Are you looking for a way to gain clarity about who you are, and how you fit and interact with the world? The Enneagram test is just for you. With philosophical and mystic roots blended with science, it's a popular typology that assigns everyone a personality type. The American Journal of Psychiatry defines the system as a personality theory describing nine strategies by which the psyche develops a worldview and relates to self and others. Ian Morgan-Krull, a psychotherapist and Enneagram teacher, explains, We adopt these in childhood as a way to cope, protect ourselves, and navigate the new world of relationships in which we find ourselves. The Enneagram is unique because it maps your psyche by breaking down personalities in terms of motivations, your basic fears, and even your most suited career. Here are the nine types of Enneagram. Type 1. Idealist You interact with the world by striving to be perfect. Also known as the reformers, you seek moralistic behavior and reform. You're naturally positive with a heart for helping others. You go by the book and make sure that you're always doing the right thing. As a result, it's difficult for you to delegate or take risks. Type 2. Caregiver Diana Redmond, a Proctioning Enneagram teacher and coach says, A tool's focus is to take care of others' needs in an effort to feel loved and accepted. As emotional thinkers and hard workers, you are extremely attentive to helping other people in any way you can. This desire to be fully loved and accepted comes from your fear of being unwanted or unloved by those around you. A two may believe consciously or subconsciously that love is only gained and earned by serving others. Type 3. Performer The Enneagram official site calls them the achiever. If you're a three, this shows you're driven by success and a desire to look as good as possible to others. You seek accomplishment and admiration by setting big goals and doing what it takes to succeed in order to earn validation. This helps mask your basic fear of being regarded as a failure or worthless. According to Redmond, your goal is to be perceived as successful in the eyes of others. Type 4. Creative Redmond says, People who get Type 4 are artistic, with an eye for beauty. Fours are sensitive souls with a passion for various art forms, and so you make great writers, designers, actors, and artists. The basic desire of the Type 4 is to build an individual and authentic identity, and to express it in the world. Fours may feel misunderstood if others fail to recognize their unique, identifying traits. Type 5. Thinker Type 5s have earned a reputation as the absent-minded professor. They're excellent deep thinkers and make great engineers, scientists, and scholars. If you get a Type 5, you're introspective and thoughtful. The most basic desire of the Enneagram Type 5 is to feel helpful and competent. So you passionately pursue knowledge and gain new skills. Fives often prioritize developing their own intellect over most other things in life. You guard against your basic fear of being incompetent by withdrawing emotionally and physically from others. This may make you feel safer, but can also lead to loneliness. Type 6. Loyalist Type 6s perform well as bankers, professors, nurses, and consultants. According to the Enneagram Institute website, the most basic desire of the Type 6 is to feel secure, which you seek through loyalty to others. The basic fear of the Type 6 is losing personal support and stability. You crave predictable, safe environments. Sixes tend to have an expect the worst hope for the best mentality. Type 7. Adventurer Here we have our upbeat travel agents, designers, and diehard photography fans. As Type 7s, you want to live life to the fullest and experience all the world has to offer. You desire to feel stimulated and engaged. You go to great lengths to seek excitement and joy from new experiences. FOMO causes you to avoid schedules and concrete planning to allow for last-minute opportunities. You appreciate a positive, open-minded attitude and out-of-the-box ideas. Type 8. Protector Think lawyers, directors, or sales experts. Type 8s are confident attention-commanding leaders, skilled in quick, effective decision-making. Your need to have control of your own fate stems from your desire to protect and defend yourself as well as others from injustice. However, at times, their need for control can be overwhelming, says Redmond. Your subconscious belief that vulnerability equals weakness makes you avoid it at all costs. This is because your greatest fear lies in being controlled or hurt by others. And Type 9. Peacekeeper Do you seek to avoid conflict at all costs? Type 9s seek to feel safe, comfortable, and at peace with themselves. They're adaptable and agreeable teammates, dedicated to considering multiple perspectives. Internal peace is the most basic desire of the Enneagram Type 9. The basic fear of the Type 9 is that you may lose or be separated from others. You prevent this by remaining peaceful and avoiding conflict, potentially adapting to others' preferences rather than stating your own. By understanding your type, you can let go of habitual patterns that hold you back and open up to your own inherent gifts. Have you taken the Enneagram test? What number did you get? Let us know in the comments! If you found this video helpful, please give it a thumbs up and share it with loved ones. Until next time, take care, and we'll see you soon!


Good afternoon ladies and gentlemen. Now today I would like to say a huge Thank you to all of my new subscribers because I know that they've come on pretty quickly since I last posted my video I posted a video a few months ago or even weeks right saying, you know, well done You know, we're a thousand subscribers now guys. That's really cool. And then like almost a month or two later We're almost at one and a half thousand. So this is really great I'd just like to say thank you very much I appreciate it. Obviously you understand and like something I'm saying or doing Because otherwise you guys wouldn't subscribe. So thank you for that. Now today's question I'm gonna get right into it. It's from my buddy Brendan and he says I have started thinking about Reading text before I go to sleep so that I may possibly dream about reading text and this will trigger me to become lucid Now the reason he wants to do this is because I've said before and It's written all over the internet pretty much that when you read text in a lucid dream Usually the text will will morph or change in some way. Okay, so you'll read say if you read a book In a dream you can read something look away and then when you look back at the text, it will have changed Okay, so this can be used as a reality check Okay, you can use this in a dream to trigger yourself to become lucid and become aware of what you're doing The problem is it's not reliable and while it is reliable in the dream when you're already sort of lucid It's not reliable as a standalone reality check in my experience It might be for you and so I would advise you to keep trying it and see if it works But I would probably couple it with another reality check and the reason for this is because in waking life It's not actually that easy to always have something to read throughout the day Excuse me. I've got a bit of a sore throat today. Um, you you might find sometimes that you don't have something Instantly available to you to read So you might find yourself say for example, you're walking down a path and you hear an unusual noise Okay, and I've said before it's a good idea to reality check when you hear or see something unusual Okay, if you're in this situation and you don't have something that you can instantly read to do this to do this reality check Then what are you gonna do you're gonna have to do another reality check anyway So I would advise doing reality checks that you can do at any time no matter what the situation So say if you're walking along the road, you've got nothing on you You've got no books to hand that you can read You can just do a reality check similar to the pushing your fingers through the palm I don't know if you noticed that's my favorite one or you could pinch your nose try and breathe You could look at your hands. You could do all of these reality checks. Okay, so obviously go ahead and try this the read the reading Reality check see if it works and obviously let me know like comment or something And I'm sure it will but I wouldn't advise it as a standalone reality check because it's not really one that you can always Replicate in waking life. Okay, so I hope this has helped if you've enjoyed this, please subscribe and I'll see you in the next video What's up guys, thanks for watching if you enjoy these videos and you like what I do in this channel You can actually support me using the patreon link down in the description or I'll put one that will come here as well And that's patreon.com forward slash how to lose it That's the way you can sort of support me with giving small monthly amounts and it really does make a huge difference to me Allows us to keep doing these videos. Also, if you'd like to get a free and lucid resource guide PDF Make sure you click the link that should be in the corner here


