update the course website

Author: wikiselev

docs/09-L3-intro-to-R.md

@@ -161,7 +161,7 @@ b
 ## [1] 5
 ```
 
-In addition to standard alphanumeric characters, strings can also store various special characters. Special characters are identified using a backlash followed by a single character, the most relevant are the special character for tab : "\t" and new line : "\n". To demonstrate the these special characters lets concatenate (cat) together two strings with these characters separating (sep) them:
+In addition to standard alphanumeric characters, strings can also store various special characters. Special characters are identified using a backlash followed by a single character, the most relevant are the special character for tab : `\t` and new line : `\n`. To demonstrate the these special characters lets concatenate (cat) together two strings with these characters separating (sep) them:
 
 ```r
 cat("Hello", "World", sep= " ")
@@ -187,7 +187,7 @@ cat("Hello", "World", sep= "\n")
 ## Hello
 ## World
 ```
-Note that special characters work differently in different functions. For instance the "paste" function does the same thing as "cat" but does not recognize special characters.
+Note that special characters work differently in different functions. For instance the `paste` function does the same thing as `cat` but does not recognize special characters.
 
 
 ```r
@@ -214,7 +214,7 @@ paste("Hello", "World", sep= "\n")
 ## [1] "Hello\nWorld"
 ```
 
-Single or double backslash is also used as an "escape" character to turn off special characters or allow quotation marks to be included in strings:
+Single or double backslash is also used as an `escape` character to turn off special characters or allow quotation marks to be included in strings:
 
 
 ```r
@@ -231,7 +231,7 @@ Special characters are generally only used in pattern matching, and reading/writ
 dat = read.delim("file.tsv", sep="\t")
 ```
 
-Another special type of character data are colours. Colours can be specified in three main ways: by name from those [available](http://bxhorn.com/r-color-tables/), by red, green, blue values using the "rgb" function, and by hue (colour), saturation (colour vs white) and value (colour/white vs black) using the "hsv" function. By default rgb and hsv expect three values in 0-1 with an optional fourth value for transparency. Alternatively, sets of predetermined colours with useful properties can be loaded from many different packages with [RColorBrewer](http://colorbrewer2.org/) being one of the most popular.
+Another special type of character data are colours. Colours can be specified in three main ways: by name from those [available](http://bxhorn.com/r-color-tables/), by red, green, blue values using the `rgb` function, and by hue (colour), saturation (colour vs white) and value (colour/white vs black) using the `hsv` function. By default rgb and hsv expect three values in 0-1 with an optional fourth value for transparency. Alternatively, sets of predetermined colours with useful properties can be loaded from many different packages with [RColorBrewer](http://colorbrewer2.org/) being one of the most popular.
 
 
 ```r
@@ -253,7 +253,7 @@ barplot(c(1,1,1), col=reds, names=c("by_name", "by_rgb", "by_hsv"))
 
 ### Logical
 
-The "logical" class stores boolean truth values, i.e. TRUE and FALSE. It is used for storing the results of logical operations and conditional statements will be coerced to this class. Most other data-types can be coerced to boolean without triggering (or "throwing") error messages, which may cause unexpected behaviour.
+The `logical` class stores boolean truth values, i.e. TRUE and FALSE. It is used for storing the results of logical operations and conditional statements will be coerced to this class. Most other data-types can be coerced to boolean without triggering (or "throwing") error messages, which may cause unexpected behaviour.
 
 
 ```r
@@ -368,14 +368,14 @@ as.numeric(as.character(x))
 ## [1] 20 25 23 38 20 40 25 30
 ```
 
-To make R read text as character data instead of factors set the environment option "stringsAsFactors=FALSE". This must be done at the start of each R session.
+To make R read text as character data instead of factors set the environment option `stringsAsFactors=FALSE`. This must be done at the start of each R session.
 
 
 ```r
 options(stringsAsFactors=FALSE)
 ```
 __Exercise__
-How would you use factors to create a vector of colours for an arbitrarily long vector of fruits like "str_vector" above?
+How would you use factors to create a vector of colours for an arbitrarily long vector of fruits like `str_vector` above?
 __Answer__
 
 
@@ -437,9 +437,9 @@ class(x)
 ```
 ## [1] "character"
 ```
-Here we tried to put character, numeric and logical data into a single vector so all the values were coerced to "character" data.
+Here we tried to put character, numeric and logical data into a single vector so all the values were coerced to `character` data.
 
