Fantastic plots and how to draw them

Transcript

• 00:00Topic of today's is about speaking
• 00:04about data visualization.
• 00:05And so it will be very in general on
• 00:09ramps how to design some strategy,
• 00:12some issues, some principles to guide in
• 00:15the visualization of the of our data.
• 00:20So data visualization is important to
• 00:22explore the data and this is particularly
• 00:25crucial since nowadays data are becoming
• 00:28much more complex and much more bigger,
• 00:31and so in general there
• 00:33is a rise of data science.
• 00:35So not only in research,
• 00:37not only in biological research.
• 00:40The second function to data
• 00:43visualization for data visualization
• 00:45is to communicate the data and
• 00:47that may be the most traditional.
• 00:50I'm so that's what that.
• 00:54Creating publication figures,
• 00:55for example,
• 00:56is about to communicate data to others
• 01:01because communicating data visually is
• 01:04more efficient than than words in general.
• 01:09So in order to represent complex data here,
• 01:13I collected 3.
• 01:16General challenges and aims.
• 01:18So whenever you plot the data is
• 01:22important that the plots are and
• 01:24the representations are are precise,
• 01:27so they're truthful.
• 01:28That means that distortion has
• 01:31to be avoided as much as possible
• 01:34is not always achievable,
• 01:36so distortion sometimes is unavoidable.
• 01:39Think about for example,
• 01:41when you plot the 2D Maps
• 01:44for representing 3D data.
• 01:46But the point is that the
• 01:48distortion doesn't have to convey
• 01:49the message of the figure,
• 01:51so it has to be something that is not
• 01:54related to the main message of the feature.
• 01:57Otherwise it's a problem.
• 01:58Then the second point is clarity.
• 02:01So data the figure has not to be ambiguous,
• 02:06and the third one is the efficiency.
• 02:09So every.
• 02:11Inca every in every pixel is precious,
• 02:14so each decision in doing your plotter,
• 02:17each decision on the color on the size
• 02:20on the number of layers said that you
• 02:24that you plotter is important and it has an.
• 02:28Everything has to be has to have a purpose,
• 02:32so you should reduce the
• 02:34so called chartjunk here.
• 02:36Below the slide you see
• 02:38quotation from Edward.
• 02:39After that I discovered by the way.
• 02:43Only yesterday is that he
• 02:45never knew I'm I'm here from.
• 02:48Since three years and they
• 02:50never known that Edward Tufte,
• 02:52it is the most one of the
• 02:56most celebrated visualization.
• 02:58Antisa is is is there in new heaven.
• 03:03And the condition is that with an
• 03:05image you have to give to the viewer
• 03:07the greatest number of ideas in the
• 03:09shortest time and with the least possible,
• 03:11Inc.
• 03:13This is another general representation
• 03:15of his that you should consider
• 03:18to make a good visualization.
• 03:20Also, this is very general, so it's not.
• 03:23It's not only about science
• 03:25and basically they criteria are
• 03:27organized in four different sets,
• 03:29so you need to represent the information,
• 03:32but the fever also need to display
• 03:34to convey to communicate a story and
• 03:37that's the concept of the figure.
• 03:40This is connected also with the goal of the.
• 03:43All of the figures, so the function.
• 03:47So what is the message that you
• 03:50want to display and also the visual
• 03:53format is important.
• 03:55Obviously the weight of these four
• 03:58different layers is different in
• 04:00different applications for images,
• 04:02so visual form probably is more
• 04:05important for artistic display,
• 04:07while for scientific displays that
• 04:09probably in formation goal and
• 04:12story are more important.
• 04:14This doesn't mean that you should not
• 04:17consider also the visual visual part.
• 04:20I ideally the perfect visualization
• 04:22is at the center of these four steps.
• 04:28So this is for the introduction now.
• 04:31The rest of the presentation will be
• 04:33structured with some very concrete examples,
• 04:35and it's also organized in
• 04:37a way that is interactive,
• 04:39so I will show something.
• 04:41Some example of figures and I will try to
• 04:44ask you what could be wrong with this figure.
• 04:48Starting with this,
• 04:49but this is a figure that is very
• 04:52frequent in scientific publication.
• 04:55It's a barplot and it's the most.
• 04:58It's actually the most frequent disk image
• 05:01that you can find in biomedical journals.
• 05:08Do you have any ideas of what
• 05:11could be wrong with this?
• 05:13Not pretty, yes. Lots of it.
• 05:18It. It's lacking data.
• 05:21It's it's not showing you
• 05:23the data distribution.
• 05:24Yeah, yes, exactly there are.
• 05:26It's putting the treatment on the left,
• 05:29which I always don't like.
• 05:31I always want the control on the
• 05:33left. Oh yes, OK.
• 05:35Yes, that's true, yes,
• 05:36so it has a lot of like. Capital A.
• 05:42Visual problems, but the main yes.
• 05:44The main thing is that it doesn't
• 05:47show the data, so that's the main.
• 05:49That's the main drawback of this image,
• 05:52and so, particularly in the last year.
• 05:54So that's the trend that a lot of also
• 05:57publishers are requesting in images.
• 05:59So the principle is that you need ideally to
• 06:02show always the data points in every figure,
• 06:05because you should show the data that
• 06:08make up your fingers and these for a
• 06:11barplot means that you have to show.
• 06:13They did data points.
• 06:16So here you see an example of how these
• 06:19bar plot can be represented with the
• 06:22data points and you see here on the
• 06:25on the right you see the the single
• 06:28data points and also you see a summary
• 06:30statistics that could be for example,
• 06:33they mean plus minus standard deviation
• 06:35for the treatment and the end the control.
• 06:38In general this showing barplots
• 06:40with only the mean with the standard
• 06:42deviation is a problem and there was
• 06:45a publication of five years ago.
• 06:47The teacher, wife and one issues,
• 06:49for example,
• 06:50that the different data distribution
• 06:52can lead to the same bar block.
• 06:55You see an example here.
• 06:57So in a you should use.
• 06:59You see a barplot representation
• 07:01of distribution of data and all the
• 07:04distribution that you see from B2ER
• 07:06representing could be represented
• 07:08by that padlock.
• 07:09So the ideal situation would be
• 07:11what you see here in plot.
• 07:14Be where you have data that are.
• 07:18Symmetrically distributed,
• 07:19so this is the if the distribution
• 07:21of your real data is.
• 07:23These are the bar plot is less problematic,
• 07:26but for example in C use your situation
• 07:28where you have an outlier and so
• 07:31for example this would mean that the
• 07:33supposed difference that you are
• 07:35showing in the padlock is not real,
• 07:38but it's present only because
• 07:39you have these outlier pointer.
• 07:41But most of the other data are
• 07:44overlapping in the two distributions.
• 07:46Sometimes as you see in the,
• 07:49this plot could hide some patterns in
• 07:52the data, so that's what you see here.
• 07:55In the do you see my cursor?
• 07:59Yes, OK,
• 08:00so this for example shows that there are.
• 08:05The distributions that you see
• 08:07here are by model.
• 08:08This could be linked, for example,
• 08:11to replicate for example,
• 08:12technical replicates and
• 08:13biological replicates,
• 08:14or it could be an important
• 08:16property of the data.
• 08:18Nevertheless,
• 08:18it's something that you cannot
• 08:20see if you represent with with
• 08:23a bar plot and the also Bartlett
• 08:25hide the number of data that are
• 08:27used to visualize the plot.
• 08:29The barplot themselves and so
• 08:31for example in EU situation,
• 08:33where you have an equal number of.