Okay, welcome everybody to the DEPCOM 15 and Heidelberg session. This is the outline. We will shortly recap our bit process because we did it slightly different than other bits used to do it in the past. So we just want to share our experience. Maga is going to do that. And then we'll talk about where it is and how you get there. Renee is going to talk about that. And then the venue that we found, which Martin will present, and I will talk a bit about conference and timing and all that stuff at the end. So without further ado, that's to Maga. So Germany had already tried to go for the DEPCOM before and it hadn't worked out very well. So when we talked about this last year, we decided to do it in a different way. So instead of looking in a particular city, like a bunch of us lived in Munich, but other people lived in Berlin or in other cities. You will see in a map later. And instead of looking in a particular city, we said we will look everywhere. Where's the best place to have the DEPCOM in Germany? Regardless of where we live. So not putting our residence address in the question. And so we looked. Okay. So first, during the DEPCOM 13, we decided that we would do this. A lot of people were interested. So we kind of created a local team and we took this decision that this was what we were going to do. After the DEPCOM 13, then... So there were more than 75 different venues that were queried. Like looked at and checked if they would work as DEPCOM venue or not. From these 75 plus venues, first we had a list of nine that were prospective for which we tried to find any possible problems that we may have with those venues. Like with the network work, with getting their work, etc. Would it be big enough? Because some of the venues were discarded because they would be like for 200 people. For Germany, we expect that we will get much more than 200 attendees. So if it was not big enough, we discarded them. And so we went from nine to five to in the end three, which were Munich, Heidelberg and Treitlingen. The one in Treitlingen was very interesting because it was like the whole city would be hosting us. There would not be like... It's a very small town. So it would be like DEPCOM takes over Treitlingen. Yeah, in the end we ended up dropping it because they didn't have a suitable venue for the conferences. So it was very nice as the idea, but it wouldn't have worked. And so when the time came for the bid submission, we had two very well worked out possibilities. That were like we had done a lot of work for both of them, trying to find the possible problems, trying to find solutions for the possible problems. The first one was in Munich. It was a split option, the accommodation in a hostel, the conference in a university that is nearby. The second one in Heidelberg, which is everything in one place. So the hostel includes the conference rooms, so everything will be in one place. And we submitted these two different bids that both had their pros and cons. So having everything in one place, it's a big pro, but the one in Munich was in the middle of a very big city. Heidelberg is a city, it's very nice, but it's smaller compared to Munich. So we thought that being in a bigger city would be a plus, and that's why we worked with the Munich bid for quite a while. When we finally got the bid, we still had these two options, although it was very clear from the global team that they very much preferred Heidelberg. So we kind of knew that we would go with Heidelberg, but we used the fact that we hadn't yet settled to negotiate with the venue, better prices, better conditions, etc., because we were saying, okay, we can also do it in Munich, so there was interest for the venue to give us better prices. This was a bit, I'm telling you, everything was great, but I know that it was a problem for the DevConf committee to make a decision because of this split option, so it also had it downsides. For the DevConf committee, it was very hard to make a decision because we were proposing two bids that were very different, and so it also had it downsides, but we really profited from the fact that we could play one bid against each other and got a very good deal from that. So, yeah, finally after it was decided, we decided to go with Heidelberg. That was like in May or April, and since then we have been working with the Heidelberg venue to create the best possible DevConf. This is you, I think? Okay, yeah, this is a map of where we are, but it's not well rendered. Technical difficulties prevent to show the most important part. Is there any way that we can put it so that Heidelberg shows up? So Heidelberg would be like here. Okay, just a machine. Heidelberg is here. Munich is here. We have a bunch of people here. So this is like where people are. Okay, yeah. Well, let me take this over because this is still a local team. Just to give you an idea, I put down these numbers. So we had three main things. We had this DevConf 13 meeting where we had around 18 people. Then on the bit we had people signing up for, as a local team, there was 17 people and we actually had a kickoff meeting, which I think was just cut off the last slide because we all went to the venue to decide and to have a look at it for a weekend, and there was 18 people going there. But actually the union of all is 37 people and 12 people were there two out of three times. So it's not the same people all the time, which is clear because there's nine months in between. So some people came after, of course, they were not at DevConf 13 and some people left or didn't make it to the kickoff meeting. So it's a bit unclear how many people there really are, but just so you get some numbers. On the other hand, of course, not everybody is as active, so I wouldn't say that there's 37 totally active people on local teams. So yeah. Yeah, well, you probably know where Germany is, but to reiterate we are in the center of Europe, middle of Europe. This is Heidelberg, actually. This is Heidelberg, actually, like the bridge to the old city. You can see the castle over the top. It's a ruin. It's 150 inhabitants cities divided by a river. It has history to the medieval times, to the Roman times even. It's a popular tourist location. I don't actually know why. But there was many Asian people and... It's not popular with Germans. I don't know why, but yeah, many Asian people and many people from other countries come in there. We have a pretty old city, the Altstadt, and it's even the White Heritage site. So this is an image I stole from Wikipedia, from the city area view. You see the venue marked. It's directly on the river banks and directly near the zoo. The old city is like here. The castle is that one. Just to say that this is the university as well, so it's not like in the middle of nowhere. This is the old city, this is the university, and we're quite close by. Yeah, the Heidelberg main station is here. So it's also quite connected to the public transport. Yeah, and Martin is taking over for the venue itself. So because you love watching how I use computers, there's of course one thing we can do. That was one of them. There we go. So now you know what Germany looks like. All right, so the venue we found in Heidelberg is a youth hostel. It's just a few kilometers out of the city center. I thought it was much less, but then when we actually walked to the city center it took a lot longer than I expected. It's wedged sort of between the river, as you saw. There's a zoo nearby, so in case you want to have a break from the conference, that'll be great. The university is nearby, so we're hoping to have some liaison with the university going on, maybe invite some people to the open day, and maybe there's some cool stuff happening at the university at the same time. And there's a bus stop right in front of the venue that'll take you to transportation sites and also to the center town, and we believe the bus goes until shortly after midnight or something like that. But one of the benefits of... There you go, look. As we saw on the map, or every view, it's right in the city center, so you need like 10 minutes from the bus from the main station. There's a bus stop on the venue, also it's hot on the last light. The next airport is Frankfurt. If you go from international flights, from there you need one hour, one hour 30 depending on the connection. And if you go from the regional airport, you can also go to Stuttgart or Karlsruhe