From Alignments to Raw Counts

In this section, we will collate all of the count data into one file for analysis in R.

1. First, go back to your 02-STAR_alignment directory. Let’s use a wildcard to list all of the counts files from all of the STAR alignment directories:

cd ~/rnasq_example/02-STAR_alignment
ls -1 */*ReadsPerGene.out.tab

Take a look at the beginning of one of these files:

head C61/C61_ReadsPerGene.out.tab

The first four lines are totals and the columns are ID, total reads, reads mapped to forward strand, and reads mapped to the reverse strand. In this experiment, it looks like the reads are from the reverse strand, due to the much higher mapping numbers in that column. So what we want is just that column of numbers (minus the first four lines), for every one of these files.

2. So let’s take one file and figure out how to do that, then we will expand it to all the files. First let’s just get the rows we want, i.e. everything but the first four:

tail -n +5 C61/C61_ReadsPerGene.out.tab | head

When you give the ‘-n’ option for the ‘tail’ command a number preceded by a ‘+’ sign, it gives you the entire file starting at the line indicated by the number. In this case, we want to skip the first 4 lines, so we start at line 5. We’re piping the command to ‘head’ just to check that it looks correct. You shouldn’t see the first four total lines.

Now, we want only the fourth column (the counts), and in order to get that we pipe the output of the tail command to the ‘cut’ command, and then redirect the output to a new file:

tail -n +5 C61/C61_ReadsPerGene.out.tab | cut -f4 > C61/C61_ReadsPerGene.out.tab.count

Now, C61_ReadsPerGene.out.tab.count contains a single column of data… counts for each of the genes for that sample.

3. Now, we want to do these steps for ALL of the read count files… and to do that we will be using a ‘for loop’ directly on the command line. First, just run a simple ‘for loop’ that will print out the names of all the files we want to use:

for x in */*ReadsPerGene.out.tab; do echo $x; done

This command takes all the files that we listed in step 1 and loops through them, one by one, and for every iteration, assigns the filename to the ‘$x’ variable. Also, for every iteration, it runs whatever commands are between the ‘do’ and ‘done’…. and every iteration the value of ‘$x’ changes. The semi-colons separate the parts of the loop. The ‘echo’ command just prints the value of $x to the screen… in this case just the filename. However, instead, we will use our previously created command, but with $x instead of the filename, and adding a few things:

cd ../    # make sure you're in the dir above 02-STAR_alignment
mkdir 03-Counts
for x in 02-STAR_alignment/*/*ReadsPerGene.out.tab; do \
    s=`basename $x | cut -f1 -d_`
    echo $s
    cat $x | tail -n +5 | cut -f4 > 03-Counts/$s.count
done

The ‘basename’ command gives you just the filename of the path in the variable, i.e. everything after the final forward slash. After this command, there should be a counts file for every sample, in 03-Counts.

4. Next, we need to get the columns for the final table. Because all of these files are sorted in the exact same order (by gene ID), we can just use the columns from any of the files:

tail -n +5 02-STAR_alignment/C61/C61_ReadsPerGene.out.tab | cut -f1 > geneids.txt
head geneids.txt

Finally, we want to combine all of these columns together using the ‘paste’ command, and put it in a temporary file:

paste geneids.txt 03-Counts/*.count > tmp.out

5. The final step is to create a header for our final counts file and combine it with the temp file. The header is just all of the sample names separated by tabs. And again, since we pasted the columns in sorted order (wildcards automatically sort in order), the columns just need to be in that same order… which is the order in our samples.txt file.

cat samples.txt | paste -s > header.txt

We take the samples.txt file and pipe that to the ‘paste’ command with the ‘-s’ option, which takes a column of values and transposes them into a row, separated by the tab character. And finally, let’s put everything together:

cat header.txt tmp.out > all_counts.txt

And now you have a raw counts file that has a count for every gene, per sample. You will use this file for the next step, which is analysis in R.