: partial match of 'val' to 'value'
```

```
## 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)

```r
plotColData(
    sce.grun,
    x = "donor",
    y = "altexps_ERCC_percent",
    colour_by = data.frame(discard = discard.ercc2)
)
```

```
## Warning: partial match of 'val' to 'value'
```

```
## 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)

```r
# Add info about which cells are outliers
sce.416b$discard <- discard2

# Look at this plot for each QC metric
plotColData(
    sce.416b,
    x = "block",
    y = "sum",
    colour_by = "discard",
    other_fields = "phenotype"
) +
    facet_wrap( ~ phenotype) +
    scale_y_log10()
```

```
## Warning: partial match of 'val' to 'value'
```

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

```r
# Another useful diagnostic plot
plotColData(
    sce.416b,
    x = "sum",
    y = "subsets_Mito_percent",
    colour_by = "discard",
    other_fields = c("block", "phenotype")
) +
    facet_grid(block ~ phenotype)
```

```
## Warning: partial match of 'val' to 'value'

## Warning: partial match of 'val' to 'value'
```

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

]

---

```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"
        )
    )
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))

# Only showing unique points for plotting speed.
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
)
```

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

```r
set.seed(100)
e.out <- emptyDrops(counts(sce.pbmc))

# See ?emptyDrops for an explanation of why there are NA # values.
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)
# Ideally, this histogram should look close to uniform.
# Large peaks near zero indicate that barcodes with total
# counts below 'lower' are not ambient in origin.
hist(all.out$PValue[all.out$Total <= limit &
        all.out$Total > 0],
    xlab = "P-value",
    main = "",
    col = "grey80")
```

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

```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")
```

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

]

---

# Exercises

* Why does `emptyDrops()` return `NA` values?

* Are the p-values the same for `e.out` and `all.out`?

* What if you subset to the non-`NA` entries?

???

* Below `lower` they are considered emptry droplets. Only used for multiple correction testing.
* No, due to the `NA`s.
* Yes: `identical(e.out$PValue[!is.na(e.out$FDR)], all.out$PValue[!is.na(e.out$FDR)])`

---

```r
# Removing low-quality cells
# Keeping the columns we DON'T want to discard
filtered <- sce.416b[,!discard2]
# Marking low-quality cells
marked <- sce.416b
marked$discard <- discard2
```

* Which of these objects is larger?

* Which one would you prefer to use?

???

* `marked` is larger than `filtered`
* I would prefer to use the `marked` one if I have enough memory to do so.

---

# Thanks!

Slides created via the R package [**xaringan**](https://github.com/yihui/xaringan) and themed with [**xaringanthemer**](https://github.com/gadenbuie/xaringanthemer).

This course is based on the book [**Orchestrating Single Cell Analysis with Bioconductor**](https://osca.bioconductor.org/) by [Aaron Lun](https://www.linkedin.com/in/aaron-lun-869b5894/), [Robert Amezquita](https://robertamezquita.github.io/), [Stephanie Hicks](https://www.stephaniehicks.com/) and [Raphael Gottardo](http://rglab.org), plus [**WEHI's scRNA-seq course**](https://drive.google.com/drive/folders/1cn5d-Ey7-kkMiex8-74qxvxtCQT6o72h) by [Peter Hickey](https://www.peterhickey.org/).

You can find the files for this course at [lcolladotor/osca_LIIGH_UNAM_2020](https://github.com/lcolladotor/osca_LIIGH_UNAM_2020).

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

]

---

# R session information

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