-A "matrix" is the two dimensional version of a vector, it also requires all data to be of the same type. 
+A `matrix` is the two dimensional version of a vector, it also requires all data to be of the same type. 
 If we combine a character vector and a numeric vector into a matrix, all the data will be coerced to characters:
 
 
@@ -517,15 +517,15 @@ class(z[,1])
 ## [1] "factor"
 ```
 
-Another difference between matrices and dataframes is the ability to select columns using the "$" operator:
+Another difference between matrices and dataframes is the ability to select columns using the `$` operator:
 
 
 ```r
 m$x # throws an error
 z$x # ok
 ```
 
-The final basic data structure is the "list". Lists allow data of different types and different lengths to be stored in a single object. Each element of a list can be any other R object : data of any type, any data structure, even other lists or functions. 
+The final basic data structure is the `list`. Lists allow data of different types and different lengths to be stored in a single object. Each element of a list can be any other R object : data of any type, any data structure, even other lists or functions. 
 
 
 ```r
@@ -564,12 +564,12 @@ ll
 ## $even_a_function
 ## function (..., deparse.level = 1) 
 ## .Internal(cbind(deparse.level, ...))
-## <bytecode: 0x7f3bf97ae978>
+## <bytecode: 0x55e4ded2f378>
 ## <environment: namespace:base>
 ```
 
 Lists are most commonly used when returning a large number of results from a function that do not fit into any of the previous data structures. 
 
 ## More information
 
-You can get more information about any R commands relevant to these datatypes using by typing "?function" in an interactive session.
+You can get more information about any R commands relevant to these datatypes using by typing `?function` in an interactive session.

docs/10-L3-Intro-to-Bioconductor.md

@@ -43,7 +43,7 @@ Task 1: In what ways is the untidy data not tidy? How could we make the untidy d
 
 Tidy data is generally easier to work with than untidy data, especially if you are working with packages such as ggplot. Fortunately, packages are available to make untidy data tidy. Today we will explore a few of the functions available in the tidyr package which can be used to make untidy data tidy. If you are interested in finding out more about tidying data, we recommend reading "R for Data Science", by Garrett Grolemund and Hadley Wickham. An electronic copy is available here: http://r4ds.had.co.nz/
 
-The untidy data above is untidy because two variables ("Wins" and "Losses") are stored in one column ("Category"). This is a common way in which data can be untidy. To tidy this data, we need to make "Wins" and "Losses" into columns, and store the values in "Counts" in these columns. Fortunately, there is a function from the tidyverse packages to perform this operation. The function is called `spread`, and it takes two arguments, `key` and `value`. You should pass the name of the column which contains multiple variables to `key`, and pass the name of the column which contains values from multiple variables to `value`. For example:
+The untidy data above is untidy because two variables (`Wins` and `Losses`) are stored in one column (`Category`). This is a common way in which data can be untidy. To tidy this data, we need to make `Wins` and `Losses` into columns, and store the values in `Counts` in these columns. Fortunately, there is a function from the tidyverse packages to perform this operation. The function is called `spread`, and it takes two arguments, `key` and `value`. You should pass the name of the column which contains multiple variables to `key`, and pass the name of the column which contains values from multiple variables to `value`. For example:
 
 
 ```r
@@ -86,7 +86,7 @@ Task 2: The dataframe `foods` defined below is untidy. Work out why and use `spr
 foods<-data.frame(student=c("Antoinette","Antoinette","Taylor", "Taylor", "Alexa", "Alexa"), Category=c("Dinner", "Dessert", "Dinner", "Dessert", "Dinner","Dessert"), Frequency=c(3,1,4,5,2,1))
 ```
 
-The other common way in which data can be untidy is if the columns are values instead of variables. For example, the dataframe below shows the percentages some students got in tests they did in May and June. The data is untidy because the columns "May" and "June" are values, not variables.
+The other common way in which data can be untidy is if the columns are values instead of variables. For example, the dataframe below shows the percentages some students got in tests they did in May and June. The data is untidy because the columns `May` and `June` are values, not variables.
 
 
 ```r

docs/16-exprs-overview_files/figure-html/expr-overview-tsne-after-qc-1.png

@@ -436,7 +436,18 @@ plotRLE(
 
 \caption{Cell-wise RLE of the tung data}(\#fig:norm-ours-rle-scran)
 \end{figure}
+scran sometimes calculates negative or zero size factors. These will completely distort the normalized expression matrix. 
+We can check the size factors scran has computed like so:
 
+```r
+summary(sizeFactors(umi.qc))
+```
+
+```
+##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
+##  0.4646  0.7768  0.9562  1.0000  1.1444  3.4348
+```
+For this dataset all the size factors are reasonable so we are done. If you find scran has calculated negative size factors try increasing the cluster and pool sizes until they are all positive.
 
 ### Downsampling