• 08:35Points for the black and the white are
• 08:39Bartlett on the left and the right.
• 08:42At this is a problem also when you
• 08:45want to show paired data in barplots.
• 08:49So again here you see a situation
• 08:53where a barplot
• 08:54displays some is the same.
• 08:57For situations that you see
• 08:59displayed in BC&D, so be Cmdr.
• 09:01Very different situations here
• 09:03you could imagine, for example,
• 09:04that this data obtained from single
• 09:06patients at treated with the dragon,
• 09:09and you measure a parameter of the patients,
• 09:11and so the information related to each
• 09:14patient has to be connected so that the
• 09:17meaning of the of the pair that plot.
• 09:20So the situation in B shows that the
• 09:22Dragon has a consistent effect on all
• 09:25the patients and you can see that.
• 09:28Calculating for each patient,
• 09:30the difference between the dots on
• 09:32the left and on the right give rise
• 09:35to this to this plot here below,
• 09:38where all the differences are
• 09:40positive and are also consistent in.
• 09:42See you see a situation where
• 09:44the drug has very big,
• 09:46very different effects depending
• 09:48on the patient.
• 09:50So that the distribution of
• 09:52the differences is skewed.
• 09:53And by the way,
• 09:55this line represents the median
• 09:57difference that you see for
• 09:59each patients for the treatment.
• 10:01And the third plot indeed that you see
• 10:03has a composition of effects that.
• 10:05So here you see that the again
• 10:08the difference is by model.
• 10:09That means that there are patients
• 10:11that do not respond to the dragon,
• 10:14and you see here with the
• 10:15horizontal lines and some patients
• 10:17that responded to the dragon.
• 10:19So the resulting distribution of the
• 10:21difference as you see here is by model.
• 10:24The problem with her plots
• 10:25are and the problem.
• 10:26Also,
• 10:27if you use barplots with paired
• 10:29data is that you don't see any.
• 10:31Any of this structure so
• 10:33you you are losing it.
• 10:34So the best way is always to show
• 10:36the dots are of your distribution,
• 10:38maybe together with the bar plots
• 10:40and if the data are paid also
• 10:42to show the single connection
• 10:44with in between the dots.
• 10:49There is also an issue about about the
• 10:52choice of displaying the meme of your data,
• 10:56for example versus the media,
• 10:58or to show the standard deviation
• 11:01versus the standard error of the mean,
• 11:04so mean versus median are ways to represent
• 11:07summary of the centrality of a distribution.
• 11:11An the mean is preferable if you
• 11:14suppose your data are for example.
• 11:17Symmetrically distributed.
• 11:18For example, if you assume that the data
• 11:21has a normal or Gaussian distribution,
• 11:23while the median represents the
• 11:25mid is the point that represents
• 11:28the middle of your data.
• 11:30The middle of your distribution,
• 11:32and it's more generally applied
• 11:34independently from the shaper
• 11:36of the distribution of the data.
• 11:38So here you see an example where you
• 11:41have four different samples population
• 11:44and you plot the mean plus the standard.
• 11:48And that's the most conventional
• 11:50way that you see in publication.
• 11:52They mean plus standard deviation.
• 11:55And the median of the population will
• 11:58receive the single point and the
• 12:00horizontal bar represents the median.
• 12:02So an important point about.
• 12:06Mean versus median is that the
• 12:08mean and can be used only with
• 12:11symmetrical distributions.
• 12:13Otherwise it can be misleading.
• 12:15While the median is more
• 12:17generally appropriate.
• 12:19When you have an outlier like that,
• 12:21you would always recommend the meat being.
• 12:24Honey. When you have an outlier
• 12:27like in the third group there,
• 12:29yeah, then it makes more sense to
• 12:31use the median.
• 12:33Yeah, nobody showed them young.
• 12:34Is that the median is more robust
• 12:37data with outliers is totally
• 12:38more robust with outliers,
• 12:40and the median is not,
• 12:42so the presence of over outlier as
• 12:44you see here in C can shift a lot the
• 12:48mean while the median is is affected,
• 12:50but not so much.
• 12:53Especially from the magnitude
• 12:55of the outlier,
• 12:56I would say. So
• 12:58tomorrow question right?
• 12:59So so also, being you know,
• 13:02knowing there's a difference
• 13:03between me and a medium,
• 13:05but one of the things I heard,
• 13:07of course, haven't looked
• 13:09myself into this deeply enough.
• 13:11Is that for the meeting the distribution,
• 13:13unlike mean not necessarily follows
• 13:15a Gaussian or normal distribution,
• 13:17so that from a statistical point of view
• 13:20is going to be a little hard to calculate,
• 13:23certain significance etc.
• 13:24Based on medium data.
• 13:26Is that true?
• 13:27Or it's simply a misnomer?
• 13:30How to calculate the
• 13:32significance of differences?
• 13:34That's a different.
• 13:36So that's the difference of the approach.
• 13:39If you choose parametric test,
• 13:41such as the tester or the ANOVA
• 13:44and those tests assume that the
• 13:47distribution is Goshen is normal.
• 13:49Yeah, so you need to be careful so he is
• 13:52usually if it is a repeated measures.
• 13:55So if you're testing repeated
• 13:56measure yes soon the error is is
• 13:59is distributed in a Goshen way,
• 14:01but that is not always the case.
• 14:03For example,
• 14:04if you're comparing two population of
• 14:07jeans with a signal for each gene.
• 14:09Just have to check it.
• 14:11Well, so so this is something that I
• 14:13think will be particularly important
• 14:15for experimental scientist, right?
• 14:17Because you know, as an experiment
• 14:18is when we are trained, we know OK,
• 14:21when we did design experiment,
• 14:22we do service replica so we can
• 14:24join error bar without thinking
• 14:26Y and how to deal with it.
• 14:28And if you go to a statistician
• 14:30that will tell you say oh look,
• 14:32if you're going to use the test,
• 14:35you have to show me first that this is
• 14:37actually largely a normal distribution
• 14:39before you can actually use the T test.
• 14:42Whereas the vast majority
• 14:43of people in the lab,
• 14:45that's not how they will
• 14:46think about in the 1st place,
• 14:49and they also not trendy enough
• 14:51to think you know how to prove
• 14:53or disprove that's the case.
• 14:55So what would you suggest,
• 14:57especially when we're doing
• 14:58experiment that you cannot do
• 15:00200 replicas for each experiment.
• 15:02So what would be a good
• 15:04approach in that regard?
• 15:06Yeah, so there is a tradeoff between
• 15:09the ideal situation where the ideal
• 15:11situation would be always to have
• 15:14enough data points so that you can
• 15:16understand the shape of the distribution
• 15:19and the real case scenario with you
• 15:22can do as many replicates as you can,
• 15:24and so usually you have to assume
• 15:27that the distribution is normal, so.
• 15:33Ideally, you should always check her.
• 15:36And again, if we are repeating measures
• 15:39and you are collecting a measure
• 15:41of the same data in a repeated,
• 15:44that way you can assume that
• 15:46if the error is stochastic,
• 15:48it should be normally distributed.
• 15:50So you assume that the distribution of
• 15:52the error is Goshen, and it makes sense.
• 15:55But for example in other situation
• 15:57where you have a lot of measurements
• 16:00and measurements of different entities,
• 16:02for example, the expression of
• 16:04different genes we're doing like.
• 16:07A compilation of Jesus.
• 16:08Then these assumption is less probable,
• 16:11is less likely,
• 16:12and you should have enough data points
• 16:15so that you can switch from parametric
• 16:18tests one on parametric, so we're not.
• 16:23That doesn't make assumption of
• 16:25on the underlying distribution.