```
## ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 3.6.3 (2020-02-29)
##  os       macOS Catalina 10.15.3      
##  system   x86_64, darwin15.6.0        
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  ctype    en_US.UTF-8                 
##  tz       America/New_York            
##  date     2020-03-23                  
## 
## ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────
##  package                * version  date       lib source                                   
##  AnnotationDbi          * 1.48.0   2019-10-29 [1] Bioconductor                             
##  AnnotationFilter       * 1.10.0   2019-10-29 [1] Bioconductor                             
##  AnnotationHub          * 2.18.0   2019-10-29 [1] Bioconductor                             
##  askpass                  1.1      2019-01-13 [1] CRAN (R 3.6.0)                           
##  assertthat               0.2.1    2019-03-21 [1] CRAN (R 3.6.0)                           
##  beeswarm                 0.2.3    2016-04-25 [1] CRAN (R 3.6.0)                           
##  Biobase                * 2.46.0   2019-10-29 [1] Bioconductor                             
##  BiocFileCache          * 1.10.2   2019-11-08 [1] Bioconductor                             
##  BiocGenerics           * 0.32.0   2019-10-29 [1] Bioconductor                             
##  BiocManager              1.30.10  2019-11-16 [1] CRAN (R 3.6.1)                           
##  BiocNeighbors            1.4.2    2020-02-29 [1] Bioconductor                             
##  BiocParallel           * 1.20.1   2019-12-21 [1] Bioconductor                             
##  BiocSingular             1.2.2    2020-02-14 [1] Bioconductor                             
##  BiocVersion              3.10.1   2019-06-06 [1] Bioconductor                             
##  biomaRt                  2.42.0   2019-10-29 [1] Bioconductor                             
##  Biostrings               2.54.0   2019-10-29 [1] Bioconductor                             
##  bit                      1.1-15.2 2020-02-10 [1] CRAN (R 3.6.0)                           
##  bit64                    0.9-7    2017-05-08 [1] CRAN (R 3.6.0)                           
##  bitops                   1.0-6    2013-08-17 [1] CRAN (R 3.6.0)                           
##  blob                     1.2.1    2020-01-20 [1] CRAN (R 3.6.0)                           
##  cli                      2.0.2    2020-02-28 [1] CRAN (R 3.6.0)                           
##  colorout               * 1.2-1    2019-05-07 [1] Github (jalvesaq/colorout@7ea9440)       
##  colorspace               1.4-1    2019-03-18 [1] CRAN (R 3.6.0)                           
##  crayon                   1.3.4    2017-09-16 [1] CRAN (R 3.6.0)                           
##  curl                     4.3      2019-12-02 [1] CRAN (R 3.6.0)                           
##  DBI                      1.1.0    2019-12-15 [1] CRAN (R 3.6.0)                           
##  dbplyr                 * 1.4.2    2019-06-17 [1] CRAN (R 3.6.0)                           
##  DelayedArray           * 0.12.2   2020-01-06 [1] Bioconductor                             
##  DelayedMatrixStats       1.8.0    2019-10-29 [1] Bioconductor                             
##  digest                   0.6.25   2020-02-23 [1] CRAN (R 3.6.0)                           
##  dplyr                    0.8.5    2020-03-07 [1] CRAN (R 3.6.0)                           
##  dqrng                    0.2.1    2019-05-17 [1] CRAN (R 3.6.0)                           
##  DropletUtils           * 1.6.1    2019-10-30 [1] Bioconductor                             
##  edgeR                    3.28.1   2020-02-26 [1] Bioconductor                             
##  ensembldb              * 2.10.2   2019-11-20 [1] Bioconductor                             
##  evaluate                 0.14     2019-05-28 [1] CRAN (R 3.6.0)                           
##  ExperimentHub            1.12.0   2019-10-29 [1] Bioconductor                             
##  fansi                    0.4.1    2020-01-08 [1] CRAN (R 3.6.0)                           
##  fastmap                  1.0.1    2019-10-08 [1] CRAN (R 3.6.0)                           
##  GenomeInfoDb           * 1.22.0   2019-10-29 [1] Bioconductor                             
##  GenomeInfoDbData         1.2.2    2019-10-31 [1] Bioconductor                             
##  GenomicAlignments        1.22.1   2019-11-12 [1] Bioconductor                             
##  GenomicFeatures        * 1.38.2   2020-02-15 [1] Bioconductor                             
##  GenomicRanges          * 1.38.0   2019-10-29 [1] Bioconductor                             
##  ggbeeswarm               0.6.0    2017-08-07 [1] CRAN (R 3.6.0)                           
##  ggplot2                * 3.3.0    2020-03-05 [1] CRAN (R 3.6.0)                           
##  glue                     1.3.2    2020-03-12 [1] CRAN (R 3.6.0)                           
##  gridExtra                2.3      2017-09-09 [1] CRAN (R 3.6.0)                           
##  gtable                   0.3.0    2019-03-25 [1] CRAN (R 3.6.0)                           
##  HDF5Array                1.14.3   2020-02-29 [1] Bioconductor                             
##  hms                      0.5.3    2020-01-08 [1] CRAN (R 3.6.0)                           
##  htmltools                0.4.0    2019-10-04 [1] CRAN (R 3.6.0)                           
##  httpuv                   1.5.2    2019-09-11 [1] CRAN (R 3.6.0)                           
##  httr                     1.4.1    2019-08-05 [1] CRAN (R 3.6.0)                           
##  interactiveDisplayBase   1.24.0   2019-10-29 [1] Bioconductor                             
##  IRanges                * 2.20.2   2020-01-13 [1] Bioconductor                             
##  irlba                    2.3.3    2019-02-05 [1] CRAN (R 3.6.0)                           
##  knitr                    