Badenbahn. FKB is Karlsruhe Badenbahn, which is a bit longer because they are not very well connected to the rest of the high-speed trains. You need one hour 45 to two hours. Karlsruhe, not in the morning. You will be stuck in the morning. Or if you go from Switzerland or from France, you have to go directly to Karlsruhe or Mannheim and you can just switch. The same ICE from Basel, also the same Karlsruhe in Mannheim. And from there you can just take your S-Bahn for 20 minutes. So it's easily reachable. What's parking like on site? Yeah, it's a city. So let me address your question, Steve. What's parking like on site? The university is nearby and we're going to be off the term. So theoretically you can park your car any way you want. The Youth Hostel also has, I believe, 27 spaces during DEPCAMP. Those are available on the first come first serve basis and during DEPCAMP there are hours. So we have again the technical problem. So that you see a bit of the, have a bit of a fashion of the airport connections. This is Frankfurt, of course, which is the biggest hub in Germany. So anyone who comes from intercontinental flights will go to Frankfurt. Suckert is over there and the region airport, Karlsruhe, is like here. So you can also take Munich, but take it a bit longer. So apparently I get to do the venue and this is a little bit of an improv. Let's look at pictures together and I'm going to tell you something about it. So we have a couple of impressions here just so that you get an idea. You can see that it's spacious. We're actually currently standing pretty much at the zoo and we're looking at the venue. So what you see up here is a balcony similar to what we have over there, with the main talk room behind it. And from the balcony you can actually see squirrels and all kinds of other animals that are in the zoo and they are sometimes loud. It's a little bit outside the venue. You can see some seating spaces here. There's probably the, I think the sports court that we may or may not be allowed to use yet. I don't know. It's over on that side. This is close by the front entrance. You can see that the venue is itself pretty modern. They pretty much redid it in 2006 I believe and you can still feel that. I mean, it's a very nice venue. Some outside eating spaces close to the cafeteria, beer garden style. Here you have some of the imagery into the zoo from the balcony. And this is the welcome lobby. So at the far back here you can see the reception which is where you're going to be received and you get to pay and get your badge and stuff. And then this could be an area where they are like, you know, sponsors presenting themselves a little bit. It's quite spacious. It's open. It's nice. Like the rest of the venue to be honest. There are a lot of spaces like this in the venue which we thought could actually then become hack areas. So maybe we're going to get rid of the entire hack labs concept and have some hack areas. But that's to be discussed. This is one of the two dining rooms. There's another one that's closer to the outside. And then also on the outside they are set up to handle people eating there and pretty much anywhere. Food's very good. We went there. Part of the reason was to convince the team that this is a venue that we could have DEVCONF at and so we ate dinner. We didn't eat dinner. We ate lunch there and then we ate breakfast the next day. And I think the general response was that this is, you know, above average youth hostel food. It's probably better than this one. Here you go. Outside. This is sort of an inner courtroom inside the venue. So you court yard. That's right. So you have rooms all around it and they eat here and there's a lot of other things that have happened. They are already at the youth hostel. For instance a band has played and all that kind of stuff. So we're looking into these sort of things and taking ideas of course. Is there a curfew on that outdoors area? During DEVCONF, yes. We have to abide strictly by the rules of the youth hostel so I think that's going to be pretty much like 10 o'clock. That doesn't mean you can't be outside but they're going to enforce quietness. During DEVCONF we have the entire venue to ourselves. Does that answer your question? So there's a volleyball court. As you can see the sports pitch is behind that. And this is one of the rooms. I don't know what kind of room this is. So the different rooms that they have available. Is this the last one? Different rooms that they have available are two, three, four and six bed rooms. Each of the rooms has their own bathroom. It's actually quite high standard. We have plenty of space for instance to accommodate for families and to accommodate for special needs. It's fully accessible. I think there are six rooms that are completely wheelchair accessible and also for people with other disabilities. Easy to reach because they're very close to where everything is happening. So I'm going to just quickly go through these facts and then in case you have any questions about the venue I guess we'll get to that later. Michael already said that. We're going to go to a night hack lab or walk to and from food. It's all going to be in one space. We're just going to make it much more intense than this week has already been. Exclusive access, I said that. This means a lot to us. So we prioritized this. We actually ended up paying a little bit more money than we had to, but now we're going to be just us. Or at least we get to decide who's in. There are about 450 beds. 430, we'll say 400 for now in two to six bedrooms. We have a number of different rooms. Three main talk rooms with the sizes 250, 120 and 40. Although 40 could also be a buff room. And then we have eight rooms for 10 to 20 people. So we would like to provide you with a lot of space to do ad hoc type things in addition to hack areas outside and in the common spaces of the venue in general. Internet. I'm not allowed to say we don't have internet there. We are currently setting it up. So we will have a one gigabit fiber link that goes via the zoo to the university. It's one of the coolest zoos, seriously. The guy that the IT technician of the zoo was hired like 10 years ago and he convinced the CEO of the zoo to like put fiber throughout the entire zoo just in case you want to have an information board. You need fiber for that everywhere, right? So this is the most modern zoo of the world and we're 25 meters from it. Food arrangements during Debcon, again, we can pretty much set the times ourselves within certain limits. Also the venue can cater for all dietary restrictions except for people who are eating strictly kosher. But we have never had a request for that as far as I know. We have? Okay, as far as I know, I'm glad I said that. Well, let's not discuss that here. It's just not possible, but that is not our choice or a statement. The cheese and wine party is theoretically not allowed to be held inside the venue, but we're going to have it inside the venue. That took a little bit of work. But it'll be great because they'll provide us with cutlery and glasses and everything and they'll clean up after us, so. We're thinking about upping the level of childcare that we offer. Seeing many kids here, I would love to see many more next year and we realize that we have some work to do on that. If you're interested, that's one of the ways you can join. Yeah, we have to do some work on that I meant the Orga team in preparing to host kids. The rest is up to you guys. And there's a disco in the basement. We're allowed to use it. There's also musical instruments all over the place. We're going to be allowed to use them. For instance, two pianos. There are going to be a couple of guitars. There's a setup with speakers and so on. There's a karaoke going somehow. If that's what we want. And I think that's it from my part about the venue. So should we do a couple of questions? I wanted to ask you two questions. First, what is the situation regarding booze? And second, what is the nearest bar? I can see over the years in Deppcon forga the priorities of things are shifting. Where's the booze? Where's the bar? It is true that the Cheesan Wine Party was very problematic to get going because they do not allow you to bring booze into the venue. As a matter of fact, they don't really allow you to bring food and drinks and all that kind of stuff. However, I already told them that there are going to be people who have their own desires and mate and all that kind of stuff needs to be able to go in and say, yeah, we'll just look up or somewhere else. There will be a bar, pretty similar, I'd say, to install to DC-13. It's going to be right at the entrance in that hall that you saw in the picture. And they are going to be serving snacks that are also more substantial, like, for instance, packaged sandwiches in case