• 16:26It is, for example,
• 16:27they will cook some test or the
• 16:30Mann Whitney test.
• 16:31And the problem is that you need them
• 16:34or replicates because if the end is
• 16:36the size is less than five, you don't.
• 16:39You cannot reach the statistical
• 16:43significance as it is accepted below 0.05,
• 16:47but it's usually the more correct way.
• 16:53Then they the standard is not to use that,
• 16:55and so I remember there was a case
• 16:57where the paper was in review.
• 16:59It was from.
• 17:02Young bean and I remember we performed
• 17:05the Wilcoxon test and the reviewers
• 17:07as to why we didn't do the parameter
• 17:10test so so they asked for the opposite.
• 17:13They asked us to go against the
• 17:15ideal situation.
• 17:18I think this is very helpful.
• 17:20I think it's really,
• 17:21you know telling about me,
• 17:22especially for people who
• 17:24are not familiar with with.
• 17:26Test and also the the World Cup test.
• 17:29I think it's really suggest
• 17:30you to look into that.
• 17:32Things can be very helpful.
• 17:33Yeah, and and obviously you're
• 17:35so it's important when you plan.
• 17:37If you if you can to have enough data
• 17:40points to perform a nonparametric test.
• 17:43In high throughput
• 17:44experiments that they see now,
• 17:45for example single cell that's
• 17:47not there anymore problem because
• 17:49you have usually a lot of data
• 17:51points and so that's less of a
• 17:53problem that sometimes we work.
• 17:55Is it after him because they thought
• 17:58the number of data are increasing?
• 18:00And not
• 18:01that generic comment comma. These people
• 18:04are a lot of these lot of our group is blood
• 18:10hematology researchers. Yeah,
• 18:11and neither blood nor blood advances require.
• 18:16The investigator in their papers
• 18:17to show all the
• 18:19data points. And now
• 18:21I'm on the publication committee.
• 18:22We've actually talked about this,
• 18:24but we go by the Journal of Cell Bio.
• 18:28Instructions to authors and prep for figures,
• 18:30and there are Rockefeller Press
• 18:32publication. And they
• 18:34haven't. So they have genome research
• 18:36and germ cell bio Med and stuff,
• 18:38so they haven't come around to making
• 18:41people show all their dots etc.
• 18:43But a number of journals, as you know,
• 18:46half like JC I you know JC AI
• 18:50advances etc. They might not
• 18:51even review your paper if
• 18:53you show, for instance,
• 18:54your plots on the left here.
• 18:56Well, they might, you know,
• 18:57might not even go out
• 18:59for review. The pre reviewer's will say
• 19:01you know your figures are inadequate
• 19:03for our instructions, authors etc etc.
• 19:05So I think some journals are
• 19:07coming around to this is the way
• 19:09we really want to see the data.
• 19:12Yeah, I think there is a shift
• 19:15in the paradigm, let's say,
• 19:16and it will take years.
• 19:18But for example, I have a slide here
• 19:21where so this is from my experience,
• 19:24so that for example,
• 19:25all the family of the network journals
• 19:28have already this policies for the figure.
• 19:31So this is something I received after
• 19:34the review of a paper as an editorial
• 19:38guidelines and the food for these like.
• 19:41Policies that I had to change a
• 19:44lot of figures and you see that.
• 19:47And so that the one of the
• 19:49policy as you see here,
• 19:50the last one is that for sample
• 19:52size that are less than 10.
• 19:54And they want you to get to plot
• 19:57the individual data points and
• 19:58so they don't accept bar graphs.
• 20:00Got bargraphs anymore.
• 20:03And then, for example,
• 20:05if you have some statistics such as
• 20:08error bars with the lesson 3 replicates,
• 20:11you have to remove,
• 20:13remove them and you have to show
• 20:15to show the data without the
• 20:18statistics without the error.
• 20:20Then this also is a point that
• 20:23you usually is not satisfied.
• 20:25So when you plot some statistical
• 20:28significance values,
• 20:29they don't accept anymore,
• 20:31they start the stars.
• 20:33But you have to provide the
• 20:35precise P value in the figure.
• 20:38It means that you have some stars.
• 20:40You have to change the stars that
• 20:42converting start to the precise P
• 20:45value before before publishing and
• 20:47then also you have to provide the
• 20:49precise number size for each of your bars.
• 20:52For example,
• 20:53I mean I,
• 20:53I think in the past it was enough
• 20:56to provide a range like from
• 20:58three to six replicates,
• 21:00but now they really want the number for each.
• 21:04For each app and population,
• 21:05for each sample that you have.
• 21:08So these are,
• 21:10in my experience were something
• 21:12that I had to provide that,
• 21:15but after the radio so it was not.
• 21:18It was the editorial like.
• 21:22At stage of acceptance of the paper,
• 21:24and I think this is true now for all
• 21:28the families of the of the natural.
• 21:32Jordans
• 21:34it can I add something?
• 21:36Although this is only for
• 21:38publication that goal of publication,
• 21:39but it's important that we start
• 21:42practicing all these rules in
• 21:44our daily life because it's so
• 21:46painful that you have to do this
• 21:49when you you're trying to get
• 21:51the figures into the Journal.
• 21:53It's a lot easier to do it while you're
• 21:56making the figures in real life.
• 21:59Yeah, so obviously it
• 22:01says worker before there.
• 22:02Yeah it says work because otherwise
• 22:05you have to repeat all day. Fevers so
• 22:10yeah also echo that,
• 22:11and also just want to say that you know
• 22:14I used to just use Excel to placings.
• 22:17But since my many of my lab members
• 22:19start to use Graphpad prism to plot,
• 22:22that makes a huge difference in
• 22:24converting between different types
• 22:25of parts such as this kind of things.
• 22:28If you had a bar bar graph,
• 22:30Indiana in that software,
• 22:31then you can very easily change that to a
• 22:34bar graph with different dots distributed.
• 22:36So it's very easy to work with.
• 22:40Yeah, that's also I have something
• 22:42at the end of the presentation.
• 22:44So basically there are a lot of tools now
• 22:46more or less commercial, but tequila.
• 22:49They aren't really available.
• 22:51U as which are too many different formats
• 22:55and starting with the same initial data,
• 22:58basically formatted as a table.
• 23:01So that from the same table you can switch
• 23:03to there too many different visualizations.
• 23:06So that's that's true,
• 23:08and it's probably easier also to plot these
• 23:11dots with single dots as it was in the past.
• 23:15Without respect.
• 23:18OK, so that was the main point of this part.
• 23:22I had a part on the standard
• 23:24deviation standard error.
• 23:26That's another issue because the
• 23:28standard error is basically the
• 23:29standard deviation divided by the square
• 23:32root of the number of experiments,
• 23:34and so usually the standard
• 23:36error is displayed.
• 23:37But you have just be careful that it's
• 23:40a measure that tends to go to zero
• 23:43just because they increase the number
• 23:45of replicates or the number of points.
• 23:48So you see an example here where
• 23:50it seems by plotting the standard
• 23:52error that the black bar and the
• 23:55white bar have the same like measure
• 23:58of spread of the data.
• 23:59But if you look at the standard
• 24:02deviation you see that this is
• 24:04an effect of the factor.
• 24:06Today the Black bar has higher spread,
• 24:08but also more points,
• 24:10and that's why the standard
• 24:12error seems seems the same.
• 24:16So that's another another issue.
• 24:18So obviously for publication at the
• 24:20standard error of the mean is preferred,
• 24:23because it usually gives an impression
• 24:26of the data being less sparse.
