Good morning, everybody. The daylight hours are winding down already. I'm waiting to get unloaded. My appointment is at 9 p.m. in Laredo, Texas. I'm sitting up about 30 miles north of Laredo in Enns... I couldn't pronounce it yesterday either. Encinida, Encynda, a little town here, and a truck stop, a road ranger. That's where we're at, waiting to go unload as it loves across the street. It's pretty warm. You're not gonna lie, it's pretty warm. I've been sitting here with the windows down most of the day and a T-shirt, it's January. It's 12 degrees Celsius, and that's cold for them. I looked at their forecast for the rest of the week, and it was like 25 degrees Celsius the rest of the week. So right now we're sitting in like the 50s Fahrenheit. Their regular temperature looks like it's in the 70s in January. I saw motorcycles riding around yesterday, and I saw palm trees. I did, I didn't show them in the vlogs, it was already nighttime, but my life is complete. Saw my first palm tree of 2024, because it was very, very exciting. I don't get to see them very often. It just had gotten used to cold weather for winter. So coming down here, my body's like, what's going on? I thought it was winter time. Is it still January? Did we fall asleep? Did we sleep all the way till like May? I don't know what's going on. So I'm like sweating away here. Everyone else is probably doing just fine, but this Canadian is melting. We're 30 miles from the Mexico border. Closest to the Mexico border I've been in about 15 years. That was an exciting trip for me. It was a little bit out of the ordinary for me. Stretched my legs a little bit. It was a good rounder. I have a reload that's taking me back to Winnipeg. I gotta go pick it up near Houston tomorrow. But for now, we gotta wait. It'll be dark already by the time we start our day. Cause I've been sitting here since yesterday. We're gonna start our day. We're gonna go and get unloaded at nine o'clock and then get as close to Houston as we can tonight yet. Probably drive past midnight. The sun is down and I'm ready to start my day. It's gonna be a night trucking day today. So the vlog will be a little dark. Apologies in advance. We're still gonna have fun.  Still attached? Fantastic, fantastic. And here we go. They were getting there at 8.45 as well. It's like a mall or retail store, delivery, back lane. So there will be plenty of room. I just don't know how many of us are down here. Cause I think I saw another one of our drivers down here with a second part to this load. I only saw him once in Kansas and then it would make sense cause I don't think my load is the whole thing. But I don't know. I just know mine. They turned their signal on for no reason. I'm just gonna wait and make sure. Yes, he is turning. Okay, well he had his signal on, so I just didn't trust him. Entrance to the load on 35 South. Patient, I guess. I just, I feel bad cutting people off.  That what he did there wasn't so bad. I mean, it doesn't really matter. That would have been one of those like gray areas where I would have felt bad about it but I probably would have still done it. I don't know, I don't know. Turning on, he was turning on at the same time. So it's just a stop sign, but stop waiting for it to turn green. Like I turned his signal on after he was already turning. Otherwise I wouldn't have known what he had done. Good, good, good. Glad to see the drivers are exactly the same in every corner of the continent. It's awesome. It's one thing that unites us all. Which that crosses into Mexico is called the World Trade Bridge. I've seen lots of signs for it trees everywhere. I wanna take exit 3A. Mexico is literally a few blocks over to our right. Take exit 3A. See them in the video? Exit 3A. I always say palm trees to me are a sign of good times. Vacation. Fun beaches. Or just warmer weather. 100 meters, turn right on West Colton road and then approaching destination on the left side. Under 100 meters. We're gonna make a right, immediate right. Cross here, cross to here. It's very humid here. Hey, so we're here. Just around the corner here. Destination 100 meters on the left side. Look at that old blue. You see that?  Something up with that light though. But here we are. I'm 15 minutes early. I just talked to the guy who's gonna come here and meet me. Told him where I was. He'll be here in about 15 minutes. In the meantime, I'm gonna go say hello to my little friend over here. There's a special place in my heart for palm trees. First I have to make sure there's no snakes or spiders or things around here that I shouldn't be near. We're not in Manitoba anymore. Okay, nothing around here. Always be vigilant of where you're stepping, especially when you're going into the grass. Okay, nothing there, nothing there. Nothing on the trunk. All right, well, had to hug it. It's a good day. I know I'm like 10 years old, whatever. Don't spoil my good mood. Palm trees put me in a good mood. Well, now that we got that over with, straight down to business. I'm gonna crawl inside the trailer here, get everything ready to go. I'm not sure if I'm gonna be unloading around here or back over there, but we'll take a look. See if I can show you inside the trailer here a little bit. I don't have a lot in there. Come on, focus. Focus. No, there's not enough light in there right now. See? Oh, oh. No, camera's not. Can you sort of see where they are there? Sorry, guys. Too dark in there. Look at this. January 2024, walking around no-took t-shirt. Pushed my wife and son right here. Then I have everything that makes me happy all in one place. My son, my wife, palm trees, and old look. Wouldn't that be great? One day. One day, that's why we got these tattoos. The Brit got one too. See? Nope. Brit's got a matching one on her leg to remind me all the time to plan vacations.  And you gotta remember to stop, slow down.  And just for the record, I know on the way down here, I wasn't familiar with this area at all. I'm still not familiar with it. And I was a little bit nervous about coming down here. I just wanna let you know that those worries were unfounded. From what I've seen here so far and where I spent the night in this area here, Laredo's very nice. And I would have had no problem staying here overnight. There would have been no problem at all. It seems like a very nice place. Though I've only been here for a very short time, so I don't know. Oh, there's two of us here unloading. So that's Roger there in his truck. They're unloading him first, and then they're gonna unload me.  They haven't unloaded anything yet. And I'm just taking the straps off the first bundle. That's what I mean. Like it wasn't that much. And I knew this couldn't be the whole thing, right? And I was right. He's got the other part. Looks like they're just about done with him though. So I better get my stuff ready to go. Gotta get two more straps off of here. I've gotta be very careful taking it off. It can be a little bit delicate. This'll be a lot easier. T-shirt, can't get over that. January. And to them it's cold here right now. To me it's nice. So my plan from here is to get as close to Houston as I can, so I'll be driving into the night. I explained this to you before already, right? We'll see how far we can get. I'll drive till I'm tired, but I slept pretty late. I slept most of the day actually, half the day away. So I've got quite a bit of energy now. And we'll see how it works out. We get to where we get to. As long as I get to my customer tomorrow between 2 p.m. and 3 p.m. We'll be good to go. I'll load everything up and start headed home. We're all done. So I wanna get back and past San Antonio. That's my goal for tonight. I wanna at least get back and through San Antonio so I don't have to do that traffic tomorrow. I'll get as close to Houston as I can. It's actually not on the west side of Houston that I'm picking up. It's the north side, near Conroe, Texas. So I will have to go through Houston tomorrow. I don't think I'll be able to go, I don't think I'll make it all the way there tonight. Yeah, that's a long way. That's about six, six and a half hours from here. So, we'll see how I feel, but I'll probably end up stopping somewhere between San Antonio and Houston. So let's get going. Turn left on. San Dario, I only met. Take the entrance to the mountain to 100 meters. Back straight up the way we came. I-35. So this was a pretty cool trip. I enjoyed it. The guys who were unloading me here were really awesome. Funny guys. Had a good time. I keep working. Turned my signal on and after I turned my signal on, he goes racing around on my left, on the side that he knew I was turning. I gotta see my palm trees. Continue on this road for 242 kilometers. For a quick little coffee. The entrance to the left on, I-35. Oh, I need to get here. No, maybe we'll get coffee, Karen. What do you got against coffee? Banser is nothing. Drill Sergeant. Lighted route. The sleep that I got there was very good. So I'm just gonna park. Temporary parking anywhere. All the city. Right on, US 90 West I-10. East US 90, East US 87, South I-10. Came to the right at 130 meters. Nice. Got that same cheap fuel way back. So I've gotta grab a little bit of fuel now, here at San Antonio. They've got the second cheapest fuel on my road home. To the right. Home of juice. Save a bit of money. Interstate 10. Exit 532. Exit 2 on this road for 12 kilometers. We're flying J there. That's where we're gonna pull in. What is the Texas likes to build? Highways in the sky. And I know we've been going through Texas mostly at nighttime length. But look at this. Can you see this? I wonder if the camera's gonna pick this up. There's like a road that they're building. Keep to the left on. I can't use take. Aggro road. No, it's not showing up. The camera is too dark. Interchange here. That highway is gonna be like 20 stories in the sky. The nurse. Take nurse. Where am I going? Hey, this road goes right. Yes. Oh, my lane ends though. Oh. Okay. 300 meters. Turn left on. Ackerman road and end. Turn right into 130 meters. Am I there yet? Oh, here's a Petro. Petro stop in town. I wanna go left. Okay. Flying Js across on the other side. All those constructions. So confusing right now. My lane. There's no lines. Oh, I see the flying J over there. Oh, I get in there. Turn right on. Connecting road and end. Approaching destination on the right side. And draw meters. Entry only. Okay. Let's go in here. Remind me not to go out this way. Tight one. And it's busy at the pumps. Hot in the world, middle of the night. Pumps, sleeping. All these trucks in the fuel aisles. No one's fueling. They're all just parked in there. I guess over there, off to the right. Two gallons should get me up to Oklahoma City. All the way to Houston and around is about 600 miles. That should be good. They're different. They have roads running parallel to it. This isn't just good. Okay, so this is the on-ramp right here. I think. Where? No, there's just a big puddle here. What? We're in Houston on I-10. Pulling into the Amigos Travel Plaza. You have arrived at your destination on the right side, Amigos Country Corner. This is how we get in here. I can't read that sign. Those lights behind it are too bright. There's some parking here for me. So I think I got some good distance behind me for driving into the night like this. The sun's not up yet, so that's a good sign. That's one good thing about this season is the sun comes up later. I don't like it that it goes down earlier, but it also comes up later. It's harder for me to fall asleep once the sun is already up. That's why I stop. One of the reasons I stop now. The main reason I stop now is because every truck stop around my shipper where I'm picking up tomorrow, charges for parking. I went and looked at all of them on the Trucker Path app. All of them charge 10 to 15 bucks a night to park.  I refuse to pay for parking. I'm gonna be a little stubborn there. This isn't Europe. We don't pay to park. Can you imagine how expensive it would be if I would have to pay to park every single night? Like, let's say 15 bucks every, every single night. 150 bucks every 10 nights. 300 bucks every 20 nights. 450 bucks, am I doing my math correctly? 150, 300, 450, I'm tired. Another reason I pulled over. 450 bucks a month just to park my truck. Just to sleep. This is my home back here when I'm on the road. That's my bed. I'm not even in a hotel.  