you're very hungry in the midnight. A range of local beers and wines. And I suppose pretty much anything else we ask them to. They will not serve to intoxicated persons and so on and so forth. So pretty much standard rules and I'm sure we're going to be able to deal with that. Just fine. The next bar would probably be in town. So you'd have to take a bus, so you'd have to walk. Which is doable and it's actually a nice walk along the river. Just before I take your question, I wanted to say one thing which I forgot. I have to say we were very lucky with this venue. They're really cool. A bit of related question. If we get sick of the food in the cafeteria and want to go to another restaurant, are there any immediately around or do we have to take the trip to the city and how, in general, is the dietary situation at the other restaurants? Yeah, well, you know, you're not going to find fantastic choice of vegan restaurants with really, really good food like you do here in Heidelberg, I'm afraid. I'm pretty sure you can find pretty much anything for any dietary requirements. It might just not be as easy as it is here, ubiquitous as it is here. Yes, I was going to get to them. So this is in town, which is two and a half kilometers. About 45 minutes is what we walked, right? Thereabouts. There are a couple of sports places nearby. Now I'm not talking about sports bars like you would see here, but like, you know, in Germany, every single sports club also has a bar and a restaurant. Sometimes they're going to be like simple pizzerias, which is good food. I think there's a Greek restaurant nearby. There's actually a sort of like beach club, which probably offers some stuff within walking distance. So there are a few choices, unless you want to walk all the way into town. Our idea was to provide high quality food that changes every day and doesn't get you sick, both ways, you know, like you don't, you won't. So that we, you don't actually have to flee. That's our goal, and we're going to keep doing that. Just a testimonial that last year at DeppConf 13, the bar and food onsite were both good enough that people who attended were mostly happy with what was available onsite. So that's evidence that it can be done well in a way that DeppConf is happy with. So I suppose a similar question in terms of accommodation. Say for people who aren't necessarily going to stay in a hostel or the other hotels nearby. In preparing this conference in an all-in-one venue, we did consider this from the very start and we believe that pretty much anyone who would want to stay at a hotel is going to be okay as well at the youth hostel. It is high standard for a youth hostel. However, I know that this is not a satisfactory answer to everyone. There's a Marriott Hotel, and it's about 1.5 kilometers. That's the closest. If you have a car onsite anyway, then it's probably not going to be a problem because they offer parking. And there's one hotel that's also reachable. I think it's the Marriott even by bus. So, I mean, it's doable, but so far we haven't allocated any resources to making special arrangements for those hotels simply because we believe that you're going to be fine at the venue and we hope that you will actually come and stay at the venue. Yeah, so the Marriott is on the other side of the river, so it's not totally clear which bridge you have to take and how long it takes, but yeah, 10 minutes. And it's a 10-minute bus ride to the city center and the main station. There's lots of hotels there, except you're not at that conference. So if you really want to be in a hotel, you can do that. We don't actually suggest that, but it's certainly doable. Hyde America has 3 million visitors each year, so there's a lot of hotels. I think Matt was first. I'm wondering if any of the different size rooms have refrigerators or kitchens and also grocery store nearby, because that would be one way of dealing with dietary requirements as well. I don't think that we're going to be very lucky on that one. There are very strict rules as to who can bring the food into a youth hostel in Germany, and they have to abide by that, so I don't think that they provide cooking facilities. The nearest grocery store is about 2 kilometers. By bus you can reach that as well. On the other hand, they are very relaxed and very cool people. I'm going to go and meet with them soon with the cooks and discuss the entire food situation and so on. If you have any input on that, I'd love to hear it. We can push for things. They've already gone a long way away from the standard to what we expect from them, including making sure that no food gets wasted and that we have literally almost all dietary requirements catered for. I'm tempted at this stage to say that people are going to be happy and okay with the exception of kosher people. If you feel strongly about this, then I would love to hear your input and maybe I can take it to them and maybe we can actually make the conference better for everyone in that sense. Some previous DEBCOMPS have had one refrigerator for the whole DEBCOMPS that people could put things in, including cheese for the wine and cheese, and that was sufficient to solve the problem. Might there be a group of bicycles available? While we're looking into that, I think it's useful. It's on the list. I'm not sure the Youth Hostel has any of them, but we will try to provide something. The Youth Hostel has about 30 bikes that they rent out on a daily basis, and there are other ways to get bikes too, so there'll be bikes. Are most of the rooms two people or four people or mostly six people? Mostly four and six. The six people rooms have actually two bathrooms, and they have split bathrooms, so you can wash your teeth when someone is using the toilet and stuff like that. Because I was a bit afraid of the bathroom situation, so I looked into that. So it's still usable, even the six people room. Any more questions about when you're on a location? This place seems really cool. Thank you for finding it. Thanks. Right, so there's not a lot of time left. We thought also that we really want to have a depth camp again. It's basically negotiated already, I believe. This will be... This will be the week before. This time we would like, as a suggestion, we would like to have an open weekend where the first two days, there will be talks like we had this Saturday, but maybe try to get this even more out. I'll spread out to a weekend. We will certainly try to arrange a conference dinner or a meeting, but we will certainly try to arrange a conference dinner or, again, a barbecue like we had last year. To get this covered, we haven't really started looking into this yet seriously. As you saw, we have two rather big rooms and one smallish, medium room. So we're right now thinking about how we can set this up. Maybe we can have two tracks of talks and one buff track at the same time and then prompt to sessions like we had this time. We're also thinking about thinning out the talks towards the end so there's more ad hoc sessions because we saw that a lot of people came up with cool ideas, tried to have ad hoc sessions and it was always kind of difficult to schedule them. But in general, I think, otherwise, the whole thing seems to have worked out really well now and we don't want to change so much compared to this year. It would be really cool if you guys had any input from how it worked this year with relation to how it worked in the previous years and you just sent that to us. Just a small thing regarding Depcamp. I think I heard that it's going to clash with the CCC camp. Probably many people will be interested in both. Right, so we have a slide on timing right after this so we'll just discuss this. We thought about possibly having a job fair at this open weekend out of the place so if regular Depcamp attendees don't necessarily have to go there but if sponsors are interested in providing that and people are interested in talking to prospective employers we might have that on site in one of the rooms and possibly also make it possible for our sponsors to present themselves again only on the weekend. So this would be opening weekend, more targeted to us public and then the rest of the week would be open or would be just a regular Depcamp. Another thing we thought about is might having a kind of a poster session where teams just present what they're working on and everybody can walk around and talk to them. So right now we have all these bits from and everybody has to go there and then we can just have a walk around as there are in some conferences. We are looking into this, we will see how it works. Music and everything, we talked about Habracona, let's just skip this. The timing, right, so these are the dates. This will be August 15th to 22th from Saturday to Saturday. Depcamp will be until Friday and probably