Unit 10: Analysing NMR Data from Start to Finish

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Author: Dr James Cumby
Email: james.cumby@ed.ac.uk

Overview

In this Unit, we will focus on handling real experimental results using Python, from the initial stage of importing data right through to fitting models and plotting the results. While you could achieve this manually using Origin or Excel, hopefully you’ll see that writing code makes the analysis easier, particularly when you have lots of data!

Note: this session does not intend to teach new concepts, but may inadvertently introduce different ways of achieving the same results. Please ask me/a demonstrator if you unsure.

Learning Objectives

This unit covers the learning outcomes of the entire course:

• Perform numerical operations such as vector algebra and calculate simple statistics on data sets.

• Write readable, well-documented and modular code.

• Break a problem into logical steps, and use loops and decision operations to solve tasks.

• Import and clean experimental data, and choose the appropriate variable types to hold information.

• Fit models to numerical data, and plot the results in a number of different formats.

Table of Contents

1. The Problem

2. Reading Data

3. NMR data exploration

4. Peak hunting

5. Analysing Results

Link to documentation:

You may wish to include links to package documentation here, if you wish to refer to them later.

# Add any packages you need to import here

1. The Problem

Do you get more peaks in the ¹H NMR spectrum if you have an odd number of heteroatoms compared with an even number?

The information available is a CSV file containing the number of heteroatoms and an identifier for the molecules, and a folder of NMR data with the matching identifiers. You do not know the chemical formulae.

Surveying the class

Tasks

You will work in pairs or small groups to try and answer this question, with help and guidance from academic demonstrators. Don’t worry if you can’t immediately solve the problem - try out a few ideas, and ask for help when you’re ready!

If you get stuck don’t panic - you will be given hints throughout the workshop, and a model answer will be made available after the session.

Task 1.1 Thinking about the problem

In small groups, discuss the following:

• the chemistry behind the problem;

• what steps are required to solve the problem.

As you discuss your ideas, add steps to the mentimeter:

Click here to see solution to Task.

Some ideas:

• Collect NMR data

• Collect information on hetero atoms

• most abundant isotopes of heteroatoms

2. Reading Data

Data: You will find all the data you need to answer the question in the directory data. The summary file tells you about the hetero atom counts, the weights file about the molecular weights. In data/NMR_data you find many files with different spectra.

Task 2

Getting started with the data

1. Import the NMR_summary.csv file

2. Work out how to read a single NMR spectrum from a file

3. Write a function read_NMR_datathat can read in all NMR files

2.1 - Import summary file

First, we need to import the summary data file; here we’ll use Pandas.

# FIXME

2.2 - Read in NMR data file

First, we need to work out how to read one file

# FIXME

2.3 - Read in all NMR files

Now we can read one file, we should write a function that can read them all.

# FIXME

3. NMR data exploration

To answer the problem

Do you get more peaks in the ¹H NMR spectrum if you have an odd number of heteroatoms compared with an even number?

we will need to determine the number of peaks in a spectrum.

Questions to ask of the data

To peak search automatically, we need the NMR data to have similar numerical values. Things to check are:

• What range of chemical shift do they cover?

• What is the maximum intensity?

• How noisy is the baseline?

Task 3

Exploring the data:

1. Write a function that can quantify the following information:

   • Range of chemical shifts

   • Maximum intensity

   • Level of noise in the spectrum background

2. Extract and store these values for all NMR data

3. Plot histograms of each of each parameter, and decide whether any corrections to the data are required

4. Make any corrections required

3.1/3.2 - Extract key features from each spectrum

#FIXME

3.3 - Plot histograms across all NMR data

# FIXME

3.4 - Apply corrections to standardise the data

For peak searching to work effectively, we need to standardise our data. The simplest change would be to normalise the intensity values so that, e.g. the maximum is 100.

# FIXME

NMR peak hunting

A number of different peak finding algorithms exist, but here we will focus on scipy.signal.find_peaks that you saw in unit 7. Use this to determine the number of peaks in each NMR spectrum, and store these values for plotting. We can optimise the peak finding using the prominence parameter (in this case; other problems might need different parameters).

Task 4

Finding Peaks:

1. Write a function capable of extracting peaks from any of the NMR spectra

2. Test how the peak-fitting parameters affect the number of peaks determined

   Hint: “prominence” is particularly useful for these peak shapes

3. Optimise these parameter(s) to determine the number of peaks in each spectrum

4.1 - Function to extract peaks

# FIXME

4.2 - Optimising the prominence parameter

To find peaks automatically we need to choose a prominence value. Let’s systematically test a few different ones and see what the effect is on number of peaks. There are many ways to tackle this, but here we’ll use the power of pandas to plot multiple curves quickly (and neatly)!

# FIXME

Click here to see some discussion on the prominence parameter.

A prominence above ~1 seems to give a fairly constant number of peaks, but anything below this gives a number of peaks strongly dependent on this parameter.

We will choose prominence = 1 to avoid biasing the number of peaks in one spectrum over another. It is possible that we are missing some “real” peaks, but we are unlikely to do better in one spectrum than another.

4.3 - Optimise the parameters and extract peak count

# FIXME

5. Analysing the results

Now we have computed the number of peaks, we can this for each spectrum in order to answer the original question.

Task 5

Plotting:

Plot graphs to determine whether the number of heteroatoms influences the number of NMR peaks.

# FIXME

Conclusions

Key points:

• Starting from a hypothesis and some available data, you have been able to work through the problem logically to reach a conclusion.

• It is important to inspect and “clean” your data before automating your analysis, as some assumptions may be incorrect.

• There is more than one way to perform these sorts of analysis, but it is important to be aware of the benefits/limitations of different approaches.

Tasks to complete after this session

• Read through the notebook with solutions, and check that you have understood the steps

• Find and read the documentation for any Python commands you are less familiar with

• Think about other ways to solve the problem, and try to implement/compare them

• Extend your analysis to explore other peak searching approaches (or parameters)

• Use statistical methods you have learned to quantify any correlations we have observed

Solutions Notebook

Unit 10 Applications III Solutions

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