Hi.
My name is Janet Iwasa.
I'm in the Department of Biochemistry at the University of Utah, and I've worked
with a number of different researchers over the years to really visualize and communicate
their data to broad audiences.
So, today, this is going to be the first in a three-part video series that will be
taking you through visual communication in biology.
And this video, and the last two videos, will really focus on using animation tools
to create molecular animations.
So, when we think about visualizations in biology, there are a number of different visualizations
you might think about.
So, we use visualizations throughout research, from thinking of how to create experiments,
how to design the experiments, to collecting data, to finally communicating our data.
And I really...
I think about visualizations as falling into a few different categories.
So, the first of course is images.
So, we use images all the time, from microscopy images of fixed cells, for example, in fluorescence,
as well as for live cell microscopy, looking at movies, for example, as an example of
these types of images, where you're really looking at 2D or 3D spatial data.
And of course, we also have structures... where we have structures of cryo-EM- or crystallographically
derived structures that we can look at, again, really showing spatial information of
cellular and molecular structures.
So, next we have data figures.
And of course we're all quite familiar with this, with graphs and plots and things like that.
And the goal here is really taking information that may not have any spatial values to them...
it could be... it could be maybe just put in in a sort of a tabular form.
But to really be able to see the patterns, and to be able to see outliers, for example,
it really helps to be able to put them in this visual form, in the forms of plots and graphs.
And I'll be talking more about how to think about making data visualizations later in this talk.
Next, we have model figures.
So, these types of figures are really where we have to get a little bit more artistic.
We're trying to tell a story about a specific process.
And trying to convey our hypotheses in this visual form.
So, this is a really great example of an illustration that was created by Dyche Mullins to show
the leading edge of actin... the actin networks at the leading edge of a crawling cell.
And so, I'll be talking a lot more about how to think about these types of model figures,
and how to create them -- hopefully a little bit better, maybe, than you have been --
in this talk as well.
So, the last type of visualization that I'm going to talk about is animation.
So, the last two videos of this series will really cover animation in great detail.
This is an example of a three-part animation that was used in a figure for a paper
that was describing ESCRT-III-mediated fission.
So, animation in general can be really great when you have both spatial... 3D spatial information
as well as temporal information to describe a specific process.
And it also can be really great for comparing these different processes, as shown
in this particular example.
So, when do we use visualizations?
As I mentioned before, visualizations are crucial in all parts of research.
When you're planning experiments, you might want to sketch out how you're going
to set up an experiment, for example.
When you're collecting data, it might come in the form of images.
But also, when you're doing analysis, of course, you're probably making a lot of graphs
and charts and things like that.
So, we're really focused today on thinking about how we communicate... how we use visualizations
to communicate our results and our hypotheses, and really share those with others.
So, first, I wanted to talk about model figures, and how to think about a process that
allows you to really create a very effective model figure.
So, the first step -- and this is the same kind of process that I do with my collaborators --
is to really describe the process.
So, what is happening?
Where is it in the cell?
How many proteins are there?
Which proteins are there?
Really trying to get into as much detail as possible, but also thinking about that figure legend.
So, maybe it's something that you write down.
Preferably, it's not just something you're thinking about in your head,
but you're actually sharing with others... maybe writing down, and sharing that with others.
Next, you really have to define your audience.
So, depending on... maybe the journal that you're submitting this to, it might have
a broader audience.
Depending on who this this illustration might get shown to... it might get shown to students,
members of the public.
So, depending on your audience, you may have to consider what kind of background information
you could... you could include in that illustration, as well as what context you might be
able to provide.
For example, for this molecular process occurring, what part of the cell is that occurring in?
What kind of... what part of the body?
That kind of information could all be included in a visual way.
So, that's worth thinking about as well.
So, the next step is really... so, at this point, you've been really describing the process,
defining your audience, and now you're finally getting a chance to start drawing.
So, I always think that it's best to start with a piece of paper and a pencil,
and just draw... draw that process, draw that first draft of your figure.
And then you might want to reiterate on that until you're kind of happy to show it to someone else,
because that's the next step.
It's to really... so, the next step is to show this drawing to someone,
preferably in that target audience that you're thinking about.
So, not to someone just in your lab, not to some... another expert in the field.
Someone who really doesn't really know the process very well.
And the idea is to give them this drawing and not to tell them what it is,
not to give them any sort of, you know, a legend or anything like that.
So, without any additional information, give them that figure, and ask them to
basically look at it and interpret it.
So, you want them to tell you what they think it is.
And you want to note where they might be struggling with different parts of the drawing.