start on the earlier Saturday. So it's useful to arrive on Friday evening or Saturday morning if you can. There are adjacent free software events who are still to be confirmed, this is what Tincho said. So there's the CCC camp. Our current information is that it will be one like the weekend before and until Wednesday so it will be super cool. People might miss a bit of Depcamp but could go to the CCC camp which is near Berlin and then move over to Depcamp. If that works out, that would be great. More so for international people, we are talking to them. That was the last information. We hope that they will not schedule it on the open weekend. We'll see. There's going to be FrostCon near Cologne. I believe that will be probably afterwards. I believe it will be one weekend later. So if people stick around, it's also a very cool conference. And of course, there's also the Devin UK Barbecue probably also one weekend later, right? So we try to not clash with that either. We would like also again, when we try this year to maybe make it even more official to say please come up with sprints during Depcamp. So Jeff, some concentrated work time. And in general, we would like to open a call for papers in January and try to get a preliminary schedule open up early and also the registrations of people can book flights early. We will see how that works out, but that's the general idea right now to get things early. But of course, we also thought about, we already have it on the plan to have late submissions for talks and not book everything up early so people can come up with cool ideas later on. So generally, that's it. We would like to have more input from locals because nobody from us is right now in Heidelberg. It's not so far away for us. It's not Cape Town. But we already talked to a couple of people from Heidelberg and there are some volunteers, but we could still use more. We could use more people from university especially because Heidelberg University is quite a big one. And if there could be some cooperation, that would be great. We're still looking for ideas for day trips. We have a couple of ideas already, but if somebody has one. And yeah, we're looking also for the sponsorship team to raise the money. So yeah, we hope to see everybody in Debcombe15 in Heidelberg. That's the website. It should be up now. Take a look. It's not much on it yet, but it's there. And we will switch our mailing list now to Debcombe15. So we used Debcombe15 team before. This is not working. And, well, we will also switch the GIC channel, I think. So we had a local channel until now, but now the organization will go to the global team and the global channel. And that's it from us. I think we're almost over time. So if there's any more questions, actually we have time for a couple of questions, I think. So go a while. Okay, that's the new webpage. Yeah. That's the view from the castle. Change channel. Not quite done yet, but... Yes. I feel fairly confident everybody in the room will agree with me when I say this looks awesome. Thanks very much for your work so far. Clearly there's a lot more to come. You're all aware of that. But this looks like a great plan. Thank you very much.


This is a fun step because we're really starting to get into the what makes a neural network a neural network We're putting in forward propagation But so far only the linear parts. We're not yet adding a non-linear activation function So to do this we go to layer dot pi go to our dense layer object We create a new method forward prop it takes in a set of inputs these are the inputs to the layer and The first thing we do is take a single bias input So it's a just a two-dimensional array, but size one by one and it has a value of one and Then concatenate so we just stack that on top of the inputs which itself We have set up as a two-dimensional array and we specify to stack it on Axis number one so axis number zero is always the row Axis number one is always the column so you can picture this as a horizontal set of inputs and The bias neuron is being tacked on to the very right side the very final column Then we have this set of full augmented inputs X and And we matrix multiply that by the weights also a two-dimensional array Doing that then let's us get the output Y Then we return that as a result Now we can go into our ANN class in framework dot pi and add a forward prop method there We take in the inputs X in this case. These are the inputs to the entire neural network Whatever form that it's in we take it and Ravel it meaning we turn it into a one-dimensional NumPy array and Then the construction on the end the square brackets new axis comma colon square bracket What this does is it takes that one-dimensional array and it stretches it out across columns puts it all into a single row But make sure it cuts across columns Then the next line takes our from our list of layers, which we only have one layer in it right now So take that one and only element Run the forward prop method on it We've already taken our input and renamed it from X to Y The reason for doing this will be clear a little bit later It just makes it easier if you're calling one layer after the other The output of the previous layer is always the input to the next layer So in this case we take and run forward prop on the inputs We get a Y as an output and then we return that We flatten it again. We ravel it. So return it as a one-dimensional array Then we can go back now Into our train and evaluate methods and we can add this forward prop step So we bring our data in we take our training set. We pull an example. We normalize it Now the next thing we do is you run it through our forward propagation We get the result Y and we'll print that out in our training method Just so we can see that it's working, but then we do the exact same thing in our evaluate method Now when we run it we can see sure enough we get what we would expect One-dimensional arrays and we're expecting a size of four and these are the outputs of this neural network So it appears to be working as intended


I'm very nervous and when I am very nervous I talk very fast, so we're gonna go really fast to this thing I'm here to talk about locality sensitive hashing a really fun algorithm I learned about when as working at SoundCloud where I serve some amazing teams of data scientists as their humble manager Clear okay, so before I talk about locality sensitive hashing I need to review a few concepts so that we're on the same page to this to wit we will Define vectors then we will talk about machine learning algorithms and how they produce vectors and after that We will show how in recommendations algorithms where I work we use these vectors and compare them each other to produce Recommendations and we're gonna talk about how slow that is and then we're gonna talk about how locality sensitive hashing helps make this faster Okay vectors without going into the axioms of a vector space a vector is a point in n dimensional space relative to an origin For example here is a vector in one dimensional space. It's a point on a line relative to this zero Here is a vector in two dimensional space is a point on a sheet of paper Here's a vector in three dimensional spaces flexing my abilities to do drawings in Google slides It is a point in a cube and here is a vector in fourth dimensional space where the fourth dimension This time this The fourth dimension is time. Okay. Here's if you want to have n dimensions You can do this by just having n components that uniquely describe a point relative to an origin Now you all know what a vector is now we can talk about machine learning Many machine learning algorithms take sequences of things and produce vectors for those things here is the real live sound cloud Recommendation algorithm taking sequences of user histories and producing for each track a vector now These algorithms do this in such a way that track vectors that are close to each other in this space Happen to also be similar and we use this to build recommendations For example here is a two-dimensional track vector space with some of my favorite albums if we want to build Recommendations for joining Mitchell. We will just compare it It either length or cosine angle to all the other tracks in the catalog and pick the five top ones If you want to then produce it for Bruce Colburn self-title album from 1970 You have to compare it to all the other tracks in the catalog and pick the five best ones and this takes a really long time It's known in computer science as the K nearest neighbor's problem because you have to compare for each track To the rest of the catalog and produce the top five tracks or as I like to call K lit tracks Thank you Now it turns out that doing all these comparisons is really complex if you have n tracks in your catalog It'll take n steps of