Spectroscopy Workshop at BioSpec and How to Merge Data

Last week Marko and I visited the land of the midnight sun – Norway! We held a two-day workshop on spectroscopy data analysis in Orange at the Norwegian University of Life Sciences. The students from BioSpec lab were yet again incredible and we really dug deep into Orange.

Related: Orange with Spectroscopy Add-on

A class full of dedicated scientists.

 

One thing we did was see how to join data from two different sources. It would often happen that you have measurements in one file and the labels in the other. Or in our case, we wanted to add images to our zoo.tab data. First, find the zoo.tab in the File widget under Browse documentation datasets. Observe the data in the Data Table.

Original zoo data set.

 

This data contains 101 animal described with 16 different features (hair, aquatic, eggs, etc.), a name and a type. Now we will manually create the second table in Excel. The first column will contain the names of the animals as they appear in the original file. The second column will contain links to images of animals. Open your favorite browser and find a couple of images corresponding to selected animals. Then add links to images below the image column. Just like that:

Extra data that we want to add to the original data.

 

Remember, you need a three-row header to define the column that contains images. Under the image column add string in the second and type=image in the third row. This will tell Orange where to look for images. Now, we can check our animals in Image Viewer.

A quick glance at an Image Viewer will tell us whether our images got loaded correctly.

 

Finally, it is time to bring in the images to the existing zoo data set. Connect the original File to Merge Data. Then add the second file with animal images to Merge Data. The default merging method will take the first data input as original data and the second data as extra data. The column to match by is defined in the widget. In our case, it is the name column. This means Orange will look at the first name column and find matching instances in the second name column.

 

A quick look at the merged data shows us an additional image column that we appended to the original file.

Merged data with a new column.

 

This is the final workflow. Merge Data now contains a single data table on the output and you can continue your analysis from there.

Find out more about spectroscopy for Orange on our YouTube channel or contribute to the project on Github.

Orange with Spectroscopy Add-on Workshop

We have just concluded our enhanced Introduction to Data Science workshop, which included several workflows for spectroscopy analysis. Spectroscopy add-on is intended for the analysis of spectral data and it is just as fun as our other add-ons (if not more!).

We will prove it with a simple classification workflow. First, install Spectroscopy add-on from Options – Add-ons menu in Orange. Restart Orange for the add-on to appear. Great, you are ready for some spectral analysis!

Use Datasets widget and load Collagen spectroscopy data. This data contains cells measured with FTIR and annotated with the major chemical compound at the imaged part of a cell. A quick glance in a Data Table will give us an idea how the data looks like. Seems like a very standard spectral data set.

Collagen data set from Datasets widget.

 

Now we want to determine, whether we can classify cells by type based on their spectral profiles. First, connect Datasets to Test & Score. We will use 10-fold cross-validation to score the performance of our model. Next, we will add Logistic Regression to model the data. One final thing. Spectral data often needs some preprocessing. Let us perform a simple preprocessing step by applying Cut (keep) filter and retaining only the wave numbers between 1500 and 1800. When we connect it to Test & Score, we need to keep in mind to connect the Preprocessor output of Preprocess Spectra.

Preprocessor that keeps a part of the spectra cut between 1500 and 1800. No data is shown here, since we are using only the preprocessing procedure as the input for Test & Score.

 

Let us see how well our model performs. Not bad. A 0.99 AUC score. Seems like it is almost perfect. But is it really so?

10-fold cross-validation on spectral data. Our AUC and CA scores are quite impressive.

 

Confusion Matrix gives us a detailed picture. Our model fails almost exclusively on DNA cell type. Interesting.

Confusion Matrix shows DNA is most often misclassified. By selecting the misclassified instances in the matrix, we can inspect why Logistic Regression couldn’t model these spectra

 

We will select the misclassified DNA cells and feed them to Spectra to inspect what went wrong. Instead of coloring by type, we will color by prediction from Logistic Regression. Can you find out why these spectra were classified incorrectly?

Misclassified DNA spectra colored by the prediction made by Logistic Regression.

 

This is one of the simplest examples with spectral data. It is basically the same procedure as with standard data – data is fed as data, learner (LR) as learner and preprocessor as preprocessor directly to Test & Score to avoid overfitting. Play around with Spectroscopy add-on and let us know what you think! 🙂