Do they understand what this representation means?
Do they understand what the arrows are pointing at and what that means?
So, you want them to kind of verbally talk you through what they think is happening.
And then after that, you want to tell them what you are trying to convey,
and then ask them for recommendations on how you might improve your illustration to better
convey those ideas.
So, next, you refine your drawing.
So, you take your drawing, and you take all of these recommendations,
these different observations that you've made,
and try change your illustration so you can really streamline
this process of someone who's not from your field... allowing them to really interpret it quickly.
So... and then you reiterate.
So, you take your drawing, your revised drawing, and give it to another person
in that target audience, and ask them to again tell you what they think is happening.
And ideally, you get to the point where you can really have a drawing that you can
show someone outside of your field and they understand.
Without reading any legend, without you telling them anything, they can tell you what
that figure is trying to show.
And only after that is done do you take it into illustration software and create
the final version.
So, I wanted to create... to tell you about some general recommendations for when you're
thinking about model figures.
The first is not to recycle, to start from a blank slate.
So, I think it's very common in our field, in biology, to really borrow figures a lot.
You see somebody else's figure.
They use it in a talk, and maybe you just kind of redraw it.
Or maybe you just take it from them and reuse it.
In general, I think this isn't a great idea.
And this is because there are a lot of kinds of problems with the...
with the original drawing, the original illustration, that you can perpetuate
when you do something like this.
So, for example, in this exam... this kind of set of images of HIV, which all look, actually,
relatively similar, there are some problems.
So, for example, the envelope protein, the kind of... the protein that's
on the outside of the membrane, there are actually only considered to be 5 or 6 of them, maybe up to 10.
And you can see that in a lot of electron micrographs of HIV.
However, in these sets, you can see that it's really coating the entire thing, which is inaccurate.
And also the way that the genome, the RNA, is depicted is also kind of strangely compact and short.
And so these kinds of things get perpetuated when people are kind of looking at
reference images that may not be the most accurate.
So, in general, I think you would... when you are using reference images, use your own data.
Use images that you know to be accurate, and really rely on those to start creating your illustration.
And another problem with recycling is that often when you recycle a figure,
it's not going to tell your story as well as when you create one from scratch.
So, really, it's always a good idea to start from scratch if... whenever possible.
Alright.
So, another suggestion is to start drawing early and often.
So, what I've found working with a lot of collaborators is this process of taking
an idea that may have been just bouncing around in your head for who knows how long
and then committing it to a piece of paper can be a really creative process.
But it can also be a process that allows you to really criticize your own ideas,
in hopefully a very helpful way for your research.
So, in this example here, there's a series of illustrations where students were taught
a particular subject -- in this... in this case, it was how cyanide kills you --
and then asked to make a drawing that depicts the sort of... the kind of...
the things that they learned to another student.
So, to be able to teach another student about this process.
And so, teachers evaluated these different drawings.
And what they found was that it was really easy for the teachers to understand
what the students didn't get... did get and didn't get from looking very...
just glancing through these illustrations.
And that could help them change the way that they teach.
But for ourselves, it also... you know, when we take an idea and we put it on a piece of paper,
we commit it to a... this idea to a piece of paper, you really get to see...
start understanding where there may be holes in your hypothesis.
It may allow you to even design experiments that can really de... kind of better describe
some of those... those problem areas.
So, again, it's a great idea to start drawing early and often.
And so, this project... if you're interested in learning about it more, you can go to
this website, Picturing to Learn.
Another general recommendation is not to start with software.
So, I think it can be general inclination when you're trying to draw a figure
to just go into PowerPoint and start, you know, making something.
It's not a great idea.
You know, for ins... for example, in PowerPoint, if you start there, chances are very good
your figure... all your proteins will look like ovals and squares.
And, you know, the arrows will all look exactly the same.
And so... and that can be problematic.
So, in general, no matter what you think your drawing skills are, I think it's a good idea
to start with a piece of paper and a pencil.
This allows you to really be more creative, and allows you to be more expressive,
and tell those ideas in a way that, I think, just starting with software doesn't really enable you to do.
So, when you're starting with software, you're thinking more about, you know, how you...
what your limitations are with the software, what the software can let you do.
And with a piece of paper and pencil, you're really free to do kind of whatever you want.
And that's where you really want to start.
So, really, try to start with a piece of paper and a pencil whenever possible.
So, another general suggestion is to try to keep things simple.
The point of the model figure is often to convey an idea
as efficiently and as quickly as possible to broad audiences.
So, it might be compelling to do some things where it looks a little bit more 3D,
and add gradients and patterns.