1.28     2020-02-06 [1] CRAN (R 3.6.0)                           
##  later                    1.0.0    2019-10-04 [1] CRAN (R 3.6.0)                           
##  lattice                  0.20-40  2020-02-19 [1] CRAN (R 3.6.0)                           
##  lazyeval                 0.2.2    2019-03-15 [1] CRAN (R 3.6.0)                           
##  lifecycle                0.2.0    2020-03-06 [1] CRAN (R 3.6.0)                           
##  limma                    3.42.2   2020-02-03 [1] Bioconductor                             
##  locfit                   1.5-9.1  2013-04-20 [1] CRAN (R 3.6.0)                           
##  magrittr                 1.5      2014-11-22 [1] CRAN (R 3.6.0)                           
##  Matrix                 * 1.2-18   2019-11-27 [1] CRAN (R 3.6.3)                           
##  matrixStats            * 0.56.0   2020-03-13 [1] CRAN (R 3.6.0)                           
##  memoise                  1.1.0    2017-04-21 [1] CRAN (R 3.6.0)                           
##  mime                     0.9      2020-02-04 [1] CRAN (R 3.6.0)                           
##  munsell                  0.5.0    2018-06-12 [1] CRAN (R 3.6.0)                           
##  openssl                  1.4.1    2019-07-18 [1] CRAN (R 3.6.0)                           
##  pillar                   1.4.3    2019-12-20 [1] CRAN (R 3.6.0)                           
##  pkgconfig                2.0.3    2019-09-22 [1] CRAN (R 3.6.1)                           
##  prettyunits              1.1.1    2020-01-24 [1] CRAN (R 3.6.2)                           
##  progress                 1.2.2    2019-05-16 [1] CRAN (R 3.6.0)                           
##  promises                 1.1.0    2019-10-04 [1] CRAN (R 3.6.0)                           
##  ProtGenerics             1.18.0   2019-10-29 [1] Bioconductor                             
##  purrr                    0.3.3    2019-10-18 [1] CRAN (R 3.6.0)                           
##  R.methodsS3              1.8.0    2020-02-14 [1] CRAN (R 3.6.0)                           
##  R.oo                     1.23.0   2019-11-03 [1] CRAN (R 3.6.0)                           
##  R.utils                  2.9.2    2019-12-08 [1] CRAN (R 3.6.1)                           
##  R6                       2.4.1    2019-11-12 [1] CRAN (R 3.6.1)                           
##  rappdirs                 0.3.1    2016-03-28 [1] CRAN (R 3.6.0)                           
##  Rcpp                     1.0.4    2020-03-17 [1] CRAN (R 3.6.0)                           
##  RCurl                    1.98-1.1 2020-01-19 [1] CRAN (R 3.6.0)                           
##  rhdf5                    2.30.1   2019-11-26 [1] Bioconductor                             
##  Rhdf5lib                 1.8.0    2019-10-29 [1] Bioconductor                             
##  rlang                    0.4.5    2020-03-01 [1] CRAN (R 3.6.0)                           
##  rmarkdown                2.1      2020-01-20 [1] CRAN (R 3.6.0)                           
##  Rsamtools                2.2.3    2020-02-23 [1] Bioconductor                             
##  RSQLite                  2.2.0    2020-01-07 [1] CRAN (R 3.6.0)                           
##  rstudioapi               0.11     2020-02-07 [1] CRAN (R 3.6.0)                           
##  rsvd                     1.0.3    2020-02-17 [1] CRAN (R 3.6.0)                           
##  rtracklayer              1.46.0   2019-10-29 [1] Bioconductor                             
##  S4Vectors              * 0.24.3   2020-01-18 [1] Bioconductor                             
##  scales                   1.1.0    2019-11-18 [1] CRAN (R 3.6.1)                           
##  scater                 * 1.14.6   2019-12-16 [1] Bioconductor                             
##  scRNAseq               * 2.0.2    2019-11-12 [1] Bioconductor                             
##  sessioninfo              1.1.1    2018-11-05 [1] CRAN (R 3.6.0)                           
##  shiny                    1.4.0.2  2020-03-13 [1] CRAN (R 3.6.0)                           
##  SingleCellExperiment   * 1.8.0    2019-10-29 [1] Bioconductor                             
##  stringi                  1.4.6    2020-02-17 [1] CRAN (R 3.6.0)                           
##  stringr                  1.4.0    2019-02-10 [1] CRAN (R 3.6.0)                           
##  SummarizedExperiment   * 1.16.1   2019-12-19 [1] Bioconductor                             
##  tibble                   2.1.3    2019-06-06 [1] CRAN (R 3.6.0)                           
##  tidyselect               1.0.0    2020-01-27 [1] CRAN (R 3.6.0)                           
##  vctrs                    0.2.4    2020-03-10 [1] CRAN (R 3.6.0)                           
##  vipor                    0.4.5    2017-03-22 [1] CRAN (R 3.6.0)                           
##  viridis                  0.5.1    2018-03-29 [1] CRAN (R 3.6.0)                           
##  viridisLite              0.3.0    2018-02-01 [1] CRAN (R 3.6.0)                           
##  withr                    2.1.2    2018-03-15 [1] CRAN (R 3.6.0)                           
##  xaringan                 0.15     2020-03-04 [1] CRAN (R 3.6.3)                           
##  xaringanthemer         * 0.2.0    2020-03-22 [1] Github (gadenbuie/xaringanthemer@460f441)
##  xfun                     0.12     2020-01-13 [1] CRAN (R 3.6.0)                           
##  XML                      3.99-0.3 2020-01-20 [1] CRAN (R 3.6.0)                           
##  xtable                   1.8-4    2019-04-21 [1] CRAN (R 3.6.0)                           
##  XVector                  0.26.0   2019-10-29 [1] Bioconductor                             
##  yaml                     2.2.1    2020-02-01 [1] CRAN (R 3.6.0)                           
##  zlibbioc                 1.32.0   2019-10-29 [1] Bioconductor                             
## 
## [1] /Library/Frameworks/R.framework/Versions/3.6/Resources/library
```

]]