• 24:30But especially with different
• 24:31number of samples in different
• 24:33in different bars that it could.
• 24:35This could be misleading.
• 24:40And all these issues were presented
• 24:42in these in this paper published
• 24:44five years ago in in plus biology.
• 24:49I would skip this,
• 24:50just that we will touch this later,
• 24:52but an alternative solution if
• 24:54you have enough data points.
• 24:56So I would say more than 10.
• 24:59An alternative solution instead
• 25:01of showing like but lotsa Ann
• 25:03is to show the distribution of
• 25:05the data is box whisker plot.
• 25:07As you see here they have some light
• 25:11model with more details on this.
• 25:14OK, so this is the next example.
• 25:17I think it's a biplot with the
• 25:19usage of the different browsers,
• 25:22so this is extra science image so.
• 25:26So this is a classic example
• 25:29in like visualization lessons.
• 25:31So what could we run with this?
• 25:39There's no end. Yeah, so that's a yes,
• 25:44so there is no endless so that you cannot.
• 25:47You don't know of how many,
• 25:50how many data points you use that in
• 25:52order to build the other frequencies.
• 25:55Obviously pie charts are used to display
• 25:59frequencies and proportions of some
• 26:02classes that sum up to 100 or or to one.
• 26:05The main problem is that so
• 26:07the idea is that you shouldn't.
• 26:10You should avoid by chance.
• 26:12So the idea for displaying an
• 26:15information of our proportion or of
• 26:17a percentage as a pie chart are is.
• 26:21Not the best choice.
• 26:23Because that it was shown that humans
• 26:27are very bad at reading angles,
• 26:30so we're not very precise,
• 26:32precise in understanding differences between
• 26:35angles and so between the designs of the.
• 26:39Slices of the pie and so usually if you
• 26:44convert the pie chart into a bar plot.
• 26:47Information is much more clear.
• 26:49It's true that the pie
• 26:51chart is more aesthetic.
• 26:53Appeared, but the bar plotter
• 26:55is in in any circumstances,
• 26:57usually more affecting in displaying girl.
• 26:59For example, differences in the
• 27:02usage of this genome browsers.
• 27:04So this has been a long issue and if you
• 27:07in many presentation so there is always
• 27:10this suggestion to avoid at all a pie charts.
• 27:13There are also some example of these.
• 27:15So these are three pie charts and you can see
• 27:19that it's they are different from each other.
• 27:22But it's very difficult to
• 27:24understand that the difference,
• 27:25so the difference is is in the size
• 27:28of the slice of the three pies,
• 27:31but it's very different.
• 27:32For example, to understand in each
• 27:35pie which one is the largest slides.
• 27:38And to draw comparison it much more
• 27:41more easier to understand these issues.
• 27:44So which pie is larger if the information is
• 27:48not displaced is not displayed as pie charts,
• 27:51but as market.
• 27:54So that's on the web.
• 27:56I also found these provocative.
• 27:59Label of pie charts as lighters.
• 28:03So in general it would be better to avoid
• 28:06displaying information as pie chart.
• 28:08And prefer a bar chart instead
• 28:10to show the same information.
• 28:14OK, so that was faster.
• 28:16This is another example.
• 28:18What could be wrong with this plot?
• 28:20Again, we have a treatment.
• 28:21We have a control.
• 28:22This time we see the data point.
• 28:26Scale is so wrong, so it covers
• 28:29the distribution of the lower end.
• 28:31Yes, exactly so this is a case where
• 28:34most of the data are compressed,
• 28:37since they have very different magnitude.
• 28:39Most of the data are compressed
• 28:42air in a very small part of the
• 28:46plot and we cannot understand.
• 28:48Very much how they are distributed
• 28:51because most of the plotter is related
• 28:53to these kind of two outliers.
• 28:56So this is an issue with the measures
• 28:58that have different magnitudes,
• 29:00so it could in my experience it happens.
• 29:03For example in gene expression measurements.
• 29:07Because they can vary,
• 29:08especially with the sequencing.
• 29:10They can value of four to five.
• 29:13Magnitude and the main way to solve this
• 29:16issue is to log transform the data.
• 29:19So instead of plotting in a
• 29:21linear scale to log normalizing,
• 29:24the scale of the data and this
• 29:26allows to restrict the distance
• 29:28between these two points,
• 29:30the outliers,
• 29:31and allow you to see also the
• 29:34distribution of the points.
• 29:36That here seems all compressed.
• 29:40So usually log transformation allow you to
• 29:43capture some information on the difference
• 29:45of your points that are more clear.
• 29:48Not in all cases, but in some
• 29:50cases rather than displaying the
• 29:52information in a linear scale,
• 29:55especially when you have a lot of range
• 29:58between your minimal and maximal.
• 30:00Measurements. An alternative way.
• 30:04Is not also a panel breaks,
• 30:07so personally I prefer log log
• 30:10transformation over panel breaker because
• 30:13there is mathematically more likely.
• 30:16Linear or elegant,
• 30:18but there are situations where you can.
• 30:20You can choose so this is an example.
• 30:24You have a bar chart.
• 30:26You have a huge difference between
• 30:28the measurements of a 2D and E&F.
• 30:31So this is how you solve the problem by
• 30:35introducing a breaker in your panel.
• 30:38So from 25 to 200 to 210 and this is the
• 30:41equivalent solution by log transformation.
• 30:44As you see,
• 30:46the solution that the two solutions
• 30:49give a fight a similar result.
• 30:51But here you insert the manual break of
• 30:54the data and this could be misleading.
• 30:57Here you saw the issue by log
• 31:00transforming all the measurements.
• 31:02So this is for example is an advantage
• 31:04because it affects all the measurement.
• 31:07And while this panel breaker
• 31:09affects only for example,
• 31:11these two bars and could distort the data.
• 31:18Another another scenario where you should
• 31:22consider log transformation is these.
• 31:26This could be a plotter that shows for gene
• 31:30expression levels from Aaron Isike for.
• 31:33Population of jeans.
• 31:35So each gene could be a doctor
• 31:37and he received it.
• 31:39There is a different year age,
• 31:41but you see that there are outliers like
• 31:44for example genes of ribosomal proteins.
• 31:46Histones usually are in these.
• 31:49Are in this part of the plot,
• 31:52but most of the gene are 90% of
• 31:54your jeans are in this part of the
• 31:56plot and you cannot really see.
• 32:01You cannot really inspect them
• 32:03because most of the plot is
• 32:05dedicated to some outliers.
• 32:07So again, here is a situation
• 32:09where you can log transform.
• 32:11Both are the coordinates.
• 32:12So let's say that here is the
• 32:15control and this is the treatment
• 32:17and this will allow you to see more
• 32:20in detail the differences in the
• 32:22expression of the bug of your jeans.
• 32:28In a situation like Visa,
• 32:30you should also consider
• 32:32issue if you're interested,
• 32:34for example in showing differences in
• 32:37expression between 3 between a control.
• 32:39For example, are one and
• 32:41the treatment are two.
• 32:43You have also the possibility to show.
• 32:48As the Y axis,
• 32:50the differences in the log values.
• 32:52So this representation
• 32:53here is the same as this,
• 32:56but it maximizes the visualization
• 32:58of the differences in the
• 33:00expression levels of genes.
• 33:01So this is something that you
• 33:04find a cold as as an MA plot.
• 33:07It was introduced with the
• 33:09analysis of microarray data,
• 33:11but you can find it also
• 33:13with sequencing data.
• 33:15Sometimes these two different
• 33:16visualization are used.