your algorithm because you have to compare it to itself and if your If your catalog of tracks is 130 million like they are at sound cloud then this takes just too damn long Okay, so thankfully some people came up with this really great algorithm called locality sensitive hashing That's gonna solve all this problems for us break. I'm gonna Tell you what we talked about so far. We talked about vectors as points in the space relative to an origin We then talked about machine learning algorithms that take sequences of things and produce tracks for those things We then talked about how to use those tracks which are close and therefore similar to produce recommendations by comparing them to each other And this takes too damn long now locality sensitive hashing is hopefully going to make this a lot easier for us before Oh, and let me define what that is locality sense of hashing is an algorithm that takes vectors as input and produces Binary as output and it does it in such a way that small changes the input Manifests themselves as small changes in the binary output and in this sense it preserves locality And I promise you you'll have an intuition for this by the end of this talk Before I talk about the algorithm, let me give you an analogy. How many of you live in Queens? Yeah, okay, how many of you live in Queens asked your friend who lived in Brooklyn over for dinner and that friend said Queens is too damn far Okay, yeah, I stayed in Queens once it was awesome I'm from Berlin by the way and originally from Canada now the reason that your friends say Where isn't your friends that it was so far away is because? We draw these arbitrarily boundaries everywhere and what we say is that we draw this line It's called the neighborhood and anyone on the other side of that line It's just too damn far and this is at the heart of locality sensitive hashing You draw arbitrarily lines that people on the other side are just too damn far So let's talk about this algorithm on the left. We have vectors and then on the right We have binary. I told you this algorithm takes vectors and produces binary So we're gonna do this this vector space is two-dimensional one axis is happiness. The other axis is how dead you are The algorithm goes as follows pick a random plane that passes through the origin then pick and an Orientation and assign vectors on one side of the plane to one and vectors on the other side of the plane at zero And don't stop pick a random line that passes through the origin pick an orientation Assign the vectors on one side of the plane a one and vectors on the other side of the plane a zero Don't stop pick a random plane pick an orientation assign vectors on one side of the plane a one and vectors on the other side of The plane is zero and don't stop pick a random plane Pick an orientation assign vectors on one side of one and vectors on the other side a zero as you can see We have vectors on the left-hand side and binary on the right-hand side. That is locality sensitive hashing Okay, so I need to show you two things one I need to show you that this algorithm preserves locality and then I need to show you how it can help us find recommendations And I'm gonna do that. Let's look at this Let's look at this binary representation very quickly I'm gonna make a claim I'm gonna claim that the probability that you picked a random line that separated to emoji is proportional only to the angle between them So think about this for a second. You have two vectors. They're really close together. We're picking random lines What is the likelihood if these two vectors are close together that your line separates them? It's pretty small now if the vectors are if the vectors are far apart The likelihood of you picking a random line that separates them is very large and in this way They are proportional now I'm gonna give you all a quiz and I'm not gonna ask you for the answer because we don't have enough time But how many lines separated these two emoji looking only at the bit strings? I give you a hint The answer is one and it is exactly the bits that are different between these two bit strings So I now make the claim that if you count the number of bits that are different between two of these binary representations that this is proportional to the angle between the emoji and of course This is the case because the only time you separate a plane is The only time you separate two emojis is when you pick a plane that separates them and the likelihood of you doing this Is proportional to the angle between them another complex way of saying this is that the hamming distance Which is the number of bits that are different divided by their length is proportional to the angle between the vectors So that's what we mean when we say they preserve locality Somehow this binary representation preserves the fact that they were one side or the other on the line and this is related to the angle But I haven't told you how to use this to find k nearest neighbors and we're gonna do that right now We're gonna take you back in your mind to the bucket analogy suppose I wanted to order these Emoji in such a way that emojis in the same bucket appear next to each other Suppose this bucket all I need to do is take this representation and lexically sort it Which means sort from the left in the right where your alphabet is just one and zero But I need to make sure that my red and blue bits are first and they are in this representation I give it a sort and Emojis that are in the same bucket appear next to each other We have some false positives like the ghost but we'll filter that out later and we can keep doing this We can do this for another bucket. Here is the bucket spanned by the blue and green line And because we chose these lines randomly in any order We can permute them in any order and so we just go and permute them Lexically sort them and we see here that emoji in the same bucket are nearest each other And we can keep doing this random permutation of bits and lexical sorting to find candidates And this is our this becomes our locality sensitive hashing algorithm So let me summarize what the algorithm is in entirety you have a vector space full of things You pick a random plane that separates this vector in half or sorry You pick a random plane that separates the space in half Then you choose an orientation and you assign two vectors on one side of that plane of one and vectors on the other side That plane is zero then you repeat this d times and if you repeat this d time for each vector You'll have a binary representation that is d bits long If you then take this representation and randomly permute the bits and lexically sort them you will find candidates for your recommendations And then you can just use cosine similarity to filter out the bad ones That's it why is this better Well first of all I told you our original algorithm was n squared But sorting is n log n and our algorithm depending on the number of times we permute bits and lexically sort them It's just s times n times log n, which is much faster than n squared when you have 130 million tracks Also, we can use The fact that the hamming distance was proportional to the angle to reduce all angle calculations to just bit math Which CPUs are very good at and this is the only time in my career as a software engineer Which is very short where a constant factor actually mattered in an algorithm, okay? That is the end of locality sensitive hashing an algorithm that takes vectors as input and produces binary as output in such a way that It preserves locality and it does this by splitting places in half and assigning each vector as ones and zeros on either sides of these planes You might ask is there more this algorithm only preserved the angle? And you might say I want to preserve the length of these vectors because hey Maybe that's important and there is such an algorithm that does this you might say hey all my vectors have a rank associated with Them because we put page rank somewhere in there and you can do this it is called rank based hashing in fact You might even say hey, why did we pick these planes so randomly this seems absurd? Why didn't I look at the data well you can do this and it's called supervised hashing you could pick the best planes you Want and because it's 2017 somebody has figured out how to do deep hashing using Using deep learning. This is my favorite webpage on the internet by the way, and there are many papers about deep hashing I have no idea how that works, but that's it. That is locality sensitive hashing may all your vectors be hashed


