Analysis pipelines with Python

Introduction to Snakemake

Overview

Teaching: 15 min
Exercises: 15 min
Questions
  • How can I make my results easier to reproduce?

Objectives
  • Understand our example problem.

Let’s imagine that we’re interested in seeing the frequency of various words in various books.

We’ve compiled our raw data i.e. the books we want to analyze and have prepared several Python scripts that together make up our analysis pipeline.

Let’s take quick look at one of the books using the command head books/isles.txt.

Our directory has the Python scripts and data files we we will be working with:

|- books
|  |- abyss.txt
|  |- isles.txt
|  |- last.txt
|  |- LICENSE_TEXTS.md
|  |- sierra.txt
|- plotcount.py
|- wordcount.py
|- zipf_test.py

The first step is to count the frequency of each word in a book. The first argument (books/isles.txt) to wordcount.py is the file to analyze, and the last argument (isles.dat) specifies the output file to write.

python wordcount.py books/isles.txt isles.dat

Let’s take a quick peek at the result.

head -5 isles.dat

This shows us the top 5 lines in the output file:

the 3822 6.7371760973
of 2460 4.33632998414
and 1723 3.03719372466
to 1479 2.60708619778
a 1308 2.30565838181

We can see that the file consists of one row per word. Each row shows the word itself, the number of occurrences of that word, and the number of occurrences as a percentage of the total number of words in the text file.

We can do the same thing for a different book:

python wordcount.py books/abyss.txt abyss.dat
head -5 abyss.dat
the 4044 6.35449402891
and 2807 4.41074795726
of 1907 2.99654305468
a 1594 2.50471401634
to 1515 2.38057825267

Let’s visualize the results. The script plotcount.py reads in a data file and plots the 10 most frequently occurring words as a text-based bar plot:

python plotcount.py isles.dat ascii
the   ########################################################################
of    ##############################################
and   ################################
to    ############################
a     #########################
in    ###################
is    #################
that  ############
by    ###########
it    ###########

plotcount.py can also show the plot graphically:

python plotcount.py isles.dat show

Close the window to exit the plot.

plotcount.py can also create the plot as an image file (e.g. a PNG file):

python plotcount.py isles.dat isles.png

Finally, let’s test Zipf’s law for these books:

python zipf_test.py abyss.dat isles.dat
Book	First	Second	Ratio
abyss	4044	2807	1.44
isles	3822	2460	1.55

Together these scripts implement a common workflow:

  1. Read a data file.
  2. Perform an analysis on this data file.
  3. Write the analysis results to a new file.
  4. Plot a graph of the analysis results.
  5. Save the graph as an image, so we can put it in a paper.
  6. Make a summary table of the analyses

Running wordcount.py and plotcount.py at the shell prompt, as we have been doing, is fine for one or two files. If, however, we had 5 or 10 or 20 text files, or if the number of steps in the pipeline were to expand, this could turn into a lot of work. Plus, no one wants to sit and wait for a command to finish, even just for 30 seconds.

The most common solution to the tedium of data processing is to write a shell script that runs the whole pipeline from start to finish.

Using your text editor of choice (e.g. nano), add the following to a new file named run_pipeline.sh.

# USAGE: bash run_pipeline.sh
# to produce plots for isles and abyss
# and the summary table for the Zipf's law tests

python wordcount.py books/isles.txt isles.dat
python wordcount.py books/abyss.txt abyss.dat

python plotcount.py isles.dat isles.png
python plotcount.py abyss.dat abyss.png

# Generate summary table
python zipf_test.py abyss.dat isles.dat > results.txt

Run the script and check that the output is the same as before:

bash run_pipeline.sh
cat results.txt

This shell script solves several problems in computational reproducibility:

  1. It explicitly documents our pipeline, making communication with colleagues (and our future selves) more efficient.
  2. It allows us to type a single command, bash run_pipeline.sh, to reproduce the full analysis.
  3. It prevents us from repeating typos or mistakes. You might not get it right the first time, but once you fix something it’ll stay fixed.

Despite these benefits it has a few shortcomings.

Let’s adjust the width of the bars in our plot produced by plotcount.py.

Edit plotcount.py so that the bars are 0.8 units wide instead of 1 unit. (Hint: replace width = 1.0 with width = 0.8 in the definition of plot_word_counts.)

Now we want to recreate our figures. We could just bash run_pipeline.sh again. That would work, but it could also be a big pain if counting words takes more than a few seconds. The word counting routine hasn’t changed; we shouldn’t need to recreate those files.

Alternatively, we could manually rerun the plotting for each word-count file. (Experienced shell scripters can make this easier on themselves using a for-loop.)

for book in abyss isles; do
    python plotcount.py $book.dat $book.png
done

With this approach, however, we don’t get many of the benefits of having a shell script in the first place.

Another popular option is to comment out a subset of the lines in run_pipeline.sh:

# USAGE: bash run_pipeline.sh
# to produce plots for isles and abyss
# and the summary table 

# These lines are commented out because they don't need to be rerun.
#python wordcount.py books/isles.txt isles.dat
#python wordcount.py books/abyss.txt abyss.dat

python plotcount.py isles.dat isles.png
python plotcount.py abyss.dat abyss.png

# This line is also commented out because it doesn't need to be rerun.
python zipf_test.py abyss.dat isles.dat > results.txt

Then, we would run our modified shell script using bash run_pipeline.sh.

But commenting out these lines, and subsequently uncommenting them, can be a hassle and source of errors in complicated pipelines. What happens if we have hundreds of input files? No one wants to enter the same command a hundred times, and then edit the result.

What we really want is an executable description of our pipeline that allows software to do the tricky part for us: figuring out what tasks need to be run where and when, then perform those tasks for us.

What is Snakemake and why are we using it?

There are many different tools that researchers use to automate this type of work. Snakemake is a very popular tool, and the one we have selected for this tutorial. There are several reasons this tool was chosen:

The rest of these lessons aim to teach you how to use Snakemake by example. Our goal is to automate our example workflow, and have it do everything for us in parallel regardless of where and how it is run (and have it be reproducible!).

Key Points