But this is often... it makes things harder for people to actually read a paper...
read a figure quickly.
So, getting rid of those patterns, getting rid of those kinds of gradients,
really allows people to kind of visually understand an illustration
much more quickly and efficiently than otherwise.
And another problem with software in general is, sometimes,
having these sorts of gradients and patterns can be a default.
So, it's another reason why starting with a piece of paper and a pencil can be a great idea.
So, another general suggestion is that you want to be able to guide your viewer
very easily through a figure, especially if it's a multi-component type of figure,
where you have a lot of different parts, and you're trying to lead people through it visually.
So, the most common way we do this is using arrows.
And you can be smart about the way you use arrows.
So, in these two examples, you have one where you're using just kind of a straight arrow,
and kind of a relatively large arrowhead.
And this is the same diagram, here, where different elements have been moved around,
and, you know, the shape of the arrows has been curved, the arrowhead has been made smaller.
And you can see the flow of this type of figure, where your eye is supposed to go
from one place to another, a lot more clearly than you can in the previous.
So, just thinking about how you can really make the eye flow from one to another is
a great thing to do.
And again, this is something that would probably come out when you're giving your figure
to other people to take a look at.
If they're, you know, looking at things in the wrong order, then you know you have
to provide a better flow to that diagram.
Another way to provide better flow is by thinking about where you place text.
So, by aligning text to the way that your diagram flows, you can often help the eye...
the person... peoples' eyes kind of follow things in the right direction.
So, that's also something to think about it, and to change if you think that, you know,
the placement of your text might be disrupting the flow.
So, those are some general recommendations to think about when you're creating model figures.
And to recap, you know, kind of the most important points.
Start with a paper and pencil, just go ahead and draw.
Show them to other people, especially in that target audience, and get their feedback
before you reiterate and go into Illustrator, or whatever kind of vector drawing program.
So, in general, I think, you know, illustration programs like Adobe Illustrator are made
for professional illustrators.
They can be quite difficult to learn.
But luckily, there are a lot of different types of tutorials and resources that
you can find online, often for free, that you can... allow you to basically take your skills
that much farther.
So, I often just, like, google whatever I want to try to do in Illustrator,
and usually I'll find a lot of different ways of doing whatever it is I want to do.
And there are a lot of free resources you can find online.
So, there are a lot of video tutorials that often are made by, like, the software manufacturers,
by people on YouTube... different places like that are places you can find different resources
for learning software.
And there are also more organized courses on places like Lynda.com, where you can
have courses on things like Photoshop and Illustrator, even the 3D animation software.
And it's always worth checking whether, you know, these kinds of organized courses
can be made... can be... you can get to them for free.
So, through your university...
I can get through to Lynda.com through the Salt Lake City Public Library, if you're a member.
So, those kinds of things are always worth looking at if you want to kind of
increase your understanding of how to use this type of software.
And the other thing I always recommend is it's also a great idea to just reach out
to other people, maybe in your lab, on your floor.
There's often someone there who knows how to use this software really well,
and is happy to show you, or to even just help you create that illustration.
And you can also reach out to the art department if you're at a university.
There may be students there that you can just hire to help you with this.
So, in general, that's the best recommendation, is to take your drawing and, really,
use a vector illustration program.
And there are several out there that you can consider.
And using presentation software like PowerPoint and Keynote can be used,
but generally you're going to have more expre... you're going to have a better...
a better kind of quality illustration, a more expressive illustration, if you use those other programs.
Alright.
So, next we're moving on to data figures.
So, data visualizations is a large... it's a large field.
And we're really just going to be able to scratch the surface.
And I'm just going to be talking about some of the things that I think are kind of
the main recommendations for kind of the biggest problems that people see in biology data visualizations.
So, one of the things that I think a lot of data visualization experts recommend is
really being careful with how we use color.
And using color specifically to represent quantitative data.
And this is because the way we perceive color is really not on an absolute scale.
It's on a relative scale.
So, we really perceive color and the value of color based on sort of the context
and the other colors around it.
So, if you take a look at these two boxes, for example, in the top box, in the blue,
the color... the color that you see in the center there, that green shade,
looks different from the lighter... when it has a lighter green background.
But you can see that these two... these two colors in the center are actually the same
when you look at them side by side.
And on the flip side, you have two... these two... this example, where you have two different
colors that are represented in these two different backgrounds, but they look to be the same value.
So, this is just to say that we're really not very good at being able to assess
the value of a color when it's... when it's presented on different backgrounds.
And so, in this example, we have a heat map that's often used to represent quantitative data,
often things like upregulation and downregulation of genes.
