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EMBO Practical Course: Bioinformatics and Genome Analysis, 5–17 June 2017.


communication + science

Nature Methods: Points of View

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Points of View column in Nature Methods. (Points of View)

Guidelines for Effective Figures

Practical and concise advise on the visual presentation of data for researchers. One topic and one page at a time.

Common Challenges in Figure Design

Andreas Dahlin runs a figure making course at Uppsala University. He was kind to share with me common questions and concerns that his students have when creating figures (emphasis is mine).

I face problems for using the tools in power point to make nice illustration figures, and in addition how one can enhance the resolution of the figures to print it in a high quality mode.

In my opinion, the most difficult thing is how to draw the good-looking pictures and design the structure of slide to make it simple and substantial in content.

I find it difficult to find the right software to draw pictures.

The most difficult thing for me, when I make a figure, is to arrange the parts of the figure in a way they look nice and understandable.

I think the most difficult part is creating the concept, how to make a figure easy and fast to understand but not lacking all essential parts.

Stepping outside of my own knowledge of what the picture presents and viewing it as someone who sees it for the first time. It's easy to assume that some things are self evident and not making them clear enough in the pictures.

Figures that not are plots can also be tricky to get to look nice.

Anytime you have to draw something in paint, gimp, or other image program it requires a lot of work to make it look even slightly better than crap.

The most difficult thing (in general) is to include as much information as possible and display it in a way that is easy to understand. Figures should be intuitive for the reader, which is sometimes difficult to achieve. There might also be technical difficulties in achieving what you've visualized.

I think the most difficult part for me is to highlight the main idea I would like to express.

For me the most difficult part is making 3-D figures. Also while making figures its hard to decide on the good colors to choose for the figure.

In my opinion, the most difficult part when making a figure is don't know which software we can use and how to use.

The most difficult part for me is to start it! Because I am so meticulous and I am a painter, then it is not so easy to make decision about my figures and which one is better and so on, then finally I give up and put just one figure which of course I don't like...

I think it is difficult to put together my ideas to something that is connected and makes it easier for the viewer to understand.

It is so easy to just get an image from internet. I don’t know what is ok to do. There seems to be different rules in different communities.

To come up with a figure that does not simplify the concept too much at the same time as it does not overwhelm the viewer. To get some ideas for this is the reason why I take the course. ;-)

To me, how to make it easy to understand is the difficult part.

I think it is to save it in the correct format: Raster or vector, png or jpg or pdf... especially if I want to make some changes in the future to the figure.

I think is to choose the most appropriate figure that really help to transmit the information we want. Then, how many words can be good enough for been part of the message. At the beginning I used to use too many.

Apart from the difficulty of making the figure clear and easy to understand, the biggest problem I'm having is the captions. How long and detailed description is appropriate, so it neither steals attention from the figure nor leaves out too much important information.

I think the most difficult part is to have high resolution image once we want to save it. My experience is when finish with drawing, the file size sometimes to large for high quality image and if we downgrade it, the image becomes bad.

The most difficult part when i making a figure is the software using part, I'm not good at computer so that part is annoying for me all the time.

I think the most difficult is to find out how to condensate many ideas in one picture without making it difficult to understand.

The most difficult part is the get the image to not look too amateurish that people focus on that instead of the message.

The most difficult part when doing a figure is to let it speak for itself, i.e. to not have long caption text.

To be able to depict all the desirable results on a single figure is sometimes not that easy. It becomes more critical when a figure is to be fitted within a certain size frame. An exact placing of a figure in some text editors often comes along with difficulties.

The most difficult part when making a figure is to make it simple and still be informative.

Depends a lot on the kind of figure, but generally it is to get clarity in the design, such that the idea is conceived easily. This requires some good outline (usually an iterative process).

The most difficult part to make a figure is the need to express abstract concepts into drawings.

The compromise between include detailed information and at the same time be readable (figures in articles)

To compress all information and ideas you have in your head into short and clear message.

I feel the difficulty in choosing a right resolution of the picture and the angle that could visualize all the details. And also choosing right test/label colour, size, font. Another difficulty for me is continuation from one slide to another.