Like, can you imagine if like the government or something like your landlord or HOA if you are unfortunate enough to live in an HOA? I don't know why anyone would want to. I don't. I would never live in an HOA. That's a story in a rant for another time.  Like homeowner's association line, like it's ridiculous, right? It doesn't make any sense. It's cruel, it's greedy, and it punishes us truckers for stopping one. It's not even, it's not even our choice. The government's telling us we have to stop. Whatever. You know how I feel about that. I won't go into that any further. I already told you the other day. So yeah, they all charged the park over there. So I'm parking here. Excuse me. I'm going to bed. Morning's going to come quick, real quick. So I'll see you then. Don't forget to subscribe, everybody. A lot of you are watching and aren't subscribed yet. Why? It's free. Down below. Go and subscribe. I'm also going to be opening a member's thing on my page here. Sorry, I can't even talk. We'll talk with this tomorrow because I can't really talk. Where you can become a member of the channel and get early access to the videos. Sort of like what I was doing over on Patreon, but I'm going to be winding Patreon down over the next few months, I think. And moving it over to YouTube memberships that keeps everything right here on this page. But I haven't made that final decision yet because I used to release the videos early over there, right? But it's a bit of a hassle to go around that way if we can give early access right here on YouTube then everything's all sort of in one place. Tomorrow after my brain is a little bit more rested, maybe we'll talk about it a little bit more. But don't worry about it right now, okay? That's just foreshadowing a conversation I'll have with you in the future. We're going to be opening up a member section of my YouTube channel here. Nothing's going to change for all of you. All my videos will still be free for you to watch when they get released. But I'm trying to find ways of whoever wants to can access them early. Stuff like that. We'll see what happens. So good night everybody. Stay safe out there. And I'll see you tomorrow. See you tomorrow.


Yes, it's 3pm in Switzerland and time to start our second talk of the afternoon. It's my great pleasure to welcome Catherine Röder here for this talk. She's an outstanding statistician, a very accomplished researcher in this field. After PhD in statistics at Pennsylvania State University, she was a professor at Yale and then moved to Carnegie Mellon University, where she's a full professor since 1998. I remember that I first heard about your work when I did a medical at the Broad Institute and I was interested in the interface between machine learning and the life sciences and they said, look at Professor Röder's work that's outstanding and I read your papers back then. I'm now really happy to really meet you here and host you for a talk. You know, almost 10 years later, but I realized over these 10 years, even though we hadn't met yet, but I realized that the high opinion that the colleagues at the Broad Institute had was shared by the whole community. So Catherine was elected to the most prestigious societies, like she's a fellow of the Institute of Mathematical Statistics of the American Statistical Association National Academy of Sciences and the American Association for the Advancement of Sciences and she has won numerous awards from the Committee of the Presidents of Statistical Societies. So all of these reflect the high standing of her work. And we are very excited to have you here and to learn more about selective inference in G-Bus. Welcome and we're looking forward to your talk. Thank you very much. You know, I thought when we started emailing, I thought, now I know that name. I know that name. That's an unusual name and I'm glad you reminded me of when we had emailed back when you were at Broad. So glad to get to meet you at least on Zoom now. So thank you for inviting me and I look forward to sharing my work with you this afternoon for you and this morning for me. I want to talk about how to improve power by using extra information that's there's so much multi-omic data that we can use extra information and various tests and we started off with GWAS, not because we were most interested in GWAS, but because there was a lot of data and we felt we could really test things out there. Ultimately, we want to try these methods on genome widest. We want to try them on whole genome sequencing for rare variants, but we're not finished with that yet and I'm going to share with you that fairly successful investigation we made of GWAS. So just a very quick review of that. So what's the idea? We've tested many, many SNPs across the genome and we do a marginal test for every SNP to see if it's associated with the phenotype, say maybe schizophrenia and these are the results that we would get and that's a happy result with a lot of signal above the significance threshold but that's not always the case and if that's not the case, we want to try to use extra information to improve the power. So we can summarize those data with our P values, a P value for many, many, many tests and for each test it either is associated or it's not and so it's null or non-null is the truth and then we either reject or we fail to reject and we're of course worried about the false positives here where we rejected and we shouldn't and R is the number of discoveries, the number of rejections we've made and typically people really control very carefully so they want the probability of any false positives to be less than alpha which leads us to only reject if the P value is less than five times ten to the negative eight which is quite stringent and but that works well for well-powered studies like schizophrenia, you can see there even in 2014 we had a lot of discoveries but for autism just look at the colored bars here for autism even in 2019 after millions and millions of dollars we have hardly any P values crossing that threshold and so we would like to do better and to learn more about autism and so we might consider instead using a false discovery rate so instead of trying to control for any false positives we might like to control so that the expected fraction of false positives is less than alpha and that'll increase the power quite a bit but still not enough with autism to have very many discoveries and so we want we want to see if we can do more so here's our P values lined up and those are maybe genes they're associated with and and of course there'd be most of the SNPs wouldn't be associated wouldn't be in a gene but we might be able to map them to a gene based on which gene they're nearby and which gene they tend to drive the expression of and so we could have X which is the what we call the side information so any kind of information that may might make us more likely to reject that test and so for instance if we were studying schizophrenia bipolar is very highly correlated with schizophrenia the same SNPs tend to drive the signal and so we could use the big studies of bipolar to increase the power of our schizophrenia test and then also more interesting in a multi-elmic way is we might look to see which SNPs drive which genes and if they have a very because those are more likely causal SNPs and if they drive a gene in in other words they control the expression of it if they have a very high impact a big beta slope in that relationship there whoops then we then we would maybe put consider that extra information that that SNP is associated associated another more interesting side information is if we if we looked at correlated genes based on gene expression and created communities of genes which are been called modules then we might say if a module has a lot of small p values associated with it then we would say other p values that are on the order of being rejected maybe they we should reject them to because this is obviously a functional set of genes and that are important to this phenotype so that's what we want to do we want to gain that information now quite a long time ago 15 years ago we wrote a paper aiming to solve that problem in 2006 and it was a very simple idea where you just you would use get a weight based on the side information and you can in theory improve the power quite a bit and that was a big hit that paper was very very popular but the problem was there was hardly any side information 15 years ago multiomics really didn't exist but now the problem is the opposite we have an excessive information we have high dimensional many many many things to include and now we don't know how to determine before we couldn't determine the weights because there wasn't much information now there's a lot of information some of the good some of it not so good and we can't snoop in the data to decide so we need new methods beast to build on these ideas we had but the main idea though really is always going to be that use the side information to be more lenient with some p values and less lenient with other p values so that you all together spend your alpha wisely okay and so the desired properties of this is we the covariates need to be independent of p value the p value you have to collect things like gene expression or other g was that are independent of your p value and then we need to be able to explore these high dimensional spaces without snooping so that we can up weight the right features without taking a penalty too severe of a penalty for for for for we must take a penalty for snooping but we want a minimum penalty that still allows us to get greater power and yet maintain our false discovery and so this is the idea so this this figure is extremely important for you to understand so on the vertical axis it's log minus log 10 p value so the bigger the value the more significant that test every point is that's the p value for a particular snip and then on the horizontal axis is possible side information a scalar side information x and the idea there is that if you did just a usual bond for only i'm or a benjamin hodgeberg fdr control you would you would reject any of the tests that were above the dashed line and you you can see there that x is obviously quite informative the bigger x gets the more small p values we have we have a lot more up there with significant significant results and so and hardly any when x is small there are no interest no small p values so we should use that and draw that curved blue line and then we can we can tilt and reject more over there when there are a lot of the near the bh threshold we'll tilt it down a little bit and then at the expense of not rejecting over there on the left when x is small and nothing interesting is going on and that if we could learn that line that blue line we could have a much more powerful test and so i'm now showing exactly the same thing but i'm change the vertical axis to be the zero to one p values so now all of the significant ones are the blue points down there that are i mean well actually this is a simulation so all of the blue points are the actual signals the truth and you can see a lot of them have small p values but they're only tend to have small p values when x is big and so we want to be able to find a lot of those and so this is the test we're going to use and we want to the way we're going to do this i feel like i i miss something oh yes i forgot to acknowledge that this is work due to lay in fifth end and it's called the adapt procedure and it's for adaptive p value threshold and so i don't i don't want to take credit for this work which is their work and my work the work of my team is bringing this to fruition in genetics okay so here we are let me explain how that adapt algorithm works okay so there this is the same figure as below but now at the p value of point five i've drawn a boundary a blue boundary just below at point four five and a red boundary that's the mere image at point five five and i'm going to reveal those p values in the the black ones and now this is kind of extreme i could make that much lower make it more like at point oh five but let's just suppose we started off with this huge rejection region any p value less than point four five we consider rejecting that and the point is is the mere image the red points those are pretty much all the null all following the null hypothesis so we're going to