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# making poetry out of spam is fun

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

# Visualization Principles Tutorial

This tutorial took place on Monday Mar 5th 2012 at VIZBI 2012 in Heidelberg Germany.

## Introduction

Jessie Kennedy · We will present fundamental principles of graphic design and visual communication that will help you create more effective interactive and print visualizations. You will learn how the purposeful use of salience, color, consistency and layout can help communicate large data sets and complex ideas with greater immediacy and clarity.

Cydney Nielsen · We will illustrate how these principles were implemented in ABySS-Explorer to visualize genome assemblies, an example to show you ways to apply design ideas to your own project.

Martin Krzywinski · At the end of the tutorial, you will apply what you have learned in an interactive group session in which you will design a figure illustrating a biological process.

## Agenda

 9:30 – 10:15 45 min Jessie KennedyPrinciples 10:15 – 10:25 10 min break 10:25 – 11:10 45 min Cydney NielsenDesign Process 11:10 – 11:20 10 min form teams + select figure to critique 11:20 – 11:30 10 min break 11:30 – 12:00 30 min Martin KrzywinskiPractical — Breakout sessiondownload papers 12:00 – 13:00 60 min team presentationsInteractive suggested solutions

It is not necessary to read the paper from which your figure was selected. I have included the papers only if you are interested in learning about the figure's context.

## Visualization and Design Resources

Designing effective visualizations in the biological sciences (PSA Genomics Workshop, Seattle, 12 July 2011)

Designing effective visualizations in the biological sciences (Genome Sciences Center bioinformatics seminar, 26 August 2011)

Drawing Data: Creaing information-rich, informative and appealing figures for publication and presentation (BCCA workshop, 8 Jun 2011)

Behind a great figure is a design principle (BCB Spring Seminar, Iowa State, 27 Feb 2012)

Visualizing Quantitative Information (Genome Sciences Center bioinformatics seminar)

## Visualization Principles VIZBI Book Chapter

Look for my chapter on visualization principles in the upcoming Visualizing Biological Data — a Practical Guide. This book is being written by VIZBI 2011 participants and edited by Sean O'Donoghue and Jim Procter.

VIEW ALL

# Essentials of Data Visualization—8-part video series

Mon 16-01-2017

In collaboration with the Phil Poronnik and Kim Bell-Anderson at the University of Sydney, I'm delighted to share with you our 8-part video series project about thinking about drawing data and communicating science.

Essentials of Data Visualization: Thinking about drawing data and communicating science.

We've created 8 videos, each focusing on a different essential idea in data visualization: encoding, shapes, color, uncertainty, design, drawing missing or unobserved data, labels and process.

The videos were designed as teaching materials. Each video comes with a slide deck and exercises.

# P values and the search for significance

Mon 16-01-2017
Little P value
What are you trying to say
Of significance?
—Steve Ziliak

We've written about P values before and warned readers about common misconceptions about them, which are so rife that the American Statistical Association itself has a long statement about them.

This month is our first of a two-part article about P values. Here we look at 'P value hacking' and 'data dredging', which are questionable practices that invalidate the correct interpretation of P values.

Nature Methods Points of Significance column: P values and the search for significance. (read)

We also illustrate how P values can lead us astray by asking "What is the smallest P value we can expect if the null hypothesis is true but we have done many tests, either explicitly or implicitly?"

Incidentally, this is our first column in which the standfirst is a haiku.

Altman, N. & Krzywinski, M. (2017) Points of Significance: P values and the search for significance. Nature Methods 14:3–4.

Krzywinski, M. & Altman, N. (2013) Points of significance: Significance, P values and t–tests. Nature Methods 10:1041–1042.

# Intuitive Design

Thu 03-11-2016

Appeal to intuition when designing with value judgments in mind.

Figure clarity and concision are improved when the selection of shapes and colors is grounded in the Gestalt principles, which describe how we visually perceive and organize information.

One of the Gestalt principles tells us that the magenta and green shapes will be perceived as as two groups, overriding the fact that the shapes within the group might be different. What the principle does not tell us is how the reader is likely to value each group. (read)

The Gestalt principles are value free. For example, they tell us how we group objects but do not speak to any meaning that we might intuitively infer from visual characteristics.

Nature Methods Points of View column: Intuitive Design. (read)

This month, we discuss how appealing to such intuitions—related to shapes, colors and spatial orientation— can help us add information to a figure as well as anticipate and encourage useful interpretations.

Krzywinski, M. (2016) Points of View: Intuitive Design. Nature Methods 13:895.

# Regularization

Fri 04-11-2016

Constraining the magnitude of parameters of a model can control its complexity.

This month we continue our discussion about model selection and evaluation and address how to choose a model that avoids both overfitting and underfitting.

Ideally, we want to avoid having either an underfitted model, which is usually a poor fit to the training data, or an overfitted model, which is a good fit to the training data but not to new data.

Nature Methods Points of Significance column: Regularization (read)

Regularization is a process that penalizes the magnitude of model parameters. This is done by not only minimizing the SSE, $\mathrm{SSE} = \sum_i (y_i - \hat{y}_i)^2$, as is done normally in a fit, but adding to this minimized quantity the sum of the mode's squared parameters, $\mathrm{SSE} + \lambda \sum_i \hat{\beta}^2_i$.

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Regularization. Nature Methods 13:803-804.