• 33:17Depending on the aim of the figure,
• 33:20so sometimes you will find these,
• 33:21especially when the message of the
• 33:23figure is that you don't see big
• 33:25differences between the two conditions,
• 33:27while if the message is that you find big
• 33:29differences between the two condition,
• 33:31you will find mostly these visualization.
• 33:35So here I would just point out that
• 33:37in any at any sequencing experiments,
• 33:40you will probably never find any gene
• 33:44that is in this area because they.
• 33:48But most of the genes,
• 33:50the main difference they make
• 33:51the main like determinant,
• 33:53is the basil expression levels.
• 33:55So usually your perturbations do not
• 33:57affect so much the expression of a gene,
• 34:00so that the gene is in these
• 34:03area of the plot of the oranges.
• 34:06And that's why this visualization
• 34:08is much more efficient in capturing
• 34:10the expression differences.
• 34:12Because they scale on on the
• 34:15expression at baseline.
• 34:19OK, so now I have a section I don't
• 34:21know that I'm I have a section
• 34:24about how to display distributions.
• 34:29So let's say that
• 34:29we have a display. One time you had 15
• 34:32minutes and if we go a little over, that's
• 34:34OK, OK? So when you have to
• 34:37represent the distribution of data,
• 34:39you have many choices.
• 34:40The histogram is one of the most used choice.
• 34:44It has the advantage that it can present.
• 34:48With detail, the shape of the
• 34:51distribution of your data.
• 34:53And so basically you have a variable of
• 34:56interest that usually is a continuous
• 34:58variable and you wanted to show
• 35:01how this variable is distributed.
• 35:03So you divide the range of the values in
• 35:06some beans and then you count the number
• 35:09of points that fall inside each being.
• 35:12The issue with the histograms is
• 35:14that you should be careful when when
• 35:17building the histograms and when looking
• 35:19at the histograms that there are
• 35:22some are being arbitrary parameters.
• 35:24In building up his histogram,
• 35:26mainly the choice of the bin size.
• 35:30So this is an example where the same
• 35:33distribution of data that is the
• 35:35distribution of the price of abedy
• 35:38apartments in French City has been
• 35:40being there in two different ways.
• 35:43So here is the price and hear the bin sizes.
• 35:47So the size of each of the bin is 10.
• 35:52Dollars.
• 35:53While it in here on the writer it is
• 35:57of $2 so you can see that using more
• 36:00granular bins allow you to see some
• 36:04the presence of some accumulations
• 36:06in your data that you cannot really
• 36:09see with the larger bin size,
• 36:11and this could be important because
• 36:14these accumulation this probably
• 36:16are accumulation of price that are
• 36:19due to the fact that they are prices
• 36:22that are commonly used.
• 36:23By many different Airbnbs, for example,
• 36:26because they are multipliers of 50 or 100,
• 36:29for example.
• 36:30But the fact is that depending on
• 36:33the choice of the bin,
• 36:34you see a different story.
• 36:38And then you should be always
• 36:42careful to select bin size.
• 36:45That doesn't affect too much data.
• 36:49There are also software tools
• 36:51that calculates depending on
• 36:53your data depending on squared,
• 36:56your points are placed the best
• 36:58and size of the bins so that you
• 37:02reduce the distortion of your data.
• 37:09An alternative way to represent
• 37:10distribution is to use a density plot.
• 37:13So a density plot is basically
• 37:15a smoothing of a histogram.
• 37:18Here you collect being said and here
• 37:21use motor the shape of the distribution
• 37:23so that you have a continuous function.
• 37:27This is graphically nice.
• 37:30And it allows to compare,
• 37:31for example distribution of
• 37:33two variables as you see here
• 37:35in green and in and in Violet,
• 37:37and the advantages that you can see also
• 37:40complex shapes of the distribution.
• 37:42For example here the bimodality
• 37:44or hear the presence of this show
• 37:46is that of the distribution.
• 37:48The pitfall is similar to the histogram,
• 37:51so you should always be careful
• 37:53in selecting the.
• 37:54How much is Martha the distribution?
• 37:56And here you see an example.
• 37:58So these are the.
• 37:59Points that were used at the single
• 38:02points that were the that were used
• 38:04in order to build the distribution.
• 38:07They were randomly chosen from a normal
• 38:10distribution and you can see that.
• 38:12Problem is similar to the bin size,
• 38:14so here you have to select basically.
• 38:19A wavelength in order to approximate
• 38:21that the function to a curve
• 38:23and depending on the wavelength,
• 38:26the resolution of the
• 38:27wavelength that you choose.
• 38:29The result is different,
• 38:31so you could have this kind of plot
• 38:34that seems to show a lot of local pixel,
• 38:38but by smoothing more you have
• 38:41instead the normal distribution
• 38:43from which you draw the data so.
• 38:46There is a balance which appear
• 38:48in choosing beings that are two
• 38:51larger or hear excessive smoothing.
• 38:53Because these over simplifies
• 38:54the original distribution,
• 38:55but on the other side,
• 38:57if you take a resolution that is too small,
• 39:01too granular,
• 39:02you can obtain that strange effects.
• 39:04So you could see for example,
• 39:06pics that are depending on the
• 39:09extraction of random numbers.
• 39:11Again,
• 39:11also in this case there are softwares
• 39:15that given the the original data,
• 39:18your original vote data can calculate the
• 39:22optimal smoothing wavelength in order
• 39:26to avoid distortions based on your data.
• 39:29A compact way to represent the
• 39:31distribution is the box whisker plot,
• 39:34and here you can see how a box
• 39:36whisker plot they can be obtained
• 39:39by this distribution of 20 points.
• 39:41So basically the box whisker plot
• 39:43represents as a box 50% of the data
• 39:46of the distribution to.
• 39:48Usually you have a central line
• 39:50that is the media.
• 39:51It's important,
• 39:52not laminar,
• 39:53but in the box whisker is always the medium.
• 39:56This is the first quartile
• 39:58and the third quartile.
• 40:00420 Percent 25th percentile of the data.
• 40:0375th percentile of the data.
• 40:05So in the box you have 50%
• 40:07of your day to the central.
• 40:09Here 50% of your data.
• 40:11Then you have the whiskers.
• 40:14They are standard definition of the
• 40:17Whisker Lanka is that they are as
• 40:20long as the interquartile range.
• 40:22That's the distance between Q1 and Q 3 * 1.5.
• 40:27And you see these as the whisker
• 40:30of your plot.
• 40:32So these collect most of the
• 40:34distribution of your data.
• 40:36The data that are outside the whiskers
• 40:38are considered to be outliers.
• 40:40For example,
• 40:41here you see there these three points.
• 40:44They are outside the whisker size,
• 40:46and so these usually are individually
• 40:49displayed in the whisker plot and are
• 40:52considered to be an outlier according
• 40:54to this definition of the whiskers.
• 40:57Yes,
• 40:58if you wanted
• 40:59to make these plots, yeah,
• 41:00is there an easy way to do it
• 41:03or do you like you personally,
• 41:05just do it by in R or something?
• 41:08Well, box plot. I don't think
• 41:10you can do them with Excel,
• 41:13but for example with Prisma
• 41:15or Origin you can totally.
• 41:20I think the only limitation is is
• 41:22Excel, but I be honest, I didn't
• 41:24check the last version of Excel.
• 41:27Right for us to think about,
• 41:28you know we can we have our data and there
• 41:31are many different ways of plotting it,
• 41:33but it sounds like prison might be the
• 41:35way to go in to try to do it in less.
• 41:37You're somebody like you.
• 41:38Who knows how to put it into our.
• 41:41Yes, probably, so please MA is it?