adjust the blue line and then the mere image one will follow naturally and we'll always find out if we're ready to quit when the estimated false discovery portion based on the red points is less than our threshold so we count how many points are above the red line and we keep adjusting until that's less than say point oh five so it's it's a clever it's a clever idea and then we're going to we're going to adjust the line using mostly the black lines but we'll also use the other data cleverly okay so this is what it looks like we start here i'm just going to say we'll show you what the algorithm does and then i'll show you how it does it so we start there and we say well that's pretty terrible so i start rejecting a little less and i'm going to reject less by going over on the those uh low x's which aren't very good and then a little more and a little more and a little more and more and more and more and i'm getting close to having the fraction in the red less than point oh five and a little more and i'm done now i have found the rejection region where i allow where i'm mostly rejection rejecting for big x's and small p values and i've learned that from the top from the red points okay and now i've just flipped it back that's exactly the same figure except now i'm showing it with the axis being log 10 p value so you can it exaggerates the the small values and you see that was that curved i found that curved blue line there okay so how do we how do we learn that red line and of course that red line can be in very high dimensions but i can show it best with a scalar um we can't break we can't cheat we can't break the symmetry between the red and the blue regions we can never look at the p values that are in the red and the blue regions but we can mask the data so that it's legal so every p value that's in the black region we can see so we can see that p value we can see the x the side information associated but we can see something about the ones in the red and the blue we will say we don't know what the p value is it's either p or it's one minus p and we're not told which one it is so that mask that clever masking lets us get a lot of information without actually cheating because a very small p value is either really significant or really not significant and so we can use that and this is going to help us to decide where to draw the line in each step we get more black points until we can never backtrack because once they're in the black region we've seen them and we can't go back okay so now the point is is we have to know model the local false discovery rate so at each step we're going to have a probability model that says what's the probability that that point is actually null given the side information and given the mask the masked values in the p one minus p and we're going to remove the hypothesis each step we're going to remove the the SNP that has the highest estimated probability of being null so it's rejected but we shouldn't it's the least likely to be associated and so to do that we need to build two models one of them we need to build a model for the probability of the hypothesis being non-null based on the side information and the other one is we have to say well given that it's non-null how how strong the signal does it have what do we think the true log p value is for that x so that's what we need to do and why are we doing that we're going to we want we want to compute this condition this local f dr and we have to to do that we have to have a model for the p values and our model is a mixture model we say pi one as a function of x is the fraction that are non-null so one minus pi one of the p values with that level of x are null so they follow the uniform distribution and then the other fraction has to follow a distribution that says that the p values tend to be small and the easiest thing to use for that distribution is beta distribution with its mean piled up near near the with very small p values so that's a natural thing and it has the one parameter which is a parameter of how big is the signal in that domain parameter the beta as a function of the side information and if we estimated those two things the pi one and the mu which are needed in that calculation we can get this local f dr and how do we do it very cleverly with the eam algorithm what we do is we duplicate the data so all the mass p values appear in our data once is p and once is one minus p we don't know which one is the true one and then the eam algorithm imputes weights which allows us to up weight the right one and down rate the wrong p value when there's enough information for it when there's not much information and they're about half and half and this allows us to figure out which is the least promising snip we remove it we refit s to put it in the in the not rejected set we keep making our rejected set smaller and smaller as I showed in the animation report okay so what do we what are the assumptions this is really very important the surprising how often this assumption isn't met so you have this the first thing you have to check if you're going to try this algorithm the mirror we're relying very heavily on the mirror conservative idea that is we're using the p values near one in order to figure out whether we're controlled or not so we need for the under the null hypothesis the fraction or less than t the greater than one minus t has to be equal to the fraction less than t and here's an example that's not near conservative we're missing some p values up there near one and that'll make a huge difference we'll make a lot of false positives if we happen to have a test how would this happen the test isn't properly calibrated so we said it followed a chi-square one but it didn't and so we didn't get enough p values up near one and so then when we're trying to figure out what our false positive rate is we can't we can't use that mirror conservative okay now it's legal if there's a pile up of p values near one but it's very low power that's very bad too because you you get a test that doesn't perform well so we we need to have tests that are well calibrated under the null or uniformly distributed okay so at any rate we're pretty excited if this is back when we're working on so very excited about this using adapt it was perfect for our genetic problem because we had a lot of a lot of information that we wanted to find and so in our literature search we found this paper from Corethar and they said they had a practical guide to controlling false discoveries in competition biology we thought wow and they scooped us they already did it but then they said they said oh adapt is terrible doesn't work weekly informative covariates degrade the performance and it requires a careful specification of the functional relationships so it's and they often got no discoveries at all so this seemed bad but we went ahead and and looked anyway to see if we could do better so let's look at what the story is you have p values you have covariates which are your features or your side information those go into the adapt machine it knows what to do with those but it you must model these parameters the non-null probability as a function of the covariates and the strength of the signal as a function of the covariates you have to have a model and of course it has to be a good model so when when Corethar at all said that it suffers when those things are bad but those are the classic modeling problems any model suffers when those are bad that's what our job is is to make good models for those and so it turns out that the off-the-shelf version of adapt just had the most naive of statistical input just generalized linear models which would lead people to fit their covariates in an additive way and perhaps at least approximately linear it's not a very sophisticated way of putting in a very high dimensional and possibly many covariates that you put in are not very good and this is not a very good way of modeling that so that's probably why they didn't find anything so our job was then to do to solve the right hand of this the modeling procedures and so we thought well we have a very high dimensional problem and whenever they do competitions who always wins it's a gradient boosted tree so we try gradient boosted trees so the idea is that of the boosting is that you you fit a very simple model but you do many many steps the first step maybe maybe you get that that tree and that path has a very predictive for for what we're modeling and but then we've still made a lot of errors so we add in another small tree and then we see oh where are we making errors we have more more more more trees eventually we have many many simple models that add together and we can capture very complex non-linear relationships and interactions and this kind of model is very good for high dimensions with some uninformative covariates so that's what we went with and remember we wanted to model the non-null probability and then we wanted to model the strength of the signal and so we used logistic for the first and gamma for the second and we did all this using the XGBoost software which I think is is really marvelous software to for for this kind of problem so we did that and now my graduate student Ron Yerco suffered for months and found out that many many many details matter the parameter space for cross validation the frequency of cross validation frequency it's very expensive to keep doing the EM algorithm in this huge space and and even where you you started your line should you start them at 0.45 or should you go more towards a sensible 0.05 for the starting rejection and so he worked out many of these details and you can find them on the on the Github repository for the software okay so how did it work out so remember we were going to we're interested in autism actually but we're gonna we're gonna learn on schizophrenia because there's a lot known about schizophrenia so we can use the 2014 data and then we we could also validate with newer data and since bipolar is is very highly correlated genetically so we're using bipolar GWAS for one of our site information we're going to use gene expression and what kind of gene expression well schizophrenia seems pretty clear that we all use brain tissues so we use the brainvard data for our gene expression information and I think so so the way we want to use that brain expression is we want to figure out what are the eqtls and what does that mean it's the SNPs that as the SNP varies across its genotypes is highly correlated with the expression of that gene so we want to use that what we're going to use is the magnitude in absolute value of the slope and that'll be one of our bits of site information a SNP that that drives a gene and drives it with a lot of signal is likely caught not necessarily causal but likely causal the other thing we're going to do is we're going to take that those gene expression in brainvard brainvard is brain expression and it's four in the developmental ages it goes from fatal up to teenagers and so it seems like the right range of data valuable data for schizophrenia risk so in those data we we assume it's approximately block diagonal we find that the communities of genes and so and this great software from Zang and Horvath called Wigna and you color you use colors to describe the different core gene communities and so like we have a yellow community and turquoise community and those are the correlated genes and we're going to we're going to use that as site information too so SNPs get assigned to modules because they drive a gene and then the gene is in that correlated set of genes and so it would be called say the turquoise that SNP goes with the turquoise module okay so finally here here's our data and this is the qq plot for whoops for SNPs and you can see that there's definitely a signal in that data if we look at all SNPs those are the blue points but we're going to we're going to restrict ourselves to the only the SNPs that are E SNPs and you can see that there's a lot more signal in those E SNPs and also we want our SNPs to be they don't have to be independent but we want we don't want them to be highly correlated for FDR won't work very well so we're only testing the E SNPs and so that's the input data the bipolar GWAS and the gene expression for grain var and we're going to test these 25,000 SNPs okay so let's see what do we have to do here we have the