Welcome to this single salt analysis tutorial. My name is Wendy Bacon. I am a lecturer at the Open University, as well as a visiting researcher at Emblem EBI. This tutorial I'm going to do on the human cell atlas galaxy instance. It should function on most galaxy instances that I know of. And if not, try galaxy.eu or galaxy.org. I'm only using the human cell atlas one because that's the first one I ever use. So it's my favorite. This tutorial is not going to be explaining a lot to the science. It's just going to be as a tool or a resource for you to be able to follow along, get your right parameters. If anything is going wrong, you're not sure where to click or what to do. Hopefully watching this tutorial will help you. For any of the scientific content, please read the tutorial itself. That is going to be your home for the information. Okay. So we start with getting data. You can either use the input history and then import it, or I'm going to import the and data object that was made in the previous tutorial. So this can sometimes take a while. So I'm going to copy the link to that input history. And I'm just going to import that. I'm going to move the history and then make sure is it H5AD? Yes, it is. As part of the first question, you might be inspecting the and data object. And again, if you're using the tutorial version, you can just click. So I can just click on that and then I'm sorted. I want to do that a few more times. I want to see the OBS. Okay, and then we can look at all sorts of our lovely information. Okay, and then we're going to plot, copy the keys over. Do the same thing again.  I'm just copying and pasting from the tutorial. And I mean, you can pretty much use anything in your OBS information. This, I just think came out the cleanest in terms of being able to visualize on this dataset because it's quite a messy dataset. So it helps clarify where you might want to put your filters, et cetera. Okay, and then we're naming our plots, which is for easy access because you end up with quite a lot of plots on here. All right, and now we have all the plots in the world. And you can also, if you want, you can enable scratch book and then you can sort of look at the plots. And resize that, you can look at the plots side by side. So if I then click off this and then I also have that, well now I can sort of look at the two plots at the same time and that's quite handy when you're using scratch book. So that's a little handy utility within Galaxy. Okay, and then you're going to be given lots of different questions to analyze what's going on in each of the plots. And then it's time to apply the filters. So very easy to accidentally hit the serrat on here, by the way, when you're searching, so make sure you don't do that. And I'm gonna do the standard. And there are a few that automatically come up. We're not using those because it's less clear because the data's quite messy. I wanna do that. Oh, I see how I'm gonna plot next. And we're looking at genotype just because that's where my important variable to be fair, you want all the variables to be as similar as possible in terms of like sequencing depth and output. So that's that important thing to keep in mind when you're setting up. And then I'm gonna inspect. And this is quite cool because even if you examine it, you can see right here, without even having to do an inspect step, the cells and the genes that are left. And then you can compare that with the original, right? Which had a lot more cells with the same number of genes. So you can look at this and see each of your different parameters and what's happened. You can compare it with the previous one. And say, oh, look at these different batches. How cool is that? I think actually the best way to set this up right now is to say we want island genotype. So I'm gonna enable scratch book island by genotype. All right. And now I also want this violin as well. So I can see what's changed. So you can look and say, that's not really done, I think. Let's look over here. And we can see now we're seeing a much more significant change because that's what we were filtering by. We've sort of done a cutoff of this bottom for better or for worse. All right. And then now we're going to filter total counts as opposed to gene counts. We do this step by total counts rather than the log one P of gene counts. We're gonna go with 6.3. It'll always be higher than your genes because you are expecting to have multiple copies of a transcript. Well, not copies, not PCR amplicons rather, but copies of the cell has made. It should have more than one transcript of a given expression. So you would want to put your cutoff higher and then we repeat what I'm so zoomed in because a lot more effort to do. These sort of repeat steps. All right. And we're there again. So I will rename everything. I know it's a little bit of a faff to rename everything all the time. This is gonna be a long tutorial and it can be so confusing when you're using the same tools over and over again which we are, a lot of the single cell tools have a lot of stuff packed into them and a lot of abilities packed into them. And it can be so confusing when you're looking back. So the more you can get in the habit of managing your data well, the better your life will be. So do it. Okay. And then I'll add this filter by counts in and now I can actually using my scratch book see each phase of filtering and what happens. This is not a great look, if I'm honest. To be frank, this is a better look because you can at least see the bottom of the violin plot, which is a lot better. But we, you know, this is, I'm putting in quite low cutoffs. So I'm being very liberal with my cutoffs. That's how I would put it to keep as much as I can because I know the data is messy and there was a lot of background. Okay. And now that we've done by genes per cell, by counts per cell, or you know, the log versions of it, we're gonna do the same thing with percent counts mito. So we know that mitochondrial, urnate, et cetera is a sign of stress and we don't wanna keep our stressed out angry, half dead cells. So we're gonna filter out our, well, this is easier, just do that. 5% is pretty standard. Always, you know, read the text, it depends. Depends on the sample that you're working with. But we're gonna go with 4.5 today just to be a bit contrary really. I always think the ultimate test of your data is how well it survives weird analyses. And as boys, relabeling, and then we can again look at all of our plots, one, two, three. Okay. And the last step we're gonna do is filtering genes. Now, the first time I ran this protocol, I got a bit lackadaisical and thought, ooh, it probably doesn't matter the order in which I do these steps. Filtering's filtering, right? No big deal. So I want rogue.    So don't make that my mistake and filter genes first. Make sure you do all of your filtering and then hit filter genes. Always love making these videos because making the videos takes, you know, as long as it takes you guys to get through the tutorial. But then the final video output at the end when you edit out all of the time of waiting for stuff is much shorter and it seems so efficient. So if steps are taking longer than you're seeing in the video, it's because I edit out all of the other time waiting. All right, on our process. You know what, I'm just gonna, just gonna, if I search scamp it here, usually it'll be a lot easier for me to find all the stuff that I want. Although if you're using the tutorial mode then you're pretty much good to go. Okay, normalizing some data. Yes, number 20. Do that. And I'm just gonna set up the next one because it's kind of a sequence of pretty standardized, mostly steps. Although there's a whole bunch of parameters here you could change if you wish. Scale data. Okay, two, 10 and then we're gonna run PCA. So this is our first, well, I guess find variable genes. You're also down sampling bit. But now we're properly going to start reducing the size of our data, reducing the dimensions. I always double check the inputs on this because you end up with quite a big history. So I do recommend that. That's how I want that. And we use 50, so we'll show 50. Okay, and I will rename that. I don't know if you can hear it, but it's a wonderful sunny day. Some lovely birds singing in the background, mocking me. All right. And then you can decide, ooh, where do I wanna put my cut off? How exciting. Okay, onto our compute graph. No space, no space. Right, so this is where we're defining our nearest neighbor graph and trying to put everything on like a single graph as opposed to 1,001 dimensions. Let's just check. I want 15 neighbors, sure. And then the number of PCs we're gonna use 20 because that's what we've determined from the previous plot or at least such as what we think. We can also do a bit of, get the rest of our plots calculated, right? So different ways of reducing dimensions, different places you're gonna be on your X, Y graph. Perplexity, yeah, so perplexity will be 30, but you can change it if you're working in a group.  