• 41:45Give you an option that is much.
• 41:47Use that if usually use them
• 41:48originally with respect to Prisma.
• 41:50I think it has more.
• 41:52I'm more power,
• 41:54so there are more things that you
• 41:56can do with origin then please MA.
• 41:59I think because it was designed
• 42:01for the for the physics community,
• 42:04but the tradeoff is always complexity,
• 42:06so please May is has less power,
• 42:09less choices, but it's easier
• 42:10to use rather than than origin,
• 42:13but both share the same philosophy
• 42:15so that you need to provide the data
• 42:18is a spreadsheet format and they are
• 42:21available in the software library at.
• 42:24OK, thank you to my can you say
• 42:27the name of the other not prism
• 42:29but the other programming?
• 42:31Or I have a slide after whether you show
• 42:34its origin? OK, thanks yeah.
• 42:37Ava question so,
• 42:38so my initial understanding is that
• 42:40the whisker lenses representing the
• 42:4295 percentile of the data range.
• 42:45But here it says the whisker
• 42:47length is 1.5 times this IQR lens.
• 42:50But if that's the case,
• 42:52why would the left side of
• 42:54the screen right side of risk
• 42:57are having different lens?
• 43:02Um? So that could be for example
• 43:06because here you have the, so that's
• 43:09the the maximal length of the whisker.
• 43:12But if the minimum of your
• 43:14data that is here is here,
• 43:17the whisker stops. So that's why.
• 43:19So I see here you have outliers and
• 43:22so that we can extend to the maximum
• 43:25point that is 1.5 at this measure.
• 43:27But if you before the the maximal distance
• 43:30here you meet the minimal pointer,
• 43:32the whisker and there and
• 43:34there so that's why.
• 43:36OK, I see it's also true that these
• 43:39whisker definition can be customized,
• 43:41so this is the default interpretation.
• 43:43I don't know who who decided this.
• 43:46I don't have the original publication,
• 43:48but you can choose whiskers to
• 43:50be differently, so that's why.
• 43:52Also in the Network Journal
• 43:54paper when you do a box plot,
• 43:56you have always to specify in the statistical
• 43:59methods how you design your box plot.
• 44:02So you have to provide how,
• 44:04for example, the skirts were defined.
• 44:07Because sometimes it's true that,
• 44:08for example,
• 44:09the whisker can represent like
• 44:1195% of the distribution.
• 44:13Right, so this is just the default,
• 44:15but it can be customized,
• 44:17so there are different choices.
• 44:21I have a question regarding the
• 44:24distribution again, maybe it's in
• 44:26continuation to what you just said.
• 44:30Some softwares allow a default value
• 44:32for the bin size and for the smoothening
• 44:36and all that say like Matlab that
• 44:38I've been trying to put this into.
• 44:41How reliable do you think that is?
• 44:44The default values and how would you suggest?
• 44:48Most of the time,
• 44:49most of the time, so I don't.
• 44:52I don't have experience with matter,
• 44:53but probably it will be that it's the
• 44:56same in our so so most of the time
• 44:58there is a sort of optimization there,
• 45:01so most of the time is fine. Uh, but.
• 45:07Sometimes, especially if you
• 45:09have a distribution of data,
• 45:11but you also have a pointer
• 45:14with cumulation of data.
• 45:16You could have problems in the.
• 45:20In the blocker so.
• 45:22But I don't have an example.
• 45:25OK, so like in 95% of the time I'm OK
• 45:30with the with the solution that is
• 45:33provided by the MATLAB or RA building tool.
• 45:38For example, sometimes when you compare
• 45:40to distribution with a different size
• 45:42with a different number of points,
• 45:44that could be that that can be a problem.
• 45:48Because sometimes there.
• 45:50If you're comparing for example
• 45:52distribution with 10 points with
• 45:54a distribution of 1000 points.
• 45:56Adopting the same wavelength
• 45:58could be a problem,
• 45:59and so you need to manually change it.
• 46:03So that's the yes,
• 46:05but that's that probably could be a.
• 46:08A practical example on when it's not ideal.
• 46:12Because the software,
• 46:13if you are trying to compare a
• 46:1610 points versus 1000 points,
• 46:18tries to define a common wavelength.
• 46:21But sometimes this leads
• 46:23to like distorted images.
• 46:25I don't have an example to show.
• 46:29That's good enough, thank you.
• 46:32And well, I can leave the note.
• 46:35Sometimes you can see also the
• 46:37nutshack inside your box whisker,
• 46:39so they're not sure is diesel
• 46:42feature that it represents a measure
• 46:44of certainty for the medium.
• 46:47So sometimes it is useful to have.
• 46:49These are 'cause if you are comparing
• 46:51a lot of box whisker plots a you can
• 46:54look at the uncertainty as if it was a
• 46:56sort of standard error of the media.
• 46:59And so if two box whisker overlapping,
• 47:02they're not.
• 47:03She's probably it means that the
• 47:05medians are not statistically.
• 47:08Inefficiently different.
• 47:09This could be a way to.
• 47:12The use of the notch or there is
• 47:15the interpretation of the data
• 47:16and the comparison of different
• 47:18distribution and that's why the
• 47:19box whisker plots are so popular,
• 47:21because they allow you to represent
• 47:24that distribution of data in
• 47:25a very compact format.
• 47:27This is another display of the
• 47:29anatomy of Big box whisker,
• 47:31but it doesn't add anything
• 47:33that I had also before.
• 47:35So here is an example where box whisker
• 47:38plots are used in order to compare
• 47:42the four different distributions.
• 47:43So the advantage is that they allow
• 47:46easy comparison so it's easy to
• 47:49compare the distribution of ABC and D.
• 47:52The problem they can have is that they
• 47:55hide the shape of the distribution.
• 47:58And also usually they hide the
• 48:01number of points that were used
• 48:03to build the box whisker.
• 48:05Sometimes you can code the number
• 48:08of points so the cardinality the
• 48:10size of the distribution as the
• 48:13width of the box whisker,
• 48:15but it's rarely used because it's not
• 48:18very visually beautiful, I would say.
• 48:21So one solution it could be to
• 48:23overlay over the box whisker,
• 48:26plot the jitter plot,
• 48:27so jitter plot represents the single
• 48:30points that were used to build
• 48:32the box whisker plot and they are.
• 48:34So while on the Y axis that there
• 48:36is the precise values on the X
• 48:39axis there randomly.
• 48:43Place that let's say there are
• 48:45also methods that do not display
• 48:47these points randomly butting up.
• 48:49Sell the random way that captures
• 48:50the shape of the distribution,
• 48:52and I think that that kind of plot
• 48:55is also present in in graph for
• 48:58the Prisma so the advantage of this
• 49:00is that you can see, for example,
• 49:03that would be the distribution is bimodal.
• 49:06So because you see that there are
• 49:08these high densities of points and
• 49:10the box whisker plot cannot capture
• 49:12that you cannot see from a box,
• 49:14whisker plot data distribution is
• 49:17bimodal and for example here you
• 49:19can see that these box whisker
• 49:21plot there has been is based on
• 49:24much less data than the others.
• 49:26So, uh, and a solution for these
• 49:29are is to enclose the box whisker
• 49:32plot into a violin plot.
• 49:35So violin plot representation
• 49:37like these allow you to see the
• 49:40same information of a box whisker,
• 49:43but also information on this shape
• 49:46of the distribution is basically
• 49:49in a violin plot.
• 49:50You add a density plot that is
• 49:54parallel to the vertical axis.
• 49:58And here, by using a violin plot you can see.