the GWAS summary statistics we're going to have to select out the E SNPs we're going to join all that information now we have an x side information high-dimensional side information and the p values we pop that into the ADAPT machine and we start the algorithm with some generous cutoff we started at we're going to reject every p value less than 0.05 and then we look at that and we say is the false discovery rate less than 0.05 say or alpha no of course it isn't so then we we use the EON algorithm to to discover what's the worst SNP that we rejected we throw it out we update our line we do this again again again again but it doesn't take it doesn't it it converges in the reasonable amount of time and when finally we get the new rejection set we look at the mere conservative we see have we control the false positive rate and when we have then we return our rejection set we look to see what we got and we want to compare this with various ways of choosing the side information to see when we get the most discoveries and we start our search with we're only going to reject the p values less than 0.05 if you remember right we had about 25,000 SNPs we were working with is that right yes and we right away we revealed most of them so our black region reveals the majority 21,000 of the SNPs and now we that that way our algorithm because remember the EOM algorithm has to have something to get going then it works very well and we can then find we can up weight down weight based on the features until we get a more powerful test so how much more powerful is it all right so remember we have all these look on the bottom here we get 300 300 well first you can just see overall this is if we don't use any covariates we just do FDR that is adapt with the intercept only is just basically an FDR controlling and you can see there's some there's of course the big strong signal on chromosome 6 in the HLA region there's a number of signals but once we put our covariates in there we get whoops we get a lot more signals and the signals are not just all piled up in the same LD friends but they're spread out over the genome and so let's see how did it go and the look at the bottom of these bars here if we use the intercept only we have 361 now if we take a very simple model and only put in the bipolar we only get three more discoveries now if we add in the EQTL slope so we get another 110 but not that much more now if we throw in the gene modules we we get another you know another increment and even if we just put in the gene modules by themselves we get the most as long as we allow for complicated interactions between those modules we get 100 up 603 so almost twice as many as we started with but if we put the full power of XG boost fitting with many simple trees but adding together with interactions we get all the way up to 843 discoveries and these discoveries so we we found a lot more discoveries and even if you count them based on correlated sets we get a lot more independent regions so it it did work very well and we published that PNAS in 2020 and now I want to show you a little bit about because I know most of you are this is machine learning so you want to know how did we look at this after the fact so you you can analyze your trees based on the relative importance of your variables and remember the tricky part of this is we keep running the ADAP model again and again and again as we at each step we change our our our curve and so a variable can be very important in the early steps and somewhat less important in the later steps so you can see that the bipolar even though now remember bipolar didn't help it all by itself but in conjunction with the other ones it is by far the most important variable and so another one that's very interesting here is this gray line and that that's a variable that was very unimportant in the beginning but as we get towards the final steps it becomes quite important and that is the Wigner gray module which seems quite weird because if you know about Wigner the gray module is the module that the gray module is the module that's uncorrelated the genes that are uncorrelated with all the other genes and the way this plays a role in our fitting is it any SNP that maps into that gray module gets down-weighted relative to the others and that and that helps us a lot in the later steps when we're really refining things and then the salmon module was important early on and then becomes less important let me show you what how that was so if you look at the SNPs that map to the salmon module you see on the bottom histogram there's a lot of small p-values but the SNPs that don't map to the salmon module are closer to uniform distributed so we up weight the SNPs in the salmon module and we find them early on okay so now we're finally so i'm kind of wrapping up on that subject but i'm just saying that we we did find a lot of extra SNPs because of the side information and we didn't worry too much about the fact that our SNPs were somewhat correlated due to LD but because ADAP maintains FDR with this amount of correlation but still correlation is really an issue when we're testing and so we want to move on we might prefer to do gene level analysis because we can put all that we put the we believe we can put the correlated SNPs together and draw conclusions about genes this might boost our power because we have fewer hypotheses to test and it certainly improves our interpretability so so that was the next thing we tried we want to put the p-values together and and do a gene testing but we have to account for that the LD within a within a gene and so so we look in the literature and there's a method called magma and it is a really popular method even though it's relatively new to the scene it has 1200 google scholar citations i'm maybe probably probably more every minute and and the magma computes the gene level p-values by using Fisher's test and a correction for correlation this is an old correction from 1975 but there's a little whoop a little problem with that correction it's a correction for one-sided test of significance so wait a minute SNP test are two-sided will that be a problem well so we we run their software and we look at our histogram of p-values this is for autism and we're happy happy to see that big spike near zero but there's a very serious problem here we're not going to be able to you we don't have mere conservative there are p-values that are missing over in the right hand side so while the magma authors aimed to fix they aimed to fix this problem of the one-sided two-sided test they managed to fix it mostly over on the left the small p-values are pretty close to correct but they they failed to fix that dearth of big p-values and most people think that doesn't matter but it really matters for FDR you have to not just for that just in general FDR needs the uniform p-values and so some other authors in 2020 used magma with an FDR test and for the low so and then we corrected magma we fixed that uniform distribution that was shocking how many false discoveries they had so for skits so look down there the orange ones we we also found those genes and the gray ones are ones that didn't didn't replicate when we fixed the p-values and so you can see for schizophrenia oh it's 13 percent of them were false but if you roll back to autism almost you know about half of them were false more than half of them were false and the reason is because the lower the power the more important the distributional assumptions and autism and ADHD are the lowest power of the psychiatric disorders and then schizophrenia is more powerful so it really that that mere conservative really matters and so um so anyway we did we did fix that and then we so once we started worrying about correlation and i need to wrap up soon but we started worrying about correlation we thought what about those what about say if p3 is the causal one that's going to be correlated with p4 which is in the neighboring gene and that's going to cause problems too because we're probably going to we should reject gene one but now we're also going to reject gene two due to that um bleeding of ld and that'll be a problem because people will try to interpret that gene it's worse than SNPs because people don't interpret SNPs but they really interpret genes that's going to be a problem and so what we recommended is you find the genes that have a lot of bleed over correlation and you analyze them as a unit and so i'm going to i'm running out of time here so we just you can look at our paper for that but we are flexible with plus minus five minutes so oh okay okay i'll slow down a little bit so what we do is we look to we first we we take the positional SNPs we put the E SNPs into the genes then we merge loci that have a lot of correlate correlations so we merge genes A and B there we leave gene C separate and then we now we have loci some of which are one gene some of which are several or two genes and we analyze them everything's we can control the FDR very well there and then after the fact we'll zoom in on the multi gene loci and try to figure out what is the causal one although that that's still just an exploratory analysis but at least we never we never call out a gene that's totally just correlated as an independent discovery so now this this figure which my student made is a little too complicated just ignore everything except the top three bars of the three phenotypes and so the black bar is just if you just did benjamin and hodgeburg and the orange bar is if you did um adapt with only but not with no covariates and the blue bar is when you put in some side information because that's our goal is to have side information help now we get a lot of discoveries for autism more like 125 genes and the side information helped us a lot now and some of the side information was gene expression we also use schizophrenia which is correlated and we use educational attainment and surprisingly enough educational attainment in europe is quite highly genetic and very well studied and correlated with autism and so we put those in and we found more for autism but what was also interesting is that the side information helped a lot for a low powered test some for a moderate powered test like schizophrenia and really hardly at all for educational attainment because that's very highly powered because it's an easy an easy phenotype to measure so there's a lot of data so side information is best when you need it and not so useful when you don't need it and so and then that's our I mean my student made the application for viewing where the signal is and I'll let you look at that on your own so we're moving towards for the last step of his PhD to try to move from common variants to rare variants and it's going to be really hard but there is a lot of side information that we can pull in and so I'm hoping that by next year we'll have a paper trying to find to interpret whole genome signals so let me summarize then want to bridge the gap between selective inference methods there's such beautiful machine learning methods in that area and almost none of them are applied to real data and so what we tried to do was get practical applications and genetic studies so that we could actually achieve the promised increases in power and we feel that we did do that so we have these three papers the one in PNAS is the main one I talked to you about about the SNPs and then we like we we have a couple of papers that are about moving to gene level tests and what we learned from all of this is it is possible to greatly improve the power by incorporating multi-omic information but it has to be done with a great deal of care so that you um not just so that you avoid false positives but if you don't do it with a lot of care you don't get any improvements so good modeling is the secret to good results so I'll stop at that point thank you and we thank you and we sent you a round of virtual applause here on zoom that was fascinating an extremely important topic it also unified several topics that were mentioned in the earlier talks today like multi-omics and making discoveries through data integration this was extremely exciting so other questions from the zoom audience yes there is one Lucas one of our students in the doctoral students in the network he Lucas Miranda will go first thank you Garcin thank you very much Catherine for such an interesting talk