Normalization

# <strong>Normalization</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).]

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---

# Slides by Peter Hickey

View them [here](https://docs.google.com/presentation/d/1_tCNLiEsQ_TgsqHHf9_1lzXSaM_LunEHxBq3k130dQI/edit#slide=id.g7cc450648d_0_118)

---

# Code and output

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

```
## 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
```

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

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## loading from cache
```

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

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

]

---

```r
# Library size factors
lib.sf.zeisel <- librarySizeFactors(sce.zeisel)

# Examine distribution of size factors
summary(lib.sf.zeisel)
```

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

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

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

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

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

]

---

# Exercise

* Are `ls.zeisel` and `lib.sf.zeisel` identical?

* Are they proportional?

* Compute `lib.sf.zeisel` manually

---

# Solution

* Check the **Details** at `?scater::librarySizeFactors`

* Compute the size factors manually

```r
## First compute the sums
zeisel_sums <- colSums(counts(sce.zeisel))
identical(zeisel_sums, ls.zeisel)
```

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

```r
## Next, make them have unity mean
zeisel_size_factors <- zeisel_sums/mean(zeisel_sums)
identical(zeisel_size_factors, lib.sf.zeisel)
```

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

* Check the [source code](https://github.com/davismcc/scater/blob/master/R/librarySizeFactors.R)

---

```r
# Normalization by convolution

library('scran')
# Pre-clustering
set.seed(100)
clust.zeisel <- quickCluster(sce.zeisel)
# Compute deconvolution size factors
deconv.sf.zeisel <-
    calculateSumFactors(sce.zeisel, clusters = clust.zeisel, min.mean = 0.1)

# Examine distribution of size factors
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[Size factor]",
    col = "grey80")
```

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

```r
plot(
    ls.zeisel,
    deconv.sf.zeisel,
    log = "xy",
    xlab = "Library size",
    ylab = "Size factor"
)
```

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

]

---

# Exercises

* How many quick clusters did we get?

* How many cells per quick cluster did we get?

* How many quick clusters will we get if we set the minimum size to 200? Use 100 as the seed.

* How many lines do you see?

???

* 12
* From 113 to 325, `sort(table(clust.zeisel))`
* 10 `set.seed(100); sort(table(quickCluster(sce.zeisel, min.size = 200)))` 
* Several ones near the diagonal. They are potentially 7 `table(factor(sce.zeisel$level1class))`

---

```r
# Library size factors vs. convolution size factors

# Colouring points using the supplied cell-types
plot(
    lib.sf.zeisel,
    deconv.sf.zeisel,
    xlab = "Library size factor",
    ylab = "Deconvolution size factor",
    log = 'xy',
    pch = 16,
    col = as.integer(factor(sce.zeisel$level1class))
)
abline(a = 0, b = 1, col = "red")
```

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

]

---

# 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()
```

```
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##  date     2020-03-23                  
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```

]]