• 50:00That this,
• 50:01that this distribution is one pick
• 50:03has one pick. This one is by model.
• 50:08And you can add also the number here.
• 50:11He said of coding the number as
• 50:13the size of the distribution as
• 50:15the width of the distribution.
• 50:19So this is an example of compare
• 50:21of comparisons between different
• 50:23ways to show distribution.
• 50:25Here you see the histogram with the density,
• 50:28corresponding density plot,
• 50:29the same distribution
• 50:31visualized as a box plot,
• 50:33and visualized as a violin plot that
• 50:36captures both the features of a box
• 50:39plot cluster the density distribution.
• 50:42And this is for a normal distribution.
• 50:44This is for a bimodal distribution where you
• 50:47can see that the box plot doesn't capture,
• 50:50so the box plot can capture the fact
• 50:53that the data are not symmetrical and
• 50:55you see the for example the distance from
• 50:58the from the from the point of the box
• 51:01and the medium is much more than these.
• 51:04So the box whisker is good in capturing
• 51:07a symmetrical distributions but not
• 51:09the presence of more than one piece.
• 51:11So not the complex shape of the distribution.
• 51:15And there is a website here where
• 51:17you can where you can see a lot of
• 51:21examples where the different choice of
• 51:23visualization can lead to different.
• 51:26Conclusion as here.
• 51:29It's true also that the violin Plata
• 51:32is not efficient because you're
• 51:35sure you're showing twice.
• 51:37The same information,
• 51:39so this is aesthetically pleasant,
• 51:42but is not efficient because
• 51:44you're repeating basically this
• 51:46density twice above and below,
• 51:49and so that's why there are two
• 51:52saver for efficiency sufficiency.
• 51:55There are recent visualization
• 51:57strategies as the rain cloud plotter.
• 52:00So the Raincloud plot that shows a box
• 52:03whisker plot in the middle half violin
• 52:06plot here and then also the single point.
• 52:09So that's probably the one of the
• 52:12most complete exhaustive ways to
• 52:14represent a distribution of data.
• 52:16And they're called the rain cloud because
• 52:18of this effect is should be the cloud.
• 52:20And this is the rain that falls
• 52:22on the proposed below.
• 52:23So you can find information on
• 52:25how to block these are following
• 52:28the following these link.
• 52:29Another yeah.
• 52:31Quick question, is there a?
• 52:35How to say the restriction or limitation
• 52:38as to how many data points are required
• 52:43for generating reliable violin plot?
• 52:50Generally not so. Probably more than 10,
• 52:55I would say because otherwise so you can
• 52:58see that you can see it empirically,
• 53:00because if the data are too few you can
• 53:03see that the violin basically have sort
• 53:06of waves around each point of your data.
• 53:10So as a general. Is a general threshold.
• 53:15I would say 10 points would be the
• 53:19like the minimum number. And asking
• 53:21that question is of course if
• 53:23you have a lot to data points,
• 53:26these would be informative.
• 53:27But if you have, let's say
• 53:29less than 10 or small number,
• 53:31this could be really distorting
• 53:33or faking the. Yeah, yeah, that's
• 53:35true. That's why I would
• 53:37say 10 because it below 10.
• 53:39Probably the best strategy is to show
• 53:41the single points and then a summary
• 53:44such as the mean or median plus
• 53:46some validation standard dialogue,
• 53:48but not the not the distribution
• 53:50as a violin plot.
• 53:51So that's for a like less than 10 data.
• 53:57Alright, when data are too much,
• 53:59for example, it doesn't make
• 54:00sense to show the single points.
• 54:03Because that they are overlap,
• 54:04they overlap each other and so you
• 54:06don't see anything that happens
• 54:08when you have more than 1000 points,
• 54:10and so the best solution in that case
• 54:12is for example to show only the violin.
• 54:18So there is a Ranger for which.
• 54:22The best solution is to show the
• 54:24single data points with the cross bar,
• 54:27so an element with captures mean or
• 54:30median plus standard deviation order.
• 54:33Confidence interval there is a
• 54:35Ranger that is in the middle from 10
• 54:38to some hundreds where the violin
• 54:40plot and the box whisker plot are
• 54:43the best option to visualize.
• 54:44And when you have many,
• 54:47many data more than 1000, probably.
• 54:48If you want to capture the distribution
• 54:51then only there the violin plot rather
• 54:53than the single points is the best way.
• 55:01Did did he? Did it answer?
• 55:06Yeah, that was awesome.
• 55:08That is a great explanation.
• 55:11OK, another another alternative
• 55:13way to maximize efficiency of the
• 55:16violence that I saw a lot in the
• 55:18with single cell data, for example,
• 55:20is the the user split violin plots are,
• 55:23so you use the violin plot to show a
• 55:26comparison between two distributions.
• 55:29So you see here are this plot shows the
• 55:32representation of Asia or female and
• 55:34males are using different social, social,
• 55:37media, Instagram, Facebook, Twitter.
• 55:38So it's a way to show using.
• 55:41Half of a violin plot are differences
• 55:44in the distributions and this can
• 55:46be used when you have a contrast
• 55:49of two conditions or you want to
• 55:52compare two distributions.
• 55:53I'm also in the single cell.
• 55:57About the violin. Plots,
• 55:58like in the such cases, yeah,
• 56:00So what determines the height of the peaks?
• 56:03Or is that everything is normalized
• 56:05so that the total area the same,
• 56:07or the maximum height is the same?
• 56:10So most of the time,
• 56:12so you have choices usually so you can
• 56:16choose to have the same maximum hate.
• 56:20And that's usually the then.
• 56:22That's usually what you find,
• 56:24so you you plot there in a way
• 56:27that the Ranger is the same from
• 56:30here to here from here to here,
• 56:33the alternative is to use the
• 56:35real criteria for a for a density,
• 56:38and that should be that the
• 56:41area under visa is equal to 1.
• 56:45And so that the two have the same area.
• 56:48An alternative is to have an
• 56:50area that is proportional to
• 56:52the number of observations,
• 56:54but I think that visually most
• 56:57of the time you find that.
• 57:00The criteria is that you have in order
• 57:02to have balanced plots are the criteria,
• 57:05is to have the same Ranger.
• 57:07Meaning from here to the maximum
• 57:09for all their pull the plot
• 57:11independently from the area and
• 57:13dependently from the number of points.
• 57:18It's not probably the best solution from
• 57:20the point of view of communication,
• 57:21but it's most used. OK, thank you.
• 57:27I variation of this is also the
• 57:30use of ridgeline plots are that.
• 57:32They allow you to compare a
• 57:35lot of different densities.
• 57:37For example, here you see a comparison
• 57:40of the density of temperatures in
• 57:43different month in allocation metadata.
• 57:45Remember Lincoln NE and this
• 57:48is used in a single cell.
• 57:52Is Alotta now in these years
• 57:54with single cell data?
• 57:55For example,
• 57:56here you see that it is used to
• 57:58compare the distribution of the
• 58:01expression of 1 gene leads A
• 58:03or CL5 in different population
• 58:05of cells that are probability
• 58:07can from some blood sample.
• 58:10Different population and
• 58:11these allow you to see.
• 58:13Sorry to see an marker genes or to
• 58:16see how the expression of a gene is
• 58:19specific for a population of cells.
• 58:22So that's why I included because I
• 58:25see that the frequency of this plot,
• 58:28specially in the single cell
• 58:31visualization field is quite increasing.
• 58:34I have visa section of the
• 58:36presentation that we could skip.
• 58:38In general the message about Visa
• 58:40is that Venn diagrams are good
• 58:43when you have two Venn diagrams,
• 58:45but if they are,
• 58:46if they're more there a bad way to
• 58:49represent intersections between sets.