I have two questions if I may if I have the time please one just out of ignorance and uh it's something that you may have mentioned during the talk but I either didn't pay enough attention or I didn't understand when you compare different methods to threshold p-values for example and um you say that one yields more discoveries than the other how do you assess how do you compare true positives with false positives understand okay that's a bear though go ahead you want to ask your second question and make a unified question yeah the second question has nothing to do with it oh okay then hold that one until I answer this one okay we don't know what is the truth so um we we can't know that those results are definitely true um we did um we we we did that that analysis on schizophrenia in 2014 data and then there's been publications since then and we we got uh the expected level of of um but you know we you know that the new data supported the findings that we had and and we felt happy with that but that's all we can know can't know for sure if they were true um likewise when we did our gene test then we found that the the genes were enriched in the genes that we expect but you never know for sure we believe it's true but we don't know thank you question second question okay yeah so from my limited understanding of these psychiatric diseases that you mentioned such as schizophrenia for example there is a lot of hypothesized heterogeneity behind them like um there are people saying that many of the diseases are defined based on symptoms but there might be different biological entities behind them do you have an opinion on whether this can have a high impact impact on the geo studies because either labels that we have are representing different genetic entities for example this might be yes that that is that is a very good question and and I I commend you for asking it because I've never given a talk when somebody didn't ask that question and the um the thing is is that people you know different people with autism you know I mean it could say say for instance autism is the one that they say it the most about and some are very high functioning some can barely talk and these differences you would think it would make sense to partition your analysis by these different categories that never works I don't I think that the truth is is that people have multiple hits having autism is really kind of like being I mean being tall or being short there are many many many different SNPs acting on that and so this it's the severity and the slight differences don't don't borrow from the fact that they do have the same problem the brain is not wired quite the same I mean there's the big hits that make you have a very very low IQ that's kind of set that aside the ones that cause us to have social challenges those are some of us have more or worse social challenges but they're still social challenges and so but when people partition the data they find nothing so they go from you know seemingly meaningful to nothing thank you then another question by Julia hello thank you so much for your talk it was very very interesting my question is very much related to the first question from Lucas so I was was interested in knowing what you're using when you have like results for natural phenotypes as you as you mentioned what you're using for analyzing those results and for understanding if those results are actually meaningful or or not so it's very much related to his question okay that that's a very good question I wish I had a much better answer the one the first thing that we did was we we looked at the the the newest data set which was at that time 2018 and we we were able to statistically work back the data is all pooled together but we were able to statistically work backwards and figure out the strength of the new test from the new data by subtraction so now we have the the second like 25 percent of the data that's come in recently and now that's not well powered enough to replicate these these things but at least we can see if do we get do we get like a 0.05 kind of level of significance in the the SNPs that we found and and even then you don't always because these things are all right on the border of significance but we get we did a simulation to see how many would you expect to replicate if everything that you had was true and we got we got what we we got what we'd expect so if you can look in the supplement of the PNAS paper to see how we did that because it was it was tricky and so we we were able to replicate as many as we thought we would if our if our if our findings were all true and so so we felt that that was a good replication study not perfect then the other things we do the same things everybody does you know we we look to see if we if the results are meaningful do do you get a lot of results that fall in certain pathways and and we didn't but neither does anybody else because schizophrenia is a broad broad it's not like it's not like that type one diabetes where you you know it's the pancreas the brain like the brain is like i i study only brain things the brain's like everything almost every gene is brain expressed and and a lot of little things can go wrong that can cause you to be mentally ill it's a it's a miracle that we're all we're mostly healthy given how many little things can happen so we were not able to replicate our work in schizophrenia by using sort of those kind of enrichment results but we did analyze type one diabetes when we knew what to expect and it came out perfect we got good enrichment of exactly the things that we all know cause type one diabetes so that is a good way to check for phenotypes that you understand better like you know the lipid things and those kind of phenotypes not so good for the brain ones does that answer yes thank you so much for this answer and i would definitely have a look at the paper excellent okay are there further questions if not i would have a question if i recall correctly then you mentioned in the beginning that the covariate information should be independent from the information that you are testing like the genotypic information um how how can you guarantee that this is the case or what would happen if it is not fully independent can you detect this empirically so how can this affect your approach yeah yes no absolutely i thought about that because i um what what that means is like you wouldn't i mean of course they're correlated in the same i mean are they conditionally independent because of course you want them to be correlated right but you want them to be independent in terms of the measurement errors of them and so you wouldn't want to use the same like if you had a g was for the same subjects and you measured let's say you measured educational attainment on them and also whether they had autism or not that would be wrong right the correlation because any measurement errors would be the same the causing the same same small p values and so that that would be unacceptable but the fact that you use like like when i use the the bipolar those were different subjects but we believe that the the underlying signal was the same that's good that's the kind of correlation that you want but not not false correlations thank you really can measurement are the measurement errors are related yes thank you for that so we've come to the end of this one hour presentation Catherine like all external speakers has kindly agreed to also meet the the doctoral students of our network for for half an hour and to to give them advice and feedback on career and research issues that that's wonderful so we also thank you for that and and for the excellent presentation that you have given here is very was very thought stimulating and an excellent talk that that was great and thanks a lot the the pis and the youtube audience say goodbye to you now thank you very much Catherine and the doctoral students will take you to a to a breakout room thank you thank you for having me you're welcome and we'll be back here in the general audience in half an hour Christoph Fisher will present at 4 30


Good evening. Recently, I started playing Garry's Mod again, with a small group of friends on a private server. We goof off with absurd custom models and maps and generally have a blast. But in the process of organizing game sessions and coordinating add-ons, it came to my attention that some of my friends were missing models and textures because they didn't have the game Counter-Strike Source purchased and mounted. And this got me thinking. See, without assets from Counter-Strike Source mounted, playing GMod Online without running into problems is nearly impossible. Some game modes like Prop, Hunt, and Murder are often played on Counter-Strike maps, servers for Dark Rp or Sandbox often make use of Counter-Strike weapons, and custom maps often utilize Counter-Strike props and textures. So if you try to play online without these assets mounted, you're going to eventually be met with gaudy missing textures and error signs. And if it's a map you're missing, you might even get flat out booted from the server. In fact, Counter-Strike Source assets are so essential to the Garry's Mod experience that somewhere along the line the GMod team actually bothered implementing tofu versions of the player models and weapons, just to try and help the game stand on its own. While this makes trouble in Terrorist Town playable on select levels, it doesn't account for the rest of the missing textures and models that new players will have to deal with if they have any intent of playing online. Steam used to promote a bundle that included both CSS and GMod together for just 15 bucks, and I have no idea why they stopped doing this. Nowadays both games are $10 each, and while that might not seem so bad, I'm of the opinion that if it isn't sold as part of a package with GMod, CSS should just be free. Here's why. First, as established, the game's files being freely accessible would simplify life for Garry's Mod players and server owners, who would no longer have to deal with asset-related headaches. Second, the slash and cost would greatly encourage new players to pick up the game. Maybe you have a friend who's willing to dish out $10 to try Garry's Mod with you, but are they also willing to dish out another $10 for a game they're never going to play just so they can properly enjoy the first one? The game having what amounts to a hidden fee is pretty discouraging for some new players. What if you're trying to get a group of people together? The collective price of GMod for 8 people goes from $80 to $160 the second you start thinking about playing on Dust2, Office, or one of the hundreds of thousands of maps that use content from those levels. I think GMod would be a much more welcoming game if it could just stand on its own. Third, CSS being free would make it legal for people to redistribute its assets online, which would help the greater source mod and community a lot. Many early source mods, like S-Mod Tactical, also relied on Counterstrike assets, but nowadays their mounting systems are broken because of complications from the source engine being updated. If Counterstrike assets could simply be downloaded online, life would be a lot simpler for anyone trying to play or tinker around with these classic mods. Fourth, I think CSS should be free because frankly I see no reason for it not to be. Making a 15 year old game that sees maybe a couple thousand sales per year free to play would not have a notable effect on Valve's profit margins at all. This is a company that thinks in terms of billions. And even if money were a concern, the influx of GMod players you'd see in the wake of this decision would probably be enough to make up for lost revenue, since Valve also receives a hefty portion of GMod sales. Some might say that CSS going free would detract from CSGO's success, but the primary player base of source is veterans from an era gone by, so it's not at all a welcoming game for new players. If someone's trying to get into Counterstrike, they're going to go play the newer, prettier installment that has an alluring esports scene and a matchmaking mode designed to help newer players improve. And that brings me to my final point. If the newest installment of a series is completely free, why should the buggy, unsupported installment from nearly 20 years ago not be as well?


