Practical-3-Processing-16S-rRNA-amplicon-data.md

@@ -28,7 +28,7 @@ What are fnFs and fnRs? Think about why we might want to make lists of files. (R
 
     > sample.names <- sapply(strsplit(basename(fnFs), ".R"), `[`, 1)
 
-This one's a bit funny syntactically. Use the ? to try and figure out what sapply does. The oddest bit is that single square bracket '['. In R, the square bracket is actually a function much like '+' and '-'! It's actually the subset function here. 
+This one's a bit funny syntactically. Use the `?` to try and figure out what sapply does. The oddest bit is that single square bracket `[`. In R, the square bracket is actually a function much like `+` and `-`! It's actually the subset function here. 
 
 Now we have our file names, we can look at the quality values of the reads:
 
@@ -44,7 +44,7 @@ Next we want to make file paths for the filtering and trimming step that will be
     > names(filtRs) <- sample.names
 
 
-Now we move on to actually filtering and quality trimming our reads. Again use ? to see what filterAndTrim does, and what the various options are here. 
+Now we move on to actually filtering and quality trimming our reads. Again use `?` to see what filterAndTrim does, and what the various options are here. 
 
     > out <-  filterAndTrim(fnFs, filtFs, fnRs, filtRs, maxN=0, maxEE=c(5,5), compress=TRUE, multithread=8)
 
@@ -73,7 +73,7 @@ Next we merge the reads to produce single sequence variants:
 
     > mergers <- mergePairs(dadaFs, filtFs, dadaRs, filtRs, verbose=TRUE)
 
-Be careful inspecting this object, it's really big! Have a go at subsetting it using square brackets and use head() to inspect just a part of the output.
+Be careful inspecting this object, it's really big! Have a go at subsetting it using square brackets and use `head()` to inspect just a part of the output.
 
 The next step is to make the ASV table (formatted like a standard OTU table with sequences and counts across samples):