• 58:51And this actually is a plot that
• 58:54was published in Nature and it it's
• 58:56about a comparison of the genome
• 58:59of banana with other species.
• 59:01So the problem in general is that
• 59:04when you have more than two, 3,
• 59:06four but also two Venn diagrams,
• 59:09it's it's not the best way to
• 59:11visualize intersection with the use
• 59:13of the traditional Venn diagrams.
• 59:15So a table is probably more effective
• 59:18than this because the areas are
• 59:21not proportional to the size.
• 59:23And it's quite confusing to see
• 59:26the specific intersection and
• 59:28so on alternative way.
• 59:30That was developed in the recent year
• 59:32was the user the concept of this
• 59:35upset plots are so to represent the
• 59:37intersections in a matrix format.
• 59:40So represent these are as a member
• 59:42as a sum object.
• 59:44Example,
• 59:44a gene that is present on only
• 59:46List A only list D only list C
• 59:49intersection between AMD origin
• 59:51present in all the intersections.
• 59:53So you can use these matrix format
• 59:55to show the intersections and then
• 59:58you can display the cardinality.
• 01:00:00Of each.
• 01:00:01Intersection so the number of genes,
• 01:00:03for example that are only in the PDF
• 01:00:06error pathway that you see here.
• 01:00:08The number of genes that are in the
• 01:00:11common between the EGFR and P-10 path.
• 01:00:14With that you see here.
• 01:00:15So this is a way to show the cardinality
• 01:00:19of the global list that you see here.
• 01:00:22And also you can rank the intersections
• 01:00:24between the different sets according
• 01:00:26to their size to their personality.
• 01:00:29So it's much more clearer.
• 01:00:30To show the structure of the intersection.
• 01:00:34Rather than using the. A Venn diagram.
• 01:00:38I skip this because they are.
• 01:00:41There were some examples of bad
• 01:00:44usage of graphic in politics.
• 01:00:47And a lot are looking at online
• 01:00:50and related to Fox News.
• 01:00:52Of bad usage of klasa display.
• 01:00:54So the final part could be how to
• 01:00:56draw this pad. There is
• 01:00:58relative they were trying to.
• 01:01:00Not make the point. Yeah,
• 01:01:02well they were trying to make him
• 01:01:04to give a message by distorting the.
• 01:01:09Yeah, this for example is an
• 01:01:11issue if you always need to
• 01:01:13include the zero in your plot.
• 01:01:15Sir, this is controversial.
• 01:01:17Let's say that. In general,
• 01:01:19in Barplots it's a bad idea,
• 01:01:21but for example is a good
• 01:01:23idea in in time series,
• 01:01:25and that's because there in barplots
• 01:01:27the height of the bar plot is that
• 01:01:30your main message of the figure,
• 01:01:32while for example here in in a
• 01:01:34time series that the main message
• 01:01:36is how the two trajectories
• 01:01:38evolve and are interconnected.
• 01:01:40So the main issue is the horizontal
• 01:01:43axis and so you can skip the zero.
• 01:01:47So again,
• 01:01:47it depends on how much these inclusion
• 01:01:50or exclusion of the zero distort
• 01:01:53your your main message of the fever.
• 01:01:56So how to draw plots?
• 01:01:58So here are there is an outline
• 01:02:00of the software that you have,
• 01:02:03so this is some commercial
• 01:02:05software from the most from Excel.
• 01:02:07It's probably the most used or available,
• 01:02:10but it doesn't allow to plot all the
• 01:02:13solutions that they did show before, but.
• 01:02:16For example,
• 01:02:17Grandpa,
• 01:02:18Graphpad prism,
• 01:02:18or Origin Pro are through software
• 01:02:21that are available and with those
• 01:02:23that you should be able in an
• 01:02:26environment that is similar to Excel
• 01:02:28to produce most of the plots that
• 01:02:30you saw in the presentation today.
• 01:02:33So this is commercial software,
• 01:02:35doesn't require programming
• 01:02:36skill on these sides.
• 01:02:38Are you see the main solutions
• 01:02:40that are used by data scientists,
• 01:02:42but that require programming
• 01:02:44skills so that the two most
• 01:02:46common languages in data science,
• 01:02:48RR and Python so far are you have is GG plot.
• 01:02:53Library for Python.
• 01:02:54You have matplotlib or Seaborn.
• 01:02:58At these require programming so,
• 01:02:59but I would say that the advantage
• 01:03:02nowadays of using visa is that you can
• 01:03:05find a lot of really a lot of examples.
• 01:03:09Because there are a lot of website
• 01:03:12that where you can choose that
• 01:03:15you're like data visualization
• 01:03:17type and you see already the code.
• 01:03:20That you can use in order
• 01:03:22to produce the blocked.
• 01:03:23So I would say that you just need
• 01:03:26to know how to insert that or how to
• 01:03:28load that in the this programming
• 01:03:31environment table of data.
• 01:03:33And then most of the difficulties
• 01:03:35are probably in fixing details,
• 01:03:37so it's very easy to realize the plot,
• 01:03:40different plot.
• 01:03:41It's more complicated to adapt the
• 01:03:44small things that we are to your taste.
• 01:03:48But so so,
• 01:03:48this suggestion is that if you
• 01:03:50do a lot of visualization,
• 01:03:52it's worth investing in this.
• 01:03:57Here you see a maybe a future perspective
• 01:04:01that could be their own online solution.
• 01:04:04They're already available so summer,
• 01:04:06for example. You can produce upset plots,
• 01:04:09Aurora rain plot, Sir,
• 01:04:10or some other like exotic type
• 01:04:13of data visualization online.
• 01:04:15So there are websites,
• 01:04:17web web servers where you can insert
• 01:04:20your data as tables and they produce at
• 01:04:23the data that you want and you have.
• 01:04:27Some sort of interactivity,
• 01:04:28so that could be the future. Sure.
• 01:04:32Where are web servers provide you with
• 01:04:34the main programming environment?
• 01:04:36You need just to interfere data
• 01:04:38and you can see by interactively.
• 01:04:44By the interaction with the web server.
• 01:04:46How to customize the data?
• 01:04:48Most of the solutions right now are.
• 01:04:53Commercial, and so you need to pay,
• 01:04:56and that's the drawback of this.
• 01:04:58But it could be probably the
• 01:05:00future of matching the programming
• 01:05:02with easiness of usage.
• 01:05:06This is a useful resource that
• 01:05:08you can use also to decide which
• 01:05:11kind of blocked are you want.
• 01:05:13So there are a lot of these trees are that
• 01:05:16depending on what you want to represent,
• 01:05:19one numeric variable to numeric
• 01:05:21variables or categorical variables,
• 01:05:22you can follow the tree and arrive
• 01:05:24to your to the best graphical
• 01:05:26solutions to display your data.
• 01:05:28So I suggest you to visit it
• 01:05:30also to look at what are the
• 01:05:33kind of possibilities for data
• 01:05:35representations that you have online.
• 01:05:37There are many of these sites and
• 01:05:39now and that's why it's easy to
• 01:05:42look at the documentation and also
• 01:05:44to retrieve and reproduce the code.
• 01:05:46This is another example I closed with Visa.
• 01:05:52Patricia, that I find particularly
• 01:05:54related to data visualization and
• 01:05:57science is not natural itself about
• 01:06:00its nature under our observation.
• 01:06:03And so the science of data visualization
• 01:06:05is a way to allow more adherence between
• 01:06:10observation science visualization.
• 01:06:17Thank you come on.