Alright, in this video I'm going to show you how to upload a video to your YouTube channel. So of course you want to go to youtube.com, log in.  But anyway, once you're logged in, you're right here. Next what you want to do is you want to find a button that's like a plus on a camcorder or a create. Again, it could look different depending on how you have your interface set up. Also your screen could be white, I'm on the dark theme, but anyway you just need to find something like a video and a plus or an ad or a create on your site. So mine's in the upper right hand corner so far, so I'm going to click on that. And then I'm going to click on upload video. Just going to transfer you to YouTube Studio and right here it says drag and drop video files to upload or select files. So I'm going to go to my desktop and I just did a video that how to export contacts from your Yahoo account. So I'm going to, you don't want to keep the name of it, you want to change the name. So you're going to right click and then you're going to find something that says rename, right? And then what I'm going to do is I am going to, you got to think of what people would say to search for it. So if I was searching for how to export my contacts from Yahoo, I would type in how and then you want to do an underscore to export contacts underscore from underscore your underscore Yahoo underscore and you put account, contacts, that type of stuff. And then I like to put underscored video. So now that we have that renamed with underscores because underscores will help connect those key words. So now what I'm going to do is I'm going to go back and now that I have my video renamed I'm going to hit select files. I'm going to look for the video which is right here and it's going to upload it. So next I'm going to do the title again. So how to export Yahoo contacts where you put how to export, okay down here is the file name. You could just copy that and paste it. But you don't want the underscores on this one because this is what people are reading. So they're not going to read the name of the video that just helps YouTube optimize your video. So we're just going to take these little things out and how to export contacts from your from your Yahoo account. Okay. So that's the title. And then in this video we show you how to export your contacts from your Yahoo account into a CSV spreadsheet, right? You're just basically telling exactly what they'll be viewing, okay? So then it's processing the video right now. So there will be some thumbnails right there. So we're going to go ahead and let that process for a few. And then playlist. You can either create a new playlist or add it to a current one. So if you want to create a new playlist, this one will be, we will put this under how to export contacts, okay? So we'll hit new playlist, title, how to export contacts, okay? Because what I'll do is I'll just create different videos of how to export contacts from your iPhone, you know, how to export contacts from Gmail, that type of stuff. And then we'll put it in that playlist. Now you don't have to have a playlist, but it makes it easier for people if they're, you know, wanting to figure out how to export contacts from various things. I mean, just watch a playlist of it and they don't have to search through videos because they go by date and time that they're uploaded. So it just makes it easier. You're going to have like a series of videos, all right? So we're going to hit, of course, we want the visibility to be public. So I'm going to go ahead and hit create. And then I'm going to check it. And then we're going to kind of look through here and see if there's any other playlist I want to add it to, which I don't. Okay, so we'll hit done. All right. And then next, what we're going to do is no, this isn't made for kids. Okay, a restriction show more. Okay, so paid promotion tags, subtitles and more. So let's go ahead and so this is showing, you kind of click and it looks like it's not showing the thumbnail right here, but you could select the best one. And I will, I can see where the contacts are right here. And this is what that screen will look like. So I'm just going to use this thumbnail right here. And you could upload a custom thumbnail if you wanted to, like a picture, but I'm just going to use this because that's what it looks like. Show more. All right, so there's no paid promotion. So I don't have to worry about anything there. And then I have some tags that are automatically placed in here. These years are probably blank. So I'm just going to go through here and these just have to do with the topic. So Twitter's not the topic, templates, QR code, conversion, sales, email notification. None of these are relevant. There's our sales managers, leads. These could be leaked. Actually, they are leads, your contacts. Assistants do this quite a bit. So I'm going to leave that there. Loan originators, real estates, that's who we teach. So I'm going to leave real estate, marketing, use contacts for marketing. Internet marketing, customer relationship management. We use that CRM, ours is Dynamics Pro, System Dynamics. Okay, cool. I'm going to add Yahoo. And I just hit the tab button and it created it. I'm going to do, I don't see contacts in here. I'm going to hit a tab. I can put database because it has to do with database, tab. So you just really think of all the different keywords that have to do with this. So export, because you're exporting contacts. So anyway, you can go through and think about it quite a bit. I'm going to keep on going just because this is an instructional video. Recording date, you could add the recording date if you want to. Reminds in English, video location. All right, so we're good to go. Now embedding, allow embedding if someone wants to use the video on their website. Publish to subscription feed, yes. And I'll notify anybody who's subscribed to our YouTube channel, because it'll just basically give them a push notification. All right, so this is how to and style education. It is a how to, it's not really style on there, but I'll put education because I'm educating people on how to do something. Let's see here, choose, all right. So you could for comments and ratings, show how many viewers like and dislike the video. So you have the option, and I'm going to hit next. And it's a little more advanced, but I'm not going to do it in this video. But you could get pretty advanced with this. And then they're going to look at copyright issues, which we're good. You have the check mark, so we're good. And then next. And then I can save or publish publicly, which I want to. So people can look at the video and search it. I could schedule a time and date for, you know, first thing in the morning, things like that if I want to. Because a lot of people watch videos first thing in the morning when they get up. So it's kind of nice to have a new, new, new notification. I'm not going to use that schedule on here. So I'm just going to go back to public and publish it now. And we're good to go. Do kids appear in this video now? So anyway, you're going to hit publish. And then right here, if you want to copy your video link, you can just click it right here. And then if you open up a new tab and there's the video of what it's going to look like once people are able to find it. And I'm going to hit publish. And when you hit publish, it's going to send out notification to people who are subscribed and are looking for new videos for you. So you could share the link on Facebook, all these different ones if you want. And then there's your video link. And that's how you upload a video to your YouTube channel.


What's going on everyone? Sam here with Leighton sports cards and I am ripping one box of 2022 Bowman baseball sapphire edition for Ryan B. So good luck Ryan. See what we can get in here for you man Uh, so another you saw another sandlot jersey. Yeah, dude, that is so the sandlot jerseys are so sick out of that It's definitely like a really cool like any of the the actor or like movie Pieces out of the Stitches and slabs would be so cool to own Yeah, us for a Garcia Marcella Meyer got autograph first pack is matt frazier for the pirates Nice one there And jack Sawinsky on the first Probably like a big wander would be my guess I'd probably say it wander franco rookies Uh, yannis in class a awesome hendrick hooper kini rickardo genoves Geez that sounds brutal german Well, we marty got pastrano Henry davis non first on the back pca For uh first of juniors and quinzen and willman Diaz We got an orange come on big orange big orange andre laura Then rodriguez nicocavadas, and we've got willman Diaz night. There you go on the orange For the Dodgers nice hit there. Grats on that one ryan jeer downes Urabil and heles cantero and hassle Yeah, big fan of sapphire big fan of sapphire apple say Cartaya First of alvin guzman harry ford and last pack for you here ryan. Good luck man malcom nunas Alondra pa and victor lizard raga on the back that will do it for your break there ryan Congrats on all the hits get those right out to you man. Thanks again


From there, when the front athlete's done, we'll move forward to the bikes, so on until everybody is done. Ten hours on the bike, ten days all over the shoulders, ten hours on the bike. Two, one, go! If you're just joining us, we've got three teams that can get you right in here. We're pretty excited moving from the EIB taskings and the E3B stuff, which is pretty technical, into some of the more physical aspects of the competition. Obviously it's a couple days in, so we're starting to get a little tired, but it's a welcome change. We're looking forward to it. I think as a squad we've been working pretty well together, primarily because we are actually an organic squad. I've worked with all my dudes for the past three years or so, so we know each other pretty well. And I think one thing that we've learned as we've been going through this competition is when to put on the gas and when to put on the brakes, so to speak. Today's challenges include a sled push, a log carry, aerosol bikes, a deadlift as a squad, and rope climbs. My squad as a whole is doing pretty well. Our spirits are pretty high. We're still laughing. We're still having a good time just making the most of each event. When I think about the best squad, I'm just incredibly proud to be here. Seeing everybody from the community show up today is just making it look like we're known and appreciated, you know? My squad, I can't say enough how proud I am of those guys. They're showing up and they're ready to work. I'm definitely looking forward to moving on in the competition. As an infantryman, I look forward to being in the field and being with my guys, and I know they feel the same way. I think our squad's doing really well. We had a good train up, and so we're all meshing pretty well and helping each other out. Everybody has strengths and weaknesses, so we're filling each other's in pretty well, and I think we're going to do pretty well as a squad. The challenge I'm looking forward to most is probably the rope climb, because our team is really good at rope climbs, so I think we'll kill that event. Continuing the competition, we're going to do well because we're just going to focus on one specific event at a time. So we're not thinking about the whole thing. We're just going in, winning one event, then moving to the next event. So as long as we break it down, event by event, I think we'll do really well.