And we have, here, two different boxes, with the stars in them, that are actually the same color.
But because of the way... the background around them, they don't look to be the same color.
So, it's really hard to interpret these types of illustrations, these types of figures,
when using color.
So, in general, the recommendation is not to use color to represent quantitative data.
So, what do we use instead?
So, what research has shown is that we're not great at being able to understand
quantitative data using color.
However, what we are really good at is being able to understand quantitative data
when used on a fixed position on axes, using either length or position.
So, this last... this last example, here, is really very... it's very clear which of these...
I guess, five different points... which is... which of them has the largest value and which does not.
Versus if you take a look at the color, over here, the volume or the area,
it's really a lot less clear.
So, in general, using something like this, like a graph, is a much better way of
representing quantitative data.
So, when is it that we use color?
So, color is really great when you're trying to distinguish different classes of data.
So, this could be on a bar chart to represent, you know, different cells or something.
Or it could be on... using spatial data, so, for example, this map example.
But also, when you're thinking about spatial data in terms of, say,
a multi-subunit kind of protein complex, and trying to color the different subunits
so that they are distinguishable from each other.
So, it's important to note that there is an upper limit to how many colors we can distinguish
in general.
And choosing colors can be pretty important, so that we can really see the differences
between these different classes.
And the good news is that there are some great tools online to really enable us to
choose colors wisely.
So, this website, called ColorBrewer, allows you to basically interactively select different palettes.
And some of these are... you can use ones that have... all have the same color, or different colors.
And it also allows you to select colors that would be, for example, colorblind-friendly.
And then you can export all of these color palettes and use them in your illustration.
Another general recommendation, and probably one that you've heard before, is not to use
3D graphs and charts.
And so in this example, we have a pie chart that's both represented in 3D and 2D.
And you can see in the 3D representation that the blue and the green wedges look to be
about the same size.
But when you look at them in 2D, it's very clear that the blue... the blue wedge is
actually significantly larger.
So, that can be... that can be really difficult to parse and understand.
And the same with bar graphs.
When you have 3D representations, it's not clear, for example, which part of this
3D bar you're supposed to be looking at to look at that measurement.
And in this example, the green and the blue, are they the same quantity?
Or are they... are they different?
It's really hard to interpret this.
So, the general recommendation is not to use a 3D representation if you're not representing
3D data, 3D spatial data.
That's really... when you have 3D spatial data, that's the only time you should be using 3D.
So, it's a great idea, when you have a data set, to take a look at different representations.
And try to figure out which representation allows you to best see the patterns that
you're expecting to see, or that you're seeing.
And so... but, you know, that can be difficult to do.
To take your data and to throw it into lots of different graphs and charts can be
kind of time-consuming.
But luckily, there's software out there that allows you to do this.
And so, in this example, I've...
I'm using Tableau Public, which is a free resource that you can access...
you can download from online.
And you can import data and then visualize it in a lot of different ways.
So, bars and graphs... and these are all... you know, you can just kind of drag and drop
your data in different ways.
And so this is a great way to look at data... multi-dimensional data in different...
using different representations.
And it allows you to really kind of test different ways of doing this.
And then you can actually output the entire thing as an interactive that you can create online.
So, I recommend taking... giving that a try.
Yeah.
So, in general, those are some of the recommendations.
But again, we're just scratching the surface.
There is a huge body of knowledge out there, there's a huge body of work,
that talks about different ways of thinking about data visualizations.
And I wanted to lead you to some resources if you're interested in learning about more.
So, some of the figures I've shown you -- actually, a good number of them --
come from the Points of View articles that were written by Bang Wong and colleagues,
that really describe lots of different ways of thinking about data visualizations.
These were published in Nature Methods from 2011 to 2013, and there are over...
there are about 40 of these articles.
There are also... if you're interested in thinking about how to do statistics and visualizations
of those, you should take a look at the Points of Significance articles, again in Nature Methods.
And there are also a number of books that you can take a look at.
So, I have a small selection of books on my bookshelf that I found to be very useful.
And so, these are those.
There's... this is just again scratching the surface.
There are a lot out there that you can take a look at and learn from.
And finally, there are a couple of meetings that I've attended that I found to be
quite useful when thinking about visualization.
One is the Gordon Research Conference on Visualization in Science and Education.
It happens every other year.
And also VIZBI, which also happens every year, either in Europe or in the US.
So, if you're interested in learning... that kind of wraps up this particular video,
but if you're interested in learning more about animation, I suggest that you watch
the next two in the series, that will really focus on that.
Thank you.