These other ones, paga and run fdk are more looking at trajectories. So that's a separate tutorial. The next one, in fact, if you're following along, now I have a lovely U map. So we're not gonna get any pretty plots out of this yet because it's just calculating the coordinates, right? And now with those coordinates, we can now try and calculate, all right? If those things are quite next to each other on this nearest graph, what's it like with their cluster? Let's start calling some clusters. I'll use the Louvain. Lots of people now are using Leiden. We're gonna use a resolution because I know this dataset and it's not the cleanest. We don't wanna make too many clusters because the more detail you have per cell, the more you can trust, the more clusters is what I would say. But it also depends on your sample. If it's a super homogeneous set, then you also don't wanna be calling a whole bunch of clusters where there are not. So you kind of have to take a few things into account on your cluster calling. All right, we have our lovely clusters. So let's now do the fun bit, which is figure out why a cluster is a cluster. So let's look at the genes that make it so. So sometimes you just need to hit that. I don't know if that glitch has been fixed yet, but for whatever reason, you do need to click that sometimes. Loads of parameters you can change there. So it will sort of automatically make clusters by Louvain clustering. We might also be interested in if there are any differences across genotype. And then, you know, the manipulate and data tool could let you filter out specific clusters. So then you could just compare those to each other. Like there's a lot of fun that you can have with the fine markers to compare different things. Okay, and again, this is actually particularly important. You want to make sure that you relabel these things appropriately. Because you've got four things that look like fine markers. And the other side of this is it will store the result of your fine markers within the object. Oh, I've just done that wrong. Oops, okay. It will store that information within the object and we are more interested in, yeah, so here's your marker table and this is what gets best. We're more interested in the cluster comparisons rather than the genotype. Because the genotype is sort of like glorified bulk RNA seek because you're just smashing everything together. So I'll just, I'll just get rid of that one so that I keep my final object. Anyway, you want to, so yeah, you want to keep stored within your end data object the results of the comparisons between clusters. Now we're going to do a little jiggery pokery, final object and I want variable information. Right, so we want misinformation because if you look at it, oh, it has the ensemble ID, which is the most accurate way of counting transcripts, but the symbols are really what we're going to be talking about when we're trying to understand it from biological point of view. And right now our lovely cluster and genotype tables, they only have the ensemble ID as their label. So we're going to do a bit of jiggery pokery to make that work. Join two datasets side-by-side, on a specified field. And their column four is the one that has the ensemble ID. Yes, that's what we want. And yes, yes, no, yes. Now at this point, I strongly recommend checking because sometimes the order will be a little bit bizarre. So you just want to make sure you have the right number. All right, and then I want to rename these tables. Should have genes and then symbol awesome. And I know that it's the shorter one, it's going to be when you're spludging everything together. It's going to be when you compare and cross genotype. Really the most interesting one is one, it's by cluster, own peace of mind. Just gonna, and I'll, I have a nice history. And now the best bit, well, it's time to plot. So we're going to see all of our lovely hard work on an object, oh yes. We're going to start with PCA, using our predefined knowledge. We're going to be plotting by a whole other different thing, lots of different bits and bobs for changing how your plot looks essentially. And then I'm going to do the same thing for Chisney and UMAP, auto correct, mocking me. Oh, there's buckets and buckets of information you can now get from these images. It would help if I zoomed out a bit. All right, so buckets and buckets of information you can analyze and think about and interpret. And I strongly recommend that you do, that the more sort of time you spend trying to get into the mind of why might you want these plots? What might they be telling you? The more easily you'll be able to direct your questions, direct your analysis. Yeah. But we're going to move on to the annotating clusters step. We're going to rename our categories. We've cunningly been able to figure out exactly what each cluster is, looking at their marker tables and our known marker genes. And so rather than have them be named cluster zero, we're going to give them their actual name names, cell type names. And then we don't want to necessarily delete that, especially if you look at the marker table, it will be cluster zero, cluster one. So it would be nice. Although I suppose you could just run the marker table again using the new categories and that would work too. We'll just add them back in. So now we're copying that cluster annotation back into your original object. And then that means we essentially have Louvain and Louvain under for zero. So that's not ideal. And now it's called Louvain zero. We don't really want that. Actually, I'm going to just get a fresh one so I don't accidentally repeat the same thing again. So we don't want it to be called Louvain. We want it to be called cell type. So this second Louvain category is getting changed. We have Louvain and cell type. So that's a lot nicer. So we're going to rename this our final cell annotated object. So if we want to now plot that so that we get our lovely labels, we can rerun one of these and just run it on the final object, add in cell type. Or you can switch Louvain to cell type. It'll color it the same way. I'll just label it differently. And now if we look at the plot, it's labeled by cell type rather than number. And that's cool. And there's whole heaps of information again across all of these different plots and what you can interpret. So please do take some time and do that. The last bit is when we're looking at some of our interactive visualization. And so if we go over here to our UCSC cell browser, we'll choose the format. So it's scan by, that's what we're using. Yes, final annotated object. Sure, sure. Yeah, Louvain is fine. We can execute that. And we're up and running. So now I can hit view data. I've zoomed far too far in. There we go. Okay, and then this is brilliant.  You can look at all sorts of different visualizations. You can color by different things. Yeah. It's a lovely thing to mess around with and to be able to interrogate your data. So spend a bit of time mucking around with seeing all the wonderful things you can do in this. It's also nice because you can just share your history and someone can immediately click on this and start playing around in it just the same way you were, which is awesome. Yeah, we're gonna just do that. All right, and it's finally changed to, I'm ready. So I'll click here to display. And this is going to take you into, it always makes me do this. That's fine. The inner root of viewers for whatever reason, always my security stuff. No, well, I will find sometimes if this happens to you and it says proxy target missing, I don't know why it glitches this way. It is, I promise it's worth the pain, okay? Just run it again and then leave it for a few minutes before trying to look at it. As I said, if this plays you up or if it says proxy target missing or something, just exit, leave it for a minute and then come back and do it again and then leave it for like five minutes. And then usually you'll get this happen, which is pretty great. So this is a whole world to explore, if I'm honest. There's all sorts of cool things you can do with it. Like, okay, I want to color it by batch. I want to color it by cell type or genotype, right? Ooh, that's, this is interesting when you color it by genotype. And then now I've created a little population and I can click it. There's all sorts of wicked stuff you can do to explore. And this is just nice because it means you're exploring your data without having to recreate plots left, right and center. And then you kind of pick which plots show what you're looking at and what you can investigate. And it's just a really nice exploration tool when you're trying to interpret your data. All right, and then just make sure to come here and hit stop when you're done looking through that tool. And that brings us to the end of this tutorial. So I hope you had a great time.