Okay, welcome back to our SuperCloud 2 event, live coverage here of stage performance in Palo Alto, syndicating around the world. I'm John Flurry with Dave Vellante. We've got exclusive news and a scoop here for SiliconANGLE in theCUBE. Shumak Tagandhi, creator of Data Mesh, has formed a new company called NextData.com, NextData, she's a CUBE alumni and contributor to our SuperCloud initiative, as well as our coverage and breaking analysis of Dave Vellante on data, the killer app for SuperCloud. Shumak, great to see you. Thank you for coming into the studio. Congratulations on your newly formed venture and can continue success on the Data Mesh. Thank you so much. It's great to be here, great to see you in person. Yeah, finally. Wonderful. Your contributions to the data conversation has been well documented, certainly by us and others in the industry, Data Mesh taking the world by storm. Some people are debating it, throwing cold water on it. Some are thinking it's the next big thing. Tell us about the Data Mesh Super data apps that are emerging out of cloud.  Everybody said, oh, why didn't I think of that?  I've been involved in many of those implementations and I guess SuperCloud is somewhat a prerequisite for it because it's Data Mesh and building applications using Data Mesh is about sharing data responsibly across boundaries and those boundaries include boundaries, organizational boundaries, cloud, technology boundaries and trust boundaries. I want to bring that up because your venture, Next Data, which is new, just formed, tell us about that. What wave is that riding? What specifically are you targeting? What's the pain point? Absolutely. Yes, so Next Data is the result of, I suppose, the pains that I suffered from implementing a Data Mesh for many of the organizations. Basically, a lot of organizations that I've worked with, they want decentralized data. So they really embrace this idea of decentralized ownership of the data but yet they want interconnectivity through standard APIs. Yet they want discoverability and governance. So they want to have policies implemented. They want to govern that data. They want to be able to discover that data and yet they want to decentralize it and do that with a developer experience that is easy and native to a generalist developer. So we try to find the, I guess, the common denominator that solves those problems and enables that developer experience for data sharing. Since you just announced the news, what's been the reaction? I just announced the news right now. So what's the reaction? But people in the industry that know you did a lot of work in the area, what have been some of the feedback on the new venture in terms of the approach, the customers problem? Yes, so we've been in stealth mode. So we haven't publicly talked about it but folks that have been close to us, in fact, have reached out. We already have implementations of our pilot platform with early customers, which is super exciting. And we're going to have multiple of those. Of course, we're a tiny, tiny company. We can have many of those where we're going to have multiple pilot implementations of our platform. In real world, we're real global, large-scale organizations that have real-world problems. So we're not going to build our platform in vacuum. And that's what's happening right now. Yermak, when I think about your role at ThoughtWorks, you had a very wide observation space with a number of clients helping them implement data mesh and other things as well prior to your data mesh initiative. But when I look at data mesh, at least the ones that I've seen, they're very narrow. I think of JPMC, I think of HelloFresh. They're generally obviously not surprising. They don't include the big vision of inclusivity across clouds, across different data stores. But it seems like people are having to go through some gymnastics to get to the organizational reality of decentralizing data. And at least pushing data ownership to the line of business. How are you approaching, are you approaching solving that problem? Are you taking a narrow slice? What can you tell us about next data? Yeah, absolutely. Gymnastics is a cute word to describe what the organizations have to go through. And one of those problems is that the data, as you know, resides on different platforms. It's owned by different people. It's processed by pipelines that who knows who owns them. So there's this very disparate and disconnected set of technologies that were very useful for when we thought about data and processing as a centralized problem. But when you think about data as a decentralized problem, the cost of integration of these technologies in a cohesive developer experience is what's missing. And we want to focus on that cohesive end-to-end developer experience to share data responsibly in these autonomous units. We call them data products, I guess, in data mesh, that constitutes computation, that governs our data, policies, discoverability.  So people have their cakes, which is data in different places, decentralization and ET2, which is interconnected access to it. So we start with standardizing and codifying this idea of a data product container that encapsulates data computation, APIs to get to it in a technology agnostic way, in an open way, and then sit on top and use existing snowflake, Databricks, whatever exists. The millions of dollars of investments that companies have made sit on top of those, but create this cohesive integrated experience where data product is a first class primitive. And that's really key here, that the language and the modeling that we use is really native to data measures that I'm building a data product, I'm sharing a data product. And that encapsulates, I'm providing metadata about this, I'm providing computation that's constantly changing the data, I'm providing the API for that. So we're trying to kind of codify and create a new developer experience based on that and develop it both from provider side and user side, connected to peer to peer data sharing with data product as a primitive first class concept. Okay, so the idea would be developers would build applications leveraging those data products which are discoverable and governed. Now, today you see some companies, take a snowflake, for example, attempting to do that within their own little walled garden. They even, at one point, use the term mesh, they pull back on that and then they sort of became aware of some of your work. But a lot of the things that they're doing within their little insulated environment, support that, that governance, they're building out an ecosystem. What's different in your vision? Exactly. So we realized that, you know, and this is a reality. Like you go to organizations, they have a snowflake and half of the organization happily operates on snowflake and on the other half, oh, we're on, you know, bear infrastructure on AWS or we are on data breaks. This is the real, this, you know, this super cloud that's written up here, it's about working across boundaries of technology. So we try to embrace that. And even for our own technology, with the way we're building it, we say, okay, nobody's going to use next data, data mesh operating system. People will have different platforms. So you have to build with openness in mind. And in case of snowflake, I think, you know, they have very, I'm sure, very happy customers as long as customers can be on snowflake. But once you cross that boundary of platforms, then that becomes a problem. And we try to keep that in mind in our solution. So it's worth, it's worth reviewing that basically the concept of data measures that whether you're a data lake or a data warehouse, an S3 bucket, an Oracle database as well, they should be inclusive inside of the data. We did a session with AWS on the startup showcase data as code. And remember, I wrote a blog post in 2007 called data is the new developer kit. Back then these call them developer kits, if you remember. And that we said at that time, whoever can code data will have a competitive advantage. Aren't the machines going to be doing that? Didn't we just hear that? Well, hey Siri, hey cube, find me that best video for data mesh. There it is. This is the point. Like what's happening is that now data has to be addressable for machines and for coding. Because as you need to call the data. So the question is, how do you manage the complexity of being things as promiscuous as possible, making it available, as well as then governing it? Because it's a trade-off. The more you make open, the better the machine learning. But yet the governance issues. So this is the, you need an OS to handle this maybe. Yes, so yes. Well, we call our mental model for our platform is an OS operating system. Operating systems have shown us how you can kind of abstract what's complex and take care of a lot of complexities, but yet provide an open and dynamic enough interface. So we think about it that way. Just we try to solve the problem of policies live with the data and enforcement of the policies happens at the most granular level, which is in this concept of the data product. And that would happen whether you read, write or access a data product. But we can never imagine what are these policies could be. So our thinking is, okay, we should have a policy, open policy framework that can allow organizations write their own policy drivers and policy definitions unencoded and encapsulated in this data product container. But I'm not going to fool myself to say that, you know, that's going to solve the problem that you just described. I think that we are in this, if I don't know, if I look into my crystal ball, what I think might happen is that right now the primitives that we work with to train machine learning model are still bits and bytes and data. They're fields, rows, columns, right?  So perhaps one of the trends that we might see is this evolution of data APIs to become more and more computational aware, to bring the compute to the data, to reduce that surface area so you can really leave the control of the data to the sovereign owners of that data, right? So that data product. So I think that evolution of our data APIs perhaps will become more and more computational. So you describe what you want and the data owner decides, you know, how to manage the data. That's interesting, Dave, because it's almost like we just talked about chat, GPT in the last segment with Allie Chu, who's a machine learning guru and been around the industry. It's almost as if you're starting to see reason come in. So the data reasoning is like, you start to see not just metadata, using the data to reason so that you don't have to expose the raw data. So almost like a, I won't say curation layer, but an intelligence layer. Exactly. Can you share your vision on that? Because that seems to be where the dots are connecting. Yes, this is perhaps further into the future because just from where we stand, we have to create still that bridge of familiarity between that future and presence. So we're still in that bridge making mode. However, by just the basic notion of saying, I'm going to put an API in front of my data and that API today might be as primitive as a level of indirection. As in, you tell me what you want.  