I believe that my biggest problem would be making nice flux charts. Generally the ones I draw look too crude, it does not look beautiful. I have no concern about making an image that can represent an idea, but making a beautiful image makes it more pleasing to the eyes of the people who will read my work.

It is very difficult to make the figure delicate. I am still not get used to put all the small components together to integrate the figure by the vector software, instead of drawing it out directly.

I think the most difficult part is to make the image simple but yet informative.

I find it very difficult to make an original clarity picture in a particular format after dimensioning it according to the requirement.

Some times it is difficult to limit the size (Bytes) of the picture when going for high clarity remake.

Making the figure as informative as you want while keeping it simple enough to grasp quickly.

For me, the more difficult part is to create a figure that contains or tells all the information that I want to transmit, but keeping the figure simple, clean and not overloaded.

The most difficult for me is make it easily to be understood meanwhile containing the essential information.

The most difficult thing when developing a figure is ... to remove the bloat but keep the message. (Besides the very most difficult: finding out what I want to tell.)

For me the most difficult part is to choose colors with right contrast and to make it more attractive and catchy.

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news + thoughts

`k` index: a weightlighting and Crossfit performance measure

Wed 07-06-2017

Similar to the `h` index in publishing, the `k` index is a measure of fitness performance.

To achieve a `k` index for a movement you must perform `k` unbroken reps at `k`% 1RM.

The expected value for the `k` index is probably somewhere in the range of `k = 26` to `k=35`, with higher values progressively more difficult to achieve.

In my `k` index introduction article I provide detailed explanation, rep scheme table and WOD example.

Dark Matter of the English Language—the unwords

Wed 07-06-2017

I've applied the char-rnn recurrent neural network to generate new words, names of drugs and countries.

The effect is intriguing and facetious—yes, those are real words.

But these are not: necronology, abobionalism, gabdologist, and nonerify.

These places only exist in the mind: Conchar and Pobacia, Hzuuland, New Kain, Rabibus and Megee Islands, Sentip and Sitina, Sinistan and Urzenia.

And these are the imaginary afflictions of the imagination: ictophobia, myconomascophobia, and talmatomania.

And these, of the body: ophalosis, icabulosis, mediatopathy and bellotalgia.

Want to name your baby? Or someone else's baby? Try Ginavietta Xilly Anganelel or Ferandulde Hommanloco Kictortick.

When taking new therapeutics, never mix salivac and labromine. And don't forget that abadarone is best taken on an empty stomach.

And nothing increases the chance of getting that grant funded than proposing the study of a new –ome! We really need someone to looking into the femome and manome.

Dark Matter of the Genome—the nullomers

Wed 31-05-2017

An exploration of things that are missing in the human genome. The nullomers.

Julia Herold, Stefan Kurtz and Robert Giegerich. Efficient computation of absent words in genomic sequences. BMC Bioinformatics (2008) 9:167

Clustering

Wed 31-05-2017
Clustering finds patterns in data—whether they are there or not.

We've already seen how data can be grouped into classes in our series on classifiers. In this column, we look at how data can be grouped by similarity in an unsupervised way.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Clustering. (read)

We look at two common clustering approaches: `k`-means and hierarchical clustering. All clustering methods share the same approach: they first calculate similarity and then use it to group objects into clusters. The details of the methods, and outputs, vary widely.

Altman, N. & Krzywinski, M. (2017) Points of Significance: Clustering. Nature Methods 14:545–546.

Background reading

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Logistic regression. Nature Methods 13:541-542.

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Classifier evaluation. Nature Methods 13:603-604.

...more about the Points of Significance column

What's wrong with pie charts?

Thu 25-05-2017

In this redesign of a pie chart figure from a Nature Medicine article [1], I look at how to organize and present a large number of categories.

I first discuss some of the benefits of a pie chart—there are few and specific—and its shortcomings—there are few but fundamental.

I then walk through the redesign process by showing how the tumor categories can be shown more clearly if they are first aggregated into a small number groups.

(bottom left) Figure 2b from Zehir et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. (2017) Nature Medicine doi:10.1038/nm.4333