Let me go process that all the policies and lineage and insert all of this intelligence that need to happen. And then I will, today, I will still give you a file, but by just defining that API and standardizing it, now we have this amazing extension point that we can say, well, the next revision of this API, you not just tell me who you are, but you actually tell me what intelligence you're after. What's the logic that I need to go and now compute on your API? And you can kind of evolve that, right? Now you have a point of evolution to this very futuristic, I guess, future where you just describe the question that you're asking from the chat. Well, this is the super close. I have a question from a fan. I got to get it in, George Gilbert. And so his question is you're blowing away the way we synchronize data from operational systems to the data stack to applications. So the concern that he has, and he wants your feedback on this, is the data product app devs get exposed to more complexity with respect to moving data between data products or maybe it's attributes between data products. How do you respond to that? How do you see, is that a problem? Is that something that is overstated or do you have an answer for that? Absolutely. So I think there is a sweet spot in getting data developers, data product developers, closer to the app, but yet not overburdening them with the complexity of the application and application logic. And yet reducing their cognitive load by localizing what they need to know about, which is that domain where they're operating within. Because what's happening right now, what's happening right now is that data engineers with a ton of empathy for them, for their high threshold of pain that they can deal with, they have been centralized, they've put into the data team and they have been given this unbelievable task of make meaning out of data, put semantic over it, curate it, cleanse it and so on. So what we're saying is that get those folks embedded into the domain closer to the application developers. These are still separately moving units. Your app and your data products are independent, but yet tightly close with each other, tightly coupled with each other based on the context of the domain. So reduce cognitive load by localizing what they need to know about to the domain, get them closer to the application, but yet have them separate from app because app provides a very different service, transactional data for my e-commerce transaction, data product provides a very different service, longitudinal data for the variety of this intelligent analysis that I can do on the data. But yet it's all within the domain of e-commerce or sales or whatnot. So a lot of decoupling and coupling create that cohesiveness architecture. So I have to ask you, this is an interesting question because it came up on theCUBE all last year. Back in the old server data center days in cloud, SRE, Google coined the term site reliability engineer for someone to look over the hundreds of thousands of servers. We asked the question to data engineering community who have been suffering, by the way, agree. Is there an SRE-like role for data? Because in a way data engineering, that platform engineering, they are like the SRE for data. In other words, managing the large scale to enable automation and self-service. What's your thoughts and reaction to that? I get it. Yes, exactly. So maybe we go through that history of how SRE came to be. So we had the first DevOps movement, which was remove the wall between dev and ops and bring them together. So you have one unit, one cross-functional unit of the organization that's responsible for you build it, you run it.  So we did that. And then we said, okay, there is a ton, as we decentralized and had this many microservices running around, we had to create a layer that abstracted a lot of the complexity around running now a lot or monitoring, observing and running a lot while giving autonomy to this cross-functional team. And that's where the SRE, a new generation of engineers came to exist. So I think if I just look at- Hence Borg, hence Kubernetes. Hence, hence, exactly. Hence chaos engineering, hence embracing the complexity and messiness, right? And putting engineering discipline to embrace that and yet give a cohesive and high integrity experience of those systems. So I think if we look at that evolution, perhaps something like that is happening by bringing data and apps closer and make them these domain oriented data product teams or domain oriented cross-functional teams full stop and still have a very advanced, maybe at the platform level, infrastructure level kind of operational team that they're not busy doing two jobs, which is taking care of domains and the infrastructure, but they're building infrastructure that is embracing that complexity inter-connectivity of this data product. So you see similarities. I see it, absolutely. But I feel like we're probably in a more early days of that movement. So it's a data DevOps kind of thing happening where scales happening, it's good things are happening, yet a little bit fast and loose and some complexities to clean up. Yes, yes. This is a different restructure. As you said, the job of this industry as a whole and architects is decompose, recompose, decompose, recompose in a new way. And now we're like decomposing centralized team, recomposing them as domains and... So is data mesh the killer app for SuperCloud? You had to do this to me. Sorry, I couldn't do that. I know. Why should I? Especially when we're the baby. Yes. Yes, of course. I mean, SuperCloud, I think it's really the terminology SuperCloud, OpenCloud, I think the inspiration of it, this embracing of diversity and giving autonomy for people to make decisions for what's right for them and not yet lock them in. I think just embracing that is baked into how data mesh assumed the world would work. Well, thank you so much for coming on SuperCloud to really appreciate it. Data has driven this conversation. Your success of data mesh has really opened up the conversation and exposed the slow-moving data industry. Been a great catalyst. That's now going, well, we can move faster. So thanks for coming on. Thank you for hosting me, it was wonderful. Okay, SuperCloud 2 live here in Palo Alto, our stage performance. I'm John Furrier with Dave Vellante. We'll be back with more after this short break. Stay with us all day for SuperCloud 2.


Hi everyone, Joe for Jaspy's CaseBreaks.com coming at you on a Friday with 2019-2020 Panini Chronicles basketball one box random team number one blaster edition. As always we're going to do the break itself first and at the end we're going to re-randomize your names and three people the top three names are going to win spots in that $600 basketball mixer which is on the site right now check it out on the website. But for the end thank you to these folks right here for getting into it. Let's do the break first. Let's roll it. Let's randomize it. A full 12 times six and a six one two way down to Robert. I actually wouldn't wait John I think I think that backlog is just going to get worse and worse. All right 12 times for the teams as more and more people discover the hobby want to get stuff graded. I think they are hiring though but I don't know how long it's going to take before they catch up and one more time 12 and final time. After 12 times I mean that's my guess Milwaukee bucks and the Mavs I don't think there's something going to be like oh people want to stop grading you know what I mean. I feel like it's just growing might as well just rip the band will slowly rip the bandaid off right now you're going to be waiting a long time anyway or maybe I maybe wait to see if you have other stuff to send along with it so you can just kind of do it all at once that'd be a good reason to wait all right so there's everybody right here wait I'm still on MBA network what's what where's the where's the other game on is it a TNT night or is it an ESPN night it is a TNT night maybe TNT says Patrick which is I don't have direct TV at home I think it's 240 something there is all right oh and then the second half of the list is right here Zach with the gris palikins are right there could get some interesting things out of a blaster box all right so let's sort like that let's get everything there blah blah blah and let's print and rip all right wow yeah the jazz are up ahead by a lot 119 to 83 with two and a half minutes left in the fourth it's gonna be a pretty exciting series then I like what's happening there all right let's see what happens in here right good luck everybody and we've got we've got a fat pack retail hanger pack versions of this as well on the site and I think Ted has personal box of this on Instagram live at Jasper's breaks on Instagram live Instagram live only at Jasper's breaks follow us there and watch us there nice Zion Williamson there Jared Culver pink some Tyler heroes some Yanis I'm assuming these pink ones are exclusive to this I don't even read the box yeah pink parallels are a blaster exclusive it says right on the box nice John Moran LeBron James my Lakers look good yesterday and a pink Kobe white is cool Moranis and he David had a good game as well for my Lakers there's pink LeBron James these aren't numbered by the way but they are exclusive to this set in Cam Reddish right there there's that Zion go there's that John Moran for the Grizzlies that goes to Zach T and there's that Zion Williamson I love that I love the luminance design in this Richard Martinez with the Palakins all right so there you go all right now let's see and I'll sleeve all these up in a little bit but let's go back to the this team list right here and let's see who's gonna win those those mixer spots so once again it's gonna be top three after nine four and a five top three after nine one two and ninth and final time after nine first of all from number four down to 30 thank you very much for giving this a shot I think there's another opportunity or two to get into that mixer and the top three congrats to Kim zoom in Greg and Eric there you go Eric Greg and zoom in in the action thanks for watching hopefully we'll be able to fill up that break tonight we should be able to jazzy's case breaks calm I'll see you next time for the next one bye bye